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Electronic Theses, Treatises and Dissertations
The Graduate School
2007
Effects of Motivation, Volition, and Belief
Change Strategies on Attitudes, Study
Habits, and Achievement in Mathematics
Education
ChanMin Kim
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF EDUCATION
EFFECTS OF MOTIVATION, VOLITION, AND BELIEF CHANGE
STRATEGIES ON ATTITUDES, STUDY HABITS, AND ACHIEVEMENT IN
MATHEMATICS EDUCATION
By
CHANMIN KIM
A Dissertation submitted to the
Department of Educational Psychology and Learning Systems
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Fall Semester, 2007
Copyright © 2007
ChanMin Kim
All Right Reserved
The members of the Committee approve the dissertation of ChanMin Kim defended on
September 21, 2007.
________________________
John M. Keller
Professor Directing Dissertation
________________________
John K. Mayo
Outside Committee Member
________________________
Robert A. Reiser
Committee Member
________________________
Amy L. Baylor
Committee Member
Approved:
_________________________________________________________________
Akihito Kamata, Chairperson, Department of Educational Psychology and Learning Systems
The Office of Graduate Studies has verified and approved the above named committee members.
ii
ACKNOWLEDGEMENT
Looking back on my last three years as a doctoral student, I first deeply thank Dr. John
Keller. He has been a true mentor to me. He has done the work of a knowledgeable teacher, a
persistent supporter, an insightful motivator, and a trustworthy counselor. As a knowledgeable
teacher and a persistent supporter, he facilitated my academic and professional development
through his classes and research projects. I have benefited greatly from involvement in research
projects in addition to his classes. Beyond teaching and guiding me, he has always treated me as
his colleague; that is, he has expressed his appreciation for our collaborative relationship and
respected my ideas and opinions. Even when my research or teaching ideas needed improvement,
he acted as an insightful motivator, giving direction and encouragement as I continued to work
through my ideas. He allowed me to learn from him through opportunities to observe his
research and teaching philosophy and practices. He motivated me to attend professional
conferences, present research outcomes, and publish papers. He was always willing to help me
accomplish these things that would lead me toward my goals. Moreover, as a trustworthy
counselor, I found not only that he sincerely listened to my academic struggle and even my
personal troubles, but also that he was eager to work with me in seeking solutions for these
problems. I will always treasure the pleasant memories of learning from my true mentor.
I also would like to thank the other members of my dissertation supervisory committee,
Drs. John Mayo, Robert Reiser, and Amy Baylor. Each of them significantly contributed to the
improvement of my dissertation with perceptive suggestions for research ideas and valuable
feedback during the writing process. In addition, I am very grateful to two professors, Drs. Mika
Seppälä and Giray Ökten, who provided me the opportunity of conducting this study in their
calculus courses and patiently supported the study over two months.
Last but not least, I would like to extend my special appreciation to my parents for their
generous support for me. Their belief in my capabilities energizes my confidence and will to
pursue my academic goals and their endless love helps me endure hardships. I am also thankful
to Mike Spector for exemplifying honesty and diligence in his work, which inspires me to
continue.
iii
TABLE OF CONTENTS
List of Tables ............................................................................................................................... viii
List of Figures ................................................................................................................................ ix
Abstract ........................................................................................................................................... x
CHAPTER I. INTRODUCTION.................................................................................................... 1
Context of the Problem ....................................................................................................... 1
Problem Statement .............................................................................................................. 4
Research Question .............................................................................................................. 5
Significance of Study.......................................................................................................... 5
CHAPTER II. REVIEW OF RELEVANT LITERATURE ........................................................... 6
Introduction......................................................................................................................... 6
Issues in Mathematics Education........................................................................................ 7
Motivation and Volition.................................................................................................... 11
Research on Motivation and Volition Change Strategies ..................................... 15
Beliefs about Learning and Mathematics Knowledge ...................................................... 15
Research on Belief Change Strategies .................................................................. 19
The Framework of Research on Motivation, Volition, and Belief Change ...................... 20
Interconnectedness of Motivation, Volition, and Belief Change with Attitudes,
Study Habits, and Achievement............................................................................ 20
Motivation, Volition, and Belief Change Strategies............................................. 24
Practical Consideration for Implementation of Change Strategies....................... 26
Hypotheses........................................................................................................................ 29
iv
CHAPTER III. METHOD ............................................................................................................ 34
Participants........................................................................................................................ 34
Research Design................................................................................................................ 34
Independent Variables ...................................................................................................... 35
Type of Message Content ..................................................................................... 35
Motivation and volition change strategies (MV) ...................................... 35
Belief Change Strategies (B) .................................................................... 39
Motivation, volition, and belief change strategies (MVB) ....................... 41
Type of Message Delivery .................................................................................... 44
Personal Messages (P) .............................................................................. 44
Group Messages (G) ................................................................................. 47
Dependent Variables......................................................................................................... 48
Attitudes................................................................................................................ 48
Study Habits.......................................................................................................... 48
Achievement ......................................................................................................... 49
Procedure .......................................................................................................................... 49
Data Analysis .................................................................................................................... 58
CHAPTER IV. RESULTS............................................................................................................ 59
Preliminary Data Analysis ..................................................................................................... 59
Group Equivalence Test........................................................................................ 59
Tests for ANOVA Assumptions ........................................................................... 61
Descriptive Data..................................................................................................................... 63
Attitudes toward Mathematics .............................................................................. 63
Study Habits.......................................................................................................... 64
Achievement ......................................................................................................... 65
Examination of Hypotheses ................................................................................................... 66
v
CHAPTER V. DISCUSSION....................................................................................................... 74
Overview........................................................................................................................... 74
Findings............................................................................................................................. 75
Attitudes toward Mathematics .............................................................................. 75
Study Habits.......................................................................................................... 80
Achievement ......................................................................................................... 81
Limitations of the Study.................................................................................................... 83
Future Research ................................................................................................................ 86
Implications for Learning and Instruction ........................................................................ 88
Conclusion ........................................................................................................................ 89
APPENDIX A. QUESTIONNAIRE ON ATTITUDES TOWARD MATHEMATICS .............. 90
APPENDIX B. QUESTIONNAIRE ON BELIEFS ABOUT LEARNING MATHEMATICS... 92
APPENDIX C. QUESTIONNAIRE ON MOTIVATION............................................................ 94
APPENDIX D. QUESTIONNAIRE ON VOLITION.................................................................. 95
APPENDIX E. QUESTIONNAIRE ON STUDY HABITS......................................................... 97
APPENDIX F. OVERALL LEVELS OF MOTIVATION, VOLITION, AND BELIEFS.......... 98
APPENDIX G. MESSAGES EMAILED AT STAGE 1.............................................................. 99
APPENDIX H. DIAGNOSTIC QUESTIONS EMAILED AT STAGE 2 ................................. 108
APPENDIX I. DESCRIPTIONS OF PERSONAL SUGGESTIONS EMAILED AT STAGE 2
..................................................................................................................................................... 110
APPENDIX J. SAMPLE MESSAGES AT STAGE 2 ............................................................... 113
APPENDIX K. DIAGNOSTIC QUESTIONS EMAILED AT STAGE 3 ................................. 124
APPENDIX L. SAMPLE MESSAGES AT STAGE 3 .............................................................. 125
APPENDIX M. DIAGNOSTIC QUESTIONS EMAILED AT STAGE 4 ................................ 131
APPENDIX N. SAMPLE MESSAGES AT STAGE 4 .............................................................. 133
APPENDIX O. SHAPIRO-WILK NORMALITY TESTS ........................................................ 143
APPENDIX P. HISTOGRAM INSPECTION ........................................................................... 145
APPENDIX Q. BOX’S TESTS OF EQUALITY....................................................................... 147
vi
APPENDIX R. LEVENE’S EQUALITY OF ERROR VARIANCE TESTS............................ 148
APPENDIX S. MAUCHLY’S TEST OF SPHERICITY ........................................................... 149
APPENDIX T. HUMAN SUBJECT COMMITTEE APPROVAL INFORMED CONSENT
FORM ......................................................................................................................................... 150
REFERENCES ........................................................................................................................... 152
BIOGRAPHICAL SKETCH ...................................................................................................... 161
vii
LIST OF TABLES
Table 3.1 The seven treatment groups involved in this study....................................................... 35
Table 3.2 The use of components from different models and theories for MV............................ 38
Table 3.3 The use of components from different models and theories for B. .............................. 41
Table 3.4 The use of components from different models and theories for MVB. ........................ 43
Table 3.5 An overview of the procedure. ..................................................................................... 50
Table 3.6 The total number of emails sent to each group ............................................................ 50
Table 3.7 The number of participants in each treatment groups................................................... 52
Table 3.8 The reassignment of the groups. ................................................................................... 55
Table 3.9 The number of participants. .......................................................................................... 56
Table 4.1 Means and standard deviations for group equivalence test. ......................................... 61
Table 4.2 Means and standard deviations for attitude score......................................................... 64
Table 4.3 Means and standard deviations for study habit score. .................................................. 65
Table 4.4 Means and standard deviations for achievement score................................................. 66
Table 4.5 Multiple comparisons for attitudes toward mathematics.............................................. 68
Table 4.6 Multiple comparisons for study habits.......................................................................... 71
viii
LIST OF FIGURES
Figure 2.1 Relationships among motivation, volition, attitudes, study habits, and achievement. 21
Figure 2.2 Relationships among beliefs, attitudes, study habits, and achievement. ..................... 22
Figure 2.3 Relationships among beliefs, motivation, and volition. .............................................. 23
Figure 2.4 Interconnectedness of motivation, volition, and beliefs with attitudes, study habits,
and achievement............................................................................................................................ 24
Figure 2.5 Research framework for this study.............................................................................. 26
Figure 3.1 An example of a motivation and volition message. .................................................... 36
Figure 3.2 An example of a belief change message...................................................................... 39
Figure 3.3 An example of a motivation, volition, and belief change message. ............................ 42
Figure 3.4 An example of a diagnostic question........................................................................... 46
Figure 3.5 An example of a personal message (actual student names are not reflected here)...... 46
Figure 3.6 An example email to describe personal messages....................................................... 54
Figure 4.1 Changes in participants’ attitudes toward mathematics. ............................................. 69
Figure 4.2 Changes in participants’ study habits. ......................................................................... 71
Figure 4.3 Changes in participants’ achievement. ........................................................................ 73
ix
ABSTRACT
The importance of students’ motivation, volition, and beliefs has been recently emphasized in
mathematics education. However, despite extensive research acknowledging students’
motivation, volition, and beliefs as critical factors for their attitudes, study habits, and
achievement, there has yet to emerge a robust framework encompassing relevant theoretical
foundations and empirical evidence. Moreover, much of the previous research has been
conducted without an integrative view of the key constructs, and, as a consequence, tends to
overlook the interconnectedness among the constructs. Given this gap, this study intended to
build a conceptual framework for research on motivation, volition, and beliefs for the
improvement of students’ attitudes, study habits, and achievement in mathematics education. The
framework was grounded in a review of relevant theories and models as well as empirical studies.
This exploratory experimental study focused on the cumulative effects of email messages
designed in accordance with the framework, and, as a consequence, it provided an initial
validation of the framework in the context of the design, development and evaluation of
interventions in mathematics education. Specifically, this study investigated the effects of
motivation and volition change strategies and belief change strategies as implemented with
targeted email as personal and group messages on students’ attitudes, study habits, and
achievement in a calculus course for non-mathematics majors.
This study involved seven groups receiving one of the following treatments: 1)
motivation and volition change strategies distributed via email with personal messages (MV-P),
2) motivation and volition change strategies distributed via email with group messages (MV-G),
3) belief change strategies distributed via email with personal messages (B-P), 4) belief change
strategies distributed via email with group messages (B-G), 5) motivation, volition, and belief
change strategies distributed via email with personal messages (MVB-P), 6) motivation, volition,
and belief change strategies distributed via email with group messages (MVB-G), and 7) neither
motivation and volition change strategies nor belief change strategies distributed via email
(Control).
Eighty four undergraduates enrolled in a calculus course were distributed among the
seven treatment groups, and they received emails over a period of 8 weeks. Their attitudes
x
toward mathematics were measured using pre- and post-tests based on the Fennema-Sherman
Mathematics Attitudes (FSMA) questionnaire (Fennema & Sherman, 1976); achievement was
measured by their grades on the first and second exams of the semester. Study habits of 52
participants from the personal message and control groups (i.e., MV-P, B-P, MVB-P, and
Control) were measured using a survey, administered four times, asking how many total hours
were spent studying calculus during the week before getting the survey from researchers. The
general message groups (i.e., MV-G, B-G, and MVB-G) were excluded for the examination of
study habits because their study hours were asked only once as described in the method chapter.
The treatment effects on the dependent variables of attitudes toward mathematics,
achievement, and study habits were examined using a one-way repeated measures ANOVA
analysis. Also, Post Hoc analysis compared each group with the others using Fisher's Least
Significant Difference (LSD) test. In addition, the graphs showing changes in each of the three
variables were also analyzed. The results indicated that the use of belief change strategies with
personal messages was effective in improving learners’ attitudes toward mathematics. Notably,
change strategies with personal messages led to more positive changes in attitudes than those
with general messages. A combination of motivation and volition change strategies and belief
change strategies seemed to have had less impact on attitudes and study habits than either
motivation and volition change strategies or belief change strategies but not both. No significant
difference was found for achievement.
Possible explanations for the findings are discussed in relation to the framework of this
study constructed based on theoretical and empirical foundations. Limitations of this study are
also described as are implications and possibilities for future studies.
xi
CHAPTER I
INTRODUCTION
Context of the Problem
Motivating learners to engage in learning tasks is of obvious interest to teachers and a
constant challenge, particularly for mathematics instructors. It is also a complex phenomenon
worthy of investigation, with the ultimate research goal being to develop improved learning and
instruction. Motivation is important in that it enables learners to make decisions to pursue
learning goals. Without motivation, a purposeful learning process is difficult to sustain (Keller,
2004).
In many undergraduate mathematics courses, students are there due to program
requirements as opposed to deciding to major in mathematics or learn challenging mathematical
concepts and procedures. These students often experience difficulty in learning and completing
courses (Croft & Ward, 2001; House, 1995). Some of this difficulty might be attributed to
inadequate motivation. Specifically, students may find it hard to become motivated not only
because mathematics is not their major but because they may see little relevance of mathematics
to their lives. Lack of initial interest in mathematics may have led to poor performance in
previous mathematics courses, which often results in lack of confidence and may increase
mathematics anxiety. Moreover, many undergraduate mathematics classes are large, which
makes it difficult for instructors to motivate individual students (Croft & Ward, 2001).
Even though the students may be extrinsically motivated by grades, and thereby want to
succeed in exams and assignments, they may still experience obstacles in pursuing their
academic goals due to the number of distractions in a university campus environment. Thus,
there can be motivational problems with regard to the intrinsic desire to study mathematics, as
well as volitional problems with regard to willingness and ability to remain focused and on task
in their mathematics studies.
1
In addition to motivational and volitional problems in required mathematics courses,
students’ personal beliefs about mathematics knowledge acquisition and their abilities or lack
thereof to acquire mathematics knowledge might influence their motivation and volition. For
example, a student who believes that the ability to acquire mathematics knowledge is fixed might
not be motivated to seek help when encountering difficulty if that student also has a low estimate
of his or her own ability to acquire mathematics knowledge. However, if a student believes that
the ability is developed over time, he or she may be more willing to seek help when struggling
with a mathematics task. If a student believes that mathematics knowledge is gradually acquired
and the acquisition process is effortful, he or she might not give up so easily and might be
persistent in studying throughout the semester.
It is worth noting that these beliefs differ from self-efficacy studied as an important factor
for a student’s motivation and learning in mathematics learning contexts. Self-efficacy is a belief
about a person’s own ability to accomplish a given mathematics task (Bandura, 1997). Although
a student’s self-efficacy about a given mathematics task may be affected by his beliefs about
mathematics knowledge acquisition (Alexander, Fives, Buehl, & Mulhern, 2000), the former is
not the same as the latter. A student who believes that the ability to acquire mathematics
knowledge is gradually developed over time, may not necessarily believe that he or she can
develop mathematics knowledge effectively or efficiently in order to accomplish a particular
mathematics task.
Beliefs about knowledge acquisition have been researched and found to affect student
motivation and achievement, frequently in terms of epistemological beliefs. Specifically, by
implementing Schommer’s (1990) five dimensions of epistemological beliefs (i.e., beliefs about
the structure of knowledge, source of knowledge, stability of knowledge, speed of learning, and
ability to learn), Buehl & Alexander (2000) investigated the relations of these beliefs to
motivation and achievement in mathematics education. They found that epistemological beliefs
about mathematics were correlated with motivation and achievement.
For this study, only two beliefs have been selected for examination: one is a belief about
the ability to learn and the other is a belief about the speed of learning. These two beliefs seem to
be directly related to the learning process rather than to the structure, source and stability of
knowledge, which are more clearly epistemological in nature. Indeed, the inclusion of these two
2
beliefs in the category of epistemological beliefs has been debated, with Hofer & Pintrich (1997)
indicating that they were beliefs about “learning and intelligence, not knowledge” (Alexander,
Fives, Buehl, & Mulhern, 2000, p. 13).
In short, what helps students develop beliefs that facilitate learning about mathematics
knowledge acquisition might also help them become motivated to study mathematics and be
persistent in their studies. These beliefs have been variously labeled as ‘availing’, ‘sophisticated’,
‘mature’ or ‘appropriate’ beliefs; the term ‘availing beliefs’ is used herein (Muis, 2004;
Schoenfeld, 1988; Schommer, 1994a). Such beliefs should be taken into account when
considering ways to help students develop positive attitudes, study habits, and achievement in
mathematics. However, despite the acknowledgment of relationships between such beliefs about
mathematics and attitudes, study habits, and achievement, how to facilitate availing beliefs has
not been extensively studied. Given this lack of research and the potential importance of beliefs
for student achievement, this study develops and implements belief change strategies to facilitate
students’ availing beliefs about mathematics knowledge acquisition along with motivation and
volition change strategies to enhance motivation and volition and to improve student attitudes,
study habits, and achievement. It is the interrelatedness of these constructs – motivation, volition
and belief change – that sets this study apart from others. The underlying assumption is that these
constructs are interrelated and should, as a consequence, be taken explicitly into account in
designing and implementing interventions to support student learning.
Motivation, volition, and beliefs typically are resistant to change, particularly with regard
to challenging learning situations. Mathematics tasks can be challenging due to contextual
factors that make motivation and volition vulnerable, such as a lack of interest, relevance, or
confidence, regardless of the intrinsic difficulty the mathematics tasks. Students need to
acknowledge the values of motivation, volition, and belief change strategies. Despite a need to
provide motivation, volition, and belief change strategies, time constraints limit an instructor’s
ability to personally implement such supportive learning aspects in a large class setting. Methods
to supplement the supportive role are needed, regardless of whether the role is in or outside of
class. Email is one potential way to implement this support. An instructor can use email to send
messages pertaining to motivation, volition, and belief change strategies directly to students.
Email can enable instructors to overcome time and place constraints (Cifuentes & Shih, 2001;
Renkl, 2002). Also, email has the advantage of asynchronism; the asynchronous aspect of email
3
allows time-lags between sending, receiving, and responding, which may be useful in
stimulating reflection and allowing respondents to gain confidence in their replies (van der Meij
& Boersma, 2002).
Several studies on the instructional effects of email have been conducted. Email has been
used for interactions between instructors and students or among students to facilitate mentoring
(Boxie, 2004; Burgstahler & Cronheim, 2001; Cascio & Gasker, 2001), collaborative work,
(Dunlap, Neale, & Carroll, 2000; van der Meij & Boersma, 2002), and class activities
(Davenport, 2006; Nicosia, 2005; Poole, 2000). However, most of the emails in these studies did
not consider students’ motivation, volition, or beliefs; rather, they focused on the delivery of
course-related information. Previous studies that did include motivational and volitional
strategies were limited in scope, did not include belief strategies, and were not conducted with
mathematics classes (Keller, Deimann, & Liu, 2005; Kim & Keller, 2007a, 2007b). Accordingly,
it is appropriate to investigate whether and to what extent motivation, volition, and belief change
strategies distributed via email are effective in improving student performance. This study
investigates the effects of motivation, volition, and belief change strategies conveyed via email,
in mathematics courses – specifically in calculus required for non-mathematics majors, since it is
clear that motivation is a persistent problem in these courses which are often large lecture classes.
Of particular interest are how and to what extent such emails facilitate students’ motivation and
volition to study challenging mathematics tasks as well as students’ availing beliefs about
mathematics knowledge acquisition. The impact of these emails on students’ attitudes, study
habits, and achievement in mathematics courses is the focus of the empirical work in this study.
Problem Statement
The general purpose of this study is to investigate the effects of motivation, volition, and
belief change strategies on attitudes, study habits, and achievement in mathematics education.
The more specific goal is to determine to what extent an integrative approach to structuring
motivational, volitional and belief change messages for individuals and groups will promote
improved learning in calculus for non-mathematics majors.
4
Research Question
The main research question of this study pertains to the effects of motivation and volition
change strategies and belief change strategies delivered with personal and group messages via
email on attitudes, study habits, and achievement in a mathematics course.
Significance of Study
This study is significant in two ways. The primary contribution is in further investigating
possible ways to improve students’ motivation, volition, and beliefs for improved study and
performance in mathematics. Research has acknowledged students’ motivation, volition, and
beliefs as critical constructs in mathematics education; however, most research appears to be
fragmented, without an integrative view of three key constructs. Therefore, one purpose of this
study is to further examine the integrative effectiveness of students’ motivation, volition, and
beliefs in mathematics education.
A second contribution of this study is to identify implications derived from the integrated
analysis of motivation, volition and belief change for designing and developing effective
strategies, especially as implemented through supportive email messages, to enhance students’
attitudes, study habits, and achievement in mathematics. By building a conceptual framework
based on theoretical foundations and empirical studies as well as by conducting an exploratory
investigation based on that framework, this study provides empirical evidence as well as research
directions and a framework for motivation, volition, and belief change strategies to be used to
improve students’ attitudes, study habits, and achievement in mathematics education. This
framework is likely to generalize to other message forms and to other subjects and contexts, but
providing evidence of that generalizability is beyond the scope of this study. Such follow-on
research will be discussed in the final chapter.
5
CHAPTER II
REVIEW OF RELEVANT LITERATURE
Introduction
The importance of students’ motivation, volition, and beliefs has been recently
emphasized in mathematics education. In this context, motivation is defined as a construct that
encompasses a learner’s interest in the subject matter, feeling of relevance, confidence in success,
desires for commitment, and expectancy for satisfaction from achieving competence (Keller,
2004). Volition is defined as a construct that pertains to a learner’s goal setting, commitment,
efforts, emotion control, and persistence in study (Keller, 2004). Belief is defined as a construct
that consists of a learner’s acceptance of the structure, source, and stability of the knowledge to
be learned as well as the speed and ability to learn the subject knowledge (Schommer, 1990).
Considering that learning is viewed as a persistent “change in abilities, attitudes, beliefs,
capabilities, knowledge, mental models, patterns of interaction or skills (Spector, 2001, p. 8),”
the attention to those constructs is not surprising. However, despite extensive research
acknowledging students’ motivation, volition, and beliefs as critical factors for their attitudes
toward mathematics, study habits, and achievement, most research is still divergent rather than
based on a robust framework encompassing theoretical foundations and empirical evidence and
starting with systematic assumptions. Moreover, much of the previous research has been
conducted without an integrative view of the constructs and tends to overlook the
interconnectedness among the constructs, particularly with regard to mathematics education.
This study develops a conceptual framework for research on motivation, volition, and
beliefs in the improvement of students’ attitudes, study habits, and achievement in mathematics
education. The framework is grounded in a review of relevant theories and models as well as in
findings from empirical studies. The derived framework is then applied to mathematics
education and investigated in an exploratory study with a quasi-experimental design as an initial
validation of the framework. This exploratory experiment implements the framework as a tool
6
for the systematic design and development of interventions; outcomes are analyzed and
discussed in subsequent chapters. Specifically, this study investigates relationships between
students’ motivation, volition, and beliefs and their attitudes, study habits, and achievement. The
study also investigates the effects of motivation, volition, and belief change strategies on
students’ attitudes, study habits, and achievement in mathematics education. The study uses
personal and group email messages to explore and validate the framework.
In order to provide a rationale for this study, reviews of relevant theories and empirical
studies are presented in four sections. The first section reviews issues in mathematics education
with regard to motivation, volition, and beliefs, which are shown to be related to positive
attitudes, study habits, and achievement. The second section reviews theories and models
concerning motivation and volition as well as previous studies on motivation and volition change
strategies. The third section presents theories and models regarding beliefs about mathematics
knowledge acquisition as well as previous studies on belief-change strategies in mathematics
education. The fourth section clarifies the research framework of this study in terms of the
interconnectedness of motivation, volition, and belief change to attitudes, study habits, and
achievement in mathematics education. The fourth section also provides practical consideration
for implementation of change strategies based on the research framework, which describes a) a
rationale for distributing the motivation, volition, and belief change strategies via email, and b)
personal and group email messages to explore and validate the framework.
Issues in Mathematics Education
Like many disciplines, engaging students in learning mathematics continues to be a
challenge for educators and an interest for researchers (Guha & Leonard, 2002). At the same
time, students’ academic engagement in mathematics is considered especially difficult to
engender, since learning mathematics requires appropriate motivation, beliefs, attitudes, study
habits, and emotion in addition to specific cognitive skills (Turner & Meyer, 2004). Researchers
have recently acknowledged the direct and indirect impact of non-cognitive constructs and nonmathematical skills on achievement in mathematics courses, and, consequently, considerable
research in this regard has been conducted.
7
Educators in college mathematics have begun to pay attention to students’ motivation,
beliefs, attitudes, study habits, emotions and other affective and cognitive factors. Especially in
the case of students who do not major in mathematics, problems with respect to such aspects in a
mathematics course are more serious. For example, some engineering students do not prefer to
study mathematics; that is, they are not intrinsically motivated, and they do not set studying
mathematics as a high priority in their studies; consequently, they are not willing to cope with
time constraints, which can cause problems with regard to attitudes and study habits (Croft &
Ward, 2001). Also, students’ beliefs about their mathematics ability, that is, self-efficacy, which
can influence their learning and achievement, might be lower than those who major in
mathematics (House, 1995, 2001). In addition, students’ tendency to focus on performance rather
than learning goals, mathematics anxiety, and low confidence negatively affect student
achievement in mathematics courses (Ironsmith, Marva, Harju, & Eppler, 2003).
Research has described the relations of motivation, beliefs, attitudes, study habits, and
emotions on achievement and attempted to improve students’ achievement through these
constructs. Specifically, researchers have examined the following constructs and their influence
on student performance:
•
motivation, attitudes, persistence, and engagement (Singh, Granville, & Dika, 2002);
•
self-efficacy, autonomy, relevance, and motivation (Schweinle, Meyer, & Turner,
2006);
•
self-beliefs and expectancies (House, 2001);
•
expectancy, motivation, and engagement (Guha & Leonard, 2002);
•
interest and study strategies (Hong, Sas, & Sas, 2006);
•
motivational goals (Gabriele & Montecinos, 2001);
•
goal setting, confidence, anxiety, attitudes, and emotions (Ironsmith, Marva, Harju, &
Eppler, 2003);
•
attitudes and beliefs (House, 2006; van Eck, 2006); and,
•
motivation, time management, and study habits (Croft & Ward, 2001).
8
These and other studies reported significant correlations among motivation, beliefs,
attitudes, study habits, emotion and achievement in mathematics as well as the effects of positive
development of these constructs on achievement. This body of evidence strongly suggests the
need for an integrated framework relating motivation, volition, and belief change to attitudes,
study habits and student achievement.
Although the aforementioned studies used different terms, overlapping constructs can be
put into three categories: a) students’ motivation including interest, attention, relevance, selfefficacy, and confidence; b) students’ volition including goal setting, study time, time
management, study strategies, emotion control, anxiety reduction; and c) students’ beliefs
including beliefs about mathematics ability, and beliefs about mathematic knowledge on their
attitudes, study habits, and achievement in various mathematics learning environments.
First, with regard to the relation of motivation to achievement, Schweinle, Meyer, and
Turner (2006) surveyed elementary students’ motivation and efficacy in mathematics and found
that these two constructs, along with task relevance, were predictors for students’ learning
mathematics. Guha and Leonard (2002) highlighted the importance of expectancy and
motivation in elementary students’ engagement in learning mathematics in a review of
expectancy theories. It seems reasonable to extend their findings to secondary and tertiary
students, although the means used to motivate and develop reasonable expectations are likely to
be different for students at different stages of development.
Second, with regard to the relations of motivation and volition to attitudes, study habits,
and achievement, Singh, Granville, and Dika (2002) investigated the effects of motivation,
attitude and study time on 8th graders’ achievement in mathematics. They found that attitude and
academic time, which they defined as motivational factors, had positive effects on achievement.
Ironsmith and his colleagues (2003) were interested in the different effects between self-paced
class and lecture class formats on students’ goal setting, confidence, mathematics anxiety and
attitudes in a remedial college mathematics course. They observed the positive impact of the
self-paced class on students’ achievement although the unique characteristics of the remedial
course might have influenced the research results in ways of students being initially motivated to
complete the course, for example. Croft and Ward (2001) were concerned about the first year of
college engineering students’ motivation, time management, and study habits in a mathematics
9
course and attempted to improve them by means of interactive online materials, although the
effects of the materials were not reported. Hong and her colleagues (2006) researched the
relationship between high school students’ interest and their study strategies with achievement in
a mathematics course and they found that the more students were interested in mathematics, the
better strategies they used and the better achievement they showed. Gabriele and Montecinos
(2001) were interested in 4th and 5th graders’ learning goals and performance goals and they
found that an instructional method inducing students’ learning goal setting had significantly
positive impact on their achievement.
Third, with regard to the relations of beliefs and motivation to attitudes, study habits, and
achievement, House (2001) examined American Indian students’ beliefs about their mathematic
ability and expectancy for their performance in a college mathematics course. He found their
beliefs and expectancies were predictors of mathematics achievement. In his 2006 study, he
investigated Japanese adolescent students’ motivation for and attitudes toward mathematics,
which he regarded as attention to the learning processes and thereby engagement. He also
examined beliefs about their mathematics ability, and both motivation and beliefs were
positively correlated with achievement in geometry (House, 2006). In addition, van Eck (2006)
studied the improvement of 7th and 8th graders’ attitudes toward and beliefs about mathematics
through innovative, instructionally related advising of pedagogical agents and games. He
reported significant findings on students’ improved achievement by reducing their mathematics
anxiety.
Most of these studies provided a basis to account for possible interconnectedness among
motivation, volition, beliefs, attitudes, study habits, and achievement. However, some of them
simply surveyed the constructs and did not conduct experimental research to establish cause and
effect links among the constructs. In addition, although the other studies implemented particular
interventions to enhance some of the constructs in students with an attempt to show empirical
evidence for the relations among the constructs, some weaknesses were found. First, these
studies merely describe the intervention rather than analyze its conceptual basis and effectiveness
in solving problems related to motivation, volition, beliefs about learning, and so on (e.g., Croft
& Ward, 2001). Second, the design and development process of the interventions used did not
sufficiently explain frameworks for the process based on theoretical foundations and empirical
studies (Gabriele & Montecinos, 2001; Ironsmith, Marva, Harju, & Eppler, 2003; van Eck, 2006).
10
Lastly, a systematic design process to diagnose and prescribe solutions to problems with regard
to motivation or beliefs, for example, were not used; that is, there was no initial investigation of
problems that could provided a basis to design the interventions (e.g., Croft & Ward, 2001;
Gabriele & Montecinos, 2001; Ironsmith, Marva, Harju, & Eppler, 2003; van Eck, 2006).
In light of the lack of a conceptual framework driving research in this area, this study
intends to provide a robust conceptual framework based on theoretical foundations and empirical
studies with regard to motivation, volition, and beliefs to enhance positive attitudes, study habits,
and achievement. The following sections will review literature with regard to motivation,
volition, and beliefs about learning, and use these reviews as the basis for a framework for the
design and development of motivation, volition, and belief change strategies used in the
exploratory study that was conducted.
Motivation and Volition
Motivation refers to those things that pertain to a person’s desires or goals and beliefs
that those goals can be achieved. Volition refers to behaviors and attitudes that help a person be
or become persistent in working toward goal accomplishment. Volition is similar to the concept
of self-regulation but more inclusive (Keller, 2004). Volition pertains to a learner’s goal setting,
commitment, efforts, emotion control, and persistence in study (Keller, 2004). Historical
connectedness and separation of motivation and volition are discussed and research in motivation
and volition change strategies also examined.
Although motivation is not the same as volition (Corno, 2004), the two constructs are not
exclusive; rather, motivation, defined as “the process whereby goal-directed activity is instigated
and sustained,” originally encompassed volition (Pintrich & Schunk, 2002, p. 5). The early work
of James (1890) distinguished between two components of motivation - will and volition - but
that distinction was not maintained in the literature and research on motivation. Keller (2004), in
his review of the literature on motivation, reintroduces this distinction. Briefly speaking, ‘will’
refers to a person’s intention to pursue a goal and ‘volition’ refers to actions taken to fulfill that
intention. In this vein, Pintrich and Schunk’s (2002) definition of motivation appears to indicate
11
both parts; that is, the process of a goal-directed activity being instigated seems to refer to will;
the process of a goal-directed activity being sustained seems to refer to volition.
Moreover, a reciprocal relation is found between motivation and volition, meaning that a
person’s will, desire, and intention to pursue a goal influence the person’s actions to achieve the
goal, and vice versa. Pintrich and Schunk’s (2002) notion of motivation indexes corresponds to
the reciprocal relation between motivation and volition although they do not use the term of
volition. That is, the indexes contain “choice of tasks, effort, and persistence” (Pintrich &
Schunk, 2002, p. 13), which represent the critical components of volition mainly discussed in
volition research (Corno, 2001, 2004; Gollwitzer, 1990, 1999; Gollwitzer & Brandstätter, 1997;
Gollwitzer, Heckhausen, & Ratajczak, 1990; Kuhl, 1987; Kuhl & Fuhrmann, 1998; Winne,
2004; Zimmerman, 1998, 2001, 2002; Zimmerman & Schunk, 2001).
Just as the motivation literature refers to the concept of volition, the volition literature
includes the concept of motivation. For example, Gollwizer and Brandstätter (1997) propose a
model illustrating how to transform desires to actions, called the Rubicon model of
implementation intention (Gollwitzer, 1990, 1999; Gollwitzer & Brandstätter, 1997). The model
consists of four phases, which are pre-decisional, pre-actional, actional, and post-actional
phases, with the first phase indicating motivation with the steps of wishing, deliberating, and
choosing. Kuhl’s (1987) action control theory also includes motivation. The theory specifies a
set of control strategies that can help a person overcome distractions interfering with the person’s
intentions and actions, and motivation control is one of the strategies.
Despite the reciprocal relationship between motivation and volition, most empirical
studies on motivation seem to have focused on only motivation and most empirical studies on
volition seem to have paid attention to only volition. In other words, the former appear to study
how people form their will, desire, and intention to pursue their goals without further inquiries
regarding their persistent actions in pursuit of the goals (e.g., Gao & Lehman, 2003; Hirumi &
Bowers, 1991; Klein & Frietag, 1991; Naime-Diefenbach, 1991; Song & Keller, 2001; Yang,
1991). The latter appear to study how people take actions on their goals without attempts to
understand the underlying reasons for the formation of their goals (e.g., Fishbach & Trope, 2005;
Gillholm, Erdeus, & Garling, 2000; McCann & Turner, 2004; Schorr, Gerjets, & Scheiter, 2003;
Sniehotta, Nagy, Scholz, & Schwarzer, 2006; Winne, 2004). Although understanding both
12
motivation and volition together is beneficial for the design and development of effective
learning and teaching environments, there are only a few studies on both motivation and volition
(Deimann, 2005; Deimann & Keller, 2006; Dornyei & Otto, 1998; Keller, Deimann, & Liu,
2005; Kim & Keller, 2007a, 2007b; Schallert, Reed, & Turner, 2004).
In mathematics education, as reviewed in the previous section, only a few studies were
concerned about both motivation and volition together. When categorizing self-efficacy and
interest into motivational components as well as goal setting, emotion control, time management,
study habits, persistence, and study strategies into volitional components, some studies have
considered both motivation and volition (Croft & Ward, 2001; Hong, Sas, & Sas, 2006;
Ironsmith, Marva, Harju, & Eppler, 2003; Singh, Granville, & Dika, 2002). However, most of
the previous studies highlighted only motivational components such as relevance, expectancies,
and confidence, without interest in or attention to volition (Gabriele & Montecinos, 2001; Guha
& Leonard, 2002; Schweinle, Meyer, & Turner, 2006; van Eck, 2006).
Considering that motivation is commonly related to learners’ achievement in addition to
their attitudes since it impacts what to learn and how to learn (Schunk, 1991) and that volition
determines actual commitments to the process of studying, both motivation and volition may
need to be taken into account in mathematics education. In addition, although volition generally
results from strong motivation, volition may not result when there are a lot of distractions taking
motivated learners away from their studies (Keller, 2004). Thus, researchers and instructors need
to take both motivation and volition into consideration in college mathematics education where
learners tend to encounter many distractions such as increased non-academic obligations and
freedom in their college life compared to in high school.
A model that integrates these constructs and concepts is necessary in order to understand
student behavior and to design appropriate support for learning. In addition, different wording
but similar concepts have been used in the research on motivation and volition, and these
concepts need to be integrated in order to build a basis for the design and development of
interventions or systems that facilitate learners’ positive attitudes, study habits, and achievement.
In fact, Keller (2004) proposes an integrative theory of motivation, volition and performance
(MVP theory) that incorporates several approaches to motivation, volition and information
processing in an attempt to explain an individual’s behavior influenced by his or her internal and
13
external factors. The main purpose of MVP theory is to provide designers with a framework to
“create effective learning environments that meet the needs of the intended audiences” (Keller,
2004, p. 3). This corresponds with a general purpose to improve the effectiveness of college
math education.
Specifically, Keller’s (2004) MVP theory consists of Keller’s (1987a) ARCS model
(attention, relevance, confidence, and satisfaction), Gollwitzer’s (1990, 1999) Rubicon model of
implementation intention, and Kuhl’s (1987) action control theory, which might be useful to
enhance students’ motivation and volition, and thereby their attitudes, study habits, and
achievement as described as follows.
First, in Keller’s (1987b) ARCS model, there are four categories: attention, relevance,
confidence and satisfaction. Attention explains how to stimulate a sense of inquiry about a given
task in learners. Relevance addresses how to relate the task to learners’ own situations.
Confidence articulates how to convince learners that they can achieve their goals. Satisfaction
shows what learners would get once they accomplish their goals. Second, Gollwitzer’s (1990,
1999) Rubicon model of implementation intentions highlights the strategies that could help
learners take action on their goals. As volition is defined as transforming desire to action (Keller,
2004), the model explains the need to set a goal, to plan for the goal, and to make a commitment
to the goal. Third, Kuhl’s (1987) action control theory provides six action control strategies: 1)
selective attention control strategy intends to encourage learners to pay attention only to the
information related to actions for their goals; 2) encoding control strategy intends to facilitate
learners’ accepting their current task as a requirement to achieve their goals; 3) emotion control
strategy intends to prevent any negative feelings from interfering with actions for their goals; 4)
motivation control strategy intends to help learners stay motivated; 5) environment control
strategy intends to protect learners from distractions; and 6) parsimonious information
processing intends to help learners make decisions on how to effectively and efficiently
distribute time and effort for their actions.
14
Research on Motivation and Volition Change Strategies
Based on Keller’s (2004) integrative theory of motivation, volition and performance,
Keller and his colleagues have recently conducted experimental research (Keller, Deimann, &
Liu, 2005; Kim & Keller, 2007a, 2007b). They found that there could be positive effects of
motivational and volitional support strategies, which were constructed based on the three models
and theories explained earlier, on students’ learning motivation and achievement. Their studies
were conducted in archeology (Keller, Deimann, & Liu, 2005; Kim & Keller, 2007b) and
technology classes (Kim & Keller, 2007a). The archeology class studies suggest that the
provision of motivational and volitional strategies with personal messages addressing specific
individual problems may be useful supports for improving students’ motivation and learning in
the situation where there are threats to motivation but there is little interaction between
instructors and students owing to a large lecture setting (Kim & Keller, 2007b). They analyzed
individual participants’ profiles or motivation and volition, and created and sent the personal
messages along with motivation and volition change strategies. In addition, the investigation of
their technology class study included the effects of motivation and volition strategies email on
participants’ attitudes (Kim & Keller, 2007b). They found participants’ improved attitudes
toward technology and speculated that enhanced motivation and volition through change
strategies given might have facilitated positive attitudes. However, their studies need to be
validated in the context of learning mathematic although their studies were grounded in a strong
foundation of motivation and volition.
Beliefs about Learning and Mathematics Knowledge
Students’ beliefs about knowledge and learning have been researched with the hope that
such beliefs can explain students’ responses to learning contexts (Hofer, 1997). Some research
has focused on how beliefs developed over time (e.g., Kloosterman, 1996; Mason, 2003) while
others have highlighted how beliefs influence the cognitive processes involved in learning (e.g.,
Garofalo, 1989; Hofer, 1999; Kloosterman, 1991; Mason, 2003; Milambiling, 2001; Schoenfeld,
1988, 1992; Schommer, 1990). Research has also investigated how beliefs mediate the factors of
attitudes toward mathematics learning (i.e., interest, willingness, etc.) and study habits (i.e.,
15
efforts, persistence, etc.), which are considered to indirectly impact learning and achievement
(e.g., Alexander, Fives, Buehl, & Mulhern, 2000; Cady, Meier, & Lubinski, 2006; Cavallo,
Potter, & Rozman, 2004; Hofer, 1999; Kloosterman, 1996; Pintrich & Schunk, 2002). The
research with regard to the relation of beliefs to attitudes and study habits is relatively newer and
there are fewer studies than one finds in research regarding the development of beliefs and the
relation of cognitive processes to beliefs.
In addition, research has been conducted on how to change students’ beliefs about
knowledge and learning to more availing beliefs that presumably facilitate learning (e.g.,
Bendixen, 2002; Carter & Yackel, 1989; Erickson, 1993; Franke & Carey, 1997; Higgins, 1997;
Lampert, 1990; Pintrich & Schunk, 2002; Yackel & Cobb, 1996). ‘Availing’ is the term first
used in Muis (2004) to characterize some beliefs as implying more possibilities to enhance
students’ learning. For example, the belief that ability to learn is not innate or fixed but it is
developed over time is an availing belief, which would influence students’ decision making
regarding effort in a challenging task. This term has the same meaning as ‘sophisticated’ and
‘mature beliefs’ in Schommer (1994a) and ‘appropriate beliefs’ in Schoenfeld (1988), as
opposed to ‘naïve and inappropriate beliefs’.
In short, researchers who consider students’ beliefs about knowledge and learning
directly or indirectly related to learning outcomes through influencing cognitive processes,
motivation, attitudes, behavior, efforts, etc. have studied the development of the beliefs and
possible ways to improve the beliefs in a variety of learning contexts. Meanwhile, most research
on students’ beliefs about knowledge and learning has been in light of epistemological beliefs.
One of the most influential scholars in the field of epistemological belief research, Schommer
has conceptualized and refined a framework of epistemological belief research (SchommerAikins, 2002; Schommer-Aikins & Hutter, 2002; Schommer-Aikins, Mau, & Brookhart, 2000;
Schommer, 1990, 1993a, 1993b, 1994a, 1994b, 1998). She indicated that “unspoken and
sometimes unconscious beliefs about the nature of knowledge and learning play a critical role” in
guiding learners’ thinking (Schommer-Aikins & Hutter, 2002, p. 13), and thereby the beliefs
affect reasoning, decision making, and persistence in challenging learning environments. She
proposed that a person’s epistemological beliefs consisted of multidimensional beliefs, which
were to some degree independent of each other (Schommer, 1990), and she validated the
proposal with students in various learning contexts and at different grade levels. Her five
16
dimensions of students’ beliefs about knowledge and learning consist of: 1) the structure of
knowledge, ranging from isolated pieces to integrated concepts; 2) the source of knowledge,
ranging from authority to reasoning; 3) the stability of knowledge, ranging from certain
knowledge to changing knowledge; 4) the speed of learning, ranging from quick learning to
gradual learning; and, 5) the ability to learn, ranging from fixed at birth to improvable
(Schommer-Aikins, Duell, & Hutter, 2005; Schommer, 1990, 1993b, 1998).
Her five dimensions of epistemological beliefs provided researchers with a foundation to
study students’ beliefs about knowledge and learning, along with her measurement tool,
Epistemological Belief Questionnaire (EBQ), commonly used in epistemological belief research.
Nonetheless, the last two beliefs (i.e., beliefs about speed of learning and ability to learn) seem to
be directly related to learning rather than to knowledge, compared to the other three representing
beliefs about knowledge itself. Considering that epistemological beliefs originally referred to
beliefs about knowledge (Schraw, 2001), the inclusion of these two beliefs in the category of
epistemological beliefs has been a question under debate as Hofer and Pintrich (1997) indicate
that these two are beliefs about “learning and intelligence, not knowledge” (Alexander, Fives,
Buehl, & Mulhern, 2000, p. 13).
In addition to the multidimensionality of epistemological beliefs, there has been an issue
with regard to domain generality versus domain specificity of the beliefs (Alexander, Fives,
Buehl, & Mulhern, 2000; Hofer, 2000, 2006; Muis, 2004; Stodolsky, 1985). Most of the early
research on students’ epistemological beliefs premised that the same beliefs were observed
across domains. However, some researchers recently started questioning the domain generality
and proposing that students’ epistemological beliefs should be separately recognized according
to domains. In fact, many of the researchers found that a person’s epistemological beliefs are
different across domains. For example, Muis, Bendixen, and Haerle (2004) supported domain
specificity through a comprehensive review of empirical studies in this regard. Also, Buehl and
her colleagues (2000) found that there were prominent differences in students’ epistemological
beliefs about knowledge and learning between history and mathematics. In addition, Hofer
(2006) argued that epistemologies were not the same across domains owing to the unique
characteristics of academic disciplines, although she criticized several studies supporting domain
specificity on methodological grounds.
17
As mentioned earlier, there is a need to have a robust conceptual framework based on
theoretical foundations and empirical studies with regard to motivation, volition, and beliefs in
order to design and develop interventions facilitating students’ attitudes, study habits, and
achievement in mathematic education. In this vein, Schommer’s five dimensions of
epistemological beliefs may be able to provide a basis for a systematic design process to
diagnose and prescribe solutions to problems with regard to nonavailing beliefs (i.e., beliefs that
interfere with learning) in mathematics education. In addition, her measurement tool,
Epistemological Belief Questionnaire (EBQ), could be useful for the systematic design process.
However, considering that the focus of this study is on learning rather than knowledge, beliefs
should be about how to acquire mathematics knowledge (i.e., learning mathematics). Thus,
among the five dimensions of epistemological beliefs, only two beliefs regarding learning (i.e.,
beliefs about speed of learning and ability to learn) seem to be appropriate for this study.
Meanwhile, beliefs about knowledge acquisition have been recognized as an important
factor for students’ learning and achievement by influencing both their attitudes toward learning
mathematics and their study habits in mathematics. Moreover, as research on domain specificity
of epistemological beliefs has indicated, students’ beliefs about learning mathematics were found
to be different from other disciplines (e.g., Alexander, Fives, Buehl, & Mulhern, 2000; Muis,
2004; Stodolsky, 1985). For instance, Stodolsky (1985) compared students’ beliefs about
mathematics with social studies knowledge acquisition, and she found that students had beliefs
about passive learning in elementary school mathematics courses. That is, differently from social
studies courses, the students did not believe they could learn from the process of knowledge
construction since they were totally dependent on formulas and rules delivered by their teacher.
Buehl and Alexander (2000) investigated differences between college students’ beliefs about
learning mathematics and history, and they found beliefs about mathematics were more nonavailing than those about history. That is, the students’ belief that knowledge acquisition came
from instructors and pre-existing texts providing unchanging facts was stronger for mathematics
than for history. In fact, these findings are consistent with the criticism of “nonavailing beliefs at
all education levels” (Muis, 2004, p. 317), which were observed in many other studies (Doyle,
1988; Franke & Carey, 1997; Schoenfeld, 1988).
Up to now, the review of previous studies revealed the possibility of using
multidimensional epistemological beliefs as a basis for research on beliefs about knowledge
18
acquisition as well as a need to investigate domain specific beliefs. Also, non-availing beliefs
were found to be a problem in mathematics education. On the basis of this review, a further
inquiry would be then on what kinds of efforts have been made to improve mathematics learning
environments in light of students’ beliefs about knowledge acquisition. Some answers to this
inquiry will be described in the following section.
Research on Belief Change Strategies
Muis (2004) indicated that beliefs develop and change over time in light of Schommer’s
(1994a) argument of recursive development of epistemological beliefs rather than sequential
development. Support for this argument can be found in several prior studies (e.g., Franke &
Carey, 1997; Higgins, 1997; Kloosterman & Cougan, 1994). For example, Kloosterman and
Cougan (1994) interviewed K-6 elementary students about their beliefs about mathematics,
including a question about whether they believed that everybody could learn mathematics. The
authors found that most students showed availing beliefs about their ability to study mathematics,
which was inferred from the success of a 2-year project to improve teaching mathematics.
Franke and Carey (1997) also found that 1st graders had availing beliefs about mathematics
knowledge acquisition in the classrooms where problem-solving was highlighted. As they
regarded this finding as “reform-minded” (p. 8), they indicated that these beliefs were developed
as a result of the uniqueness of the classroom environment.
Although the two studies above explained the possibility that students’ beliefs about
mathematics learning develop and change in certain environments, they did not specify the
characteristics of experiences that the environments provided. In addition, the discussions about
findings in both studies seem to be more or less weak, lacking comparison groups of students
who were not in the environments of the studies. Differently from these two studies, Higgins
(1997) compared problem-solving enhanced classrooms with traditional ones within a scope of
students’ beliefs about mathematics knowledge acquisition, and found more availing beliefs in
the former than the latter. Also, she differentiated the characteristics of the problem-solving
enhanced classrooms from the traditional ones in terms of teaching techniques and students’
responses. Still, there was no attempt to specifically connect possible cause-effect links between
19
the classroom characteristics and students’ belief change, which might have provided some
guidelines for researchers hoping to design and develop interventions for students’ belief change.
Aside from the aforementioned weaknesses, these studies provided preliminary evidence
showing that students’ beliefs about mathematics knowledge were developmental and
changeable by means of external factors. In addition, the findings of the studies found that the
change of students’ beliefs to availing ones was positively related to enhancing their attitudes,
study habits, and achievement (Higgins, 1997; Kloosterman & Cougan, 1994). Nonetheless, it
still remains how to systematically investigate what kinds of strategies are needed for belief
change.
In fact, Bendixen (2002) proposed a belief change model, which consisted of four
necessary conditions originally proposed by Pintrich, Marx, & Boyle (1993) for conceptual
change: 1) dissatisfaction with current beliefs, 2) understanding of alternative beliefs, 3) feltpossibility of use of the alternative beliefs, and 4) felt-usefulness of the alternative beliefs. Also,
she observed persons’ belief change processes and found that her proposed model reflected the
belief change processes. However, there was no attempt to investigate possible ways to facilitate
the change processes. Empirical research needs to be conducted based on a robust conceptual
framework in order to develop a consistent body of research. Given this gap, the next section will
first illustrate a possible framework for belief change along with previously reviewed motivation
and volition change, which might be a basis for experimental research.
The Framework of Research on Motivation, Volition, and Belief Change
Interconnectedness of Motivation, Volition, and Belief Change with Attitudes, Study
Habits, and Achievement
Thus far, a potential research framework has emerged, which is grounded in the
interconnectedness of motivation, volition, and beliefs with attitudes, study habits, and
achievement. The framework is described on the basis of the combination of three rationales as
follows:
20
First, students’ motivation and volition are directly and indirectly related to their attitudes,
study habits, and achievement in mathematics learning environments. In the studies reviewed in
the motivation and volition section of this chapter, a reciprocal relation was found between
motivation and volition (Corno, 2004; Gollwitzer, 1999; Gollwitzer & Brandstätter, 1997; Keller,
2004; Kuhl, 1987; Pintrich & Schunk, 2002). Several studies reviewed in the section of issues in
mathematics education reported the relations of students’ motivation and volition to their
attitudes, study habits, and achievement although there were not consistent uses of terms across
the studies (Croft & Ward, 2001; Gabriele & Montecinos, 2001; Hong, Sas, & Sas, 2006;
Ironsmith, Marva, Harju, & Eppler, 2003; Schweinle, Meyer, & Turner, 2006; Singh, Granville,
& Dika, 2002; van Eck, 2006). Figure 2.1 illustrates the relationships among motivation, volition,
attitudes, study habits, and achievement found in the previous studies. Solid lines show direct
relations and dashed ones show indirect relations. An arrow begins from the construct that
affects the other construct where that the arrow ends. For example, Figure 2.1 shows that
motivation influences attitudes and volition influences study habits and that as motivation and
volition affect each other. Figure 2.1 also shows that motivation and volition together indirectly
affect achievement.
Figure 2.1 Relationships among motivation, volition, attitudes, study habits, and achievement.
21
Second, students’ beliefs about mathematics knowledge acquisition are directly and
indirectly related to their attitudes, study habits, and achievement in mathematics learning
environments. As reviewed in the section of beliefs about mathematics, several studies indicated
the relations of students’ beliefs to their attitudes, study habits, and achievement, although there
were methodological weaknesses found in some of them (Alexander, Fives, Buehl, & Mulhern,
2000; Cady, Meier, & Lubinski, 2006; Cavallo, Potter, & Rozman, 2004; Garofalo, 1989;
Higgins, 1997; Hofer, 1999; Kloosterman, 1996; Kloosterman & Cougan, 1994; Milambiling,
2001; Pintrich & Schunk, 2002; Schoenfeld, 1988, 1992; Schommer, 1990). Figure 2.2 illustrates
the relationships among beliefs, attitudes, study habits, and achievement found in the previous
studies.
Figure 2.2 Relationships among beliefs, attitudes, study habits, and achievement.
Lastly, students’ beliefs about mathematics knowledge acquisition are directly and
indirectly related to motivation and volition in mathematics learning environments. Although this
was not specifically described in the previous sections, some of the studies reviewed implicitly
or explicitly indicated the positive relations of students’ beliefs to their motivation and volition
(Alexander, Fives, Buehl, & Mulhern, 2000; Kloosterman & Cougan, 1994; Muis, 2004;
Schommer-Aikins, Duell, & Hutter, 2005). For example, Alexander and her colleagues (2000)
22
argued that students’ “beliefs are influential factors in their thinking, motivation, and behavior
and are related to their task choice, persistence, and performance” (Alexander, Fives, Buehl, &
Mulhern, 2000, p. 698). In fact, they investigated the interrelationships among college students’
beliefs about mathematics, motivation, and performance, and their findings supported their
argument. Also, Muis (2004) indicated that much of the research on students’ beliefs found that
there were positive relationships of availing beliefs with motivation and volition (i.e.,
engagement, study time, and persistence in her words). In addition, an empirical study by
Kloosterman and Cougan (1994) found that students’ beliefs about mathematics impacted their
motivation and actions as well as their availing beliefs which produced the tendency of studying
hard with persistent effort. Figure 2.3 illustrates the relationships among beliefs, motivation, and
volition found in the previous studies.
Figure 2.3 Relationships among beliefs, motivation, and volition.
On the basis of the three rationales above, a potential research framework is built on the
interconnectedness of motivation, volition, and beliefs with attitudes, study habits, and
achievement. In other words, motivation and volition influence attitudes and study habits, which
affect achievement as shown in Figure 2.1. Beliefs influence attitudes and study habits, which
affect achievement as shown in Figure 2.2. Beliefs also influence motivation and volition as
shown in Figure 2.3. Figure 2.4 summarizes the interconnectedness of motivation, volition, and
beliefs with attitudes, study habits, and achievement found in previous studies.
23
Figure 2.4 Interconnectedness of motivation, volition, and beliefs with attitudes, study habits,
and achievement.
Based on this potential framework, what facilitates availing beliefs about mathematics
knowledge acquisition as well as motivation and volition might help students develop positive
attitudes and study habits, and thereby improve achievement in mathematics learning
environments. However, how to facilitate availing beliefs, motivation, and volition has not been
extensively studied, and it is only presumed that the provision of motivation, volition, and belief
change strategies might be an effective means of enhancing students’ attitudes, study habits, and
achievement as described in the next section.
Motivation, Volition, and Belief Change Strategies
Several strategies have been implemented to change students’ motivation, volition, or
beliefs in the previous studies. For example, there have been innovative technologies such as
interactive online materials (Croft & Ward, 2001), videos (Milambiling, 2001), pedagogical
agents and games (van Eck, 2006); classroom environment such as problem-solving enhanced
instructional methods (Franke & Carey, 1997; Higgins, 1997; Kloosterman & Cougan, 1994);
and email messages (Keller, Deimann, & Liu, 2005; Kim & Keller, 2007a, 2007b). As reviewed
in the earlier sections of this paper, however, some researchers tried new strategies but did not
report the results (Croft & Ward, 2001). Other researchers reported positive changes by means of
24
their strategies but did not specify the characteristics of the strategies (Franke & Carey, 1997;
Higgins, 1997; Kloosterman & Cougan, 1994) or sufficiently provide the theoretical or empirical
foundations to explain rationales for the design and development of the strategies used
(Milambiling, 2001). Therefore, although they showed some significant changes in students’
motivation, volition, or beliefs, their strategies appear to be difficult to replicate in other learning
environments.
Meanwhile, Keller and his colleagues’ studies describe the underlying foundations of
motivation and volition theories for designing and developing their strategies. Thus, it seems that
their research framework might be implemented in different learning contexts. Still, since their
studies were conducted in archeology (Keller, Deimann, & Liu, 2005; Kim & Keller, 2007b) and
technology classes (Kim & Keller, 2007a), their framework needs to be validated in the
mathematics learning environment. Still, the framework that they used for motivation and
volition change strategies appears to be appropriate for the purpose of this study. That is, it is
assumed that the provision of messages containing change strategies based on the motivation and
volition theories and models might be an effective intervention to facilitate student’s motivation
and volition in mathematics learning environments. Moreover, this assumption gives rise to
another assumption that the provision of messages containing change strategies based on
epistemological beliefs theories and models might also be an effective intervention to facilitate
students’ availing beliefs about mathematics knowledge acquisition (i.e., beliefs about ability to
learn and the speed of learning). As reviewed in previous sections, applicable motivation and
volition theories and models include Keller’s (1987a) ARCS model, Gollwitzer’s (1990, 1999)
implementation intentions, and Kuhl’s (1987) action control theory. Epistemological beliefs
theories and models applicable to this study include Schommer’s (1990) multidimensional
beliefs, various researchers’ domain specificity (Alexander, Fives, Buehl, & Mulhern, 2000;
Muis, 2004; Stodolsky, 1985), and Bendixen’s (2002) belief change model.
Based on the framework for motivation, volition, and belief change strategies discussed
so far, it was expected that motivation, volition, and belief change strategies would help improve
students’ motivation, volition, and beliefs, and thus attitudes, study habits, and achievement
would be enhanced. This study intended to investigate the effects of such strategies on attitudes,
study habits, and achievement in mathematics education. Figure 2.5 illustrates the research
framework of this study.
25
Figure 2.5 Research framework for this study.
Practical Consideration for Implementation of Change Strategies
To explore and validate the framework for motivation, volition, and belief change
strategies discussed so far and enhance the effectiveness of the strategies, this section discusses
practical considerations in terms of email as well as personal and group messages.
Strategy delivery via email: There is a need for methods to implement the supportive role of
motivation, volition, belief change strategies, regardless of whether they are in or outside of class.
Considering that factors such as time constraints may limit an instructor's ability to provide such
supports, email may be an effective way for instructors to provide motivation, volition, and
belief change strategies. Students might view email messages as a type of personal attention
since this medium is commonly used (Woods, 2002). It has also become a familiar technology as
26
email has been implemented in numerous contexts that require interactions between instructors
and learners or among learners (e.g., Boxie, 2004; Burgstahler & Cronheim, 2001; Cascio &
Gasker, 2001; Dunlap, Neale, & Carroll, 2000; van der Meij & Boersma, 2002). Emails have
potential for improving interactions between instructors and students by providing a means of
sending supportive information directly to each student. A benefit of using emails is that they
enable one to overcome time and place constraints that instructors might have (Buehl, Alexander,
& Murphy, 2002; Cifuentes & Shih, 2001). Email also has the advantage of asynchronism; it
permits time-lags between sending, receiving, and responding, which may stimulate students'
reflection (van der Meij & Boersma, 2002).
In fact, many studies on the effects of emails have been conducted in a variety of contexts.
For example, email has been used for interactions between instructors and students or among
students to facilitate mentoring (Boxie, 2004; Burgstahler & Cronheim, 2001; Cascio & Gasker,
2001), collaborative work, (Dunlap, Neale, & Carroll, 2000; van der Meij & Boersma, 2002),
and class activities (Davenport, 2006; Nicosia, 2005; Poole, 2000). However, most of the emails
in these studies did not seem to consider students’ motivation, volition, beliefs, attitudes, study
habits, etc.; rather, they focused on course-related information delivery. This might be because
the studies were conducted in classes with a small number of students where it might have been
possible for instructors to already pay sufficient attentions to the students. Therefore, the results
of these studies regarding the effectiveness of emails might not generalize to large lecture classes.
Also, since there was no preliminary analysis of students’ motivation, volition, beliefs, attitudes,
and study habits, there was no information about whether or not students might have already
been motivated, for example. They might also have had strong volition, positive attitudes, or
study habits before email was used, as their situations might have been different from those in
large, required courses where students might not have much intrinsic interest. Accordingly, it is
necessary to determine what type of email will best maximize the possible effectiveness of
motivation, volition, and belief change strategies in large required mathematics courses.
Personal versus group messages: Change in this study refers to improvement as described
previously. In other words, motivation, volition, and belief change strategies aim to support
improvement of students’ motivation, volition and beliefs in the hope that their attitudes, study
27
habits and achievement would thereby be improved. Then, since improvement is considered a
process from the current status to the desired status, change strategies implemented in this study
should be constructed based on students’ current status of motivation, volition, and beliefs. In
fact, the research framework for this study implies systematic design and development process of
change strategies including diagnosis of students’ needs for such strategies, which has rationales
in Keller and his colleagues’ studies (Kim & Keller, 2007a, 2007b).
Meanwhile, there are two ways to diagnose students’ needs in previous studies: one is to
survey overall status of the whole group and the other is individual examination of each student.
Keller & Visser (1990) implemented both methods and found overall positive effects. However,
they did not distinguish the effects between both methods. Meanwhile, Kim & Keller (2007b)
specifically investigated differences between the effects of the two: individually constructed
strategies versus generally constructed ones. They found more positive impact of the former;
however, the process used by other researchers to diagnose individual students’ needs and to
construct and distribute personal change strategies would not be easy for practitioners to
implement.
Along these lines, both methods seem to be worthy to be implemented to construct
change strategies in that message construction based on individual diagnosis (i.e., delivery type
of personal messages in this study) could be effective and in that message construction based on
general diagnosis (i.e., delivery type of general messages in this study) could be efficient. In
addition, considering that emails appearing irrelevant could be viewed as useless and the whole
content could be easily discarded by receivers (Kim & Keller, 2007b), change strategies with
group message might be improved in a way to include prompt questions that give choices to
receivers to select the most relevant ones from the whole content.
28
Hypotheses
This study was designed to investigate elements of the following question: How do
motivation and volition change strategies and belief change strategies delivered with personal
and group messages via email affect students’ attitudes, study habits, and achievement in a
mathematics course? In regard to this general question, three research questions and major
hypotheses followed by specific hypotheses are presented in this section. Seven conditions were
implemented in this study to investigate the treatment effects on attitudes, study habits, and
achievement: 1) motivation and volition change strategies distributed via email with personal
messages (MV-P), 2) motivation and volition change strategies distributed via email with group
messages (MV-G), 3) belief change strategies distributed via email with personal messages (BP), 4) belief change strategies distributed via email with group messages (B-G), 5) motivation,
volition, and belief change strategies distributed via email with personal messages (MVB-P), 6)
motivation, volition, and belief change strategies distributed via email with group messages
(MVB-G), and 7) neither motivation and volition change strategies nor belief change strategies
distributed via email (Control).
Research Question 1:
What are the effects of motivation and volition change strategies and belief change strategies
distributed as group and personal messages via email on attitudes toward mathematics?
Hypothesis 1: The first major hypothesis was that the type of email messages would result in
positive changes in participants’ attitudes toward mathematics. As shown in the research
framework of this study (see Figure 2.5, p. 26), this hypothesis stemmed from both theoretical
and empirical foundations indicating that a learner’s attitudes toward mathematics could become
more positive ones through motivation, volition, and belief change strategies.
Hypothesis 1.1: Specifically, it was expected that the participants receiving motivation
and volition change strategies (i.e., MV-P and MV-G) would show more positive changes in
attitudes than the participants receiving belief change strategies (i.e., B-P and B-G). The
rationale for this specific hypothesis was grounded in that the motivation and volition change
29
strategies included direct descriptions to control negative emotions about mathematics study and
consider calculus useful, which would be key components to form attitudes toward mathematics
(Fennema & Sherman, 1976). Although belief change strategies aimed to help learners obtain
more positive attitudes toward learning mathematics, such strategies described fundamental
reasons to build positive beliefs rather than to explain techniques to control feelings, for example.
Hypothesis 1.2: It was expected that those in the personal message groups (i.e., MVB-P,
MV-P, and B-P) would show more positive changes of attitudes than those in the general
message groups (i.e., MVB-G, MV-G, and B-G). As described earlier, personal messages
constructed based on individual needs were effective than group messages based on overall
status of the whole group although the former was less efficient than the latter (Kim & Keller,
2007b). Change strategies with personal messages were expected to be more effective on
participants’ attitudes than ones with group messages.
Hypothesis 1.3: It was expected that the participants receiving a combination of
motivation and volition change strategies and belief change strategies (i.e., MVB-P and MVB-G)
would show more positive changes in attitudes than the participants receiving either motivation
and volition change strategies (i.e., MV-P and MV-G) or belief change strategies (i.e., B-P and
B-G) but not both. As Figure 2.4 (p. 24) illustrated, both motivation and volition to study
mathematics as well as beliefs about learning mathematics have an impact on a learner’s
attitudes. That is, change strategies to improve the motivation, volition, and beliefs together
might have the most influence on a learner’s attitudes.
Hypothesis 1.4: It was expected that those in the control group would show the least
positive changes in attitudes among all the groups. The emails without change strategies were
predicted to have no impact on the participants’ attitudes.
Research Question 2:
What are the effects of motivation and volition change strategies and belief change strategies
distributed as group and personal messages via email on study habits? The hypotheses in this
section are parallel with the previous, but with the focus on the dependent variable of study
habits.
30
Hypothesis 2: The second major hypothesis was that the type of email messages would result in
positive changes in participants’ study habits. As shown in the research framework of this study
(see Figure 2.5), this hypothesis was grounded from both theoretical and empirical foundations
indicating that a learner’s study habits could transform to more positive ones through motivation,
volition, and belief change strategies.
Hypothesis 2.1: Specifically, it was expected that the participants receiving motivation
and volition change strategies (i.e., MV-P) would show more positive changes in study habits
than the participants receiving belief change strategies (i.e., B-P). The rationale for this specific
hypothesis was grounded in the fact that the motivation and volition change strategies included
direct descriptions to develop willingness to study calculus and persistence on the study, which
could bring more positive study habits. Although more positive beliefs obtained through belief
change strategies also could help increase participants’ willingness and persistence to study
calculus, motivation and volition change strategies were expected to have direct, immediate
impact on changes in study habits because of specific guidelines to control environment,
emotions, etc. based on Kuhl’s (1987) action control strategies.
Hypothesis 2.2: It was expected that the participants receiving a combination of
motivation and volition change strategies and belief change strategies (i.e., MVB-P) would show
more positive changes in study habits than the participants receiving either motivation and
volition change strategies (i.e., MV-P) or belief change strategies (i.e., B-P) but not both. As
illustrated in Figure 2.4, both motivation and volition to study mathematics as well as beliefs
about learning mathematics have impact on a learner’s study habits. That is, change strategies to
improve all the motivation, volition, and beliefs together might have the best influence on a
learner’s study habits.
Hypothesis 2.3: It was expected that those in the control group would show the least
positive changes in study habits among all the groups. The emails without change strategies were
predicted to have no impact on the participants’ study habits.
31
Research Question 3:
What are the effects of motivation and volition change strategies and belief change strategies
distributed as group and personal messages via email on achievement? Again, this section
employees an organizational structure that is parallel with the previous two sections.
Hypothesis 3: The third major hypothesis was that the type of email messages would result in
positive changes in participants’ achievement. This hypothesis was based on the research
framework of this study (see Figure 2.5) that showed improved attitudes toward mathematics and
study habits resulting from motivation, volition, and belief change strategies could help
participants get better grades because they would study more with more positive attitudes and
study habits.
Hypothesis 3.1: Specifically, it was expected that the participants receiving motivation
and volition change strategies (i.e., MV-P and MV-G) would show more positive changes in
achievement than the participants receiving belief change strategies (i.e., B-P and B-G). This
specific hypothesis was also grounded in the expectations described in Hypotheses1.1. and 2.1;
that is, the participants receiving motivation and volition change strategies (i.e., MV-P and MVG) would show more positive changes in attitudes and study habits than the participants
receiving belief change strategies (i.e., B-P and B-G), and thereby the achievement of the MV-P
and MV-G groups would be more improved than the B-P and B-G groups.
Hypothesis 3.2: It was expected that those in the personal message groups (i.e., MVB-P,
MV-P, and B-P) would show more positive changes of achievement than those in the general
message groups (i.e., MVB-G, MV-G, and B-G). This specific hypothesis stemmed from the
expectation described in Hypothesis 1.2; that is, the participants receiving change strategies as
personal messages (i.e., MVB-P, MV-P, and B-P) would show more positive changes in attitudes
than the participants receiving change strategies as group messages (i.e., MVB-G, MV-G, and BG), and thereby the achievement of the MVB-P, MV-P, and B-P groups would be more
improved than the MVB-G, MV-G, and B-G groups.
Hypothesis 3.3: It was expected that the participants receiving combined change
strategies (i.e., MVB-P and MVB-G) would show more positive changes in achievement than the
participants receiving either motivation and volition change strategies (i.e., MV-P and MV-G) or
belief change strategies (i.e., B-P and B-G) but not both. The rationale for this specific
32
hypothesis was grounded in the expectations described in Hypotheses 1.3 and 2.3; that is, the
participants receiving combination of motivation and volition change strategies and belief
change strategies would show more positive changes in attitudes and study habits, and thereby
the achievement of the MVB-P and MVB-G groups would be more improved than MV-P, MV-G,
B-P, and B-G groups.
Hypothesis 3.4: It was expected that those in the control group would show the least
positive changes in achievement among all the groups. This specific hypothesis was based on the
expectation described in Hypotheses 1.4 and 2.3; that is, the emails without change strategies
were predicted to have no impact on the participants’ attitudes or study habits.
33
CHAPTER III
METHOD
Participants
The participants were college undergraduate students enrolled in the course “Calculus
with Analytic Geometry I” in a large public university located in the southeastern United States.
This course was one of the required courses for undergraduate students whose majors were not in
mathematics, such as biology, chemistry, meteorology, and engineering. All participants were
recruited from two sections of the course and no extra credit was offered for participating in this
study. There were 115 among 126 students who agreed to participate in this study by signing the
consent. The initial sample size, 115, was determined based on the alpha level of .10 and a
moderate effect size and a power level .80 according to Cohen’s (1977, reprinted 1987) index.
During the study, 31 of 115 participants either dropped the course or cancelled their participation,
and, as a consequence, 84 students constitute the sample for this study.
Research Design
This investigation was an exploratory study with a quasi-experimental design. It focused
on the cumulative effects of email messages designed in accordance with the framework of
motivation and volition change strategies and belief change strategies discussed in the review of
relevant literature chapter. In order to test hypotheses, participants were assigned into one of
seven treatment groups. There were two independent variables and three dependent variables. All
the variables are discussed in detail in the variable sections of this chapter.
34
Independent Variables
The independent variables were the type of message content and the type of message
delivery emailed to participants. The first independent variable, the type of message content, was
implemented with three levels: 1) motivation and volition change strategies (MV), 2) belief
change strategies (B), and 3) motivation, volition, and belief change strategies (MVB) – a
combination of the first two levels. The second independent variable, the type of message
delivery, was implemented with two levels: 1) personal messages (P) and 2) group messages (G).
There were six conditions assigned based on the combinations of the independent variables, and
the seventh condition that contained neither change strategies nor personal messages was
considered the control group. The control group received emails only asking about their study
hours.
Condition
Table 3.1 The seven treatment groups involved in this study.
1
2
3
4
5
6
7
Message Content Type
Message Delivery Type
Motivation and volition change strategies
Motivation and volition change strategies
Belief change strategies
Belief change strategies
Motivation, volition, and belief change
Motivation, volition, and belief change
No change strategies
Personal messages
Group messages
Personal messages
Group messages
Personal messages
Group messages
Group messages
Acronym of
Condition
MV-P
MV-G
B-P
B-G
MVB-P
MVB-G
CONTROL
Type of Message Content
Motivation and volition change strategies (MV)
The first level of the first independent variable, the type of message content, was
motivation and volition change strategies (hereafter, acronym MV will be used). MV are defined
35
as messages that are constructed based on the following three models and theory: 1) Keller’s
ARCS model (Keller, 1987b), 2) Gollwitzer’s Rubicon model of implementation intentions
(Gollwitzer, 1990, 1999; Gollwitzer & Brandstätter, 1997), and 3) Kuhl’s action control theory.
The definition of MV also includes messages that are distributed via email. An example of MV
is shown in Figure 3.1.
If you would like to have an idea of actual, real-world, problems that you can solve with calculus,
please read the situation below. Even though you might never have to solve a problem like this,
it will be nice to know that you could do it. And who knows, you might even want to impress a
friend or parent by showing them that you can use calculus in the “real world.”
Suppose your family bought a big dog, a giant Schnauzer and your mom asks you to build a
fence for it. You’re supposed to spend no more than $900 on the fence and want the largest
size you can get for the money. One side of the property of your family house meets a river. So
you don’t need to put a fence on that side. The side of the fence parallel to the river will cost $5
per foot to build, whereas the sides perpendicular to the river will cost $3 per foot. What
dimensions should you choose?
Calculus gives you an answer to this situation. If you want to see the answer, just click here.
You will see the answer and how it was derived even though you won’t understand all the
computations at this point. However, you can expect that you will find yourself understanding
the solution later in this semester!
Figure 3.1 An example of a motivation and volition message.
First, the motivation components of MV addressed the four categories of Keller’s
(1987b) ARCS model: attention, relevance, confidence and satisfaction. The attention enhanced
message incorporated a tactic to stimulate a sense of inquiry about calculus in students. The
relevance enhanced message used a tactic to relate calculus to students’ own situations. The
confidence enhanced message utilized a tactic to convince them that they would achieve their
goals once they read the strategies given and used them. The satisfaction enhanced message
implemented a tactic to show what they would get after accepting the strategies given and using
them.
Second, the volition components of MV consisted of the volitional messages based partly
on Gollwitzer’s (1990, 1999) Rubicon model of implementation intentions. As volition is defined
as transforming desire to action (Keller, 2004), this part highlighted the strategies that could help
36
students take action on their goals. That is, this part explained the need to set a goal, to plan for
the goal, and to make a commitment to the goal. These components corresponded with the subconcepts of implementation intentions; that is, commitment to goal, formation of intention
commitment, intentions for action, and so on.
Lastly, another set of the volition components of MV comprised the volitional messages
implementing Kuhl’s (1987) six action control strategies: 1) selective attention intended to
encourage students’ to pay attention only to the information related to actions for their goals; 2)
encoding control intended to facilitate accepting their current task as a requirement to achieve
their goals; 3) emotion control intended to prevent any negative feelings from interfering with
actions for their goals; 4) motivation control was covered in the aforementioned motivational
messages; 5) environment control intended to protect them from distractions or social
commitments by advising them to let people know their goals and plans; and 6) parsimonious
information processing intended to help them make decisions on how to effectively and
efficiently distribute time and effort for their actions.
Sequence of motivation and volition change strategies: One issue may be with regard to how
all the components from different models and theories could be integrated into one kind of
change strategy. Potential problems with this issue were resolved through sequencing four
different emails according to one model, which best represented the whole framework of the
strategies. That is, this study was designed to send four different kinds of emails involving MV.
Each of the emails followed the sequence of four phases in Gollwitzer’s implementation
intention model and integrated other components from different models and theory into each of
the four phases as follows: 1) the motivational phase, 2) the pre-actional phase, 3) the actional
phase, and 4) the post-actional phase. According to this sequence, the components from other
models and theory were chosen to facilitate each phase. Table 3.2 shows how components from
different models and theories were used to construct MV.
37
Table 3.2 The use of components from different models and theories for MV
STAGE 1: Motivational Phase
ARCS categories (Keller, 1987)
- Attention: Perceptual arousal; Inquiry arousal
- Relevance: Goal orientation; Motive matching; Familiarity
- Confidence: Success opportunities; Success opportunities; Personal control
- Satisfaction: Natural consequences; Positive consequences
STAGE 2: Pre-actional Phase
Implementation intentions (Gollwitzer, 1990)
- Commitment to goal
- Formation of intention commitment
- Intentions for action; Planning for action
STAGE 3: Actional phase
Implementation intentions (Gollwitzer, 1990)
- Initiation of action
- Goal-oriented action
Action control strategies (Kuhl, 1987)
- Selective attention
- Encoding control
- Emotion control
- Motivation control
- Environment control
- Parsimonious information processing
STAGE 4: Post-actional Phase
Implementation intentions (Gollwitzer, 1990)
- Realization termination of action
- Assessment; evaluation (Self-reflection)
38
Belief Change Strategies (B)
The second level of the first independent variable, the type of message content, was belief
change strategies (hereafter, acronym B will be used for convenience). B are defined as messages
that are constructed based on two models: 1) Schommer’s (1990) model of multidimensional
epistemological beliefs, and 2) Bendixen’s (2002) belief change model. The definition of B also
includes messages that are distributed via email. An example of B is shown in Figure 3.2.
Do you believe that a person has to have a natural ability for math in order to be good at
calculus? If so, please read the story below. It might help you change your mind.
Suppose your family bought a big dog, a giant Schnauzer and your mom asks you to build a
fence for it. You’re supposed to spend no more than $900 on the fence and want the largest
size you can get for the money. One side of the property of your family house meets a river. So
you don’t need to put a fence on that side. The side of the fence parallel to the river will cost $5
per foot to build, whereas the sides perpendicular to the river will cost $3 per foot. What
dimensions should you choose?
This question was given to all the students in a university classroom. What happened? Some of
students produced a correct solution and an answer. The rest of the students shouted to them
“You’re geniuses!” But, you know what? There was only one difference between students who
could solve the problem and those who couldn’t. It was whether or not they previously learned
about the formulas used for the solution. What does this mean? It means they didn’t need to be
math geniuses to solve the problem! It meant that math is a learnable skill and that they simply
used what they had learned in the classes they had previously taken. You have the ability to
learn calculus or you would not have gotten into this class, but it can be a challenge and it
requires persistent effort from you!
By the way, calculus gives you an answer to this situation. If you want to see the answer, just
click here. You will see the answer and how it was derived even though you won’t understand
all the computations at this point. However, you can expect that you will find yourself
understanding the solution later in this semester!
Figure 3.2 An example of a belief change message.
First, B addressed two beliefs from Schommer’s (1990) model of multidimensional
epistemological beliefs; that is, a belief about speed of learning, ranging from quick learning to
gradual learning, and a belief about ability to learn, ranging from fixed at birth to improvable.
Also, the questions of her Epistemological Belief Questionnaire (EBQ) to measure these two
beliefs (see Appendix B) were utilized as a checklist to address all the components that pertained
to the beliefs. That is, B utilized the statements for the EBQ questions, for example, “Some
people are born good learners, others are just stuck with limited ability,” for constructing
messages such as “Do you believe that a person has to have a natural ability for math in order to
39
be good at calculus? If so, please read the story below. It might help you change your mind” as
shown in Figure 3.2.
Second, B implemented Bendixen’s (2002) belief change model, which consisted of four
necessary conditions originally proposed by Pintrich, Marx, & Boyle (1993) for conceptual
change: 1) dissatisfaction with current beliefs, 2) understanding of alternative beliefs, 3) feltpossibility of use of the alternative beliefs, and 4) felt-usefulness of the alternative beliefs. Her
model was chosen for this study because of its characteristics representing change processes
implying the five attributes of perception in Rogers’s (2003) diffusion of innovation model (i.e.,
relative advantage, complexity, compatibility, trialability, and observability). That is, her model
reflected Rogers’s argument that there is a higher probability of a person being persuaded to
accept something new (i.e., an ‘innovation’ is Rogers’ word for what are called availing beliefs
in this study) when people perceive the new thing as being better than they are currently using
(relative advantage), as being consistent with their current beliefs and practices (compatibility),
and as being easy to use (complexity) as well as when they have opportunities to see successful
outcomes of using it (observability) and to try it in their own contexts (trialability).
Sequence of belief change strategies: The issue of integrating different components into one
kind of change strategies discussed earlier was also resolved through sequencing of four different
emails in this strategy. Each of four different emails of B followed the sequence of four
conditions in Bendixen’s belief change model and integrated other components from different
models into each of the four conditions as follows: 1) dissatisfaction with current beliefs, 2)
understanding of alternative beliefs, 3) felt-possibility of use of the alternative beliefs, and 4)
felt-usefulness of the alternative beliefs. According to this sequence, the components from other
models were chosen to facilitate each condition. Table 3.3 shows how components from different
models and theories were used to construct B.
40
Table 3.3 The use of components from different models and theories for B.
STAGE 1: Dissatisfaction with Current Beliefs
Two of multidimensional epistemological beliefs (Schommer, 1990)
- A belief about speed of learning, ranging from quick learning to gradual learning
- A belief about ability to learn, ranging from fixed at birth to improvable
1st condition of the belief change model (Bendixen, 2002)
- Intrigue reflection on beliefs about speed of learning and ability to learn math
- Explain shortcomings of the beliefs of quick learning and fixed ability
STAGE 2: Understanding of Alternative Beliefs
Two of multidimensional epistemological beliefs (Schommer, 1990)
- A belief about speed of learning, ranging from quick learning to gradual learning
- A belief about ability to learn, ranging from fixed at birth to improvable
2nd condition of the belief change model (Bendixen, 2002)
- Introduce the beliefs of gradual learning and improvable ability to learn math
STAGE 3: Felt-possibility of Use of the Alternative beliefs
Two of multidimensional epistemological beliefs (Schommer, 1990)
- A belief about speed of learning, ranging from quick learning to gradual learning
- A belief about ability to learn, ranging from fixed at birth to improvable
rd
3 condition of the belief change model (Bendixen, 2002)
- Tell stories showing positive impacts of the beliefs of gradual learning and improvable
ability to learn math
STAGE 4: Felt-usefulness of the Alternative Beliefs
Two of multidimensional epistemological beliefs (Schommer, 1990)
- A belief about speed of learning, ranging from quick learning to gradual learning
- A belief about ability to learn, ranging from fixed at birth to improvable
4th condition of the belief change model (Bendixen, 2002)
- Ask about students’ changes in beliefs about gradual learning and improvable ability to
learn math
- Describe potential positive impacts of their changes on attitudes, behaviors, and
learning
Motivation, volition, and belief change strategies (MVB)
The third level of the first independent variable, the type of message content, was
motivation, volition, and belief change strategies (hereafter, acronym MVB will be used for
convenience). MVB are defined as messages that are constructed based on the following five
models and theory:1) Keller’s ARCS model (Keller, 1987b), 2) Gollwitzer’s Rubicon model of
implementation intentions (Gollwitzer, 1990, 1999; Gollwitzer & Brandstätter, 1997), 3) Kuhl’s
action control theory, 4) Schommer’s (1990) model of multidimensional epistemological beliefs,
and 5) Bendixen’s (2002) belief change model. The definition of MVB also includes messages
41
that are distributed via email. One issue may be with regard to whether MVB could be too much
longer than each of the other two messages (MV and B). In order to prevent any potential
problem with this issue, the same example cases or scenarios were used for MV and B when
constructing messages. Thus, although all the critical components in MV and B were included
into MVB, using only one case or scenario for embedding both kinds of components in MVB
could let MVB shorter than the length of the combination of MV and B with different scenarios.
An example of MVB is shown in Figure 3.3.
If you would like to have an idea of actual, real-world, problems that you can solve with calculus,
please read the situation below. Even though you might never have to solve a problem like this,
it will be nice to know that you could do it. And who knows, you might even want to impress a
friend or parent by showing them that you can use calculus in the “real world.”
Suppose your family bought a big dog, a giant Schnauzer and your mom asks you to build a
fence for it. You’re supposed to spend no more than $900 on the fence and want the largest
size you can get for the money. One side of the property of your family house meets a river. So
you don’t need to put a fence on that side. The side of the fence parallel to the river will cost $5
per foot to build, whereas the sides perpendicular to the river will cost $3 per foot. What
dimensions should you choose?
Calculus gives you an answer to this situation. If you want to see the answer, just click here.
You will see the answer and how it was derived even though you won’t understand all the
computations at this point. However, you can expect that you will find yourself understanding
the solution later in this semester!
By the way, do you believe that a person has to have a natural ability for math in order to be
good at calculus? If so, please read the story below. It might help you change your mind.
This fence problem above was given to all the students in a university classroom. What
happened? Some of students produced a correct solution and an answer. The rest of the
students shouted to them “You’re geniuses!” But, you know what? There was only one
difference between students who could solve the problem and those who couldn’t. It was
whether or not they previously learned about the formulas used for the solution. What does this
mean? It means they didn’t need to be math geniuses to solve the problem! It meant that math
is a learnable skill and that they simply used what they had learned in the classes they had
previously taken. You have the ability to learn calculus or you would not have gotten into this
class, but it can be a challenge and it requires persistent effort from you!
Figure 3.3 An example of a motivation, volition, and belief change message.
Sequence of motivation, volition, and belief change strategies: The issue of integrating
different components into one kind of change strategies was also resolved through sequencing of
four different emails in this message like the other two discussed earlier. Each of the four emails
42
followed the combined sequence of four phases in Gollwitzer’s implementation intention model
and four conditions in Bendixen’s belief change model and integrated other components from
different models into each of the four as follows: 1) dissatisfaction with current beliefs in the
motivational phase, 2) understanding of alternative beliefs in the pre-actional phase, 3) feltpossibility of use of the alternative beliefs in the actional phase, and 4) felt-usefulness of the
alternative beliefs in the post-actional phase. According to this sequence, the components from
other models were chosen to facilitate each condition. Table 3.4 shows how components from
different models and theories were used to construct MVB.
Table 3.4 The use of components from different models and theories for MVB.
STAGE 1: Dissatisfaction with Current Beliefs in Motivational Phase
ARCS categories (Keller, 1987)
- Attention: Perceptual arousal; Inquiry arousal
- Relevance: Goal orientation; Motive matching; Familiarity
- Confidence: Success opportunities; Success opportunities; Personal control
- Satisfaction: Natural consequences; Positive consequences
Two of multidimensional epistemological beliefs (Schommer, 1990)
- A belief about speed of learning, ranging from quick learning to gradual learning
- A belief about ability to learn, ranging from fixed at birth to improvable
1st condition of the belief change model (Bendixen, 2002)
- Intrigue reflection on beliefs about speed of learning and ability to learn math
- Explain shortcomings of the beliefs of quick learning and fixed ability
STAGE 2: Understanding of Alternative Beliefs in Pre-actional Phase
Implementation intentions (Gollwitzer, 1990)
- Commitment to goal
- Formation of intention commitment
- Intentions for action; Planning for action
Two of multidimensional epistemological beliefs (Schommer, 1990)
- A belief about speed of learning, ranging from quick learning to gradual learning
- A belief about ability to learn, ranging from fixed at birth to improvable
2nd condition of the belief change model (Bendixen, 2002)
- Introduce the beliefs of gradual learning and improvable ability to learn math
STAGE 3: Felt-possibility of Use of the Alternative beliefs in Actional Phase
Implementation intentions (Gollwitzer, 1990)
- Initiation of action
- Goal-oriented action
Action control strategies (Kuhl, 1987)
- Selective attention
- Encoding control
- Emotion control
43
Table 3.4 Continued.
- Motivation control
- Environment control
- Parsimonious information processing
Two of multidimensional epistemological beliefs (Schommer, 1990)
- A belief about speed of learning, ranging from quick learning to gradual learning
- A belief about ability to learn, ranging from fixed at birth to improvable
3rd condition of the belief change model (Bendixen, 2002)
- Tell stories showing positive impacts of the beliefs of gradual learning and improvable
ability to learn math
STAGE 4: Felt-usefulness of the Alternative Beliefs in Post-actional Phase
Implementation intentions (Gollwitzer, 1990)
- Realization termination of action
- Assessment; evaluation (Self-reflection)
Two of multidimensional epistemological beliefs (Schommer, 1990)
- A belief about speed of learning, ranging from quick learning to gradual learning
- A belief about ability to learn, ranging from fixed at birth to improvable
4th condition of the belief change model (Bendixen, 2002)
- Ask about students’ changes in beliefs about gradual learning and improvable ability to
learn math
- Describe potential positive impacts of their changes on attitudes, behaviors, and
learning
Type of Message Delivery
There were two different ways to deliver messages to learners. One way was to construct
group messages according to the appropriate framework. The other way was to construct
personalized messages based on specific knowledge about the learner. Each is described next.
Personal Messages (P)
The first level of the second independent variable, the type of message delivery, was a
personal email message directed at a specific learner. Personal messages are defined as messages
that are constructed based on the analyses of individual participants’ problems with regard to
motivation and volition and/or beliefs about learning mathematics, and that are delivered to
individual participants via email.
44
The conceptual foundation and method for the construction of personal messages was on
the basis of an approach developed and validated by Visser & Keller’s (1990) for the
development of motivational messages, which incorporated a systematic approach for analyzing
learners’ motivational requirements and then designing strategies that pertain specifically to the
identified needs. The setting of this study was different from Visser & Keller (1990) in several
ways: 1) personal messages were presented to participants on paper in class in their study but
messages were presented via email; 2) the sender of messages was the instructor of the course in
their study but researchers were the senders in this study; and 3) the theoretical foundation of
their study was mainly motivational concepts and strategies but this study added things
pertaining to learner volition and beliefs. Kim & Keller (2007a, 2007b) also implemented Visser
& Keller’s (1990) approach using email as well as expanded theoretical foundation to volition in
an undergraduate archeology class. Considering Kim & Keller (2007a, 2007b) found positive
effects on participants’ motivation, the use of personal message seemed to be worthwhile to be
included as a variable in this study where theoretical foundation is once more expanded from
motivation and volition to adding beliefs.
In order to analyze individual participants’ such problems, diagnostic questions were sent
to participants, their responses were analyzed, and personal messages were constructed for
individual participants based on the analysis results. For example, for the group of MV with
personal message (MV-P), questions investigated individuals’ motivational and volitional
problems by asking about their willingness to study calculus, their time management strategies of
study, etc. If one student answered that although she knew she had to study calculus, she did not
want, the personal message specifically addressed her problems and mentioned some ways to
encourage her to see the course more than a requirement and to try to change her attitudes to
positive ones. If one student indicated that he did not schedule study times for calculus, the
personal message informed him of the importance of planning and advised him how to do so.
Examples of diagnostic questions as well as personal message constructed based on the
responses to the questions are shown in Figure 3.4 and Figure 3.5. These examples are from the
messages specifically for the MV-P group. All the diagnostic questions sent to each of the three
groups (i.e., MV-P, B-P, and MVB-P) are shown in Appendix H, K, and M. A timeline of the
activities of sending diagnostic questions, analyzing responses, and composing personal
messages is described in the procedure section of this chapter.
45
1. To me, calculus is something I have to do, but I don’t really want to. Agree___ Disagree___
2. Each week I have good intentions about studying and keeping up with homework, but I have
trouble doing it. Agree___
Disagree___
3. I already have specific blocks of time scheduled on my calendar to study for the first major
exam. Yes___
No___
4. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course
Hours spent on this course last week, not counting class time: _________
5. (Optional) A motivational problem I am having in this class right now is: _____________
Figure 3.4 An example of a diagnostic question.
Kevin, in question three you said that you don’t have specific times scheduled in advance for
studying for your test. If things change in the future and you get into a bind, you might want to
consider the information in the following message:
Putting specific times in your schedule to study is a simple strategy, but it can be highly
beneficial.
Just before Christmas break one year, a psychologist named Gollwitzer told his students that he
wanted them to write an essay about their Christmas experience and to turn it in at the first
class meeting after the holidays. He told them to write a brief plan about when they would do it
and he collected them before his students left for the holiday break. Later, when he compared
what people said in their plans to whether or not they did the project in a timely way, he found
out something very interesting. The students who made very specific commitments (“I will set
aside two hours on the morning after Christmas to write a draft, and on the 27th I will schedule
two more hours to review it and prepare my final copy.”) had much higher completion rates than
the students who made a more general commitment (“I will do it sometime during the week
between Christmas and the New Year holiday.”).
This is an example of what Gollwitzer calls strong versus weak intentions. Everyone has good
intentions about getting their work done, but most intentions, like New Year’s resolutions, don’t
go anywhere because they are weak intentions. They are desires but without a concrete plan of
action. Scheduling specific times to do necessary tasks is truly helpful in accomplishing more.
However, you must use good judgment when scheduling your work, especially tasks that are
difficult. Plan on doing it at a time when there will be minimal distractions and when you will not
have to rush through it. Then, be sure you do it. If you do this successfully a few times, you will
be surprised by the satisfying feeling of accomplishment, and it will become easier to keep
doing it, to build a positive and productive new habit!
These are called strong intentions combined with effective self-regulation or action control
strategies. People who are most successful usually have a well-structured schedule and they
stick with it. If you schedule a specific time to do a task and get into the habit of following your
schedule, then you will be more successful. It also becomes easier to follow your schedule the
more you do it.
Figure 3.5 An example of a personal message (actual student names are not reflected here).
46
Group Messages (G)
The second level of the second independent variable, the type of message delivery, was
group messages. Group messages are defined as messages that are constructed based on a priori
analyses of anticipated problems of participants with regard to motivation and volition and/or
beliefs about learning mathematics, and that are distributed to groups via email. There were
neither individual diagnoses nor analyses of participants’ levels of motivation, volition, and/ or
beliefs. The conceptual foundation and method for the construction of group messages was on
the basis of another approach implemented by Visser & Keller’s (1990), which was pre-planned
to send to the whole group of participants. However, email group messages might have been
viewed somewhat impersonal compared to Visser & Keller’s group messages delivered face-toface in class. Thus, differently from their study, group messages in this study addressed
individual participants’ names in emails, which was considered a way to prevent messages being
ignored.
During the first week of the semester, all the participants responded to a survey
investigating the levels of their motivation, volition, and beliefs. Motivation was measured using
an abbreviated version of Keller’s Course Interest Survey (CIS), volition was measured using
Kuhl and Fuhrmann’s (1998) Volitional Components Inventory (VCI), and belief about learning
mathematics was measure using Schommer’s (1990) Epistemological Belief Questionnaire
(EBQ). Based on Cronbach’s alpha measure, the reliability estimate for the total scales of each of
three questionnaires was > .70. The questions are shown in Appendices B, C, and D.
After reviewing their responses to the questions, the overall levels of motivation, volition,
and beliefs were used to design and develop MV as well as MVB. Specifically, first, since the
overall level of motivation was medium (3.85 on a scale from 1 to 5), group messages for the
MV and MVB groups were constructed to boost their motivation levels to high. Second, since
the overall level of volition was medium (5.24 on a scale from 1 to 7), group messages for the
MV and MVB groups were also constructed to help them raise strong volitional strategies. Last,
since the overall level of beliefs about learning calculus was low (scored 2.18 on a scale of 1 to
5), group messages to the B and MVB groups were constructed to build positive beliefs about
learning mathematics in general. The review of responses is in detail shown in Appendix F.
47
In order to help participants not to miss the messages that they needed, the group
messages were categorized by questions that might initiate their reflection on their status of
motivation, volition, and/ or beliefs. For example, the group message for the MV group started
with the heading of the question, “Except for being a requirement, do you believe that calculus
will actually be worthwhile to you?” and said “If not, please read the message below.” The group
message for the belief change strategies showed the question, “Do you believe that you should be
a math genius to be good at calculus?” and “If so, the story below might help you change your
mind.” In other words, according to the relevance of the questions given, participants could
select certain sections of change strategies for their needs.
Dependent Variables
There were three dependent variables in this study: attitudes, study habits, and
achievement.
Attitudes
Participants’ attitudes toward mathematics were measured using pre- and post-tests of
Fennema-Sherman Mathematics Attitudes (FSMA) questionnaire (Fennema & Sherman, 1976).
The questionnaire contained 24 items and each 12 of them were related to one of the following:
usefulness and math anxiety. Responses to the items were in the form of a five-point Likert scale.
Based on Cronbach’s alpha measure, the total scale has a reliability of .97. The questionnaire is
shown in Appendix A.
Study Habits
Participants’ study habits were measured using a survey, administered four times, asking
how many total hours were spent studying calculus during the week before getting the survey
from researchers. The first survey was distributed to each of the MV, B, and MVB groups at the
48
end of the emails containing change strategies, and it was also emailed to the control group
without any change strategies. The second, third, and last surveys were attached to diagnostic
questions in order to avoid requesting too many replies to participants. The control group was
given the second, third, and fourth surveys constructed in a consolidated manner as was done for
the first one. Thus, only MV-P, B-P, and MVB-P groups and the control group responded to all
of the survey four times. This issue is specifically explained in the procedure section of this
chapter. The survey is shown in Appendix E.
Achievement
Participants’ achievement was measured by their grades on the two tests that were
administered after second messages and after last messages. Students were required to solve
several problems using calculus concepts and formulas on the tests. These were the regular class
tests administered by the instructors.
Procedure
The procedure for this study consisted of five stages. The process of assigning
participants was conducted over two stages. First, participants were randomly assigned to one of
the change strategy groups (i.e., MV, B, and MVB) and the control group at the Stage 1. Then, at
the Stage 2, the change strategy groups were asked whether or not they wanted to receive
personal suggestions (i.e., change strategies with personal messages). In other words, the MV
group was divided to the MV-P and MV-G groups; the B group was divided to the B-P and B-G
groups; and the MVB group was divided to the MVB-P and MVB-G groups according to their
decisions with regard to personal suggestions. As a result, there were seven groups including the
control group (i.e., MV-P, MV-G, B-P, B-G, MVB-P, MVB-G, and Control). The loss of
participants during the study and the construction and distribution of messages sent to each
groups are described next. Table 3.5 shows an overview of the procedure and each stage is
specifically described in this section. Table 3.6 shows the total number of emails sent to each
group also described in this section.
49
Table 3.5 An overview of the procedure.
STAGE 1
Week 1
- Collected consent form signed
- Conducted a pre-treatment survey
- Assigned 115 participants group to one of the MV, B, MVB, and control groups
- Constructed messages based on overall levels of motivation, volition, and beliefs for
each group
Week 2
- Emailed the messages to participants
- Collected the survey of study hours
- Decided to add personal suggestions to the messages owing to low number of survey
returns
STAGE 2
Week 3
- Sent descriptions of the modified approach and asked the MV, B, and MVB groups
about a choice to get personal suggestions with diagnostic questions
- Reassigned participants according to the responses and became seven groups (MV-P,
MV-G, B-P, B-G, MVB-P, MVB-G, and Control)
Week 4
- Constructed messages based on responses to diagnostic questions for the personal
message groups (MV-P, B-P, and MVB-P) and emailed the messages
- Constructed messages based on the overall levels of motivation, volition, and beliefs for
the general message groups (MV-G, B-G, and MVB-G) and emailed the messages
- Emailed the survey of study hours to the control group
STAGE 3
Week 5
- Emailed diagnostic questions to the personal message groups
Week 6
- Constructed messages based on responses to diagnostic questions for the personal
message groups and emailed the messages
- Constructed messages based on the overall levels of motivation, volition, and beliefs for
the general message groups and emailed the messages
- Emailed the survey of study hours to the control group
STAGE 4
Week 8
- Emailed diagnostic questions to the personal message groups
Week 7
- Constructed messages based on responses to diagnostic questions for the personal
message groups and emailed them
- Constructed messages based on the overall levels of motivation, volition, and beliefs for
the general message groups and emailed the messages
- Emailed the survey of study hours to the control group
STAGE 5
Week 9
- Conducted a post-treatment survey
50
Table 3.6 The total number of emails sent to each group
Stage 1
MV-P
1
MV-G
1
B-P
1
B-G
1
MVB-P
1
MVB-G
1
CONTROL
1
Stage 2
3–4
2
3–4
2
3–6
2
1
Stage 3
1–3
1
1
1
1–4
1
1
Stage 4
1–4
1
1
1
1–5
1
1
Stage 5
1
1
1
1
1
1
1
Total
7 – 13
6
7–8
6
7 – 16
6
5
Stage 1
Consent Form and Pre-treatment Survey
The researcher and her professor visited classrooms together during the first week of the
Spring semester in order to inform students about the purpose of the research and to recruit
participants. There were 115 among 126 students who agreed to participate in the research by
signing the consent. They responded to the pre-treatment survey in class for proximately 20
minutes. The survey consisted of the Fennema & Sherman’s (1976) Mathematics Attitudes
(FSMA) questionnaire (Fennema & Sherman, 1976) measuring attitudes toward mathematics
(see Appendix A), Schommer’s (1990) Epistemological Belief Questionnaire (EBQ) measuring
beliefs about mathematics knowledge acquisition (see Appendix B), an abbreviated version of
Keller’s Course Interest Survey (CIS) measuring motivation (see Appendix C), and Kuhl and
Fuhrmann’s (1998) Volitional Components Inventory (VCI) measuring volition (see Appendix
D). Motivation, volition, and beliefs were measured to see students’ overall levels of each in
order to design and develop the motivation, volition and belief change strategies. Attitudes
toward mathematics were measured to see if there were initial group differences as well as to
compare with the results of the post-treatment survey. Since the pre-treatment survey was
conducted right before instructors started the class on the first day, participants’ responses were
based on their prior experiences.
51
Group Assignment
One hundred and fifteen participants were randomly assigned to one of the four treatment
groups: 1) the MV group receiving motivation and volition change strategies via email, 2) the B
group receiving belief change strategies via email, 3) the MVB group receiving motivation and
volition change strategies and belief change strategies via email, and 4) the Control group
receiving neither motivation and volition change strategies nor belief change strategies but
receiving a survey questioning study hours during previous week via email. The numbers of
participants in each group are shown in Table 3.7.
Table 3.7 The number of participants in each treatment groups.
Groups
MV
The number of Participants
29
B
29
MVB
29
CONTROL
28
Total
115
Message Construction
Based on the review of participants’ responses to the questionnaires (see Appendix F),
the overall levels of motivation, volition, and beliefs were used to design and develop the
motivation, volition and beliefs change strategies discussed earlier in the variable section of this
chapter. The messages constructed for each group are shown in Appendix G.
Message Distribution and Survey Return
During the second week of the semester, motivation and volition change strategies to the
MV group, belief change strategies to the B group, and both motivation and volition strategies
and belief change strategies to the MVB group were distributed via email along with a survey
questioning the hours spent studying on calculus during the previous week. All the messages sent
52
are shown in Appendix G. Only the survey was emailed to Control group. By the end of the
week, 12 of 115 participants either cancelled their participants or dropped the course and the
number of the whole participants became 103. In addition, only 64 of 103 participants returned
their responses to the survey.
Approach Modification
Originally, the researcher expected to have active participation from students because the
instructors initially agreed with the request of the researcher to give extra credits to students who
volunteered to participate in the research. However, one instructor was hesitant to do so and the
request had to be cancelled because the researcher did not want anyone to feel uncomfortable
with our research. Still the researcher hoped that the fact that there was no extra credit would not
interfere with the research, and continued to follow the original plan, which was emailing change
strategies without personal messages. However, since getting only 62% survey responses, the
researcher decided to add personal messages to the emails of change strategies, which was
expected to make emails more appealing to individual participants based on the previous
research where no extra credit was given to participants (Kim & Keller, 2007a, 2007b).
Stage 2
Diagnostic Questions and Group Reassignment
At the beginning of the third week of the semester, diagnostic questions were sent to the
groups of MV, B, and MVB (see an example in Figure 3.4). The researcher also emailed the
three groups to explain that they would receive more personal suggestions with regard to their
specific situations to learn calculus as well as to ask whether they wanted to get such suggestions.
An example of the email is shown in Figure 3.6. This example is from the messages sent to the B
group. The diagnostic questions are shown in Appendix H and all the descriptions of personal
suggestions are shown in Appendix I.
53
Hi, Kevin!
If you want to hear about techniques that will help you build positive and productive beliefs about your
ability to learn calculus, please read the following and consider sticking with this support process. Our
future messages will be much more personal with regard to your specific situation. And we know
from past successful experiences that we might be able to help.
Your motivation and performance in this class are influenced by the way the course is taught and the
behaviors of the instructor, but they are most strongly influenced by your own beliefs and behaviors. Your
beliefs can help you or hinder you, and if they are hindering you there are specific things you can do to
give yourself a boost, and that is what this message is about.
To begin, there are three important principles that you must realize and agree with in order to improve
your beliefs and performance:
First, you are not alone. No matter what you are feeling, you are a human being and millions of others
have probably had the same or very similar feelings.
Second, you can learn from other people’s experiences. The suggestions that we can send you are not
abstract psychological principles. They are based on the successes that huge numbers of people have had
in overcoming challenges by improving their beliefs and performance. These people are from all walks of
life: philosophers, businessmen, students, poets, architects, mathematicians, and even, yes, psychologists.
The important thing is that there is a tremendous amount of consistency in what these people say.
Third, it isn’t easy to change. It takes desire and effort. In other words, you have to want to overcome
attitudes of resentment, fear, or whatever is holding you back, and you have to use specific techniques to
change your attitudes and behaviors.
If you are curious to know what some of the suggestions are based on the experiences of all these other
people and my personal experience (yes, indeed, I have had to overcome with many types of challenges to
my beliefs and performance to get where I am, and still do!), then please click Reply and put a check
mark at the end of the next sentence.
Yes, send me the suggestions. I can tell you later if I want to stop at any time. __________
Figure 3.6 An example email to describe personal messages.
By the end of the week, it was found that 34 of 77 participants of the MV, B, and MVB
groups wanted to receive personal suggestions and sent the researcher their responses to the
diagnostic questions. They were included the personal message groups: the MV-P group (N=14),
the B-P group (N=9), and the MVB-P group (N=11). It was also found that 43 of 77 did not want
to receive personal suggestions and especially 11 of 43 did not want to get any of suggestions. 11
of 43 were excluded from the research and only 32 were included the group message groups: the
MV-G group (N=8), the B-G group (N=11), and the MVB-G group (N=13). The summary of
group reassignment is shown in Table 3.8.
54
Table 3.8 The reassignment of the groups.
Initial
Participants
Groups
MV
B
MVB
Loss of
Reassigned Groups
Participants
Condition 1 (MV-P)
14
Condition 2 (MV-G)
8
Condition 3 (B-P)
9
Condition 4 (B-G)
11
Condition 5 (MVB-P)
11
Condition 6 (MVB-G)
13
Participants
25
3
26
6
26
2
CONTROL
26
N/A
Condition 7 (CONTROL)
26
Total
103
11
Total
92
Message Construction & Distribution
During the fourth week of the semester, the motivation, volition, or/and belief change
strategies with personal messages were constructed based on the responses to the diagnostic
questions. The messages were divided according to the diagnostic questions and were
individually emailed to the participants of the MV-P, B-P, and MVB-P groups several times
during the week. For example, one student of the MV-P group said that he agreed to the
statement, “To me, calculus is something I have to do, but I don’t really want to” in the
diagnostic questions. Also, he agreed to the statement, “Each week I have good intentions about
studying and keeping up with homework, but I have trouble doing it.” The motivation and
volition change strategies with personal messages for him were constructed based on his
responses as explained earlier. All the change strategies were not emailed to him at once. The
first email had an introductory sentence, such as “Murphy, regarding your answer to first
question in which you agreed that you have to take the class but don’t really want to, please read
the following message,” and suggested change strategies for him. Then, the second email next
day had also an introductory sentence but based on his response to the other diagnostic question,
such as “Murphy, in question two you said that although you I had good intentions about
studying and keeping up with homework, I had trouble doing it. You might want to consider the
information in the following.” The change strategies with personal messages emailed to the MVP, B-P, and MVB-P groups are shown in Appendix J.
55
The motivation, volition, or/and belief change strategies with general messages were
constructed for some likely problems about motivation, volition, or/and beliefs. As described
earlier, the messages were categorized by the questions that they could allow them to select the
portions that they thought they needed to read. The messages were emailed to the participants of
the MV-G, B-G, and MVB-G groups once during the week. For example, the email sent to the
B-G group had an introductory sentence, “You might find the following strategies useful.” A
portion of the belief change strategies in the email started with the heading of the question, “Do
you believe that even if you study hard, your basic math ability isn’t going to change?” and said
“If so, please read the message below.” The change strategies with general messages are also
shown in Appendix J.
Stage 3
During the fifth week of the semester, new sets of diagnostic questions (see Appendix K)
were emailed to the groups of MV-P, B-P, and MVB-P, and a question asking study hours was
emailed to the control group. However, 8 of 26 participants in the control group dropped the
course after the first exam, and the number of participants in the group became 18. The total
number of participants again changed from 92 to 84. The numbers of participants in each group
are shown in Table 3.9.
Table 3.9 The number of participants.
Groups
Condition 1 (MV-P)
Participants
14
Condition 2 (MV-G)
8
Condition 3 (B-P)
9
Condition 4 (B-G)
11
Condition 5 (MVB-P)
11
Condition 6 (MVB-G)
13
Condition 7 (CONTROL)
18
Total
84
56
During the sixth week, like Stage 2, the motivation, volition, or/and belief change
strategies with personal messages were constructed based on the participants’ responses to the
diagnostic questions. The messages were divided according to the diagnostic questions and were
individually emailed to the participants of the MV-P, B-P, and MVB-P groups several times
during the week. The motivation, volition, or/and belief change strategies with general messages
were constructed based the suggestions for some likely problems about motivation, volition,
or/and beliefs. The messages were emailed to the participants of the MV-G, B-G, and MVB-G
groups once during the week. The change strategies with personal messages and general
messages are also shown in Appendix L.
Stage 4
This stage had basically the same procedures as Stage 3. During the seventh week of the
semester, new sets of diagnostic questions (see Appendix M) were emailed to the groups of MVP, B-P, and MVB-P, and a question asking study hours was emailed to the control group. During
the eighth week, the motivation, volition, or/and belief change strategies with personal messages
were constructed based on the participants’ responses to the diagnostic questions. The messages
were divided according to the diagnostic questions and were individually emailed to the
participants of the MV-P, B-P, and MVB-P groups several times during the week. The
motivation, volition, or/and belief change strategies with general messages were constructed
based the suggestions for some likely problems about motivation, volition, or/and beliefs. The
messages were emailed to the participants of the MV-G, B-G, and MVB-G groups once during
the week. The change strategies with personal messages and general messages are also shown in
Appendix N. The second exam took place on at the end of the eighth week.
Stage 5
During the ninth week after the exam, 84 participants completed the post-treatment
survey either online or on paper in class for approximately 5 minutes; this survey consisted of the
Fennema & Sherman’s (1976) Mathematics Attitudes (FSMA) questionnaire (Fennema &
Sherman, 1976) measuring attitudes toward mathematics (see Appendix A). It was announced
that $10 checks would be given to online respondents. During the week, 55 participants
57
completed the survey online, and at the end of the week, the researcher visited classes to get
responses on paper from 29 participants who did not complete the survey online.
Data Analysis
The data were analyzed according to the three dependent variables: attitudes, study habits,
and achievement. One-way repeated measures ANOVA was employed to determine if there were
significant differences among the seven groups in attitudes, study habits, and achievement. Each
group was compared with others using Fisher's Least Significant Difference (LSD) test. Alpha
was set at .10 for all analyses in this study for several reasons. First, the primary goal of this
exploratory study was to determine a basis for justifying more controlled and rigorous studies.
Second, the sample size was relatively small due to unexpected problems in the implementation
of the study. Last, findings obtained at this level can be presumed to be sufficiently unlikely to
be due to chance as to provide a basis for further research even though they could not be viewed
as strong conclusions. Results of these analyses are discussed in the next chapter.
58
CHAPTER IV
RESULTS
The purpose of this study was to investigate the effects of motivation and volition change
strategies and belief change strategies delivered via email with personal and group messages on
students’ attitudes, study habits, and achievement in a calculus course for non-mathematics
majors. In order to test the hypotheses formulated in the chapter II (pp. 29-33), the treatment
effects were examined using one-way repeated measures ANOVA. Each group was compared
with others using Fisher's Least Significant Difference (LSD) test. The alpha level was set to .10
for all analyses in this study. SPSS version 11.5 software was used for conducting statistical
analyses.
This chapter consists of three sections reporting: 1) the results of preliminary data
analyses prior to the statistical analysis of the dependent measures; 2) descriptive statistics for
dependent measures; and 3) the results of testing hypotheses.
Preliminary Data Analysis
There were two types of preliminary analysis: (a) a group equivalence test, and (b) a
detection of violations of assumptions. Each is described below.
Group Equivalence Test
This investigation was an exploratory study with a quasi-experimental design.
Participants were not randomly selected from the entire population of undergraduate students but
selected from an undergraduate calculus course where they were enrolled in. In addition,
assignment to one of the seven treatment groups was partially random. That is, first, participants
were randomly assigned into to groups of different change strategies (i.e., MV, B, MVB, and
Control) and then participants of each groups except for the control group were re-assigned to
59
either personal message or group message groups (i.e., MV-P, MV-G, B-P, B-G, MVB-P, and
MVB-G) based on their decision to get personal suggestions for calculus study or not (i.e.,
change strategies with personal versus group messages). Although there was no perfect random
assignment, since initial assignment was random, the researcher hoped to still see group
equivalence. In order to verify the group equivalence statistically, participants’ attitudes toward
mathematics and study habits were surveyed at the beginning of the semester. Since it was not
allowed to administer a test that was not on the syllabus and also all of the participants had taken
a precalculus course before coming to this course, their prior knowledge about calculus was not
examined. A one-way between-groups analysis of variance was performed to investigate the
difference among the seven conditions with regard to the five components.
Prior attitudes toward mathematics: Prior attitudes data were collected using the FennemaSherman Mathematics Attitudes (FSMA) questionnaire (Fennema & Sherman, 1976) in the first
week of the semester. The questionnaire contained 24 items and each 12 of them were related to
one of usefulness and math anxiety. Responses to the items were in the form of a five-point
Likert scale. Based on Cronbach’s alpha measure, the total scale has a reliability of .97. The
questionnaire is shown in Appendix A. Results revealed that there was no significant difference
in prior attitudes toward mathematics across seven groups, F(6, 77) = 1.462, p = .203, which
indicated that the group equivalence of prior attitudes was statistically verified. Table 4.1 shows
means and standard deviations for the group equivalence test of prior attitudes.
Prior study habits: Prior study habits data were collected using a survey asking how many total
hours were spent studying calculus during the first week of the semester when any treatment was
not given. Results revealed that there was no significant difference in prior study habits across
the seven groups, F(6, 77) = 1.807, p = .109, which indicated that the group equivalence of study
habits was statistically verified. Table 4.1 shows means and standard deviations for the group
equivalence test of study habits.
60
Table 4.1 Means and standard deviations for group equivalence test.
Prior
Prior
Attitudes
a
study habits b
M
SD
M
SD
MV-P
(n=14)
3.98
.45
6.90
5.50
MV-G
(n=8)
4.17
.40
5.50
1.13
B-P
(n=9)
3.52
.79
7.13
6.68
B-G
(n=11)
3.91
.40
7.77
5.50
MVB-P
(n=11)
4.09
.41
2.74
1.13
MVB-G
(n=13)
3.84
.51
6.58
1.25
CONTROL
(n=18)
3.89
.59
6.86
3.66
Total
(n=84)
3.91
.53
6.30
4.26
Note.
a.
Possible score range for attitudes: 1-5.
b.
Possible score range for study habits: 0-12.
Tests for ANOVA Assumptions
For attitudes toward mathematics, study habits, and achievement, one-way repeated
measures ANOVA tests were conducted. Three assumptions were tested.
Assumption 1 (Independence of observation)
As described in the method section, all the observations were independent within and
across groups. There was no interaction among students. Therefore, this assumption was satisfied.
61
Assumption 2 (Normal distribution)
The assumption of normal distribution refers that observations are taken from normally
distributed populations. Repeated measures ANOVA recommends to test this assumption for
both between subjects and within subjects.
Between subjects: Normal distribution assumption was tested with Shapiro-Wilk’s normality
test applicable for small sample sizes. Attitudes of all the groups were normally distributed and
only a few achievement and study habits were not normally distributed as shown in Appendix O.
Therefore, this assumption for attitudes was perfectly satisfied and for achievement and study
habits was roughly satisfied.
Within subjects: Inspection of histograms indicated that scores of attitudes and achievement
were normally distributed for both measures: presurvey and postsurvey and 1st exam and 2nd
exam, respectively. Thus, this assumption for attitudes and achievement was satisfied. However,
inspection of histograms showed that scores of study habits were not normally distributed except
for 4th survey. Therefore, this assumption for study habits was not satisfied. The violation of
normality could cause loss of power (i.e., an increased probability of a Type II error); however,
since an advantage of repeated measures ANOVA was to provide greater power to detect effects,
this violation was expected to have a less impact on this study than other ANOVA tests (Field,
2005). All the histograms are shown in Appendix P.
Assumption 3 (Homogeneity of variance)
The assumption of homogeneity of variance is that samples are taken from the
populations with equal variances. It is recommended this assumption is tested in three ways for
repeated measures ANOVA as follows:
Overall: Box’s test of equality was used to test overall homogeneity of variance. For attitudes
and achievement, the test indicated that there was equal variances overall. However, for study
habits, the test showed that the variances were not equal. Therefore, this assumption was
satisfied for attitudes and achievement but violated for study habits. However, most of specific
examinations of homogeneity of variance for study habits described below showed that the
assumption was satisfied for between subjects or its violation for within subjects was corrected.
All the Box’s tests are shown in Appendix Q.
62
Between subjects: For attitudes, the Levene’s test of the assumption of homogeneity of variance
resulted in failing to reject decisions that the variances were equal across the groups for attitudes
measured by both presurvey and postsurvey. This assumption for attitudes was satisfied. For
study habits, the Levene’s test of the assumption of homogeneity of variance also resulted in
failing to reject decisions that the variances were equal across the groups for study habits
measured three of four times. This assumption for study habits was roughly satisfied. For
achievement, the Levene’s test of the assumption of homogeneity of variance resulted in
rejecting decisions that the variances were equal across the groups for both achievement
measured by the 1st and 2nd exams. Thus, this assumption for achievement was violated. Since
ANOVA could tolerate the effect of heterogeneity (Aron & Aron, 1994; Weiss, 1993), it was not
viewed severe to use different analytic techniques and thus repeated measures ANOVA was
implemented for this exploratory experimental study. All the Levene’s tests are shown in
Appendix R.
Within subjects: This assumption refers that relationship between one pair of outcomes on each
time is equal to the other pairs. For attitudes and achievement, there was no need to check this
assumption since there was only one pair of outcomes: a pair of attitude scores measured through
the pre-survey and the post-survey, and a pair of achievement scores measured through the first
exam and second exam. For study habits, Maulchly’s test indicated that there were significant
differences between the variance of differences (see Appendix S). Thus, this assumption was
violated. To correct for the violation of sphericity, Greenhouse-Geisser correction was used to
report data analysis results in the section of testing hypothesis for study habits.
Descriptive Data
Attitudes toward Mathematics
Descriptive statistics for attitudes are shown in Table 4.2. The total mean scores were
3.92 (SD = .53) for the pretest and 3.75 (SD = .67) for the posttest. The mean score for the MVG group was highest (4.17) and the mean score for the B-P group was lowest (3.52) before the
63
treatment. The mean score for the MVB-P group was highest (4.05) and the mean score for the
MVB-G was lowest (3.45) after the treatment. The possible range for this measure was 1 to 5.
Table 4.2 Means and standard deviations for attitude score.
MV-P (n=14)
MV-G (n=8)
B-P (n=9)
B-G (n=11)
MVB-P (n=11)
MVB-G (n=13)
CONTROL (n=18)
Total (n=84)
Pre
M
3.98
SD
.45
Post
3.75
.77
Pre
4.17
.40
Post
3.99
.48
Pre
3.52
.79
Post
3.86
.64
Pre
3.91
.40
Post
3.56
.88
Pre
4.09
.41
Post
4.05
.46
Pre
3.84
.51
Post
3.45
.63
Pre
3.89
.59
Post
3.76
.61
Pre
3.91
.53
Post
3.75
.67
Note. Possible score range for attitudes: 1-5.
Study Habits
As mentioned in the previous chapter, there were only four groups (MV-P, B-P, MVB-P,
and Control) examined on study habit changes since the general message groups (i.e., MV-G, BG, and MVB-G) were not asked to respond to the 2nd, 3rd, and 4th survey asking study hours.
Descriptive statistics for study habits are shown in Table 4.3. The total mean scores were 6.05
(SD = 4.74) for the 1st survey, 3.94 (SD = 2.40) for the 2nd survey, 4.04 (SD = 3.30) for the 3rd
survey, and 3.92 (SD = 2.26) for the last survey. The mean score for the B-P group was highest
as 7.13 for the 1st survey, the mean scores for the control group were highest as 4.80 for the 2nd
survey, as 5.11 for the 3rd survey, and as 4.36 for the last survey.
64
Table 4.3 Means and standard deviations for study habit score.
MV-P (n=14)
st
M
6.90
SD
5.50
2nd
3.87
2.11
rd
3.44
1.55
th
4
3.85
1.86
1st
7.13
6.68
2nd
3.94
3.34
rd
4.16
3.19
th
3.72
1.95
st
2.74
1.13
2nd
2.61
1.19
3rd
2.95
1.80
th
3.45
2.57
st
6.86
3.66
nd
2
4.80
2.42
3rd
5.11
4.68
4th
4.36
2.59
st
6.05
4.74
nd
3.94
2.40
rd
3
4.04
3.30
4th
3.92
2.26
1
3
B-P (n=9)
3
4
MVB-P (n=11)
1
4
CONTROL (n=18)
Total (n=52)
1
1
2
Note. Possible score range for study habits: 0-12.
Achievement
Descriptive statistics for achievement are shown in Table 4.4. The total mean scores were
68.54 (SD = 19.42) for the 1st exam and 64.35 (SD = 24.71) for the 2nd exam. The mean score for
the B-G group was highest (77.90) and the mean score for the B-P group was lowest (58.88) for
the 1st exam. The mean score for the B-P group was highest (70.33) and the mean score for the
MV-G was lowest (55.25) for the 2nd exam. The possible range for this measure was 0 to 100.
65
Table 4.4 Means and standard deviations for achievement score.
MV-P (n=14)
MV-G (n=8)
B-P (n=9)
B-G (n=11)
MVB-P (n=11)
MVB-G (n=13)
CONTROL (n=18)
Total (n=84)
M
SD
1 exam
67.92
23.01
2nd exam
61.71
25.97
1st exam
74.00
19.10
2nd exam
55.25
26.77
1st exam
58.88
14.79
2nd exam
70.33
19.02
1st exam
77.90
14.22
2nd exam
70.00
18.58
1st exam
65.54
23.21
2nd exam
60.63
36.88
1st exam
60.53
22.53
2nd exam
59.46
21.57
1st exam
73.33
13.78
2nd exam
69.83
22.91
1st exam
68.54
19.42
2nd exam
64.35
24.71
st
Note. Possible score range for achievement: 0-100.
Examination of Hypotheses
Testing of Hypothesis 1
The first major hypothesis was that the type of email messages would result in positive
changes in participants’ attitudes toward mathematics [Hypothesis 1]. Specifically, it was
expected that the participants receiving motivation and volition change strategies (i.e., MV-P and
MV-G) would show more positive changes in attitudes than the participants receiving belief
change strategies (i.e., B-P and B-G) [Hypothesis 1.1]. Also, it was expected that those in the
personal message groups (i.e., MVB-P, MV-P, and B-P) would show more positive changes of
66
attitudes than those in the general message groups (i.e., MVB-G, MV-G, and B-G) [Hypothesis
1.2]. And, it was expected that the participants receiving a combination of motivation and
volition change strategies and belief change strategies (i.e., MVB-P and MVB-G) would show
more positive changes in attitudes than the participants receiving either motivation and volition
change strategies (i.e., MV-P and MV-G) or belief change strategies (i.e., B-P and B-G) but not
both [Hypothesis 1.3]. In addition, those in the control group would show the least positive
changes in attitudes among all the groups [Hypothesis 1.4].
The change of attitudes was analyzed through one-way repeated measures ANOVA with
the scores of Fennema-Sherman Mathematics Attitudes (FSMA) questionnaire (Fennema &
Sherman, 1976) surveyed twice: pre-survey before and post-survey after messages were sent.
With alpha set at .10 and with a sample size of 84, it was determined that the power for detecting
moderate effects was .73. Results revealed that there was a significant interaction between the
two factors of time and treatment [F(6, 77) = 2.009, p = .074, η2 = .135]. This result indicated
that participants’ attitudes depended on time (before change strategies were sent and after change
strategies were sent) according to which groups they were in (i.e., MV-P, MV-G, B-P, B-G,
MVB-P, MVB-G, or Control). Hypothesis 1, the type of email messages would result in positive
changes in participants’ attitudes toward mathematics, was therefore supported.
The time main effect was found to be significant [F(6, 77) = 5.197, p = .025, η2 = .063]
and the treatment main effect was found be non-significant [F(6, 77) = 1.116, p = .361, η2
= .080]. Post Hoc analysis using LSD test showed that changes in the attitudes of the MVB-P
group were significantly more positive than that of the MVB-G group [p = .053]. Thus,
Hypothesis 1.2, those in the personal message groups would show more positive changes of
attitudes than those in the general message groups, was partially supported. Also, Post Hoc
analysis indicated that changes in the attitudes of the MV-G group were significantly more
positive than MVB-G. Thus, Hypothesis 1.3, the participants receiving combined change
strategies would show more positive changes in attitudes than the participants receiving either
motivation and volition change strategies or belief change strategies, was not supported. Post
Hoc analysis indicated that there was no statistically significant differences in attitude changes
between the motivation and volition change strategy groups (i.e., MV-P and MV-G) and the
belief change strategy groups (B-P and B-G). It was also found that there was no difference in
67
attitude changes between the control group and the other groups. Therefore, neither Hypothesis
1.1 nor Hypothesis 1.4 was supported. Table 4.5 shows pair-wise comparisons among the seven
groups.
The graph showing changes in attitudes of each groups indicated that the mean of the B-P
group’s attitude scores increased while the mean of the other groups’ attitude scores decreased
(Figure 4.1). This result indicated that Hypothesis 1.2 was partially supported but Hypothesis 1.3
was not supported.
Table 4.5 Multiple comparisons for attitudes toward mathematics.
Mean Difference
(I) GROUP
(J) GROUP
Std. Error
(I-J)
MV-P
MV-G
-.2132
.23670
B-P
.1789
.22817
B-G
.1357
.21518
MVB-P
-.2060
.21518
MVB-G
.2236
.20570
Control
.0407
.19031
MV-G
MV-P
.2132
.23670
B-P
.3922
.25951
B-G
.3489
.24816
MVB-P
.0073
.24816
MVB-G
.4369
.23998
Control
.2539
.22693
B-P
MV-P
-.1789
.22817
MV-G
-.3922
.25951
B-G
-.0433
.24004
MVB-P
-.3849
.24004
MVB-G
.0447
.23158
Control
-.1382
.21803
B-G
MV-P
-.1357
.21518
MV-G
-.3489
.24816
B-P
.0433
.24004
MVB-P
-.3416
.22772
MVB-G
.0880
.21879
Control
-.0950
.20439
MVB-P
MV-P
.2060
.21518
MV-G
-.0073
.24816
B-P
.3849
.24004
B-G
.3416
.22772
MVB-G
.4296
.21879
68
p
.370
.435
.530
.341
.280
.831
.370
.135
.164
.977
.073*
.267
.435
.135
.857
.113
.847
.528
.530
.164
.857
.138
.689
.644
.341
.977
.113
.138
.053*
Table 4.5 Continued
Control
MV-P
MV-G
B-P
B-G
MVB-P
Control
MV-P
MV-G
B-P
B-G
MVB-P
MVB-G
MVB-G
Control
.2467
-.2236
-.4369
-.0447
-.0880
-.4296
-.1829
-.0407
-.2539
.1382
.0950
-.2467
.1829
.20439
.20570
.23998
.23158
.21879
.21879
.19438
.19031
.22693
.21803
.20439
.20439
.19438
.231
.280
.073*
.847
.689
.053*
.350
.831
.267
.528
.644
.231
.350
Note. * p < .10.
4.4
Attitude Score
4.2
Groups
MV-P
4.0
MV-G
B-P
3.8
B-G
MVB-P
3.6
MVB-G
CONTROL
3.4
Before Emails
After Emails
TIME
Figure 4.1 Changes in participants’ attitudes toward mathematics.
Testing of Hypothesis 2
The second major hypothesis was that the type of email messages would result in positive
changes in participants’ study habits [Hypothesis 2]. Specifically, it was expected that the
69
participants receiving motivation and volition change strategies (i.e., MV-P) would show more
positive changes in study habits than the participants receiving belief change strategies (i.e., B-P)
[Hypothesis 2.1]. Also, it was expected that the participants receiving a combination of
motivation and volition change strategies and belief change strategies (i.e., MVB-P) would show
more positive changes in study habits than the participants receiving either motivation and
volition change strategies (i.e., MV-P) or belief change strategies (i.e., B-P) but not both
[Hypothesis 2.2]. In addition, it was expected that those in the control group would show the
least positive changes in study habits among all the groups [Hypothesis 2.3].
The change of study habits was analyzed through one-way repeated measures ANOVA
with the study hours surveyed four times. With alpha set at .10 and with a sample size of 52, it
was determined that the power for detecting moderate effects was .64. Results revealed that there
was no significant interaction between the two factors of time treatment [F(3, 48) = 1.211, p
= .304, η2 = .070]. This result indicated that participants’ study habits did not depend time (while
change strategies were sent four times) according to which groups they were in (i.e., MV-P, B-P,
MVB-P, or Control). Thus, Hypothesis 2, the type of email messages would result in positive
changes in participants’ study habits, was not supported. Figure 4.2 illustrates overall changes in
participants’ study habits.
The time main effect and the treatment main effect were both significant, F(3, 48) =
7.219, p = .001, η2 = .131 and F(3, 48) = 2.442, p = .076, η2 = .132, respectively. Post Hoc
analysis using LSD test showed that changes in the study habits of both MV-P and B-P groups
were significantly more positive than that of the MVB-P group. Thus, Hypothesis 2.2, the
participants receiving combined change strategies would show more positive changes in study
habits than the participants receiving either motivation and volition change strategies or belief
change strategies, was not supported. Post Hoc analysis indicated that there was no statistically
significant differences in study habit changes between the motivation and volition change
strategy group (i.e., MV-P) and the belief change strategy group (B-P). It was also found that
there was no difference in study habit changes between the control group and the other groups.
Therefore, neither Hypothesis 2.1 nor Hypothesis 2.3 was supported. Table 4.6 shows pair-wise
comparisons among four groups.
70
Table 4.6 Multiple comparisons for study habits.
(I) GROUP
MV-P
(J) GROUP
B-P
B-P
MVB-P
Control
Mean Difference (I-J)
Std. Error
-.2214
.97814
.822
MVB-P
1.5783
.92242
.094*
Control
-.7673
.81582
.352
MV-P
.2214
.97814
.822
MVB-P
1.7997
1.02901
.087*
Control
-.5460
.93464
.562
MV-P
-1.5783
.92242
.094*
B-P
-1.7997
1.02901
.087*
Control
-2.3457
.87617
.010
MV-P
.7673
.81582
.352
B-P
.5460
.93464
.562
MVB-P
2.3457
.87617
.010
Note. * p < .10.
8
Study habits (Study hours)
7
6
5
Groups
4
MV-P
B-P
3
MVB-P
2
CONTROL
1
p
2
3
4
TIME
Figure 4.2 Changes in participants’ study habits.
71
Testing of Hypothesis 3
The third major hypothesis was that the type of email messages would result in positive
changes in participants’ achievement [Hypothesis 3]. Specifically, it was expected that the
participants receiving motivation and volition change strategies (i.e., MV-P and MV-G) would
show more positive changes in achievement than the participants receiving belief change
strategies (i.e., B-P and B-G) [Hypothesis 3.1]. Also, it was expected that those in the personal
message groups (i.e., MVB-P, MV-P, and B-P) would show more positive changes of
achievement than those in the general message groups (i.e., MVB-G, MV-G, and B-G)
[Hypothesis 3.2]. And, it was expected that the participants receiving combined change strategies
(i.e., MVB-P and MVB-G) would show more positive changes in achievement than the
participants receiving either motivation and volition change strategies (i.e., MV-P and MV-G) or
belief change strategies (i.e., B-P and B-G) but not both [Hypothesis 3.3]. In addition, it was
expected that those in the control group would show the least positive changes in achievement
among all the groups [Hypothesis 3.4].
The change of achievement was analyzed through one-way repeated measures ANOVA
with the scores of the first and second exams. With alpha set at .10 and with a sample size of 84,
it was determined that the power for detecting moderate effects was .73. Results revealed that
there was no significant interaction between the two factors of time and treatment [F(6, 77) =
1.138, p = .348, η2 = .081]. This result indicated that participants’ achievement did not depend
time according to which groups they were in (i.e., MV-P, MV-G, B-P, B-G, MVB-P, MVB-G, or
Control). Thus, Hypothesis 3, that the type of email messages would result in positive changes in
participants’ achievement, was not supported. Figure 4.3 shows changes in participants’
achievement.
The time main effect and the treatment main effect were both non-significant F(6, 77) =
2.472, p = .120, η2 = .031 and F(6, 77) = .910, p = .492, η2 = .066, respectively. Post Hoc
analysis using LSD test showed that changes in the achievement of any group was not
significantly more positive than that of the others. Thus, all the hypotheses were not supported.
72
80
Groups
70
Achievement
MV-P
MV-G
B-P
60
B-G
MVB-P
MVB-G
50
CONTROL
1st Exam
2nd Exam
TIME
Figure 4.3 Changes in participants’ achievement.
73
CHAPTER V
DISCUSSION
Overview
The purpose of this study was to investigate the effects of motivation and volition change
strategies and belief change strategies, implemented with personal and group email messages, on
students’ attitudes, study habits, and achievement in a calculus course for non-mathematics
majors. Specifically, this study focused on the cumulative effects of email messages designed in
accordance with the framework of motivation, volition, and belief change strategies discussed in
the literature review chapter. It investigated the effects of both personal and group messages
constructed in accordance with the underlying theoretical models for motivation, volition and
belief change.
This study involved seven groups receiving one of the following treatments: 1)
motivation and volition change strategies distributed via email with personal messages (MV-P),
2) motivation and volition change strategies distributed via email with group messages (MV-G),
3) belief change strategies distributed via email with personal messages (B-P), 4) belief change
strategies distributed via email with group messages (B-G), 5) motivation, volition, and belief
change strategies distributed via email with personal messages (MVB-P), 6) motivation, volition,
and belief change strategies distributed via email with group messages (MVB-G), and 7) neither
motivation and volition change strategies nor belief change strategies distributed via email
(Control).
Eighty four undergraduates enrolled in a calculus course were distributed among the
seven treatment groups, and they received emails over a period of 8 weeks. Their attitudes
toward mathematics were measured using pre- and post-tests based on the Fennema-Sherman
Mathematics Attitudes (FSMA) questionnaire (Fennema & Sherman, 1976); achievement was
measured by their grades on the first and second exams of the semester. Study habits of 52
participants from the personal message and control groups (i.e., MV-P, B-P, MVB-P, and
74
Control) were measured using a survey, administered four times, asking how many total hours
were spent studying calculus during the week before getting the survey from researchers. The
general message groups (i.e., MV-G, B-G, and MVB-G) were excluded for the examination of
study habits because their study hours were asked only once as described in the method chapter.
The treatment effects on the dependent variables of attitudes toward mathematics,
achievement, and study habits were examined using a one-way repeated measures ANOVA
analysis. Also, Post Hoc analysis compared each group with the others using Fisher's Least
Significant Difference (LSD) test. In addition, the graphs showing changes in each of the three
variables were also analyzed.
In this chapter, the findings are summarized according to the three dependent variables as
well as possible explanations are discussed in relation to the framework of this study constructed
based on theoretical and empirical foundations. Limitations of this study are described as are
implications and possibilities for future studies.
Findings
Attitudes toward Mathematics
The first major hypothesis was that the type of email messages would result in positive
changes in participants’ attitudes toward mathematics [Hypothesis 1]. Specifically, it was
expected that the participants receiving motivation and volition change strategies (i.e., MV-P and
MV-G) would show more positive changes in attitudes than the participants receiving belief
change strategies (i.e., B-P and B-G) [Hypothesis 1.1]. Also, it was expected that those in the
personal message groups (i.e., MVB-P, MV-P, and B-P) would show more positive changes of
attitudes than those in the general message groups (i.e., MVB-G, MV-G, and B-G) [Hypothesis
1.2]. And, it was expected that the participants receiving a combination of motivation and
volition change strategies and belief change strategies (i.e., MVB-P and MVB-G) would show
more positive changes in attitudes than the participants receiving either motivation and volition
75
change strategies (i.e., MV-P and MV-G) or belief change strategies (i.e., B-P and B-G) but not
both [Hypothesis 1.3]. In addition, those in the control group would show the least positive
changes in attitudes among all the groups [Hypothesis 1.4].
The major hypothesis that the type of email messages would result in positive changes in
participants’ attitudes toward mathematics was partially supported. One-way repeated measures
ANOVA revealed that there was a significant interaction between the two factors of time and
treatment. That is, participants’ attitudes depended on time (before change strategies were sent
and after change strategies were sent) according to which groups they were in (i.e., MV-P, MVG, B-P, B-G, MVB-P, MVB-G, or Control). Especially, the mean of the B-P group’s attitude
scores increased while the mean of the other groups’ attitude scores decreased (see Figure 4.1, p.
69). The research framework of this study based on theoretical and empirical foundations
indicating that a learner’s attitudes toward mathematics could become more positive ones
through motivation, volition, and belief change strategies was partially validated.
It was not possible to make inferences about the B-P group’s attitudes compared with the
other groups since there was no statistically significant treatment main effect. However,
according to the graph showing changes in attitudes of each groups in Figure 4.1, Hypothesis 1.1,
the participants receiving motivation and volition change strategies would show more positive
changes in attitudes than the participants receiving belief change strategies, was not supported. In
other words, the cumulative effects of belief change strategies with personal messages seem to
have had the most influence on participants’ attitudes.
Four reasons for this finding are discussed next. First, the survey measuring participants’
attitudes asked about their perceptions about usefulness of mathematics and anxiety about
mathematics (Fennema & Sherman, 1976). Initially, it was expected that motivation and volition
change strategies would have more positive impact on attitudes than belief change strategies.
The former (motivation and volition change strategy messages) described techniques to control
negative emotions about mathematics and how calculus could be useful to students. The latter
(belief change strategy messages) only described fundamental reasons to build positive beliefs
about learning mathematics; they did not provide participants with an explanation of how to
control their feelings. However, according to the visual inspection of the attitude graph (see
Figure 4.1), attitudes of many of participants changed but in a negative way rather, which might
76
have resulted from the perception of the general difficulty of the course (which is probably why
many students dropped the course – historically true for this course). Thus, fundamental beliefs
about learning mathematics might have had more impact than predicted because participants
might have learned from positive beliefs that they could still consider mathematics useful and did
not need to feel anxiety about it despite the difficulties of the course. That is, if a student
believed that the ability to learn mathematics was developed over time, he or she might have
been more willing to seek help when struggling with calculus tasks. If a student believed that
mathematics knowledge was gradually acquired and the acquisition process is effortful, he or she
might have had less anxiety than the case that they did not believe so. This seems to be
consistent with van Eck’s study (2006) that found the improvement of participants’ beliefs about
mathematics through the interesting and instruction-related advisement of pedagogical agents
and games reduced their mathematics anxiety. Also, although emotional control among
volitional change strategies was provided, this support was intended to help participants maintain
focus on their calculus study by controlling negative emotions. Motivational strategies showing
relevance of calculus to participants’ lives would have helped them become interested in calculus
and acknowledge the usefulness of mathematic knowledge. Nonetheless, such interest and
usefulness without fundamental positive beliefs about learning mathematics might have been
interfered by too many difficulties in the course.
Second, the B-P group’s attitudes may have changed more in a positive way compared
with the other groups probably because messages for the B-P were shorter than messages for the
other groups except for the control group (see Appendix G, J, L, and N). Diagnostic questions for
B-P were less than MV-P or MVB-P as well (see Appendix H, K, and M). Owing to the
characteristics of emails, longer messages could have had more probabilities to be ignored or
postponed to be read and forgot to do so. Also, longer messages for the other groups might have
caused more cognitive loads than shorter messages for the B-P group especially in a busy,
difficult course with a lot of assignments; as a result, information in the longer messages might
have not been processed as well as ones in the shorter messages for the B-P group.
Third, it is inferred that the number of participants who dropped out of the course or the
study might account for the increased attitude scores of the B-P group. That is, loss of
participants in the groups receiving belief change strategies (i.e., B-P and B-G) was two times
greater than the loss the groups receiving change strategies (i.e., MV-P, MV-G, MVB-P, and
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MVB-G) as described in the procedure section of the method chapter (see Table 3.7, p. 52). This
might have caused that participants who were consciously unwilling to change their beliefs were
removed from the study differently from the other groups that might had some participants who
did not know whether or not they were willing to change. That is, in the B-P group, only the
participants who were willing to change their beliefs may have remained.
Last, the lower baseline of attitude scores of the B-P group as shown in Figure 4.1 might
have had a potential effect of their positively increased attitudes compared to the other groups
although the initial difference in attitudes among groups was not statistically significant. In other
words, the other groups who started with more positive attitudes might have been more
disappointed about mathematics as the semester went on with challenging tasks than the B-P
group who already started with less positive attitudes.
Hypothesis 1.2, those in the personal message groups would show more positive changes
of attitudes than those in the general message groups, was partially supported. Post Hoc analysis
using LSD test showed that changes in the attitudes of the MVB-P group were significantly more
positive than that of the MVB-G group. Also, the attitude change graph (see Figure 4.1) showed
that B-P group showed increased attitude scores and B-G group showed decreased ones.
It was not possible to conclude that all the personal message groups had more positive
effects than the general message groups since the overall treatment effect was not statistically
significant. However, at least two pairs of groups could be compared: one pair was MVB-P and
MVB-G, and the other pair was B-P and B-G. Like the previous studies although they had only
motivation and volition change strategies, personal messages constructed based on individual
needs were effective than group messages based on overall status of the whole group (Kim &
Keller, 2007b). It is speculated that personal messages addressing specific individual problems
may have been more appealing to the participants than group messages including all the
suggestions for possible problems about motivation, volition, or beliefs. That is, a kind of
customized messages might have facilitated the personal message groups’ attention to the email
messages and acceptance of change strategies. Recall that participants decided whether they
would receive personal or group messages; that is to say, that assignment to the seven conditions
was not completely random – only assignment to the type of messages was random. Participants
in the personal message groups who wanted to receive personal supports could have been more
78
willing to change their attitudes than those in the general message groups. Such willingness
might have worked as a motive for them to obtain useful information even without the personal
messages.
Another element to consider in the significant results for the personal message groups on
attitudes is the number and length of messages sent to groups. The personal message groups
received more number of messages with shorter lengths than the general message groups
received. In other words, change strategies for the personal message groups were divided
according to diagnostic questions and emailed to them several times. However, the group
messages had all the suggestions for possible problems about motivation, volition, or beliefs that
could allow them to select the portions that they thought they needed to read; that is to say that
the group messages covered all likely problems and students had to decide what might be most
appropriate for them. Once again, longer messages might have had more probabilities to be
ignored and to cause more cognitive loads than shorter messages. Also, this result is consistent
with Keller and his colleagues’ expectation that a distributed group who received motivational
and volitional supports at intervals would show more improved attitudes than a bundled group
who received such supports all at once (Keller, Demien, and Liu, 2004).
Hypothesis 1.3, the participants receiving combined change strategies would show more
positive changes in attitudes than the participants receiving either motivation and volition change
strategies or belief change strategies, was not supported. Post Hoc analysis indicated that
changes in the attitudes of the MV-G group were significantly more positive than MVB-G.
Although it was not possible to compare the change of the MVB-P and MVB-G groups’ attitudes
with all the other groups since there was no statistically significant treatment main effect, at least
the MVB-G group could be statistically compared with the MV-G group. This result might have
been caused by the different length between the messages for each group. In fact, the researchers
anticipated the potential problem with the length issue and thus used the same example cases or
scenarios for MV and B when constructing strategies. Thus, although all the critical components
in MV and B were included into MVB, using only one case or scenario for embedding both
kinds of components in MVB could let MVB shorter than the length of the combination of MV
and B with different scenarios. Despite such implementation, the messages for the MVB groups
could not be as short as desired because more components were involved than used in either the
79
MV or B groups. Therefore, as described earlier, longer messages might have been more likely
to be ignored and might have caused more cognitive load than shorter messages.
Hypothesis 1.4, those in the control group would show the least positive changes in
attitudes among all the groups, was not supported. There might have been a potential effect of
the emails to the control group. That is, the question in the email about study hours may have
facilitated the participants’ self-monitoring of their attitudes in the course while reflecting on
their study hours. In short, sending messages to the control group constituted an intervention that
may have had an unanticipated effect.
Study Habits
The second major hypothesis was that the type of email messages would result in positive
changes in participants’ study habits [Hypothesis 2]. Specifically, it was expected that the
participants receiving motivation and volition change strategies (i.e., MV-P) would show more
positive changes in study habits than the participants receiving belief change strategies (i.e., B-P)
[Hypothesis 2.1]. Also, it was expected that the participants receiving a combination of
motivation and volition change strategies and belief change strategies (i.e., MVB-P) would show
more positive changes in study habits than the participants receiving either motivation and
volition change strategies (i.e., MV-P) or belief change strategies (i.e., B-P) but not both
[Hypothesis 2.2]. In addition, it was expected that those in the control group would show the
least positive changes in study habits among all the groups [Hypothesis 2.3].
The major hypothesis that the type of email messages would result in positive changes in
participants’ study habits was not supported. One-way repeated measures ANOVA revealed that
there was no significant interaction between the two factors of time and treatment. That is,
participants’ study habits did not depend on time (while change strategies were sent four times)
according to which groups they were in (i.e., MV-P, B-P, MVB-P, or Control).
The time main effect and the treatment main effect were both significant. Post Hoc
analysis using LSD test indicated that changes in the study habits of both MV-P and B-P groups
were significantly more positive than that of the MVB-P group. Therefore, it is concluded that
Hypothesis 2.2, the participants receiving combined change strategies would show more positive
80
changes in study habits than the participants receiving either motivation and volition change
strategies or belief change strategies, was not supported. Like the results on the change of
attitudes previously mentioned, it is speculated that shorter messages for the MV-P and B-P
groups would have had more positive impact on changes in study habits than longer messages
for the MVB-P group because of less probabilities to be ignored and to cause more cognitive
loads.
Post Hoc analysis using LSD test also indicated that there was no statistically significant
difference among MV-P, B-P and control groups. Thus, Hypothesis 2.1, the participants
receiving motivation and volition change strategies would show more positive changes in study
habits than the participants receiving belief change strategies, was not supported. It is inferred
that despite specific strategies for effective study habits in motivation and volitional change
strategies, a lack of improved attitudes in the MV-P group might have interfered with possible
improvement of study habits.
Also, this Post Hoc analysis result indicated that Hypothesis 2.3, those in the control
group would show the least positive changes in study habits among all the groups, was not
supported. Like the results on the change of attitudes previously mentioned, there might have
been a potential effect of the emails to the control group. The question in the emails to ask about
their study hours may have facilitated the participants’ self-monitoring about their study habits.
While responding to the emails, they may have practiced reflection, a critical component of
volitional strategies, that is, reflection (Gollwitzer, 1990).
Achievement
The third major hypothesis was that the type of email messages would result in positive
changes in participants’ achievement [Hypothesis 3]. Specifically, it was expected that the
participants receiving motivation and volition change strategies (i.e., MV-P and MV-G) would
show more positive changes in achievement than the participants receiving belief change
strategies (i.e., B-P and B-G) [Hypothesis 3.1]. Also, it was expected that those in the personal
message groups (i.e., MVB-P, MV-P, and B-P) would show more positive changes of
achievement than those in the general message groups (i.e., MVB-G, MV-G, and B-G)
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[Hypothesis 3.2]. And, it was expected that the participants receiving combined change strategies
(i.e., MVB-P and MVB-G) would show more positive changes in achievement than the
participants receiving either motivation and volition change strategies (i.e., MV-P and MV-G) or
belief change strategies (i.e., B-P and B-G) but not both [Hypothesis 3.3]. In addition, it was
expected that those in the control group would show the least positive changes in achievement
among all the groups [Hypothesis 3.4].
The major hypothesis that the type of email messages would result in positive changes in
participants’ achievement was not supported. One-way repeated measures ANOVA showed that
there was no significant interaction between the two factors of time and treatment. That is,
participants’ achievement did not depend time according to which groups they were in (i.e., MVP, MV-G, B-P, B-G, MVB-P, MVB-G, or Control). The time main effect and the group main
effect were both non-significant as well as Post Hoc analysis using LSD test showed that
changes in the achievement of any group was not significantly more positive than that of the
others. Therefore, all the specific hypotheses were not supported.
The research framework of this study based on theoretical and empirical foundations
indicated that a learner’s achievement could positively change because of improved attitudes
toward mathematics and study habits through motivation, volition, and belief change strategies.
The framework was validated since without positive changes in both attitudes and study habits, it
might have been natural that there was no improvement of achievement. Another possible
influence on the outcomes might be that two months of increased attitudes and study habits were
not enough to lead to development of achievement as shown in previous studies (Kim & Keller,
2007a). That is, even though the B-P group showed more positive changes in attitudes compared
to the other groups and also in study habits compared to the MVB-P group, it might have needed
a longer intervention for them to actually show positive changes in achievement.
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Limitations of the Study
There are several limitations that might reduce the generalizability of the results of this
study. First, as described in the procedure of the method chapter, participants were asked to make
their own decisions to be in either personal message or general message groups during the
second stage of the study. The researcher hoped to facilitate participants’ feeling of commitment
to continue their participation by giving them a choice at this point in the study; there were
already a number of participants who had chosen not to participate, so the idea was to make
participants feel like they could actively determine part of the way the study was conducted and
thus gain commitment. However, in addition to the weakness of internal validity owing to this
non-random assignment, one should be also cautious about the different results between personal
message and general message groups. Participants in the personal message groups might have
chosen to receive personal supports because they felt a need for such supports. Considering feltneed results from a discrepancy between a person’s expectations and actual status (Pintrich &
Schunk, 2002), their wish to fill in the discrepancy might have worked as a motive for them to
pay attention to change strategies.
Second, as regarded as a potential problem several times earlier, the personal message
groups received more number of messages with shorter lengths than the general message groups
received. Also, the messages for the MVB groups were longer compared to the messages for
either the MV groups or B groups because of the combination of motivation and volition change
strategies and belief change strategies. As described earlier, it should be acknowledged that
longer messages might have had more probabilities to be ignored and to cause more cognitive
loads than shorter messages. In addition, participants who clicked the Website links embedded in
the middle of the messages for the MVB groups (e.g., Figure 3.3, p. 42) might have been less
likely to go back to the message to complete reading it after visiting the Website.
Third, during the study, 31 of 115 participants either dropped the course or cancelled
their participation, and, as a consequence, only 84 students completed the study – only data from
those who completed the study were used in the analysis. Even after reduced number of
participants, the study had reasonable (i.e., .73 for attitudes and achievement and .64 for study
habits) power for detecting moderate treatment effects. Also, repeated measures ANOVA was
83
used, which had an advantage to provide greater power to detect effects. Nonetheless, if there
were more participants as well as equal numbers in each group, data analyses could have been
even stronger.
Fourth, it was not investigated if there were differences between the two classes in which
the participants enrolled. Although each of the seven conditions had almost the same numbers of
participants from each of the two classes, there may have been effects due to two different
instructors. A more rigorous study would have included more instructors to minimize the effect
of the instructor. Another possibility would be to use computer-controlled instruction and
eliminate the effect of an instructor.
Fifth, it was not examined if there were differences among various majors of the
participants. Even though it was common that all of participants’ majors were not mathematics,
still there may have been different in the changes of attitudes, study habits, or achievement
according to their majors. Given the wide distribution of non-mathematics in various disciplines,
there were not sufficient numbers to investigate such differences in this study. A study with
many more subjects might well investigate this possibility, which is likely to have implications
for personalizing messages as well as determining which kinds of messages might be most
effective for various students.
Sixth, as described in the results section, it should be noted that some of the assumptions
for repeated measures ANOVA were not fully satisfied. It would have been a more robust study
if all the assumptions were perfectly satisfied. Again, a larger number of participants would have
increased the rigor of the study and associated analysis.
Seventh, there were some measurement issues. Attitudes toward mathematics were
measured by the Fennema-Sherman Mathematics Attitudes (FSMA) questionnaire (Fennema &
Sherman, 1976) consisting of questions about participants’ perceptions about usefulness of
mathematics and their mathematics anxiety. Although usefulness and anxiety are regarded as
prominent variables to examine students’ attitudes toward mathematics, the two variables might
have not been sufficient to determine participants’ attitudes. Study habit was operationally
defined as study hours and participants were asked to report their study hours on the week before
the survey was sent. Although it was not practically possible to ask participants to keep logs of
study hours each day, if that could be done, the consistency of study hours during the week could
84
have been another evidence of study habits. Further, it should be acknowledged that self-reported
study hours might have been inaccurate to be regarded as a valid measure for study habits.
Eighth, the emails sent by the researcher might have been perceived as less important
than those sent by the instructors with whom the participants learned from for the course credits.
The researcher used the researcher group title as “MAC2311 Support Center” in order for them
to acknowledge that the emails could be important for their success of the course.
Ninth, since there was already much participation requested from the students due to the
eight weeks of the study and several messages requiring their responses, it was not practically
possible to conduct confirmation procedures to see if they actually opened the emails and read
the messages. However, it would have made this study stronger if their actual reading and uses of
strategies from the emails were known. This is an instance when targeted interviews or even a
focus group follow-up meeting might have provided additional insight into the effects and uses
of the messages.
Lastly, there might have been a potential effect of the emails to the control group. The
question in the emails to ask about their study hours may have facilitated the participants’ selfmonitoring about their study habits. While responding to the emails, they may have practiced
reflection, which was a critical component of volitional strategies (Gollwitzer, 1990). In short,
the control group was not a completely independent group. Another way would be to use a pure
control group that does not get any email messages or any treatment related directly to the
planned interventions; this kind of pure control group is recommended for future research that
builds on this study.
85
Future Research
The findings and limitations of this study provide several directions for future research.
First, the treatment in this study, while longer than in many studies, was only eight weeks. If
messages were sent over the entire semester, the results might have been stronger. When trying
to bring about changes in established attitudes and habits, it would be good to investigate the
effects of an even longer treatment to determine if positive changes persisted over time. Also,
longer research period would see improved achievement as a result of more positive attitudes and
study habits. In addition, longitudinal research would track the sustainability of their positively
changed attitudes and study habits.
Second, it would be meaningful to examine whether the sender of the email has an effect.
Messages sent by the instructor might be perceived as more personal and meaningful than
messages sent by a researcher or other support person with whom the student is not familiar. On
the other hand, the students might be more candid with a support person who is helping them
than with an instructor because the student might concerns about his or her image in the eyes of
the instructor. A comparison study would show the different sender effects, if there is, between a
support person and an instructor: one is sent by a support person and the other is sent by an
instructor but diagnosis of problems and construction of change strategies are done by the same
support person.
Third, all the change strategies of this study were constructed based on the research
framework encompassing theoretical foundations and empirical evidence. Also, all of the
strategies sent to participants were validated with an expert, who was a major advisor of the
researcher. Nonetheless, the participants’ perceptions about the strategies were not specifically
examined as well as their actual uses of strategies were not tracked down. Future studies might
use qualitative research methods to investigate participants’ perceptions about such strategies
and actual uses of specific strategies. This might provide suggestions to improve the strategies.
Fourth, it would be interesting to obtain a more in-depth understanding of students’
attitudes and habits by means of interviews and qualitative analysis. In order to conduct some of
these extended studies it would be necessary to have greater access to the participants. In the
86
present study, the researchers had limited time for interacting with the students due to other
course activities that placed high demands on their time.
Fifth, research could also investigate ways of making the process more efficient for the
design of personal messages. For instance, a complete database of all the changes strategies for
as many as possible cases might be a tool for instructors to easily access and to refer to for their
students’ specific problems.
Sixth, future research might consider a provision of incentives to participants in order not
to lose them in the middle of the study. Keeping as many participants as possible would secure
statistical power to analyze data so that the researcher would have high probabilities to see the
actual effectiveness of change strategies.
Seventh, many components were implemented into each change strategies based on the
research framework. It might provide useful information to validate the research framework and
to improve change strategies if comparison studies about separated components are done.
Eighth, the length and number of change strategies sent to participants should be
controlled so that the researcher could be sure if the effectiveness resulted from the change
strategies themselves.
Lastly, online courses might be a good context to test the effects of change strategies
emails since students of online classes would consider emails a main communication method.
There would be less possibility for emails to be ignored by the students.
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Implications for Learning and Instruction
By building a conceptual framework based on theoretical foundations and empirical
studies as well as by conducting an exploratory investigation based on that framework, this study
provides empirical evidence as well as research directions and a framework for motivation,
volition, and belief change strategies to be used to improve students’ attitudes, study habits, and
achievement in mathematics education. Although there should be more studies to validate the
framework and to improve the use of change strategies, this study opens a door to investigate
possible ways to improve students’ study and performance in mathematics through implementing
an integrative view of motivation, volition, and beliefs. Also, this study provides a basis for
implications for learning and instruction by designing and developing effective strategies,
especially as implemented through supportive email messages, to enhance students’ attitudes,
study habits, and achievement in mathematics.
This framework is likely to generalize to other message forms and to other subjects and
contexts, but providing evidence of that generalizability is beyond the scope of this study. As an
exploratory experimental investigation, the researcher wanted to determine which of the seven
treatments seemed most promising for implications in learning and instruction. Overall, the study
provides preliminary evidence to suggest that provision of change strategies with personal
messages addressing specific individual problems may be useful in improving students’ attitudes
in the situation where there are threats to mathematics study in required, large lecture classes.
Although the construction of personal messages was less efficient than general messages, it
seems that effectiveness of personal messages is worthwhile to be used for the implementation of
change strategies. There should be some efforts to find ways to more efficient ways to diagnose
individual problems and construct personal messages so that more studies could be done to
increase the effectiveness of change strategies using efficient personal message construction.
The combination of motivation and volition change strategies and belief change strategies
seem not to be necessary as long as the length constraints are resolved. The different effects
between motivation and volition change strategies and belief change strategies still need to be
investigated for the more concrete research framework as well as better practical uses.
Nonetheless, belief change strategies appear to be effective as well as the concept of beliefs
88
about learning mathematics expanded the potentials of the motivation and volition studies (e.g.,
Kim & Keller, 2007a, 2007b; Suzuki & Keller, 1996; Visser & Keller, 1990) that used
systematic design of messages for the improvement of students’ attitudes and learning.
Conclusion
This study aimed at constructing a conceptual framework for research on motivation,
volition, and beliefs for the improvement of students’ attitudes, study habits, and achievement in
mathematics education. The framework was grounded in a review of relevant theories and
models as well as empirical studies. The study investigated the effects of motivation and volition
change strategies and belief change strategies, implemented with personal and group email
messages, on students’ attitudes, study habits, and achievement.
The results indicated that the use of belief change strategies with personal messages was
effective in improving learners’ attitudes toward mathematics. Notably, change strategies with
personal messages led to more positive changes in attitudes than general messages. A
combination of motivation and volition change strategies and belief change strategies seemed to
have had less impact on attitudes and study habits than either motivation and volition change
strategies or belief change strategies but not both. No significant difference was found for
achievement for any combination of strategies.
I believe that this study, despite its several limitations, provides support for the basic
theoretical assumptions of students’ motivation, volition, and beliefs as critical factors for their
attitudes, study habits, and achievement. I also believe that an integrative view of the key
constructs of motivation, volition, and beliefs is necessary in the context of the design,
development and evaluation of interventions for enhancing mathematics education. Finally, I
believe that further research is warranted based on this study.
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APPENDIX A. QUESTIONNAIRE ON ATTITUDES TOWARD
MATHEMATICS
Instructions:
In this part of this questionnaire, you are asked about your attitudes toward mathematics. Please read
the following statements carefully and then decide if you agree or disagree with each statement. Please
indicate how true each statement is for you.
Strongly
Disagree
Disagree
Undecided
Agree
Strongly
Agree
I’ll need mathematics for my future work.
c
d
e
f
g
I study mathematics because I know how useful
it is.
c
d
e
f
g
Knowing mathematics will help me earn a living.
c
d
e
f
g
Mathematics is a worthwhile and necessary
subject.
c
d
e
f
g
I’ll need a firm mastery of mathematics for my
future work.
c
d
e
f
g
I’ll use mathematics in many ways as an adult.
c
d
e
f
g
Mathematics is of no relevance to my life.
c
d
e
f
g
Mathematics will not be important to me in my
life’s work.
c
d
e
f
g
I see mathematics as a subject I will rarely use in
my daily life as an adult.
c
d
e
f
g
Taking mathematics is a waste of time.
c
d
e
f
g
In terms of my adult life it is not important for me
to do well in mathematics in college.
c
d
e
f
g
I expect to have little use for mathematics when I
get out of school.
c
d
e
f
g
Math doesn’t scare me at all.
c
d
e
f
g
It wouldn’t bother me at all to take more math
courses.
c
d
e
f
g
I haven’t usually worried about being able to
solve math problems.
c
d
e
f
g
I almost never have gotten shook up during a
math test.
c
d
e
f
g
I usually have been at ease during math tests.
c
d
e
f
g
I usually have been at ease in math classes.
c
d
e
f
g
Mathematics makes me feel uncomfortable,
restless, irritable, and impatient.
c
d
e
f
g
90
Strongly
Disagree
Disagree
Undecided
Agree
Strongly
Agree
Mathematics usually makes me feel
uncomfortable and nervous.
c
d
e
f
g
I get a sinking feeling when I think of trying hard
math problems.
c
d
e
f
g
My mind goes blank and I am unable to think
clearly when working math problems.
c
d
e
f
g
A math test would scare me.
c
d
e
f
g
Mathematics makes me feel uneasy and
confused.
c
d
e
f
g
91
APPENDIX B. QUESTIONNAIRE ON BELIEFS ABOUT LEARNING
MATHEMATICS
Instructions:
In this part of this questionnaire, you are asked about your beliefs about the acquisition of
mathematics knowledge. Please read the following statements carefully and then decide if you agree or
disagree with each statement. Please indicate how true each statement is for you.
Strongly Disagree
Disagree
Neutral
Agree
Strongly
Agree
If you are ever going to be able to understand
something, it will make sense to you the first time
you hear it.
c
d
e
f
g
Successful students understand things quickly.
c
d
e
f
g
A good teacher’s job is to keep his students from
wandering from the right track.
c
d
e
f
g
The most successful people have discovered how to
improve their ability to learn.
c
d
e
f
g
To me studying means getting the big ideas from the
text, rather than details.
c
d
e
f
g
Going over and over a difficult textbook chapter
usually won’t help you understand it.
c
d
e
f
g
The most important part of scientific work is original
thinking.
c
d
e
f
g
If I find the time to re-read a textbook chapter, I get a
lot more out of it the second time.
c
d
e
f
g
Genius is 10% ability and 90% hard work.
c
d
e
f
g
Everyone needs to learn how to learn.
c
d
e
f
g
Wisdom is not knowing the answers, but knowing how
to find the answers.
c
d
e
f
g
If a person can’t understand something within a short
amount of time, they should keep on trying.
c
d
e
f
g
Getting ahead takes a lot of work.
c
d
e
f
g
Some people are born good learners, others are just
stuck with limited ability.
c
d
e
f
g
The really smart students don’t have to work hard to
do well in school.
c
d
e
f
g
Working hard on a difficult problem for an extended
period of time only pays off for really smart
students.
c
d
e
f
g
92
Strongly Disagree
Disagree
Neutral
Agree
Strongly
Agree
Almost all the information you can learn from a
textbook you will get during the first reading.
c
d
e
f
g
Usually you can figure out difficult concepts if you
eliminate all outside distractions and really
concentrate.
c
d
e
f
g
The best thing about science courses is that most
problems have only one right answer.
c
d
e
f
g
Learning is a slow process of building up knowledge.
c
d
e
f
g
Self-help books are not much help.
c
d
e
f
g
93
APPENDIX C. QUESTIONNAIRE ON MOTIVATION
Instructions:
In this part of this questionnaire, you are asked about your expectations for this course. Give the
answer that truly applies to you, and not what you would like to be true, or what you think others want to
hear. Think about each statement by itself. Do not be influenced by your answers to other statements.
Please indicate how true each statement is for you.
Not
true
Slightly
true
Moderately
true
Mostly
true
Very
true
The things I learn in this course will be useful to me.
c
d
e
f
g
I feel confident that I will do well in this course.
c
d
e
f
g
This class will have many things in it that will capture
my attention.
c
d
e
f
g
I expect that the amount of work I will have to do will be
appropriate for this type of course.
c
d
e
f
g
I expect that the instructor will make the subject matter
of this course seem important.
c
d
e
f
g
I expect that the instructor will use an interesting variety
of teaching techniques.
c
d
e
f
g
I expect that a person has to be lucky to get good
grades in this course.
c
d
e
f
g
I do NOT see how the content of this course relates to
anything I already know.
c
d
e
f
g
Whether or not I will succeed in this course is up to me.
c
d
e
f
g
I feel that the grades or other recognition I will receive
will be fair compared to other students.
c
d
e
f
g
I expect that the instructor will do unusual or surprising
things that are interesting.
c
d
e
f
g
To accomplish my goals, it is important that I do well in
this course.
c
d
e
f
g
I expect to feel satisfied with what I will get from this
course.
c
d
e
f
g
I expect that my curiosity will be stimulated by the
questions asked or the assignments given on the
subject matter in this class.
c
d
e
f
g
I expect to find the challenge level in this course to be
about right: neither too easy nor too hard.
c
d
e
f
g
I feel that I will get enough recognition of my work in
this course by means of grades, comments, or
other feedback.
c
d
e
f
g
94
APPENDIX D. QUESTIONNAIRE ON VOLITION
Instructions:
There are two sections. Please read the introductory material for each sections and respond to the
questions as instructed.
Sometimes it is easy to concentrate on difficult or unpleasant matters and to pay full attention to them.
But often it is difficult to keep your attention on them because you are too excited or too nervous or
because your thoughts wander. Then it could happen that you end up neglecting difficult or unpleasant
things.
What's my experience when I want to
concentrate completely on something?
These days, this is how often I am like that:
Almost
never
Seldom
Somewhat
seldom
Sometimes
Somewhat
often
Often
Almost
always
Concentrating only on whatever is
important at the moment.
c
d
e
f
g
h
i
Staying focused on the business at hand
without any effort.
c
d
e
f
g
h
i
Starting an activity with full concentration.
c
d
e
f
g
h
i
Instinctively keeping the goal in mind.
c
d
e
f
g
h
i
Deliberately paying attention to anything
that is important to the matter at
hand.
c
d
e
f
g
h
i
Finding my attention riveted to what I am
doing.
c
d
e
f
g
h
i
Keeping my mind on the main thing.
c
d
e
f
g
h
i
Automatically paying attention only to
those things that will bring me closer
to my goal.
c
d
e
f
g
h
i
95
While you are occupied with a difficult or unpleasant matter, different things may cross your mind.
Sometimes these thoughts and sensations are positively toned (e.g., hopeful, optimistic); on other
occasions they may instead be negative (e.g., doubts, apprehensions).
What crosses my mind when I pursue a
challenging goal?
These days, this is how often I am like that:
Almost
never
Seldom
Somewhat
seldom
Sometimes
Somewhat
often
Often
Almost
always
Knowing that I really want to reach a
particular goal.
c
d
e
f
g
h
i
Being consciously aware that it is I who
want the goal.
c
d
e
f
g
h
i
Taking action in the knowledge that I am
acting on my own free will.
c
d
e
f
g
h
i
Knowing that I really want something.
c
d
e
f
g
h
i
96
APPENDIX E. QUESTIONNAIRE ON STUDY HABITS
Your responses are totally confidential. Neither the instructor nor anyone else other than us will
see your individual responses to these items. We will remove your name from the questionnaire
by substituting a coded identity as soon as we enter it into the computer.
Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5, etc.)
that you spent LAST WEEK: studying/working on this calculus course. Hours spent on this
calculus course last week, not counting class time: _________
97
APPENDIX F. OVERALL LEVELS OF MOTIVATION, VOLITION, AND
BELIEFS
Means and standard deviations for motivation scores
GROUP
MV-P
MV-G
B-P
B-G
MVB-P
MVB-G
Control
Total
Mean
3.9107
4.0313
3.5972
3.7159
4.0284
3.7692
3.9062
3.8557
Std.
Deviation
.65812
.49439
.49815
.53646
.48923
.57926
.46388
.53615
N
14
8
9
11
11
13
18
84
Means and standard deviations for volition scores
GROUP
MV-P
MV-G
B-P
B-G
MVB-P
MVB-G
Control
Total
Mean
5.2440
5.6250
5.2222
4.9621
5.2424
4.9872
5.4676
5.2490
Std.
Deviation
.76058
.69293
.54327
1.05301
1.08427
.86123
.63538
.81880
N
14
8
9
11
11
13
18
84
Means and standard deviations for belief scores
GROUP
MV-P
MV-G
B-P
B-G
MVB-P
MVB-G
Control
Total
Mean
2.0759
2.1328
2.3472
2.3920
2.1477
2.3221
2.0035
2.1838
Std.
Deviation
.31980
.41111
.37644
.25934
.35496
.26251
.30353
.34224
N
14
8
9
11
11
13
18
84
98
APPENDIX G. MESSAGES EMAILED AT STAGE 1
A. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY (MV) GROUP
NOTE: This message contains pictures and special formatting. If your email reader does not
support these features, you can open the attachment which is a Word document with the same
information. Please contact me if you have any problems ([email protected]).
Greetings! My name is ChanMin and you are receiving this message from the Student Support
Center for this class. This is a new feature and you are one of the people we (me, my professor,
and your professor) are testing it with. Our goal is to help you with motivational and study habits
problems you might encounter while studying calculus. I believe that I, with the support of the
professors, can help you because I took calculus when I was working on my bachelor’s degree.
I was not a math major and I still remember some of the challenges and worries that I had.
This message is the first of four messages you will receive. This one is long, but the rest of them
will be shorter. I know you are busy, but I will greatly appreciate it if you take time to read each
message and reply to a question at the end. Everything in this message has been proven by
research to be helpful!
How many times have you heard …?
How many times have you heard that your attitudes influence your behavior, that you can
control your attitudes, and that positive expectations help you succeed? Are you skeptical about
this? Well, guess what? You shouldn’t be because it is true.
But, when you are immersed in the study of calculus it is easy to get frustrated because it is a
difficult subject for most people and to forget that with enough effort you can succeed. That is
why we are sending you this message. The purpose of this message is to help you with your
motivation and beliefs in order to succeed in this class.
In the rest of this message, I will ask several questions about your motivation about learning
calculus. Depending on how you answer each question, you can read or skip those parts of this
message. You will also find website links under the questions. I’m sure you’ll find fun stuff in the
links once you click them. At the end of the message I will ask you to click on “Reply” and
answer one question to send to me.
Except for being a requirement, do you believe that calculus will actually be worthwhile to you?
If you do not believe that calculus will be useful to you, please try the following exercise. In spite
of your doubts, try to imagine at least one way in which calculus might possibly be worthwhile to
you in the future. Do not limit yourself to specific course related tasks. For example, one person
said that it would help her be a role model for her children in the future. She did not want her
children to be afraid of math.
Then, go to our “Futures” blog and post your reason (Click here to go to the blog). Or, just go to
the Futures blog, read what others have written, and then see if you can add something. After
you have posted your first comment, come back for at least one more visit to read what others
have posted and to enter a second comment of your own. Most people find that it helps their
own motivation to see what others have said.
99
Do you have some idea of actual, real-world, problems that you can solve with
calculus?
If you would like to have an idea of actual, real-world, problems that you can solve with calculus,
please read the situation below. Even though you might never have to solve a problem like this,
it will be nice to know that you could do it. And who knows, you might even want to impress a
friend or parent by showing them that you can use calculus in the “real world.”
Suppose your family bought a big dog, a giant Schnauzer and your mom asks you to build a
fence for it. You’re supposed to spend no more than $900 on the fence and want the largest
size you can get for the money. One side of the property of your family house meets a river. So
you don’t need to put a fence on that side. The side of the fence parallel to the river will cost $5
per foot to build, whereas the sides perpendicular to the river will cost $3 per foot. What
dimensions should you choose?
Calculus gives you an answer to this situation. If you want to see the answer, just click here.
You will see the answer and how it was derived even though you won’t understand all the
computations at this point. However, you can expect that you will find yourself understanding
the solution later in this semester!
Do you believe that calculus will be interesting?
Maybe you think calculus is going to totally dry and abstract. Well, guess what? You can find
interesting examples of calculus in everyday life. For example, Tim Pennings (a mathematician
of Hope College in Holland) discovered that his dog Elvis seemed to find the optimal path to
fetch a ball by instinctively solving a calculus problem. Here is the picture of Elvis that is now
famous, playing fetch at the beach. If you want to see Elvis’ calculus instinct, please click here
to see an explanation. And, believe it or not, you will see a list of articles about whether dogs
have calculus knowledge!
Are you strongly confident that you will succeed in this calculus class?
If you are not confident that you will succeed in this calculus class just consider that not
everything in calculus will be new to you. You will recognize many of the things you already
know as you study calculus. For example, does this quadratic formula look familiar to you that
100
Even if you’ve forgotten it, you will
you learned in your algebra class?
recall it once you watch this fun music video on YouTube (click here to watch it).
How about the Pythagorean Theorem: 32+42=52? Calculus includes this too. If you want to
check this, please click here and look at the example at the top of the page (“Calculating
Swimmers Total Distance”).
But, if you don’t remember the quadratic formula or Pythagorean Theorem, it is okay because
this class will review and teach you all related concepts and formulas. I said any little thing
about math you already know about will be a part of calculus. Addition and multiplication, for
instance. And, you know how to use a calculator. Believe this: any of these will help you learn
calculus.
If you want to be amazed by how much you’ve already heard about some concepts of calculus,
click here. Just have a look at Mike Kelley’s website. You will be surprised to see how
interesting he makes some aspects of calculus by using flash, and by the positive comments of
many calculus students.
Now, a question
Instructions:
If you are working online in your email program, please click on Reply, answer the following
question, and send it to me.
If you opened the attachment and are reading your Word document, please answer the
following question and save this file to one of your folders. Then, go back to your original email
message from me, click on Return, attach this document, and then send it to me.
NOTICE: Your answers are confidential. Your professor will not see them. I will put all of your
answers into the data base using your code number, and your professor will not see your
individual answers.
Thank you VERY MUCH!
Here is the question:
Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent studying/ working on this calculus course. Hours spent on this course last week, not
counting class time: _________
B. TO THE BELIEF CHANGE STRATEGY (B) GROUP
NOTE: This message contains pictures and special formatting. If your email reader does not
support these features, you can open the attachment which is a Word document with the same
information. Please contact me if you have any problems ([email protected]).
Greetings! My name is ChanMin and you are receiving this message from the Student Support
Center for this class. This is a new feature and you are one of the people we (me, my professor,
and your professor) are testing it with. Our goal is to help you with study habits problems you
might encounter while studying calculus. I believe that I, with the support of the professors, can
101
help you because I took calculus when I was working on my bachelor’s degree. I was not a
math major and I still remember some of the challenges and worries that I had.
This message is the first of four messages you will receive. This one is long, but the rest of them
will be shorter. I know you are busy, but I will greatly appreciate it if you take time to read each
message and reply to a question at the end. Everything in this message has been proven by
research to be helpful!
How many times have you heard …?
How many times have you heard that your attitudes influence your behavior, that you can
control your attitudes, and that positive expectations help you succeed? Are you skeptical about
this? Well, guess what? You shouldn’t be because it is true.
But, when you are immersed in the study of calculus it is easy to get frustrated because it is a
difficult subject for most people and to forget that with enough effort you can succeed. That is
why we are sending you this message. The purpose of this message is to help you with your
beliefs in order to succeed in this class.
In the rest of this message, I will ask several questions about your beliefs about learning math.
Depending on how you answer each question, you can read or skip those parts of this
message. You will also find website links under the questions. I’m sure you’ll find fun stuff in the
links once you click them. At the end of the message I will ask you to click on “Reply” and
answer one question to send to me.
Do you believe that you should be a math genius to be good at calculus?
Do you believe that a person has to have a natural ability for math in order to be good at
calculus? If so, please read the story below. It might help you change your mind.
Suppose your family bought a big dog, a giant Schnauzer and your mom asks you to build a
fence for it. You’re supposed to spend no more than $900 on the fence and want the largest
size you can get for the money. One side of the property of your family house meets a river. So
you don’t need to put a fence on that side. The side of the fence parallel to the river will cost $5
per foot to build, whereas the sides perpendicular to the river will cost $3 per foot. What
dimensions should you choose?
This question was given to all the students in a university classroom. What happened? Some of
students produced a correct solution and an answer. The rest of the students shouted to them
“You’re geniuses!” But, you know what? There was only one difference between students who
could solve the problem and those who couldn’t. It was whether or not they previously learned
about the formulas used for the solution. What does this mean? It means they didn’t need to be
math geniuses to solve the problem! It meant that math is a learnable skill and that they simply
used what they had learned in the classes they had previously taken. You have the ability to
learn calculus or you would not have gotten into this class, but it can be a challenge and it
requires persistent effort from you!
102
By the way, calculus gives you an answer to this situation. If you want to see the answer, just
click here. You will see the answer and how it was derived even though you won’t understand
all the computations at this point. However, you can expect that you will find yourself
understanding the solution later in this semester!
Do you believe that ability to learn math is fixed or flexible?
In this country there is a strong tendency to believe that you either have math ability or you don’t,
and that if you have it, math should be easy. This is very different from most other parts of the
world, especially Asia, where they believe that basically anyone can learn math if they work at it.
Certainly there are differences in levels of ability, but many, many experiments have shown that
people can learn math if they try.
However, if you believe that ability to learn math is fixed and difficult or impossible to change,
please try the following exercise. Try to contradict your own belief by imagining or remembering
at least one experience that shows that your ability for learning math could be improved. Do not
limit yourself to specific course related tasks. For example, one person said that she always
thought she was terrible in learning math in high school. But she found that she enjoyed solving
Sudoku puzzles and realized that this actually required mathematical thinking and probabilities.
She then realized that she was better at mathematical reasoning than she thought she was.
After thinking about this, please go to the Successes blog (click here to go to the blog), read
what other students have written and add your own example if you can think of one. You, like
many others, might find this to be interesting and helpful with your own thoughts about learning
math. Because, in fact, math ability is improved with effort and practice, just like learning to ride
a bicycle, learning to dance, learning to read, or any other human behavior.
Now, a question
Instructions:
If you are working online in your email program, please click on Reply, answer the following
question, and send it to me.
If you opened the attachment and are reading your Word document, please answer the
following question and save this file to one of your folders. Then, go back to your original email
message from me, click on Return, attach this document, and then send it to me.
NOTICE: Your answers are confidential. Your professor will not see them. I will put all of your
answers into the data base using your code number, and your professor will not see your
individual answers.
Thank you VERY MUCH!
Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent studying/ working on this calculus course. Hours spent on this course last week, not
counting class time: _________
103
C. TO THE MOTIVATION, VOLITON, AND BELIEF CHANGE STRATEGY (MVB)
GROUP
NOTE: This message contains pictures and special formatting. If your email reader does not
support these features, you can open the attachment which is a Word document with the same
information. Please contact me if you have any problems ([email protected]).
Greetings! My name is ChanMin and you are receiving this message from the Student Support
Center for this class. This is a new feature and you are one of the people we (me, my professor,
and your professor) are testing it with. Our goal is to help you with motivational and study habits
problems you might encounter while studying calculus. I believe that I, with the support of the
professors, can help you because I took calculus when I was working on my bachelor’s degree.
I was not a math major and I still remember some of the challenges and worries that I had.
This message is the first of four messages you will receive. This one is long, but the rest of them
will be shorter. I know you are busy, but I will greatly appreciate it if you take time to read each
message and reply to a question at the end. Everything in this message has been proven by
research to be helpful!
How many times have you heard …?
How many times have you heard that your attitudes influence your behavior, that you can
control your attitudes, and that positive expectations help you succeed? Are you skeptical about
this? Well, guess what? You shouldn’t be because it is true.
But, when you are immersed in the study of calculus it is easy to get frustrated because it is a
difficult subject for most people and to forget that with enough effort you can succeed. That is
why we are sending you this message. The purpose of this message is to help you with your
motivation and beliefs in order to succeed in this class.
In the rest of this message, I will ask several questions about your motivation and beliefs about
learning calculus. Depending on how you answer each question, you can read or skip those
parts of this message. You will also find website links under the questions. I’m sure you’ll find
fun stuff in the links once you click them. At the end of the message I will ask you to click on
“Reply” and answer one question to send to me.
Except for being a requirement, do you believe that calculus will actually be
worthwhile to you?
If you do not believe that calculus will be useful to you, please try the following exercise. In spite
of your doubts, try to imagine at least one way in which calculus might possibly be worthwhile to
you in the future. Do not limit yourself to specific course related tasks. For example, one person
said that it would help her be a role model for her children in the future. She did not want her
children to be afraid of math.
Then, go to our “Futures” blog and post your reason (Click here to go to the blog). Or, just go to
the Futures blog, read what others have written, and then see if you can add something. After
you have posted your first comment, come back for at least one more visit to read what others
have posted and to enter a second comment of your own. Most people find that it helps their
own motivation to see what others have said.
104
Do you have some idea of actual, real-world, problems that you can solve with
calculus?
If you would like to have an idea of actual, real-world, problems that you can solve with calculus,
please read the situation below. Even though you might never have to solve a problem like this,
it will be nice to know that you could do it. And who knows, you might even want to impress a
friend or parent by showing them that you can use calculus in the “real world.”
Suppose your family bought a big dog, a giant Schnauzer and your mom asks you to build a
fence for it. You’re supposed to spend no more than $900 on the fence and want the largest
size you can get for the money. One side of the property of your family house meets a river. So
you don’t need to put a fence on that side. The side of the fence parallel to the river will cost $5
per foot to build, whereas the sides perpendicular to the river will cost $3 per foot. What
dimensions should you choose?
Calculus gives you an answer to this situation. If you want to see the answer, just click here.
You will see the answer and how it was derived even though you won’t understand all the
computations at this point. However, you can expect that you will find yourself understanding
the solution later in this semester!
Do you believe that you should be a math genius to be good at calculus?
Do you believe that a person has to have a natural ability for math in order to be good at
calculus? If so, please read the story below. It might help you change your mind.
This fence problem above was given to all the students in a university classroom. What
happened? Some of students produced a correct solution and an answer. The rest of the
students shouted to them “You’re geniuses!” But, you know what? There was only one
difference between students who could solve the problem and those who couldn’t. It was
whether or not they previously learned about the formulas used for the solution. What does this
mean? It means they didn’t need to be math geniuses to solve the problem! It meant that math
is a learnable skill and that they simply used what they had learned in the classes they had
previously taken. You have the ability to learn calculus or you would not have gotten into this
class, but it can be a challenge and it requires persistent effort from you!
Do you believe that calculus will be interesting?
Maybe you think calculus is going to totally dry and abstract. Well, guess what? You can find
interesting examples of calculus in everyday life. For example, Tim Pennings (a mathematician
of Hope College in Holland) discovered that his dog Elvis seemed to find the optimal path to
fetch a ball by instinctively solving a calculus problem. Here is the picture of Elvis that is now
famous, playing fetch at the beach. If you want to see Elvis’ calculus instinct, please click here
to see an explanation. And, believe it or not, you will see a list of articles about whether dogs
have calculus knowledge!
105
Do you believe that ability to learn math is fixed or flexible?
In this country there is a strong tendency to believe that you either have math ability or you don’t,
and that if you have it, math should be easy. This is very different from most other parts of the
world, especially Asia, where they believe that basically anyone can learn math if they work at it.
Certainly there are differences in levels of ability, but many, many experiments have shown that
people can learn math if they try.
However, if you believe that ability to learn math is fixed and difficult or impossible to change,
please try the following exercise. Try to contradict your own belief by imagining or remembering
at least one experience that shows that your ability for learning math could be improved. Do not
limit yourself to specific course related tasks. For example, one person said that she always
thought she was terrible in learning math in high school. But she found that she enjoyed solving
Sudoku puzzles and realized that this actually required mathematical thinking and probabilities.
She then realized that she was better at mathematical reasoning than she thought she was.
After thinking about this, please go to the Successes blog (click here to go to the blog), read
what other students have written and add your own example if you can think of one. You, like
many others, might find this to be interesting and helpful with your own thoughts about learning
math. Because, in fact, math ability is improved with effort and practice, just like learning to ride
a bicycle, learning to dance, learning to read, or any other human behavior.
Are you strongly confident that you will succeed in this calculus class?
If you are not confident that you will succeed in this calculus class just consider that not
everything in calculus will be new to you. You will recognize many of the things you already
know as you study calculus. For example, does this quadratic formula look familiar to you that
Even if you’ve forgotten it, you will
you learned in your algebra class?
recall it once you watch this fun music video on YouTube (click here to watch it).
How about the Pythagorean Theorem: 32+42=52? Calculus includes this too. If you want to
check this, please click here and look at the example at the top of the page (“Calculating
Swimmers Total Distance”).
But, if you don’t remember the quadratic formula or Pythagorean Theorem, it is okay because
this class will review and teach you all related concepts and formulas. I said any little thing
about math you already know about will be a part of calculus. Addition and multiplication, for
instance. And, you know how to use a calculator. Believe this: any of these will help you learn
calculus.
If you want to be amazed by how much you’ve already heard about some concepts of calculus,
click here. Just have a look at Mike Kelley’s website. You will be surprised to see how
interesting he makes some aspects of calculus by using flash, and by the positive comments of
many calculus students.
106
Now, a question
Instructions:
If you are working online in your email program, please click on Reply, answer the following
question, and send it to me.
If you opened the attachment and are reading your Word document, please answer the
following question and save this file to one of your folders. Then, go back to your original email
message from me, click on Return, attach this document, and then send it to me.
NOTICE: Your answers are confidential. Your professor will not see them. I will put all of your
answers into the data base using your code number, and your professor will not see your
individual answers.
Thank you VERY MUCH!
Here is the question:
Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent studying/ working on this calculus course. Hours spent on this course last week, not
counting class time: _________
D. TO THE CONTROL GROUP
Greetings! My name is ChanMin and you are receiving this message from the Student Support
Center for this class. This is a new feature and you are one of the people we (me, my professor,
and your professor) are testing it with.
This message is the first of four messages you will receive. I know you are busy, but I will
greatly appreciate it if you take time to reply to the following question.
Now, a question
Instructions:
If you are working online in your email program, please click on Reply, answer the following
question, and send it to me.
If you opened the attachment and are reading your Word document, please answer the
following question and save this file to one of your folders. Then, go back to your original email
message from me, click on Return, attach this document, and then send it to me.
NOTICE: Your answers are confidential. Your professor will not see them. I will put all of your
answers into the data base using your code number, and your professor will not see your
individual answers.
Thank you VERY MUCH!
Here is the question:
Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent studying/ working on this calculus course. Hours spent on this course last week, not
counting class time: _________
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APPENDIX H. DIAGNOSTIC QUESTIONS EMAILED AT STAGE 2
A. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY (MV) GROUP
1. To me, calculus is something I have to do, but I don’t really want to. Agree___
Disagree___
2. Each week I have good intentions about studying and keeping up with homework, but I have
trouble doing it. Agree___
Disagree___
3. I already have specific blocks of time scheduled on my calendar to study for the first major
exam. Yes___
No___
4. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course
Hours spent on this course last week, not counting class time: _________
5. (Optional) A motivational problem I am having in this class right now is: _____________
6. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course. Hours spent on this course
last week, not counting class time: _________
B. TO THE BELIEF CHANGE STRATEGY (B) GROUP
1. I believe that people who are good at math should be able to learn calculus quickly. Agree
___ Disagree ___
2. I believe that even if I study hard my basic math ability isn’t going to change. Agree___
Disagree___
3. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course, not counting class time:
_________
C. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY (MVB)
GROUP
1. To me, calculus is something I have to do, but I don’t really want to. Agree___
Disagree___
2. Each week I have good intentions about studying and keeping up with homework, but I have
trouble doing it. Agree___
Disagree___
3. I believe that people who are good at math should be able to learn calculus quickly. Agree
___ Disagree ___
4. I believe that even if I study hard my basic math ability isn’t going to change. Agree___
Disagree___
108
5. I already have specific blocks of time scheduled on my calendar to study for the first major
exam. Yes___
No___
6. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5, etc.)
that you spent LAST WEEK: studying/working on this calculus course, not counting class time:
_________
7. (Optional) A motivational problem I am having in this class right now is: _____________
8. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course, not counting class time:
_________
109
APPENDIX I. DESCRIPTIONS OF PERSONAL SUGGESTIONS
EMAILED AT STAGE 2
A. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY (MV) GROUP
If you have some worries about whether you are going to pass or are not happy with your level
of motivation and study habits there are things you can do to make it better. Please read the
following and consider sticking with this support process. Our future messages will be much
more personal with regard to your specific situation. And we know from past successful
experiences that we might be able to help.
Your motivation is influenced by the way the course is taught and the behaviors of the instructor,
but it is most strongly influenced by your own attitudes and behaviors. This is called selfmotivation and it consists of a combination of attitudes and behaviors that can help or hinder
you. If you are feeling “down” there are specific things you can do to help yourself up, and that is
what this message is about.
To begin, there are three important principles that you must realize and agree with in order to
improve your motivation and study habits:
First, you are not alone. No matter what you are feeling, you are a human being and millions of
others have probably had the same or very similar feelings.
Second, you can learn from other people’s experiences. The suggestions that we can send you
are not abstract psychological principles. They are based on the successes that huge numbers
of people have had in overcoming their motivational challenges. These people are from all
walks of life: philosophers, businessmen, students, poets, architects, mathematicians, and even,
yes, psychologists. The important thing is that there is a tremendous amount of consistency in
what these people say.
Third, it isn’t easy to change. It takes desire and effort. In other words, you have to want to
overcome attitudes of resentment, fear, or whatever is holding you back, and you have to use
specific techniques to change your attitudes and behaviors.
If you are curious to know what some of the suggestions are based on the experiences of all
these other people and my personal experience (yes, indeed, I have had to deal with many
types of motivational challenges to get where I am, and still do!), then please click Reply and put
a check mark at the end of the next sentence.
Yes, send me the suggestions. I can tell you later if I want to stop at any time. __________
B. TO THE BELIEF CHANGE STRATEGY (B) GROUP
If you want to hear about techniques that will help you build positive and productive beliefs
about your ability to learn calculus, please read the following and consider sticking with this
support process. Our future messages will be much more personal with regard to your specific
situation. And we know from past successful experiences that we might be able to help.
Your motivation and performance in this class are influenced by the way the course is taught
and the behaviors of the instructor, but they are most strongly influenced by your own beliefs
110
and behaviors. Your beliefs can help you or hinder you, and if they are hindering you there are
specific things you can do to give yourself a boost, and that is what this message is about.
To begin, there are three important principles that you must realize and agree with in order to
improve your beliefs and performance:
First, you are not alone. No matter what you are feeling, you are a human being and millions of
others have probably had the same or very similar feelings.
Second, you can learn from other people’s experiences. The suggestions that we can send you
are not abstract psychological principles. They are based on the successes that huge numbers
of people have had in overcoming challenges by improving their beliefs and performance.
These people are from all walks of life: philosophers, businessmen, students, poets, architects,
mathematicians, and even, yes, psychologists. The important thing is that there is a tremendous
amount of consistency in what these people say.
Third, it isn’t easy to change. It takes desire and effort. In other words, you have to want to
overcome attitudes of resentment, fear, or whatever is holding you back, and you have to use
specific techniques to change your attitudes and behaviors.
If you are curious to know what some of the suggestions are based on the experiences of all
these other people and my personal experience (yes, indeed, I have had to overcome with
many types of challenges to my beliefs and performance to get where I am, and still do!), then
please click Reply and put a check mark at the end of the next sentence.
Yes, send me the suggestions. I can tell you later if I want to stop at any time. __________
C. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY (MVB)
GROUP
if you have any worries about whether you are going to pass or you’re not happy with your level
of motivation and study habits there are things you can do to make it better. Also, if you want to
build positive beliefs that will help you succeed in learning calculus in this class there are things
you can do to accomplish this. Please read the following and consider sticking with this support
process. Our future messages will be much more personal with regard to your specific situation.
And we know from past successful experiences that we might be able to help.
Your motivation and performance in this class are influenced by the way the course is taught
and the behaviors of the instructor, but it is most strongly influenced by your own attitudes and
behaviors. This is called self-motivation and it consists of a combination of attitudes and
behaviors that can help or hinder you. If you are feeling “down” there are specific things you can
do to help yourself up, and that is what this message is about.
Your motivation and performance in this class are influenced by the way the course is taught
and the behaviors of the instructor, but they are most strongly influenced by your own beliefs
and behaviors. Your beliefs can help you or hinder you, and if they are hindering you there are
specific things you can do to give yourself a boost, and that is what this message is about.
To begin, there are three important principles that you must realize and agree with in order to
improve your beliefs and performance:
First, you are not alone. No matter what you are feeling, you are a human being and millions of
others have probably had the same or very similar feelings.
111
Second, you can learn from other people’s experiences. The suggestions that we can send you
are not abstract psychological principles. They are based on the successes that huge numbers
of people have had in overcoming challenges by improving their beliefs, motivation and
performance. These people are from all walks of life: philosophers, businessmen, students,
poets, architects, mathematicians, and even, yes, psychologists. The important thing is that
there is a tremendous amount of consistency in what these people say.
Third, it isn’t easy to change. It takes desire and effort. In other words, you have to want to
overcome attitudes of resentment, fear, or whatever is holding you back, and you have to use
specific techniques to change your attitudes and behaviors.
If you are curious to know what some of the suggestions are based on the experiences of all
these other people and my personal experience (yes, indeed, I have had to overcome with
many types of challenges to my beliefs and performance to get where I am, and still do!), then
please click Reply and put a check mark at the end of the next sentence.
“Yes, send me the suggestions. I can tell you later if I want to stop at any time. __________”
112
APPENDIX J. SAMPLE MESSAGES AT STAGE 2
A. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY WITH
PERSONAL MESSAGES (MV-P) GROUP
Dear Mandy,
We are happy that you want to continue getting messages!
Here is more specific feedback on your questionnaire answers from Dr. Keller:
Regarding your answer to first question in which you agreed that you have to take the class but
don’t really want to, please read the following message:
Actually, that is okay if you are satisfied with how well you are doing in the course. But, your
motivation to succeed will be much higher if you decide that you really want to learn calculus.
Looking at it only as a requirement keeps you from becoming fully engaged and intrinsically
motivated to learn. If you are struggling to keep up and “fighting with yourself” to get your
homework done and prepare for quizzes, here are some suggestions that might help you
improve your attitude.
First, consider this comment: “Your attitudes influence your performance.” This is a simple
statement that has been uttered in various ways by people throughout history. You have
probably heard it before, and it is absolutely true. Struthers Burt, a famous American author,
dude rancher and poet, put it like this, “Men are failures, not because they are stupid, but
because they are not sufficiently impassioned.” Without a doubt, developing a more positive
attitude helps you to perform better.
Now consider this comment: Your attitudes are under your control. This is another simple but
powerful principle. It isn’t always easy to manage your attitudes, but there is a world of evidence
to show that it is possible and can have amazing results. One of the most dramatic examples of
this is Norman Cousins who overcame two crippling and life-threatening diseases in his lifetime.
He wrote a book after each of these events (Anatomy of an Illness, and The Healing Heart)
describing how he took control of his attitudes, infused positive thinking and even humor into his
life, and survived. You can not only survive but prevail in this class if you convince yourself of
the value of learning something new, and overcome resentment about being in a required
course.
Finally, you have a choice. Either develop a positive attitude toward the course, or just stop
thinking about it. “Stop thinking about it, you say?” Yes, that is what I am saying. Just accept the
fact that you need the course and treat it like a job. Just do it. Stop indulging yourself in negative
thinking about it. But, if you can, go for the positive attitude development! It will benefit you.
Good luck!
Sincerely,
ChanMin and Dr. Keller
113
Good evening, Mandy! Here is one more message from Dr. Keller. This is the last suggestion of
this week:
Daniel, in question two you said that although you I had good intentions about studying and
keeping up with homework, I had trouble doing it. You might want to consider the information in
the following:
Just before Christmas break one year, a psychologist named Gollwitzer told his students that he
wanted them to write an essay about their Christmas experience and to turn it in at the first
class meeting after the holidays. He told them to write a brief plan about when they would do it
and he collected them before his students left for the holiday break. Later, when he compared
what people said in their plans to whether or not they did the project in a timely way, he found
out something very interesting. The students who made very specific commitments (“I will set
aside two hours on the morning after Christmas to write a draft, and on the 27th I will schedule
two more hours to review it and prepare my final copy.”) had much higher completion rates than
the students who made a more general commitment (“I will do it sometime during the week
between Christmas and the New Year holiday.”).
This is an example of what Gollwitzer calls strong versus weak intentions. Everyone has good
intentions about getting their work done, but most intentions, like New Year’s resolutions, don’t
go anywhere because they are weak intentions. They are desires but without a concrete plan of
action. Scheduling specific times to do necessary tasks is truly helpful in accomplishing more.
However, you must use good judgment when scheduling your work, especially tasks that are
difficult. Plan on doing it at a time when there will be minimal distractions and when you will not
have to rush through it. Then, be sure you do it. If you do this successfully a few times, you will
be surprised by the satisfying feeling of accomplishment, and it will become easier to keep
doing it, to build a positive and productive new habit!
These are called strong intentions combined with effective self-regulation or action control
strategies. People who are most successful usually have a well-structured schedule and they
stick with it. If you schedule a specific time to do a task and get into the habit of following your
schedule, then you will be more successful. It also becomes easier to follow your schedule the
more you do it.
Good luck!
Sincerely,
ChanMin and Dr. Keller
B. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY WITH GENERAL
MESSAGES (MV-G) GROUP
Dear Randy,
You might find the following tips useful:
Do you feel that calculus is something you have to do, but you don’t really want to? If so,
please read the story below. It might help you change your mind.
Actually, that is okay if you are satisfied with how well you are doing in the course. But, your
motivation to succeed will be much higher if you decide that you really want to learn calculus.
Looking at it only as a requirement keeps you from becoming fully engaged and intrinsically
motivated to learn. If you are struggling to keep up and “fighting with yourself” to get your
114
homework done and prepare for quizzes, here are some suggestions that might help you
improve your attitude.
First, consider this comment: “Your attitudes influence your performance.” This is a simple
statement that has been uttered in various ways by people throughout history. You have
probably heard it before, and it is absolutely true. Struthers Burt, a famous American author,
dude rancher and poet, put it like this, “Men are failures, not because they are stupid, but
because they are not sufficiently impassioned.” Without a doubt, developing a more positive
attitude helps you to perform better.
Now consider this comment: Your attitudes are under your control. This is another simple but
powerful principle. It isn’t always easy to manage your attitudes, but there is a world of evidence
to show that it is possible and can have amazing results. One of the most dramatic examples of
this is Norman Cousins who overcame two crippling and life-threatening diseases in his lifetime.
He wrote a book after each of these events (Anatomy of an Illness, and The Healing Heart)
describing how he took control of his attitudes, infused positive thinking and even humor into his
life, and survived. You can not only survive but prevail in this class if you convince yourself of
the value of learning something new, and overcome resentment about being in a required
course.
Finally, you have a choice. Either develop a positive attitude toward the course, or just stop
thinking about it. “Stop thinking about it, you say?” Yes, that is what I am saying. Just accept the
fact that you need the course and treat it like a job. Just do it. Stop indulging yourself in negative
thinking about it. But, if you can, go for the positive attitude development! It will benefit you.
Each week you have good intentions about studying and keeping up with homework, but
do you have trouble doing it? If so, you might want to consider the information in the
following:
Just before Christmas break one year, a psychologist named Gollwitzer told his students that he
wanted them to write an essay about their Christmas experience and to turn it in at the first
class meeting after the holidays. He told them to write a brief plan about when they would do it
and he collected them before his students left for the holiday break. Later, when he compared
what people said in their plans to whether or not they did the project in a timely way, he found
out something very interesting. The students who made very specific commitments (“I will set
aside two hours on the morning after Christmas to write a draft, and on the 27th I will schedule
two more hours to review it and prepare my final copy.”) had much higher completion rates than
the students who made a more general commitment (“I will do it sometime during the week
between Christmas and the New Year holiday.”).
This is an example of what Gollwitzer calls strong versus weak intentions. Everyone has good
intentions about getting their work done, but most intentions, like New Year’s resolutions, don’t
go anywhere because they are weak intentions. They are desires but without a concrete plan of
action. Scheduling specific times to do necessary tasks is truly helpful in accomplishing more.
However, you must use good judgment when scheduling your work, especially tasks that are
difficult. Plan on doing it at a time when there will be minimal distractions and when you will not
have to rush through it. Then, be sure you do it. If you do this successfully a few times, you will
be surprised by the satisfying feeling of accomplishment, and it will become easier to keep
doing it, to build a positive and productive new habit!
These are called strong intentions combined with effective self-regulation or action control
strategies. People who are most successful usually have a well-structured schedule and they
stick with it. If you schedule a specific time to do a task and get into the habit of following your
115
schedule, then you will be more successful. It also becomes easier to follow your schedule the
more you do it.
Good luck!
Sincerely,
ChanMin and Dr. Keller
C. TO THE BELIEF CHANGE STRATEGY WITH PERSONAL MESSAGES (B-P)
GROUP
Dear Ben,
We are happy that you want to continue getting messages!
In question one you said that you believe that people who are good at math should be able to
learn calculus quickly. You might want to consider the information in the following message from
Dr. Keller:
Many people believe this, but it isn’t true. Yes, it is true that many people who are good at math
learn calculus quickly, but there are also many people who are good at math who learn it slowly.
The most important criterion for success is effort. Some people require more time than others
because of differences in cognitive style and learning strategies, but that by itself is not an
indication of differences in basic math ability.
In this country there is a general belief that if you are smart then school should be easy.
Therefore, if you are good at math, it should come easy. It is very typical for people to pretend
that they didn’t really have to work hard to achieve a goal. And, for some people this might be
true. In high school it is not uncommon for school to be relatively easy for smart students, but
this isn’t true in college. In college, everyone is smart, relatively speaking. Therefore, the
standards are higher and you are also left on your own to a much greater degree than in high
school. Thus, it is normal to have to work hard to succeed.
Almost everyone knows that in Asia the students tend to do better in math than here and that
there are basically no differences in math performance between males and females. Why do
you think this is? Because of differences in basic ability? No, according to the evidence. From
the time they enter school, Asian kids are told that everyone can succeed in school if they try
hard enough, and everyone is expected to learn math. This is a result of the philosopher
Confucius whose beliefs provide the basis, traditionally, for education in the north Asian
countries. I don’t know why we have such a different philosophy here, but we do and it creates
problems for many children and young adults, especially for many females in regard to math.
You can’t change the culture you are growing up in, but you can change your attitudes and
personal beliefs. It isn’t easy, but you can do it if you are determined. The more you can accept
the fact that success is a result of effort and hard work, and stop comparing yourself to others,
the more you will achieve. After all, if it takes you longer than someone else to learn calculus, all
I can say is, “What difference does it make?” You should just plan to allow extra time to study.
The important thing is, are you learning it? And, if you take enough time and study effectively
you can learn it. Don’t judge yourself about being fast or slow. You are you. Do what YOU need
to do to succeed.
Many famous men and women in history have made this same point. Thomas Edison, one of
the greatest inventors in history, said, “Genius is one percent inspiration and 99 percent
116
perspiration.” When asked for an explanation of this, he said, "I never did anything worth doing
by accident, nor did any of my inventions come by accident. They came by work." In other
words, you must be persistent to succeed.
Good luck!
Sincerely,
ChanMin and Dr. Keller
Good evening, Ben! Here is one more message from Dr. Keller. This is the last suggestion of
this week:
Ben, in question two, you said you agreed with the statement “I believe that even if I study hard
my basic math ability isn’t going to change.”
This is another belief that many people think is true. Why? Because they believe that you either
have the talent or you don’t. This kind of belief is prevalent in our society and it applies to many
things: music, leadership ability, dancing, and so on. I can give you many examples from my
own life: learning to dance when I was 57, learning to downhill ski when I was 60 and so forth.
But, you might say, “Those are physical skills. They just require guidance and practice.” Well,
guess what, the same thing applies to mental skills.
You might believe that success in math is only for people with native ability who can quickly
acquire math knowledge. How many times have you heard (or said!) someone say, “I don’t have
a talent for math.” Certainly it is true that some people have a greater talent for math and some
enjoy it more than others. But, you know what? Even the well-acknowledged mathematicians
are not famous for being geniuses. They are famous for producing great math works.
Furthermore, their works were not made overnight. Their works as well as their ability to
produce these works were gradually developed over a long period of time. Math ability is in
large part a learned skill, and just like any other complex learned skill it requires consistent effort
over a long period of time. Therefore, learning and persistence are more important than native
ability for building your math skills. The human brain is not like a stone that has fixed and firm
qualities. If you use it on challenging activities, it grows, if you don’t, it weakens. One of the big
causes of mental deterioration in older people is that they become passive and stop challenging
themselves. People who continue to use their brains remain alert and intelligent their entire lives
unless, of course, they have a physical disease that has a negative impact. So, don’t start
getting old already! Let this course be an opportunity to strengthen and grow your mental
abilities in math!
This reinforces the points made in the preceding message. Your ability at math grows with
continued effort, and your speed might or might not increase, it depends on your cognitive style.
The bottom line is to develop your belief in your own ability to learn this subject. Seek ways to
increase your understanding if the lectures are not clear or sufficient. Don’t expect to get
everything from the lectures. Be proactive and look at other resources. For example, an
excellent website that we listed in last week’s message is http://www.calculushelp.com/funstuff/phobe.html. Have a look if you didn’t before. You might be pleasantly
surprised.
Good luck!
Sincerely,
ChanMin and Dr. Keller
117
D. TO THE BELIEF CHANGE STRATEGY WITH GENERAL MESSAGES (B-G)
GROUP
Dear Monika,
You might find the following tips useful:
Do you believe that people who are good at math should be able to learn calculus
quickly? If so, please read the story below. It might help you change your mind.
Many people believe this, but it isn’t true. Yes, it is true that many people who are good at math
learn calculus quickly, but there are also many people who are good at math who learn it slowly.
The most important criterion for success is effort. Some people require more time than others
because of differences in cognitive style and learning strategies, but that by itself is not an
indication of differences in basic math ability.
In this country there is a general belief that if you are smart then school should be easy.
Therefore, if you are good at math, it should come easy. It is very typical for people to pretend
that they didn’t really have to work hard to achieve a goal. And, for some people this might be
true. In high school it is not uncommon for school to be relatively easy for smart students, but
this isn’t true in college. In college, everyone is smart, relatively speaking. Therefore, the
standards are higher and you are also left on your own to a much greater degree than in high
school. Thus, it is normal to have to work hard to succeed.
Almost everyone knows that in Asia the students tend to do better in math than here and that
there are basically no differences in math performance between males and females. Why do
you think this is? Because of differences in basic ability? No, according to the evidence. From
the time they enter school, Asian kids are told that everyone can succeed in school if they try
hard enough, and everyone is expected to learn math. This is a result of the philosopher
Confucius whose beliefs provide the basis, traditionally, for education in the north Asian
countries. I don’t know why we have such a different philosophy here, but we do and it creates
problems for many children and young adults, especially for many females in regard to math.
You can’t change the culture you are growing up in, but you can change your attitudes and
personal beliefs. It isn’t easy, but you can do it if you are determined. The more you can accept
the fact that success is a result of effort and hard work, and stop comparing yourself to others,
the more you will achieve. After all, if it takes you longer than someone else to learn calculus, all
I can say is, “What difference does it make?” You should just plan to allow extra time to study.
The important thing is, are you learning it? And, if you take enough time and study effectively
you can learn it. Don’t judge yourself about being fast or slow. You are you. Do what YOU need
to do to succeed.
Many famous men and women in history have made this same point. Thomas Edison, one of
the greatest inventors in history, said, “Genius is one percent inspiration and 99 percent
perspiration.” When asked for an explanation of this, he said, "I never did anything worth doing
by accident, nor did any of my inventions come by accident. They came by work." In other
words, you must be persistent to succeed.
Do you believe that even if you study hard, your basic math ability isn’t going to change?
If so, you might want to consider the information in the following:
This is another belief that many people think is true. Why? Because they believe that you either
have the talent or you don’t. This kind of belief is prevalent in our society and it applies to many
things: music, leadership ability, dancing, and so on. I can give you many examples from my
118
own life: learning to dance when I was 57, learning to downhill ski when I was 60 and so forth.
But, you might say, “Those are physical skills. They just require guidance and practice.” Well,
guess what, the same thing applies to mental skills.
You might believe that success in math is only for people with native ability who can quickly
acquire math knowledge. How many times have you heard (or said!) someone say, “I don’t have
a talent for math.” Certainly it is true that some people have a greater talent for math and some
enjoy it more than others. But, you know what? Even the well-acknowledged mathematicians
are not famous for being geniuses. They are famous for producing great math works.
Furthermore, their works were not made overnight. Their works as well as their ability to
produce these works were gradually developed over a long period of time. Math ability is in
large part a learned skill, and just like any other complex learned skill it requires consistent effort
over a long period of time. Therefore, learning and persistence are more important than native
ability for building your math skills. The human brain is not like a stone that has fixed and firm
qualities. If you use it on challenging activities, it grows, if you don’t, it weakens. One of the big
causes of mental deterioration in older people is that they become passive and stop challenging
themselves. People who continue to use their brains remain alert and intelligent their entire lives
unless, of course, they have a physical disease that has a negative impact. So, don’t start
getting old already! Let this course be an opportunity to strengthen and grow your mental
abilities in math!
This reinforces the points made in the preceding message. Your ability at math grows with
continued effort, and your speed might or might not increase, it depends on your cognitive style.
The bottom line is to develop your belief in your own ability to learn this subject. Seek ways to
increase your understanding if the lectures are not clear or sufficient. Don’t expect to get
everything from the lectures. Be proactive and look at other resources. For example, an
excellent website that we listed in last week’s message is http://www.calculushelp.com/funstuff/phobe.html. Have a look if you didn’t before. You might be pleasantly
surprised.
Good luck!
Sincerely,
ChanMin and Dr. Keller
E. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY WITH
PERSONAL MESSAGES (MVB-P) GROUP
Dear Rachael,
We are happy that you want to continue getting messages!
Here is more specific feedback on your questionnaire answers from Dr. Keller:
Regarding your answer to first question in which you agreed that you have to take the class but
don’t really want to, please read the following message:
Actually, that is okay if you are satisfied with how well you are doing in the course. But, your
motivation to succeed will be much higher if you decide that you really want to learn calculus.
Looking at it only as a requirement keeps you from becoming fully engaged and intrinsically
motivated to learn. If you are struggling to keep up and “fighting with yourself” to get your
119
homework done and prepare for quizzes, here are some suggestions that might help you
improve your attitude.
First, consider this comment: “Your attitudes influence your performance.” This is a simple
statement that has been uttered in various ways by people throughout history. You have
probably heard it before, and it is absolutely true. Struthers Burt, a famous American author,
dude rancher and poet, put it like this, “Men are failures, not because they are stupid, but
because they are not sufficiently impassioned.” Without a doubt, developing a more positive
attitude helps you to perform better.
Now consider this comment: Your attitudes are under your control. This is another simple but
powerful principle. It isn’t always easy to manage your attitudes, but there is a world of evidence
to show that it is possible and can have amazing results. One of the most dramatic examples of
this is Norman Cousins who overcame two crippling and life-threatening diseases in his lifetime.
He wrote a book after each of these events (Anatomy of an Illness, and The Healing Heart)
describing how he took control of his attitudes, infused positive thinking and even humor into his
life, and survived. You can not only survive but prevail in this class if you convince yourself of
the value of learning something new, and overcome resentment about being in a required
course.
Finally, you have a choice. Either develop a positive attitude toward the course, or just stop
thinking about it. “Stop thinking about it, you say?” Yes, that is what I am saying. Just accept the
fact that you need the course and treat it like a job. Just do it. Stop indulging yourself in negative
thinking about it. But, if you can, go for the positive attitude development! It will benefit you.
Good luck!
Sincerely,
ChanMin and Dr. Keller
Good evening, Rachael! Here is one more message from Dr. Keller. This is the second
suggestion of this week, and you will have one more tomorrow:
Everett, in question two you said that although you I had good intentions about studying and
keeping up with homework, I had trouble doing it. You might want to consider the information in
the following:
Just before Christmas break one year, a psychologist named Gollwitzer told his students that he
wanted them to write an essay about their Christmas experience and to turn it in at the first
class meeting after the holidays. He told them to write a brief plan about when they would do it
and he collected them before his students left for the holiday break. Later, when he compared
what people said in their plans to whether or not they did the project in a timely way, he found
out something very interesting. The students who made very specific commitments (“I will set
aside two hours on the morning after Christmas to write a draft, and on the 27th I will schedule
two more hours to review it and prepare my final copy.”) had much higher completion rates than
the students who made a more general commitment (“I will do it sometime during the week
between Christmas and the New Year holiday.”).
This is an example of what Gollwitzer calls strong versus weak intentions. Everyone has good
intentions about getting their work done, but most intentions, like New Year’s resolutions, don’t
go anywhere because they are weak intentions. They are desires but without a concrete plan of
action. Scheduling specific times to do necessary tasks is truly helpful in accomplishing more.
However, you must use good judgment when scheduling your work, especially tasks that are
difficult. Plan on doing it at a time when there will be minimal distractions and when you will not
120
have to rush through it. Then, be sure you do it. If you do this successfully a few times, you will
be surprised by the satisfying feeling of accomplishment, and it will become easier to keep
doing it, to build a positive and productive new habit!
These are called strong intentions combined with effective self-regulation or action control
strategies. People who are most successful usually have a well-structured schedule and they
stick with it. If you schedule a specific time to do a task and get into the habit of following your
schedule, then you will be more successful. It also becomes easier to follow your schedule the
more you do it.
Good luck!
Sincerely,
ChanMin and Dr. Keller
Good evening, Everett! Here is one more message from Dr. Keller. This is the last suggestion of
this week:
Everett, in question three you said that you believe that people who are good at math should be
able to learn calculus quickly. You might want to consider the information in the following
message:
Many people believe this, but it isn’t true. Yes, it is true that many people who are good at math
learn calculus quickly, but there are also many people who are good at math who learn it slowly.
The most important criterion for success is effort. Some people require more time than others
because of differences in cognitive style and learning strategies, but that by itself is not an
indication of differences in basic math ability.
In this country there is a general belief that if you are smart then school should be easy.
Therefore, if you are good at math, it should come easy. It is very typical for people to pretend
that they didn’t really have to work hard to achieve a goal. And, for some people this might be
true. In high school it is not uncommon for school to be relatively easy for smart students, but
this isn’t true in college. In college, everyone is smart, relatively speaking. Therefore, the
standards are higher and you are also left on your own to a much greater degree than in high
school. Thus, it is normal to have to work hard to succeed.
Almost everyone knows that in Asia the students tend to do better in math than here and that
there are basically no differences in math performance between males and females. Why do
you think this is? Because of differences in basic ability? No, according to the evidence. From
the time they enter school, Asian kids are told that everyone can succeed in school if they try
hard enough, and everyone is expected to learn math. This is a result of the philosopher
Confucius whose beliefs provide the basis, traditionally, for education in the north Asian
countries. I don’t know why we have such a different philosophy here, but we do and it creates
problems for many children and young adults, especially for many females in regard to math.
You can’t change the culture you are growing up in, but you can change your attitudes and
personal beliefs. It isn’t easy, but you can do it if you are determined. The more you can accept
the fact that success is a result of effort and hard work, and stop comparing yourself to others,
the more you will achieve. After all, if it takes you longer than someone else to learn calculus, all
I can say is, “What difference does it make?” You should just plan to allow extra time to study.
The important thing is, are you learning it? And, if you take enough time and study effectively
you can learn it. Don’t judge yourself about being fast or slow. You are you. Do what YOU need
to do to succeed.
121
Many famous men and women in history have made this same point. Thomas Edison, one of
the greatest inventors in history, said, “Genius is one percent inspiration and 99 percent
perspiration.” When asked for an explanation of this, he said, "I never did anything worth doing
by accident, nor did any of my inventions come by accident. They came by work." In other
words, you must be persistent to succeed.
Good luck!
Sincerely,
ChanMin and Dr. Keller
Good evening, Rachael Here is one more message from Dr. Keller. This is the last suggestion
of this week:
Tatiana, in question four, you said you agreed with the statement “I believe that even if I study
hard my basic math ability isn’t going to change.”
This is a belief that many people think is true. Why? Because they believe that you either have
the talent or you don’t. This kind of belief is prevalent in our society and it applies to many
things: music, leadership ability, dancing, and so on. I can give you many examples from my
own life: learning to dance when I was 57, learning to downhill ski when I was 60 and so forth.
But, you might say, “Those are physical skills. They just require guidance and practice.” Well,
guess what, the same thing applies to mental skills.
You might believe that success in math is only for people with native ability who can quickly
acquire math knowledge. How many times have you heard (or said!) someone say, “I don’t have
a talent for math.” Certainly it is true that some people have a greater talent for math and some
enjoy it more than others. But, you know what? Even the well-acknowledged mathematicians
are not famous for being geniuses. They are famous for producing great math works.
Furthermore, their works were not made overnight. Their works as well as their ability to
produce these works were gradually developed over a long period of time. Math ability is in
large part a learned skill, and just like any other complex learned skill it requires consistent effort
over a long period of time. Therefore, learning and persistence are more important than native
ability for building your math skills. The human brain is not like a stone that has fixed and firm
qualities. If you use it on challenging activities, it grows, if you don’t, it weakens. One of the big
causes of mental deterioration in older people is that they become passive and stop challenging
themselves. People who continue to use their brains remain alert and intelligent their entire lives
unless, of course, they have a physical disease that has a negative impact. So, don’t start
getting old already! Let this course be an opportunity to strengthen and grow your mental
abilities in math!
This reinforces the points made in the preceding message. Your ability at math grows with
continued effort, and your speed might or might not increase, it depends on your cognitive style.
The bottom line is to develop your belief in your own ability to learn this subject. Seek ways to
increase your understanding if the lectures are not clear or sufficient. Don’t expect to get
everything from the lectures. Be proactive and look at other resources. For example, an
excellent website that we listed in last week’s message is http://www.calculushelp.com/funstuff/phobe.html. Have a look if you didn’t before. You might be pleasantly
surprised.
Good luck!
Sincerely,
ChanMin and Dr. Keller
122
F. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY WITH
GENERAL MESSAGES (MVB-G) GROUP
The combination of the messages sent to the MV-G and B-G groups above.
G. TO CONTROL GROUP
Hi! This message is the second of four messages you will receive. I know you are busy, but I will
greatly appreciate it if you take time to reply to the following question.
Here is the question:
Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent studying/ working on this calculus course. Hours spent on this course last week, not
counting class time: _________
123
APPENDIX K. DIAGNOSTIC QUESTIONS EMAILED AT STAGE 3
A. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY WITH
PERSONAL MESSAGES (MV-P) GROUP
1. During this past week, did you have a study plan indicating when and where you would
study calculus?
2. Do you have trouble avoiding distractions from your environment when it is time to study
calculus?
3. Do you experience any emotions (for example, anger, boredom, anxiety, or indifference)
that interfere with your ability to study calculus?
4. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course, not counting class time:
_________
B. TO THE BELIEF CHANGE STRATEGY WITH PERSONAL MESSAGES (B-P)
GROUP
1. I believe that the primary influence on success in this class is a person’s math ability, not the
amount of effort they put into it. Agree___
Disagree___
2. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course, not counting class time:
_________
C. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY WITH
PERSONAL MESSAGES (MVB-P) GROUP
1. Did you make a study plan indicating when and where you would study to prepare for the
class?
2. Did you study when you planned to?
3. Did you experience any emotions (for example, anger, boredom, anxiety, indifference) that
interfered with your ability to study calculus?
4. I believe that the primary influence on success in this class is a person’s math ability, not the
amount of effort they put into it. Agree___
Disagree___
5. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5, etc.)
that you spent LAST WEEK: studying/working on this calculus course. Hours spent on this
course last week, not counting class time: _________
124
APPENDIX L. SAMPLE MESSAGES AT STAGE 3
A. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY WITH
PERSONAL MESSAGES (MV-P) GROUP
Mandy, thank you for answering those questions!
Here is more specific feedback on your questionnaire answers from Dr. Keller:
Regarding the first question about having a study plan, you said that you did not. I highly
recommend that you do develop study plans and follow them. This is an important part of
developing a habit of study that helps you get the most benefit from your study time. Here are
some suggestions based on what successful people do. Basically, you should decide when and
where you are going to study. It’s that simple. There is nothing complicated about this tip. I
recommend that you do the following two things:
1. Make appointments for yourself. Put them on your calendar. Making commitments to study
and keeping those commitments are critical. You might have good intentions, but you have to
act on those intentions to make them real.
2. Choose your place of study. Think of it like going to an office. If you try studying in your
apartment near your refrigerator, and the refrigerator door is open more often than your
textbook, then make yourself go someplace where there are minimal or no distractions. Do your
work, and then leave to go somewhere else. You will accomplish much more and in less time.
One caution: Many people can’t work well in a place that is too dead. Silence can be deafening.
I like to have some music in the background. But, I play it at a low level so that when I am
studying, I don’t even hear it.
Good luck!
Sincerely,
ChanMin and Dr. Keller
Mandy, this is the second suggestion of the week from Dr. Keller:
Regarding the second question, you said that you do have trouble avoiding distractions from
your environment when it is time to study calculus. This is understandable, especially when you
are stressed or not highly motivated to do the task. Here is a simple, two-step process that
might help you, just it has helped many other people:
1. You are the one who will benefit from this work, so consider it to be important. Ask yourself,
“What kinds of obstacles or distractions will tempt me to not keep my study appointments? Are
these obstacles from other people or my own reluctance to “get into it”?
2. After you identify the obstacles, you can expect them to happen, but you can be prepared for
them. Tell yourself, “I know from past experience that these obstacles will come up, and I will be
tempted to think they are more important than studying. I know I will try to tell myself that I can
always study later. But this time, I am going to study when I scheduled it, and do the other
things later.” If you can develop this discipline and enjoy the rewards of good study habits, you
will be surprised at how good you feel.
125
Good luck!
Sincerely,
ChanMin and Dr. Keller
Mandy, here is the last suggestion of the week from Dr. Keller:
Regarding the third question, you said that you do have emotional feelings that distract you
when it is time to study calculus. To overcome this, here are two things to consider that can help
you develop a positive attitude of commitment and avoid procrastination. that can help you.
The first thing that can allow emotions to interfere with studying is that deep inside you really
don’t want to do it. You might not have made a deep commitment, or are “not in the mood”.
Have you ever said, “I’m just not in the mood to study.” Of course you have. Who hasn’t? But,
you can control this. It it happens at one of your scheduled times to study, you just have to say
to yourself, “So what? Yes, I know I am not in the mood to study, but this is my time to study, so
I am going to study.” As Pearl Buck said, “I don't wait for moods. You accomplish nothing if
you do that. Your mind must know it has got to get down to work.” (Who, you might ask,
was Pearl Buck? She was a fabulously adventuresome woman who won the Nobel Prize for
literature in 1938. )
The second is that there really is something that is emotionally upsetting in your life that is
making it difficult to concentrate. You can’t make the cause of that strong emotion suddenly
disappears, but there is a trick you can use to manage it. Visualize your mind as having a set of
small lockers in it, like the lockers at a gym. Tell yourself, “I feel very strongly about this situation
that is on my mind and I don’t really want to think about anything else. I can’t make it go away.
But what I can do is to put it into one of the lockers in my mind, shut the door, and then I can
open the door later after I get my homework done.” People in extremely stressful situations,
such as soldiers or athletes, do this all the time in order to concentrate on the vital task in front
of them. It works because you are not denying your feelings; you are just putting them away for
a little while so that you can indulge them later.
Good luck!
Sincerely,
ChanMin and Dr. Keller
B. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY WITH GENERAL
MESSAGES (MV-G) GROUP
Dear Randy,
You might find the following tips useful:
During this past week, did you have a study plan indicating when and where you would
study calculus? If not, please read the story below.
I highly recommend that you do develop study plans and follow them. This is an important part
of developing a habit of study that helps you get the most benefit from your study time. Here are
some suggestions based on what successful people do. Basically, you should decide when and
126
where you are going to study. It’s that simple. There is nothing complicated about this tip. I
recommend that you do the following two things:
1. Make appointments for yourself. Put them on your calendar. Making commitments to study
and keeping those commitments are critical. You might have good intentions, but you have to
act on those intentions to make them real.
2. Choose your place of study. Think of it like going to an office. If you try studying in your
apartment near your refrigerator, and the refrigerator door is open more often than your
textbook, then make yourself go someplace where there are minimal or no distractions. Do your
work, and then leave to go somewhere else. You will accomplish much more and in less time.
One caution: Many people can’t work well in a place that is too dead. Silence can be deafening.
I like to have some music in the background. But, I play it at a low level so that when I am
studying, I don’t even hear it.
Do you have trouble avoiding distractions from your environment when it is time to study
calculus? If so, you might want to consider the information in the following:
This is understandable, especially when you are stressed or not highly motivated to do the task.
Here is a simple, two-step process that might help you, just it has helped many other people:
1. You are the one who will benefit from this work, so consider it to be important. Ask yourself,
“What kinds of obstacles or distractions will tempt me to not keep my study appointments? Are
these obstacles from other people or my own reluctance to “get into it”?
2. After you identify the obstacles, you can expect them to happen, but you can be prepared for
them. Tell yourself, “I know from past experience that these obstacles will come up, and I will be
tempted to think they are more important than studying. I know I will try to tell myself that I can
always study later. But this time, I am going to study when I scheduled it, and do the other
things later.” If you can develop this discipline and enjoy the rewards of good study habits, you
will be surprised at how good you feel.
Do you experience any emotions (for example, anger, boredom, anxiety, or indifference)
that interfere with your ability to study calculus? If so, the following tips might be helpful
for you:
To overcome this, here are two things to consider that can help you develop a positive attitude
of commitment and avoid procrastination. that can help you.
The first thing that can allow emotions to interfere with studying is that deep inside you really
don’t want to do it. You might not have made a deep commitment, or are “not in the mood”.
Have you ever said, “I’m just not in the mood to study.” Of course you have. Who hasn’t? But,
you can control this. It it happens at one of your scheduled times to study, you just have to say
to yourself, “So what? Yes, I know I am not in the mood to study, but this is my time to study, so
I am going to study.” As Pearl Buck said, “I don't wait for moods. You accomplish nothing if
you do that. Your mind must know it has got to get down to work.” (Who, you might ask,
was Pearl Buck? She was a fabulously adventuresome woman who won the Nobel Prize for
literature in 1938. )
The second is that there really is something that is emotionally upsetting in your life that is
making it difficult to concentrate. You can’t make the cause of that strong emotion suddenly
disappears, but there is a trick you can use to manage it. Visualize your mind as having a set of
small lockers in it, like the lockers at a gym. Tell yourself, “I feel very strongly about this situation
that is on my mind and I don’t really want to think about anything else. I can’t make it go away.
127
But what I can do is to put it into one of the lockers in my mind, shut the door, and then I can
open the door later after I get my homework done.” People in extremely stressful situations,
such as soldiers or athletes, do this all the time in order to concentrate on the vital task in front
of them. It works because you are not denying your feelings; you are just putting them away for
a little while so that you can indulge them later.
Good luck!
Sincerely,
ChanMin and Dr. Keller
C. TO THE BELIEF CHANGE STRATEGY WITH PERSONAL MESSAGES (B-P)
GROUP
Ben, thank you for answering those questions!
Here is specific feedback on your questionnaire answers from Dr. Keller:
Regarding the first question about ability and effort, you said that you believe ability is the most
important thing. I hope you will read the following because it might be helpful to you. When I first
read about the concepts I describe below and applied them to myself, they helped me overcome
some challenges that were stressful in my own life.
Consider the following:
•
Having the basic ability to learn math is necessary for success.
•
Believing you have the ability is also necessary for success.
•
And, putting forth the effort to learn is absolutely critical.
Years of formal research by psychologists such as Bernard Weiner, John Nichols, and Carol
Dweck demonstrate that your attitudes about learning math are the most important elements of
success. The fact is that most people have the ability to learn math but success depends
primarily on your belief in your ability and your efforts.
So, if you don’t have these positive beliefs, what can you do? It is possible to change your
attitudes by, first of all, accepting the new belief and, secondly, changing your behavior to
become more task oriented when studying math or any other subject instead of being what is
called “ego oriented.”
The ego-oriented person is preoccupied by thoughts such as, “Am I smart enough to learn this?
Am I going to pass this test? Am I going to forget everything I know when it is time to take the
test? What will people think of me if I don’t do well?”
In contrast, a task-oriented person is more likely to ask him or herself, “What are the steps I
should take to be prepared? Do I understand this concept or formula that I am studying? I am
going to work on a few of the kinds of problems I was doing yesterday to make sure I
remember. Are there key steps in this process that I should memorize?”
In other words, the task-oriented person focuses on the task instead of worrying about the
outcomes.
Naturally, you will probably think about both things, but where is the primary preoccupation of
your mind? Task or ego? An ego orientation robs time from you; it takes away time that could be
spent productively. A task orientation helps you prepare as well as you possibly can.
128
So, the bottom line is that ability is necessary, but you have the ability to learn math or you
wouldn’t be here. Therefore, the most important thing is effort. The more you develop a taskoriented perspective and concentrate on the task, the more benefit you will get from your study
time.
Give it a try! It works. I know from personal experience, and I am not making this up.
Good luck!
Sincerely,
ChanMin and Dr. Keller
D. TO THE BELIEF CHANGE STRATEGY WITH GENERAL MESSAGES (B-G)
GROUP
Dear Monika,
You might find the following tips useful:
Do you believe that the primary influence on success in this class is a person’s math
ability, not the amount of effort they put into it? If so, you might want to consider the
information in the following:
When I first read about the concepts I describe below and applied them to myself, they helped
me overcome some challenges that were stressful in my own life.
Consider the following:
•
Having the basic ability to learn math is necessary for success.
•
Believing you have the ability is also necessary for success.
•
And, putting forth the effort to learn is absolutely critical.
Years of formal research by psychologists such as Bernard Weiner, John Nichols, and Carol
Dweck demonstrate that your attitudes about learning math are the most important elements of
success. The fact is that most people have the ability to learn math but success depends
primarily on your belief in your ability and your efforts.
So, if you don’t have these positive beliefs, what can you do? It is possible to change your
attitudes by, first of all, accepting the new belief and, secondly, changing your behavior to
become more task oriented when studying math or any other subject instead of being what is
called “ego oriented.”
The ego-oriented person is preoccupied by thoughts such as, “Am I smart enough to learn this?
Am I going to pass this test? Am I going to forget everything I know when it is time to take the
test? What will people think of me if I don’t do well?”
In contrast, a task-oriented person is more likely to ask him or herself, “What are the steps I
should take to be prepared? Do I understand this concept or formula that I am studying? I am
going to work on a few of the kinds of problems I was doing yesterday to make sure I
remember. Are there key steps in this process that I should memorize?”
In other words, the task-oriented person focuses on the task instead of worrying about the
outcomes.
129
Naturally, you will probably think about both things, but where is the primary preoccupation of
your mind? Task or ego? An ego orientation robs time from you; it takes away time that could be
spent productively. A task orientation helps you prepare as well as you possibly can.
So, the bottom line is that ability is necessary, but you have the ability to learn math or you
wouldn’t be here. Therefore, the most important thing is effort. The more you develop a taskoriented perspective and concentrate on the task, the more benefit you will get from your study
time.
Give it a try! It works. I know from personal experience, and I am not making this up.
Good luck!
Sincerely,
ChanMin and Dr. Keller
E. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY WITH
PERSONAL MESSAGES (MVB-P) GROUP
The combination of the messages sent to the MV-P and B-P groups above.
F. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY WITH
GENERAL MESSAGES (MVB-G) GROUP
The combination of the messages sent to the MV-G and B-G groups above.
G. TO CONTROL GROUP
Hi! This message is the third of four messages you will receive. I know you are busy, but I will
greatly appreciate it if you take time to reply to the following question.
Here is the question:
Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent studying/ working on this calculus course. Hours spent on this course last week, not
counting class time: _________
130
APPENDIX M. DIAGNOSTIC QUESTIONS EMAILED AT STAGE 4
A. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY WITH
PERSONAL MESSAGES (MV-P) GROUP
1. How satisfied were you with the quality of your study for the course?
____ Extremely highly effective
____ Highly effective
____ Moderate effective
____ Somewhat ineffective
____ Extremely ineffective
2. When you studied, to what degree did you go to a specific place where you could study
effectively?
____ Extremely high degree
____ High degree
____ Moderate degree
____ Low degree
____ Extremely low degree
3. Did you experience any negative emotions that interfered with your ability to study the
course? If so, how did you take care of them? Yes____ ( ___________________) No____
4. Do you get discouraged because you haven’t studied as much as you should have and it
seems like there is too much left to do? Yes____ No____
5. Do you have too much anxiety before a test in this class: that is, is your anxiety high enough
that it might keep you from doing your best? Yes____ No____
6. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course, not counting class time:
_________
B. TO THE BELIEF CHANGE STRATEGY WITH PERSONAL MESSAGES (B-P)
GROUP
1. Do you think the following thoughts are useful to your study of calculus? Yes ____ No ____
Learning math is gradual.
Ability to learn math is improvable.
2. On a scale of 1 (low) to 10 (high), how strong do you think your ability to learn calculus was
AT THE BEGINNING OF THE CLASS? ____
131
3. On a scale of 1 (low) to 10 (high), how strong do you think your ability to learn calculus is
NOW? ____
4. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course, not counting class time:
_________
C. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY WITH
PERSONAL MESSAGES (MVB-P) GROUP
1. Do you think the following thoughts are useful to your study of calculus? Yes ____ No ____
Learning math is gradual.
Ability to learn math is improvable.
2. On a scale of 1 (low) to 10 (high), how strong do you think your ability to learn calculus was
AT THE BEGINNING OF THE CLASS? ____
3. On a scale of 1 (low) to 10 (high), how strong do you think your ability to learn calculus is
NOW? ____
4. How satisfied were you with the quality of your study for the course?
____ Extremely highly effective
____ Highly effective
____ Moderate effective
____ Somewhat ineffective
____ Extremely ineffective
5. When you studied, to what degree did you go to a specific place where you could study
effectively?
____ Extremely high degree
____ High degree
____ Moderate degree
____ Low degree
____ Extremely low degree
6. Did you experience any negative emotions that interfered with your ability to study the
course? If so, how did you take care of them? Yes____ ( ___________________) No____
7. Do you get discouraged because you haven’t studied as much as you should have and it
seems like there is too much left to do? Yes____ No____
8. Do you have too much anxiety before a test in this class: that is, is your anxiety high enough
that it might keep you from doing your best? Yes____ No____
9. Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent LAST WEEK: studying/working on this calculus course, not counting class time:
_________
132
APPENDIX N. SAMPLE MESSAGES AT STAGE 4
A. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY WITH
PERSONAL MESSAGES (MV-P) GROUP
Mandy, here is a message from Dr. Keller:
You indicated that you experienced negative feeling such as failure that interfered your ability to
study the course. To overcome this, here is one thing to consider that can help you develop a
positive attitude of commitment that can help you.
There really is something that is emotionally upsetting in your life and making it difficult to
concentrate on your study. You can’t make the cause of that strong emotion suddenly
disappears, but there is a trick you can use to manage it. Visualize your mind as having a set of
small lockers in it, like the lockers at a gym. Tell yourself, “I feel very strongly about this situation
that is on my mind and I don’t really want to think about anything else. I can’t make it go away.
But what I can do is to put it into one of the lockers in my mind, shut the door, and then I can
open the door later after I get my homework done.” People in extremely stressful situations,
such as soldiers or athletes, do this all the time in order to concentrate on the vital task in front
of them. It works because you are not denying your feelings; you are just putting them away for
a little while so that you can indulge them later.
You will receive additional suggestions tomorrow. That will be helpful for you to move to the next
step!
Good luck!
Sincerely,
ChanMin and Dr. Keller
Dear Mandy,
Here is another message from Dr. Keller:
You indicated that you get discouraged because haven’t studied as much as you should have
and it seems like there is too much left to do. This certainly can be a discouraging situation but
there are two things you can do to help yourself get through it.
First, accept the situation for what it is. It is normal to feel guilty about not having done the
things you said you were going to do, or to feel overwhelmed by the amount of material you
have to learn in the time you have. But, these feelings do not help you move ahead. So, the
thing to do is tell yourself, Yes, I feel these ways. But, it doesn’t matter. I will be in charge of my
feelings, and not let my feelings be in charge of me. I will, once again, accept the fact that this is
work, and I will go on from here to do the best job I can. Once again, it is helpful to think of your
task as a job, or as work. This is precisely why the phrase course work exists. Having accepted
the situation, you now go to the second step.
Second, ask yourself the question, How can I best use the time I have left? This means that
133
you get rid of regrets and you do some hardheaded planning. In fact, you read the suggestions
under Make a Plan that Works and you apply those techniques to the best of your time and
ability. These are simple suggestions, but they are extremely important. By accepting and
focusing, you can help yourself tremendously.
Make a Plan that Works
Do you actually make a plan for success in this course? Or, do you just wait until the time is
short and the pressure is mounting, and then study like crazy?
Planning for success is quite simple. You may have read some study guidance material in the
past, and maybe it is good. But, sometimes they make it too difficult.
To plan for success, take a few minutes to ask yourself the following questions, and then
answer them. Like everything else in life, success at this is a combination of techniques and
attitudes.
1.
How much work do I have to do to be prepared for the next test? Do you know how to
answer this question? Try this:
•
•
2.
Count the number of pages of text, notes, and other things you have to read. Multiply it
by three, or four if you really want to master it. This way, you are allowing yourself time
for multiple readings of the material, and you will enjoy the good feelings of early
preparation. Also, you will feel relief from the anxiety that comes from last minute
cramming.
Decide how many periods of study it will take to do this. For example, maybe you can do
it in four periods of study, maybe three, or maybe five. Just make a decision. You can
always change it later.
Decide when and where you are going to study.
•
•
3.
Make appointments for yourself. Put them on your calendar. Making commitments to
study and keeping those commitments are critical. You might have good intentions, but
you have to act on those intentions to make them real.
Choose your place of study. Think of it like going to an office. If you try studying in your
apartment near your refrigerator, and the refrigerator door is open more often than your
textbook, then make yourself go someplace where there are minimal or no distractions.
Do your work, and then leave to go somewhere else. You will accomplish much more
and in less time. One caution: Many people can’t work well in a place that is too dead.
Silence can be deafening. I like to have some music in the background. But, I play it at a
low level so that when I am studying, I don’t even hear it.
Make and keep your commitments with yourself.
•
•
You are the one who will benefit from this work, so consider it to be important. Ask
yourself, What kinds of obstacles or distractions will tempt me to not keep my study
appointments? Are these obstacles from other people or my own reluctance to get into
it?
After you identify the obstacles, you can expect them to happen, but you can be
prepared for them. Tell yourself, I know from past experience that these obstacles will
come up, and I will be tempted to think they are more important than studying. I know I
134
will try to tell myself that I can always study later. But this time, I am going to study when
I scheduled it, and do the other things later. If you can develop this discipline and enjoy
the rewards of good study habits, you will be surprised at how good you feel.
Good luck!
Thank you very much,
ChanMin and Dr. Keller
Mandy, here is another message from Dr. Keller, which is the last suggestion for you:
You said that you have too much anxiety before a test in this class, and that your anxiety can be
high enough to keep you from doing your best. This is a longer message than most of them
have been, but this is a serious problem and I think you will find that this message might be very
helpful.
As a matter of fact, it has been proven in many research studies that anxiety can improve your
performance when it comes in small doses, just enough to energize you. But, too much anxiety
can be destructive. However, like every other condition we have described that keeps you from
doing your best, there is something you can do about it. Just this past week, a headline on an
MSN news article said: “Bad at Math? Worrying makes matters worse.”
(http://www.msnbc.msn.com/id/17243349/). The article went on to explain how working takes up
real space in your working memory and interferes with your ability to learn and recall
information.
This kind of anxiety is called performance anxiety, or nonstop worry about how things are going
to turn out. For example, even if you have studied a lot, you just can’t stop worrying about the
test. As the research points out, this kind of anxiety can make you less effective in your
preparations than you are capable of, and it can keep you from remembering information that
you actually know.
How do you overcome this type of anxiety? This problem is more difficult to understand and
change than just being discouraged; but even so, there is a fairly simple and powerful technique
you can use. How do I know this? Because it worked for me, and I have seen it work for others.
In my early years as professor I would go on consulting trips and make presentations or conduct
workshops. I would get so worried about whether I would succeed that I would stay up most of
the night, or all of it, “preparing.” That is, I would keep going over my notes, making new notes,
and doing various other things. But, mostly, I began to realize, I was just worrying. I would go
over the material again and again, but it didn’t make me feel better. Sometimes I would even get
migraine headaches. Ironically, I almost always did a good job, but success didn’t lessen my
anxiety the next time I went out. Sometimes I didn’t do so well, but that was usually when my
anxiety was especially bad, and it caused me to be too tense during my presentation. In other
words, when I didn’t perform well, it wasn’t because I didn’t have the knowledge or ability to do
well, it was purely because of anxiety.
But, one time I read some material in a book that I was reviewing, and I was thunderstruck by
how well one of the concepts fit me.
The author, John Nichols, was talking about goal orientation. He made the distinction between
task orientation and ego orientation. Task orientation means that you are focusing on the goal to
be achieved and the tasks you must do to prepare for it. Ego orientation means that you are
focusing on how well you will do, whether you will fail, what people will think of you, and other
135
such things. We all have a mixture of these orientations, and the mix is different in different
situations. But, the more you have an ego orientation, the less effective you will be in your
studying, preparation, and performance.
So, now that you know about this, how can you control it? What you have to do, is to be more
rational in your approach. Self-talk is important here. Accept the fact that you are worried: You
don’t want to look ridiculous, you don’t want to fail, you MUST get an A, or whatever it is that is
worrying you. Having accepted it, you then do two things.
First, tell yourself, “If I am well prepared for this task, and perform as well as I am capable of, I
will succeed, or at least I will know that I have given it my best effort.” This is a big part of what
coaches do to help their athletes overcome debilitating anxiety.
Second, tell yourself, I know I am worried, but I am going to lock those worries in a small box in
my head, and just concentrate on studying and preparing. The worries will try to escape from
time to time and take over your mind again, but now you will understand what is happening, and
you can push them back into the box. You are at greatest risk at points when you are
discouraged about whether you will ever finish reading the material or when you have been
studying too long and are fatigued. At those points, a break is good. Go wash the dishes, throw
a few hoops, or go for a walk. It helps a great deal. If you make a serious effort to use these
strategies, you will find them to be helpful!
Good luck!
Sincerely,
ChanMin and Dr. Keller
B. TO THE MOTIVATION AND VOLITION CHANGE STRATEGY WITH GENERAL
MESSAGES (MV-G) GROUP
Dear Randy,
You might find the following tips useful:
Did you experience negative feeling such as failure that interfered your ability to study
the course? If so, here is one thing to consider that can help you develop a positive
attitude of commitment that can help you.
There really is something that is emotionally upsetting in your life and making it difficult to
concentrate on your study. You can’t make the cause of that strong emotion suddenly
disappears, but there is a trick you can use to manage it. Visualize your mind as having a set of
small lockers in it, like the lockers at a gym. Tell yourself, “I feel very strongly about this situation
that is on my mind and I don’t really want to think about anything else. I can’t make it go away.
But what I can do is to put it into one of the lockers in my mind, shut the door, and then I can
open the door later after I get my homework done.” People in extremely stressful situations,
such as soldiers or athletes, do this all the time in order to concentrate on the vital task in front
of them. It works because you are not denying your feelings; you are just putting them away for
a little while so that you can indulge them later.
136
Did you get discouraged because you haven’t studied as much as you should have and it
seems like there is too much left to do? If so, please read the tips below:
First, accept the situation for what it is. It is normal to feel guilty about not having done the
things you said you were going to do, or to feel overwhelmed by the amount of material you
have to learn in the time you have. But, these feelings do not help you move ahead. So, the
thing to do is tell yourself, Yes, I feel these ways. But, it doesn’t matter. I will be in charge of my
feelings, and not let my feelings be in charge of me. I will, once again, accept the fact that this is
work, and I will go on from here to do the best job I can. Once again, it is helpful to think of your
task as a job, or as work. This is precisely why the phrase course work exists. Having accepted
the situation, you now go to the second step.
Second, ask yourself the question, How can I best use the time I have left? This means that
you get rid of regrets and you do some hardheaded planning. In fact, you read the suggestions
under Make a Plan that Works and you apply those techniques to the best of your time and
ability. These are simple suggestions, but they are extremely important. By accepting and
focusing, you can help yourself tremendously.
Make a Plan that Works
Do you actually make a plan for success in this course? Or, do you just wait until the time is
short and the pressure is mounting, and then study like crazy?
Planning for success is quite simple. You may have read some study guidance material in the
past, and maybe it is good. But, sometimes they make it too difficult.
To plan for success, take a few minutes to ask yourself the following questions, and then
answer them. Like everything else in life, success at this is a combination of techniques and
attitudes.
1.
How much work do I have to do to be prepared for the next test? Do you know how to
answer this question? Try this:
•
•
2.
Count the number of pages of text, notes, and other things you have to read. Multiply it
by three, or four if you really want to master it. This way, you are allowing yourself time
for multiple readings of the material, and you will enjoy the good feelings of early
preparation. Also, you will feel relief from the anxiety that comes from last minute
cramming.
Decide how many periods of study it will take to do this. For example, maybe you can do
it in four periods of study, maybe three, or maybe five. Just make a decision. You can
always change it later.
Decide when and where you are going to study.
•
•
Make appointments for yourself. Put them on your calendar. Making commitments to
study and keeping those commitments are critical. You might have good intentions, but
you have to act on those intentions to make them real.
Choose your place of study. Think of it like going to an office. If you try studying in your
apartment near your refrigerator, and the refrigerator door is open more often than your
textbook, then make yourself go someplace where there are minimal or no distractions.
Do your work, and then leave to go somewhere else. You will accomplish much more
and in less time. One caution: Many people can’t work well in a place that is too dead.
137
Silence can be deafening. I like to have some music in the background. But, I play it at a
low level so that when I am studying, I don’t even hear it.
3.
Make and keep your commitments with yourself.
•
•
You are the one who will benefit from this work, so consider it to be important. Ask
yourself, What kinds of obstacles or distractions will tempt me to not keep my study
appointments? Are these obstacles from other people or my own reluctance to get into
it?
After you identify the obstacles, you can expect them to happen, but you can be
prepared for them. Tell yourself, I know from past experience that these obstacles will
come up, and I will be tempted to think they are more important than studying. I know I
will try to tell myself that I can always study later. But this time, I am going to study when
I scheduled it, and do the other things later. If you can develop this discipline and enjoy
the rewards of good study habits, you will be surprised at how good you feel.
Do you have too much anxiety before a test in this class and do you feel your anxiety is
high enough to keep you from doing your best? If so, please read the following message.
Although, this is a longer message than most of them have been, but this is a serious
problem and I think you will find that this message might be very helpful.
As a matter of fact, it has been proven in many research studies that anxiety can improve your
performance when it comes in small doses, just enough to energize you. But, too much anxiety
can be destructive. However, like every other condition we have described that keeps you from
doing your best, there is something you can do about it. Just this past week, a headline on an
MSN news article said: “Bad at Math? Worrying makes matters worse.”
(http://www.msnbc.msn.com/id/17243349/). The article went on to explain how working takes up
real space in your working memory and interferes with your ability to learn and recall
information.
This kind of anxiety is called performance anxiety, or nonstop worry about how things are going
to turn out. For example, even if you have studied a lot, you just can’t stop worrying about the
test. As the research points out, this kind of anxiety can make you less effective in your
preparations than you are capable of, and it can keep you from remembering information that
you actually know.
How do you overcome this type of anxiety? This problem is more difficult to understand and
change than just being discouraged; but even so, there is a fairly simple and powerful technique
you can use. How do I know this? Because it worked for me, and I have seen it work for others.
In my early years as professor I would go on consulting trips and make presentations or conduct
workshops. I would get so worried about whether I would succeed that I would stay up most of
the night, or all of it, “preparing.” That is, I would keep going over my notes, making new notes,
and doing various other things. But, mostly, I began to realize, I was just worrying. I would go
over the material again and again, but it didn’t make me feel better. Sometimes I would even get
migraine headaches. Ironically, I almost always did a good job, but success didn’t lessen my
anxiety the next time I went out. Sometimes I didn’t do so well, but that was usually when my
anxiety was especially bad, and it caused me to be too tense during my presentation. In other
words, when I didn’t perform well, it wasn’t because I didn’t have the knowledge or ability to do
well, it was purely because of anxiety.
But, one time I read some material in a book that I was reviewing, and I was thunderstruck by
how well one of the concepts fit me.
138
The author, John Nichols, was talking about goal orientation. He made the distinction between
task orientation and ego orientation. Task orientation means that you are focusing on the goal to
be achieved and the tasks you must do to prepare for it. Ego orientation means that you are
focusing on how well you will do, whether you will fail, what people will think of you, and other
such things. We all have a mixture of these orientations, and the mix is different in different
situations. But, the more you have an ego orientation, the less effective you will be in your
studying, preparation, and performance.
So, now that you know about this, how can you control it? What you have to do, is to be more
rational in your approach. Self-talk is important here. Accept the fact that you are worried: You
don’t want to look ridiculous, you don’t want to fail, you MUST get an A, or whatever it is that is
worrying you. Having accepted it, you then do two things.
First, tell yourself, “If I am well prepared for this task, and perform as well as I am capable of, I
will succeed, or at least I will know that I have given it my best effort.” This is a big part of what
coaches do to help their athletes overcome debilitating anxiety.
Second, tell yourself, I know I am worried, but I am going to lock those worries in a small box in
my head, and just concentrate on studying and preparing. The worries will try to escape from
time to time and take over your mind again, but now you will understand what is happening, and
you can push them back into the box. You are at greatest risk at points when you are
discouraged about whether you will ever finish reading the material or when you have been
studying too long and are fatigued. At those points, a break is good. Go wash the dishes, throw
a few hoops, or go for a walk. It helps a great deal. If you make a serious effort to use these
strategies, you will find them to be helpful!
Good luck!
Sincerely,
ChanMin and Dr. Keller
C. TO THE BELIEF CHANGE STRATEGY WITH PERSONAL MESSAGES (B-P)
GROUP
Ben, here is a message from Dr. Keller:
Based on your responses to the questions about your beliefs of your ability to learn calculus at
the beginning of the semester compared to now, it appears that you do not see improvement or
have doubts about there being positive growth in your ability.
It might very well be that your ability has improved even if you don’t think it has, and it is
certainly true that if you believe that your intelligence can grow and increase then you will do
better in school. This was proven by a renowned psychologist named Carol Dweck who
conducted a study in which a group of students who were doing poorly in math. One group was
given lessons on how to study well. The other group was taught about the nature of the brain
and how new connections are made inside the brain each time you learn something new. As
these students were taught about the neurophysiology of the brain and how it can grow, they
actually visualized it as growing while they were studying.
Clearly this research supports the principle that intelligence, and especially math ability, is not
fixed or limited by heredity but will increase as you study and as you actually visualize your
brain forming new connections.
139
Isn’t this great news! Many psychologists have believed this for many years, but now there is
proof that it works. So, if you view calculus as a task that can be learned instead of a test of
your native ability, you will in fact improve. I hope you will try it!
You can read more about her study at
http://www.npr.org/templates/story/story.php?storyId=7406521.
Good luck!
Sincerely,
ChanMin & Dr. Keller
Bess, here is a message from Dr. Keller:
Based on your responses to the questions about your beliefs of your ability to learn calculus at
the beginning of the semester compared to now, it appears that you are confident in the growth
of your ability, which is great! I’m sure you will do better and better with your efforts in the class
because you believe your ability to learn calculus is stronger than before. Yes, a belief about
there being positive growth in your ability is influential in study and performance.
It is certainly true that if you believe that your intelligence can grow and increase then you will
do better in school. This was proven by a renowned psychologist named Carol Dweck who
conducted a study in which a group of students who were doing poorly in math. One group was
given lessons on how to study well. The other group was taught about the nature of the brain
and how new connections are made inside the brain each time you learn something new. As
these students were taught about the neurophysiology of the brain and how it can grow, they
actually visualized it as growing while they were studying.
Clearly this research supports the principle that intelligence, and especially math ability, is not
fixed or limited by heredity but will increase as you study and as you actually visualize your
brain forming new connections.
Isn’t this great news! Many psychologists have believed this for many years, but now there is
proof that it works. So, if you view calculus as a task that can be learned instead of a test of
your native ability, you will in fact improve.
You can read more about her study at
http://www.npr.org/templates/story/story.php?storyId=7406521.
Good luck!
Sincerely,
ChanMin & Dr. Keller
D. TO THE BELIEF CHANGE STRATEGY WITH GENERAL MESSAGES (B-G)
GROUP
140
Dear Monika,
You might find the following tips useful:
Do you now believe that the following thoughts are useful to your study of calculus?
“Learning math is gradual.” “Ability to learn math is improvable.”Here is some benefits if
you believe these thoughts as follows:
It is certainly true that if you believe that your intelligence can grow and increase then you will
do better in school. This was proven by a renowned psychologist named Carol Dweck who
conducted a study in which a group of students who were doing poorly in math. One group was
given lessons on how to study well. The other group was taught about the nature of the brain
and how new connections are made inside the brain each time you learn something new. As
these students were taught about the neurophysiology of the brain and how it can grow, they
actually visualized it as growing while they were studying.
Clearly this research supports the principle that intelligence, and especially math ability, is not
fixed or limited by heredity but will increase as you study and as you actually visualize your
brain forming new connections.
Isn’t this great news! Many psychologists have believed this for many years, but now there is
proof that it works. So, if you view calculus as a task that can be learned instead of a test of
your native ability, you will in fact improve.
You can read more about her study at
http://www.npr.org/templates/story/story.php?storyId=7406521.
Good luck!
Sincerely,
ChanMin and Dr. Keller
E. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY WITH
PERSONAL MESSAGES (MVB-P) GROUP
The combination of the messages sent to the MV-P and B-P groups above.
F. TO THE MOTIVATION, VOLITION, AND BELIEF CHANGE STRATEGY WITH
GENERAL MESSAGES (MVB-G) GROUP
The combination of the messages sent to the MV-G and B-G groups above.
G. TO CONTROL GROUP
Hi! This message is the last message. I know you are busy, but I will greatly appreciate it if you
take time to reply to the following question.
Here is the question:
141
Please indicate the amounts of time, in hours and parts of hours (for example, 0, 1, 2.5,) that
you spent studying/ working on this calculus course. Hours spent on this course last week, not
counting class time: _________
142
APPENDIX O. SHAPIRO-WILK NORMALITY TESTS
Shapiro-Wilk normality tests for attitudes
Measured by
Presurvey
Postsurvey
Groups
MV PM
MV GM
B PM
B GM
MVB PM
MVB GM
Control
MV PM
MV GM
B PM
B GM
MVB PM
MVB GM
Control
Shapiro-Wilk
Statistic
.946
.982
.943
.914
.956
.945
.954
.912
.953
.926
.890
.924
.955
.943
df
Sig.
14
8
9
11
11
13
18
14
8
9
11
11
13
18
.504
.970
.609
.271
.716
.522
.490
.171
.741
.446
.138
.351
.673
.329
Shapiro-Wilk normality tests for achievement
Measured by
1st Exam
2nd Exam
Groups
MV PM
MV GM
B PM
B GM
MVB PM
MVB GM
Control
MV PM
MV GM
B PM
B GM
MVB PM
MVB GM
Control
Shapiro-Wilk
Statistic
.887
.815
.978
.784
.979
.962
.938
.922
.915
.878
.931
.843
.958
.930
143
df
Sig.
14
8
9
11
11
13
18
14
8
9
11
11
13
18
.073
.042
.954
.006
.958
.783
.265
.232
.390
.148
.423
.035
.729
.193
Shapiro-Wilk normality tests for study habits
Measured by
1st Survey
2nd Survey
3rd Survey
4th Survey
Groups
MV PM
B PM
MVB PM
Control
MV PM
B PM
MVB PM
Control
MV PM
B PM
MVB PM
Control
MV PM
B PM
MVB PM
Control
Shapiro-Wilk
Statistic
.781
.858
.942
.960
.934
.806
.901
.974
.917
.812
.836
.742
.865
.893
.819
.968
144
df
Sig.
14
9
11
18
14
9
11
18
14
9
11
18
14
9
11
18
.003
.092
.544
.594
.346
.024
.191
.867
.196
.028
.028
.000
.035
.216
.017
.751
APPENDIX P. HISTOGRAM INSPECTION
Histograms for attitudes
20
30
20
10
10
Std. Dev = .67
Std. Dev = .53
Mean = 3.76
Mean = 3.92
N = 84.00
0
2.25
2.75
3.25
2.50
3.00
3.75
3.50
4.25
4.00
N = 84.00
0
1.75
4.75
4.50
2.25
2.00
5.00
2.75
2.50
3.25
3.00
3.75
3.50
4.25
4.00
4.75
4.50
5.00
Attitudes (Postsurvey)
Attitudes (Presurvey)
Histograms for achievement
10
14
12
8
10
6
8
6
4
4
2
Std. Dev = 24.71
Std. Dev = 19.43
2
Mean = 64.4
Mean = 68.5
25.0
35.0
30.0
45.0
40.0
55.0
50.0
65.0
60.0
75.0
70.0
85.0
80.0
10.0
95.0
90.0
N = 84.00
0
N = 84.00
0
20.0
15.0
100.0
30.0
25.0
40.0
35.0
50.0
45.0
60.0
55.0
65.0
2nd Exam
1st Exam
145
70.0
80.0
75.0
90.0 100.0
85.0
95.0
Histograms for study habits
30
14
12
10
20
8
6
10
4
Std. Dev = 4.86
Mean = 3.9
N = 65.00
0
2.0
6.0
4.0
10.0
8.0
14.0
12.0
18.0
16.0
N = 59.00
0
22.0
20.0
Std. Dev = 2.32
2
Mean = 6.3
1.0
24.0
3.0
2.0
5.0
4.0
Study Habits (1st Survey)
7.0
6.0
9.0
11.0
8.0
10.0
12.0
Study Habits (2nd Survey)
14
30
12
10
20
8
6
10
4
Std. Dev = 2.27
2
Std. Dev = 3.31
Mean = 3.9
Mean = 4.0
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
N = 52.00
0
N = 52.00
0
0.0
20.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
Study Habits (4th Survey)
Study Habits (3rd Survey)
146
8.0
9.0
10.0
APPENDIX Q. BOX’S TESTS OF EQUALITY
Box’s test of equality for attitudes
Box's M
F
Df1
Df2
Sig.
28.625
1.459
18
13892.171
.094
Box’s test of equality for attitudes
Box's M
F
df1
df2
Sig.
21.838
1.113
18
13892.171
.331
Box’s test of equality for study habits
Box's M
F
df1
df2
Sig.
99.565
2.754
30
3905.486
.000
147
APPENDIX R. LEVENE’S EQUALITY OF ERROR VARIANCE TESTS
Levene’s test of equality of error variances for attitudes
F
df1
df2
Sig.
Presurvey
.675
6
77
.670
Postsurvey
1.015
6
77
.422
Levene’s test of equality of error variances for study habits
st
1 Study habits
2nd Study habits
3rd Study habits
4th Study habits
F
4.656
1.781
2.188
.823
df1
df2
3
3
3
3
48
48
48
48
Sig.
.006
.163
.102
.488
Levene’s test of equality of error variances for achievement
st
1 exam
2nd exam
F
2.217
3.393
df1
df2
6
6
77
77
Sig.
.050
.005
148
APPENDIX S. MAUCHLY’S TEST OF SPHERICITY
Mauchly's Test of sphericity for study habits
Within
Subjects
Effect
TIME
Mauchly's W
.590
Approx.
Chi-Square
24.662
df
5
149
Sig.
Epsilon(a)
Greenhous
e-Geisser Huynh-Feldt
.000
.742
.828
Lowerbound
.333
APPENDIX T. HUMAN SUBJECT COMMITTEE APPROVAL
INFORMED CONSENT FORM
150
151
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Erlbaum.
159
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice,
41(2), 64-70.
Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement:
Theoretical perspectives (2ns ed.). Mahwah, NJ: Erlbaum.
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BIOGRAPHICAL SKETCH
EDUCATION
Ph.D.
Florida State University, Tallahassee, FL
Specialization: Instructional Systems
Major professor: John M. Keller
Degree received December, 2007
M. A.
Boston University, Boston, MA
Specialization: Educational Media and Technology
Degree received August, 2004
M. A.
Yonsei University, Seoul, Korea
Specialization: Educational Technology
Degree received February, 2003
B. A.
Ewha Woman’s University, Seoul, Korea
Specialization: Special Education
Degree received February, 1998
WORK EXPERIENCE
Instructor
EME2040 Introduction to Educational Technology
Jan 2007- April 2007
Teach required technology courses for pre-service teachers
Online Mentor
EME5601 Introduction to Instructional Systems
Jan 2007- April 2007
Assist main faculty with preparing course materials
Manage course Website and discussions in Blackboard
Mentor students and assess course work
Teaching Assistant
EDP5217 Principles of Leaner Motivation
EME5601 Introduction to Instructional Systems
Aug 2006 – Dec 2006
Assisted main faculty with preparing course materials
Managed course blackboard system for online discussion
Mentored students and assessed course work
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Teaching Assistant
EDP5217 Principles of Leaner Motivation
May 2006 – June 2006
Assisted main faculty with preparing course materials
Mentored students and assessed course work
Graduate Assistant
Instructional Systems, EPLS, Florida State University
May, 2006 – Aug 2006
Assisted in study using motivation measurement tools (IMMS & CIS)
Editorial Assistant
Educational Technology Research & Development (ETR&D)
August 2004 – April 2006
Managed the review process of manuscripts for ETR&D-Development Section
Educational Software Development Facilitator
South End Technology Center, Boston, MA
May 2004 – July 2004
Taught teachers how to design and develop educational software with HyperStudio
4.0, Inspiration, SoundEditor, and iMovie
Special Educator
Dongsu Elementary School, Incheon, Korea
March 2001- April 2003
Taught the learning disabled, the mentally retarded and the physically handicapped
Designed and developed educational Website and integrated it into classroom
course
Special Educator
Inhay School for the Mental Retarded, Incheon, Korea
March 2000- February 2001
Taught the mentally retarded
Designed and developed creative educational software and applied it to class
Special Educator
Yeunpong Bokji School for the Mental Retarded, Seoul, Korea
September 1997 – October 1997
Taught the mentally retarded
Designed and developed interactive lessons
RESEARCH EXPERIENCE
Motivation, Volition, and Belief Change Strategy Research
Researcher
Fall 2006 – Fall 2007
My dissertation research project in collaboration with Dr. John M. Keller
Responsibilities include reviewing related literatures on motivation and volition,
building motivation, volition, and belief change strategies, designing research study,
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analyzing collected data, and writing up findings from experiments in undergraduate
calculus classes
Animated Pedagogical Agents Research
Researcher
Summer 2006 – Fall 2007
An NSF funded research project in collaboration with Drs. John M. Keller & Amy L.
Baylor
Responsibilities include reviewing related literatures on attitude and belief change,
designing research study, developing pedagogical agents, and analyzing collected
data, and writing up findings from the experiments in undergraduate engineering
introductory classes
Motivation and Volitional Strategy Research
Researcher
Spring 2006 - Fall 2006
A research project in collaboration with Dr. John M. Keller
Responsibilities include reviewing related literatures on motivation and volition,
building motivational and volitional strategies, designing research study, analyzing
collected data, and writing up findings from the experiments in preservice technology
classes
Motivation and Volitional Strategy Research
Research Assistant
Spring 2005 - Fall 2005
A research project in collaboration with Dr. John M. Keller
Responsibilities to assist reviewing related literatures on motivation and volition,
building motivational and volitional strategies, designing research study, analyzing
collected data, and writing up findings from the experiments in undergraduate
archeology classes
PUBLICATION
ARTICLES IN REFEREED JOURNALS
Kim, C., & Baylor, A. L. (in press). A virtual change agent (VCA) to motivate pre-service
teachers to integrate technology. Journal of Educational Technology and Society.
Kim, C., & Keller, J. M. (in press). Effects of motivational and volitional email messages
(MVEM) with personal messages on undergraduate students’ motivation, study habits
and achievement. British Journal of Educational Technology.
Kim, C., & Suh, S. (2003). Study on elementary school teachers’ stages of concern and levels of
use about ICT teaching & learning. Korean Journal of Elementary Education Research
Center, 20, 275-299.
INVITED BOOK CHAPTERS
Kim, C., Lee, J., van Merriënboer, J. J. G., Merrill, M. D., & Spector, J. M. (2007). Foundations
for the future. In J. M. Spector, M. D. Merrill, J. J. G. van Merriënboer, & M. P. Driscoll
163
(Eds.), Handbook of research for educational communications and technology (3rd ed., pp.
2443-2471). Mahwah, NJ: Erlbaum.
REFEREED CONFERENCE PROCEEDINGS
Kim, C., Keller, J. M., & Baylor, A. L. (2007, December). Effects of motivational and volitional
messages on attitudes toward engineering: Comparing text messages with animated
messages delivered by a pedagogical agent. Proceedings of the IADIS International
Conference CELDA, Algarve, Portugal.
Kim, C., & Keller, J. M. (2007, April). Effects of motivational and volitional email messages
(MVEM) on preservice teachers’ motivation, volition, performance, and attitudes toward
technology integration. Proceedings of the Annual Meeting of American Educational
Research Association (AERA), Chicago, IL.
Suh, S., & Kim, C. (2006, October). Factors influencing the use of web-based instruction in
higher education. Proceedings of World Conference on E-Learning in Corporate,
Government, Healthcare, & Higher Education (E-Learn), Honolulu, Hawaii.
Kim, C., & Keller, J. M. (2006, June). Motivational and volitional email messages (MVEM) as a
change agent to facilitate preservice teachers’ technology integration. Proceedings of
World Conference on Educational Multimedia, Hypermedia & Telecommunications (EDMedia). Orlando, Florida.
Turner, J. E., & Kim, C. (2006, June). Professional development that considers teachers’
attitudes toward an innovation. Proceedings of International Conference of the Learning
Sciences (ICLS), Bloomington, Indiana.
Kim, C. (2006, March). Are we learning technology integration? Reflection on preservice
teachers’ perceptions of the educational use of technology. Proceedings of Society for
Information Technology & Teacher Education (SITE) Conference. Orlando, Florida.
Kim, C., & Baylor, A. L. (2006, February). A pedagogical agent as an organizational change
agent. Proceedings of Society for Applied Learning Technology (SALT). Orlando, Florida.
Kim, C., Keller, J. M., & Chen, H. (2005, October). Using motivational and volitional messages
to promote undergraduate students’ motivation, study habits and achievement. Proceedings
of Association for Educational Communication and Technology (AECT) International
Conference. Orlando, Florida.
NATIONAL AND INTERNATIONAL PRESENTATIONS
Kim, C., Keller, J. M., & Baylor, A. L. (2007, October). Effects of agent versus text delivered
motivational and volitional messages on the attitudes of introductory engineering
students. Paper presented at Association for Educational Communication and Technology
(AECT) International Conference. Anaheim, California.
Kim, C., & Keller, J. M. (2007, October). Effects of motivation, volition, and belief change
strategies on attitudes, study habits, and achievement in mathematics education.
Paper presented at Association for Educational Communication and Technology (AECT)
International Conference. Anaheim, California.
Kim, C., & Keller, J. M. (2007, April). Effects of motivational and volitional email messages
(MVEM) on preservice teachers’ motivation, volition, performance, and attitudes toward
technology integration. Paper presented at American Educational Research Association
(AERA) Conference, Chicago, Illinois.
164
Suh, S., & Kim, C. (2006, October). Factors influencing the use of web-based instruction in
higher education. Paper presented at World Conference on E-Learning in Corporate,
Government, Healthcare, & Higher Education (E-Learn), Honolulu, Hawaii.
Kim, C., & Keller, J. M. (2006, June). Motivational and volitional email messages (MVEM) as a
change agent to facilitate preservice teachers’ technology integration. Paper presented at
World Conference on Educational Multimedia, Hypermedia & Telecommunications (EDMedia). Orlando, Florida.
Turner, J. E., & Kim, C. (2006, June). Professional development that considers teachers’ attitudes
toward an innovation. Paper presented at International Conference of the Learning
Sciences (ICLS), Bloomington, Indiana.
Kim, C., & Turner, J. E. (2006, April). Investigating teachers’ incentives, beliefs, and
characteristics on their attitudes toward professional development. Paper presented at
American Educational Research Association (AERA) Conference, San Francisco,
California.
Turner, J. E., Kim, C., Grove, C., Simmons, C., & Meng, Q. G. (2006, April). Teachers as
students: Investigating teachers’ motivation and emotions for whole-school reform
professional development. Paper presented at American Educational Research Association
(AERA) Conference, San Francisco, California.
Kim, C. (2006, March). Are we learning technology integration? Reflection on preservice
teachers’ perceptions of the educational use of technology. Paper presented at Society for
Information Technology & Teacher Education (SITE) Conference. Orlando, Florida.
Kim, C., & Baylor, A. L. (2006, February). A pedagogical agent as an organizational change
agent. Paper presented at Society for Applied Learning Technology (SALT). Orlando,
Florida.
Kim, C. (2006, March). A pedagogical agent as a change agent to motivate preservice teachers to
integrate technology for their future classrooms. Paper presented at Society for Information
Technology & Teacher Education (SITE) Conference. Orlando, Florida.
Kim, C., Keller, J. M., & Chen, H. (2005, October). Using motivational and volitional messages
to promote undergraduate students’ motivation, study habits and achievement. Paper
presented at Association for Educational Communication and Technology (AECT)
International Conference. Orlando, Florida.
Kim, C., & Suh, S. (2005, October). The relationship between elementary school teachers’ stages
of concern and levels of use of technology in their classrooms. Paper presented at
Association for Educational Communication and Technology (AECT) International
Conference. Orlando, Florida.
MASTER’S THESIS
Kim, C. (2003). The relationship between elementary school teachers’ stages of concern and
levels of use about ICT teaching and learning. Unpublished Master’s Thesis: Younsei
University.
165
INVITED PRESENTATIONS
How to Formulate Your Concept paper, Presentation to EME6635. Doctoral Colloquium Class,
Florida State University. Tallahassee, FL (2006, October).
Introduction to John Keller’s ARCS Motivation Model, Presentation at one-day workshop for
Preservice Teachers, Chuncheon National University of Education, South Korea (2006,
May).
Discussant for the AECT Socratic Seminar 2005, International Division, AECT. Orlando, FL
(2005, October).
AWARDS
Award of Strohbehn Intern by Educational Communication Technology (ETC) Foundation,
the International Conference of Association of Educational Communications and
Technology (AECT) of 2007 in Anaheim, California.
PacifiCorp Design and Development Award, Design and Development Division, the
International Conference of Association of Educational Communications and Technology
(AECT) of 2007 in Anaheim, California.
Liliana Mulhman Masoner Outstanding International Student Award (2006-2007),
Educational Psychology & Learning Systems, College of Education, Florida State
University.
Finalist for the Gagné & Briggs Outstanding Doctoral Student Award (2006-2007),
Educational Psychology & Learning Systems, College of Education, Florida State
University.
Award of Graduate Student Professional Meeting by the Council on Research in Education
(CORE) for the Spring Semester of 2007 funded by the College of Education, Florida
State University.
Dissertation Research Grant Award for the Spring Semester of 2007 funded by Dissertation
Research Grant Committee, Graduate Studies, Florida State University.
Ruby Diamond Future Professor Award (2005-2006), Educational Psychology & Learning
Systems, College of Education, Florida State University.
Finalist for the Gagné & Briggs Outstanding Service Award (2005-2006), Educational
Psychology & Learning Systems, College of Education, Florida State University.
Silver (Second) Prize, General Field for Teachers, The 10th Prize Contest for National
Educational Software, December 2001. Awarded by Deputy Prime Minister and
Minister of Education.
First Prize, The 8th Prize Contest for Municipal Educational Software, August 2001.
Awarded by Deputy Prime Minister and Minister of Education.
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PARTICIPATION IN PROFESSIONAL ASSOCIATIONS
Association for the Advancement of Computing in Education (AACE)
Association for Educational Communications and Technology (AECT)
American Educational Research Association (AERA)
Florida Educational Research Association (FERA)
International Association for Development of the Information Society (IADIS)
International Society for Learning Sciences (ISLS)
SERVICE TO THE PROFESSION
Editorial Review Board member (2007), Journal of Educational Technology & Society.
International Forum of Educational Technology & Society.
Proposal Reviewer of AERA (2007), American Educational Research Association (AERA)
2008 Annual Meeting, New York, NY.
Proposal Reviewer of FERA (2007), Florida Educational Research Association (FERA)
conference, Tallahassee, Florida, USA.
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