High Educ (2012) 64:455–471
DOI 10.1007/s10734-012-9504-9
Relations between teacher students’ approaches
to learning, cognitive and attributional strategies,
well-being, and study success
Annamari Heikkilä • Kirsti Lonka • Juha Nieminen • Markku Niemivirta
Published online: 24 January 2012
Springer Science+Business Media B.V. 2012
Abstract Current theories of learning emphasize the role of motivational and affective
aspects in university student learning. The aim of the present study was to examine the
interrelations among approaches to learning, self-regulated learning, and cognitive strategies in the context of teacher education. Cognitive-motivational profiles were identified
among novice teacher students. It was also looked at, whether well-being, epistemological
beliefs, and study success in an activating lecture course were related to these profiles. The
participants were 213 first year teacher students, who participated in an activating lecture
course at a major Finnish university. The students filled in a questionnaire including items
based on the MED NORD instrument (Lonka et al. in Med Teach 30:72–79, 2008). The
structural validity of the scales was tested by means of a series of factor analyses. Latent
class clustering was used for clustering students into homogeneous groups. Finally, a series
of ANOVAs was conducted to examine between-group differences across the criterion
variables. Three groups of students were identified (1) non-regulating students (50%), (2)
self-directed students (28%), and (3) non-reflective students (22%). Non-regulating students expressed the highest levels of stress, exhaustion, and Lack of Interest. Self-directed
students received the highest grades. The profiles were not only related to study success,
but also to the general well-being of the students. It was concluded that motivational
profiles may have not been optimal, even in this highly-selected population. It is of interest
to see, how these students shall develop during their studies.
A. Heikkilä (&) J. Nieminen
Research and Development Unit for University Pedagogy, Faculty of Behavioural Sciences,
University of Helsinki, Helsinki, Finland
e-mail: [email protected]
K. Lonka
Research Centre for Educational Psychology, Faculty of Behavioural Sciences, University of Helsinki,
Helsinki, Finland
M. Niemivirta
Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland
M. Niemivirta
Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
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Keywords Approaches to learning Cognitive and attributional strategies Teacher education Well-being Higher education
Introduction
The Finnish school system is known worldwide for its exceptionally high quality of
learning outcomes. A distinctive feature of the Finnish system is that even elementary
school teachers are trained in universities, gaining certification by completing a researchbased, professionally-oriented master’s program (Toom et al. 2010). The profession is very
popular, with large numbers of applicants, and only about fifteen per cent get accepted to
the program. Consequently, the criteria for selection are very strict, and thus a highly
selective group of students enters the 5-year program. The first-year students are, however,
often very young and inexperienced, and many of them come directly from high school.
The purpose of the present study is to explore how these teachers-to-be view learning and
regulate their studies at the beginning of their educational journey.
Our previous studies with students from other fields, such as humanities, agriculture,
and law, have demonstrated the fruitfulness of combining cognitive, motivational, and
emotional aspects when investigating university students (Heikkilä and Lonka 2006;
Heikkilä et al. 2011). Motivation and students’ approaches to learning are dynamically
related to each other (Cano and Berben 2009). Recent research has also shown that even
the rather general cognitive and attributional strategies students apply in achievement
situations play a role in how they go about learning and regulate their studying (Heikkilä
et al. 2011). As future teachers are required to go through an ambitious program, it would
be important to investigate how prepared they are for such studies. However, to our
knowledge, not much is known about the relationships between these various aspects of
motivation and learning among teacher students.
Approaches to learning, epistemologies, and orientations to studying
During the last 30 years, research on university student learning has investigated how
students approach academic tasks. In qualitative and quantitative research, two main
approaches have been identified (Marton and Säljö 1976; Entwistle and Ramsden 1983;
Biggs 1987). ‘‘Deep approach’’ is characterized as a search for meaning and understanding,
whereas ‘‘surface approach’’ is described as the memorizing of details and verbatim
repeating of material (Marton and Säljö 1976; Entwistle and Tait 1990). From the point of
view of motivation these approaches differ from each other greatly: surface approaches are
seen as being motivated by the learner’s desire to meet minimum requirements with
minimum effort while deep approaches are characterized by an intention to understand the
material being studied. The use of surface approaches results in study behaviours that
enable students to reproduce material in the required form without integration into the old
knowledge, leading to low quality learning outcomes. The intentions to understand, in
contrast, result in active integration of new and old knowledge, and with information from
other sources as well. Today, our understanding empahasizes that approaches are not
mutually exclusive: for example, Marton et al. (1997) stated that deep approaches may
involve strategies typically associated with both deep and surface orientations with an
overall intention to understand.
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457
Students’ approaches to learning are dependent on both the context and content of
learning. Most students may be classified as adopting either a surface or a deep approach to
majority of tasks, meaning that they have predispositions to adopt particular processes
(Biggs 1993). Entwistle (1988) named this cross-situational consistency as ‘orientation
to studying’. He identified four different orientations: Meaning-, Reproducing, Nonacademic, and Achieving orientation (Entwistle 1988; Entwistle and Ramsden 1983).
Vermunt et al. (Vermunt 1998; Vermunt and Vermetten 2004) also concluded that four
qualitatively different ways of learning can be repeatedly found: (1) Reproductive learning,
(2) Meaning-oriented learning. (3) Application-oriented learning, and (4) Undirected
learning. A student showing an application orientation to learning views education mainly
as a way to enter a profession, and prefers knowledge that can be directly applied to
practice. An undirected student lacks a sense of purpose in studying, does not seem to
know how to study, and experiences problems in learning.
In Vermunt’s model (1998), different ways of learning include, in addition to learning
orientations, cognitive processing activities, mental models of learning and teaching, and
metacognitive regulation activities that students use to direct their learning processes.
Conceptually the regulation dimension is clearly a separate dimension (Entwistle and
McCune 2004), but its empirical relations to approaches to learning have been demonstrated repeatedly: self-regulation is most often related to the deep approach (Lonka and
Lindblom-Ylänne 1996; Rozendaal et al. 2001; Heikkilä and Lonka 2006; Heikkilä et al.
2011). This is understandable, since in high-quality learning, self-regulatory processes are
harnessed to serve students’ intentions and goals. In deep approach to learning, searching
for meaning involves monitoring and reshaping thoughts, while the Surface Approach is
merely reactive (Biggs 1988).
Lonka and Lindblom-Ylänne (1996) examined two highly selected professionally
oriented student groups: students of psychology and medicine. In addition to the two main
orientations, (1) reproducing, and (2) meaning orientations, two domain-specific orientations were found: (3) relativist epistemology, referring to a theoretical orientation typical to
psychology students, and (4) application orientation, which was more typical to medical
students. Dualistic epistemology—believing that true knowledge is absolute and certain—
was associated with a reproducing orientation to learning. In a 3 year follow-up study
Mäkinen et al. (2004) looked at general study orientations in several domains, and found
that in a Finnish multi-faculty university, work-life oriented students, compared to study
oriented and non-committed students, were the most successful in terms of both grade
point average and accumulation of credits.
Cognitive and attributional strategies
The more general aspects of regulation of action and the differing ways students approach
challenges in the university environment have been looked from the perspective of
thinking and attribution strategies (Nurmi et al. 2003). A variety of thinking and attribution
strategies have been introduced describing how students deal with their studies and studyrelated threats to self-worth (Eronen et al. 1998; Nurmi et al. 2003). Some students will
deliberately avoid challenging goals rather than make an active effort to deal with the
challenges. Such thinking strategies have been described in terms of task avoidance,
pessimism, and as a maladaptive motivational style. Task avoidance predicts low academic
achievement and dissatisfaction, which in turn predict subsequent Task-Avoidance, a
pattern which may lead to vicious circle of poor intellectual adaptation (Nurmi et al. 2003).
Some students, on the other hand, use active, task-focused strategies, such as optimism and
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active coping, when faced with challenging goals. Students’ success expectations predict
their academic achievement and satisfaction, which in turn increase their subsequent
success expectation, contributing to a positive cycle.
It has been shown that thinking and attribution strategies are related to university
students’ approaches to learning and their readiness to regulate their own learning: task
avoidance was related to problems in self-regulation, whereas active, task-focused strategies were related to a deep approach in studies by Heikkilä and Lonka (2006) and
Heikkilä et al. (2011).
Stress and exhaustion
Different kinds of motivational dispositions and approaches to learning are related—not
only to learning outcomes—but also to the general well-being of university students
(Heikkilä et al. 2011). Interest in stress and study-related burnout as experienced by higher
education students has recently been growing (Salmela-Aro and Kunttu 2010; Salmela-Aro
et al. 2008; Robotham and Julian 2006; Law 2007; Schaufeli et al. 2002).
It is interesting to examine stress since it can have many kinds of consequences. Lazarus
and Folkman (1984) defined stress as a result of an individual’s perceptions that they do
not have the resources to cope with a perceived situation from the past, present or future.
Not all stress is, however, negative: at its best, stress can also have a positive effect
enabling individuals to respond effectively into demanding situations.
Study-related-exhaustion, a component of study-related-burnout (Salmela-Aro and
Kunttu 2010; Schaufeli et al. 2002), can be defined as study-related experiences of strain,
particularly chronic fatigue resulting from an overtaxing study load. Another aspect of
study-related-burnout, cynicism, refers to an indifferent or a distant attitude towards
studies, to losing one’s interest, and feeling that studying has lost its meaning. Mäkinen
et al. (2004) found that a reported Lack of Interest by the first year predicted dropout
during their third year across domains. Study-related burnout is a promising concept for
examining university students’ well-being since it, unlike many other concepts, is a context-specific construct, conceptualized strictly in the context of higher education (SalmelaAro and Kunttu 2010).
Aims
We were interested in looking at how first-year teacher students would experience learning
at the beginning of their studies. We applied a person-oriented approach (see Bergman
et al. 2003; Niemivirta 2002) in order to explore what kinds of cognitive-motivational
profiles, consisting of approaches to learning, and cognitive and attributional strategies,
naturally occur among teacher students. A person-oriented approach has proved to be a
promising way to investigate what kinds of homogenous groups of students exist. Our
previous studies have shown that approaches to learning, regulation of learning, and
cognitive and attributional strategies are intertwined, and further, that these are related to
study success and general well-being (Heikkilä and Lonka 2006; Heikkilä et al. 2011).
However, we do not know what kinds of groups of students exist among novice teacher
students.
The aim of this study was to examine following research questions:
• How are approaches to learning and cognitive and attributional strategies among firstyear teacher students correlated?
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• What kinds of cognitive-motivational profiles can be identified among teacher
students?
• Are there differences between students with differing profiles
•
•
•
in terms of exhaustion, stress, and Lack of Interest?
in terms of epistemological beliefs?
in terms of study success?
Methodology
Context
In Finland, teacher training takes place at faculties of education. All primary school
teachers (grades 0–6) are expected to get a MA degree in educational science or educational psychology as their major, requiring (on average) 5–6 years of full-time study. The
degree also includes, internships in schools affiliated with a university, and at least one
term of research work. At the beginning of their studying most teacher students have no
prior experience of teaching.
Instructional procedures
Students who participated in this study attended an introductory course in educational
psychology, named Human growth, development, and learning (5 ECTS). There were six
activating lectures, and each of them took 4 h. Two professor level instructors were
responsible for the course.
Activating instruction is a framework constructed by the second author of this article
(Lonka and Ahola 1995). Originally it was a theoretical synthesis based on Vygotsky’s
(1962, 1978) ideas, research on applied cognitive science (Bereiter and Scardamalia 1987;
Chi et al. 1988), and models of process-oriented instruction (Applebee and Langer 1983;
Vermunt 1989). The main idea of an activating lecture is to approach university teaching as
fostering expertise in students, and to help develop functional mental models of the
materials to be learned. The central idea of an activating lecture is that the teacher does not
view a lecture as situation where knowledge is directly transmitted by the teacher and
passively acquired by the students (Trigwell et al. 1994). Rather, the intention is to promote conceptual change in the students. The control of the learning process is shared
(Vermunt 1989), in a way that students have the right and the responsibility for active
participation. Already in the beginning of the course, the lecturer and the students collectively set the goals for the learning process. Dialogue between the participants and the
lecturers was maintained throughout the course by minute papers, short discussions,
reflection tasks, learning logs, and so on (e.g., McKeachie 1994). The lecturers summarised
the discussions by collecting the participants’ ideas on a Power Point slide. In this way, the
students participated in the construction of course materials. Diagnostic feedback was
constantly collected and fed back to the students during the course. This course was
theoretical in nature and the intention was to introduce the theories and concepts of
educational psychology. Even though the teachers attempted to activate a meaningful
context in the students’ minds, this was merely done by using examples, paper cases and
group discussion. Neither school visits nor real-life teaching were included in the course
plan of this specific course.
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The final course examination took place 1 week after the data collection. It was
designed to measure thorough understanding and application of the main concepts of the
course. After the course, the official web-based Faculty course feedback showed that
the course received very positive ratings by the participants. In this official feedback the
students did not evaluate the development of their own study skills or regulatory strategies,
he questions were quite general. The students were asked, for instance, how adequate the
instructional procedures were, how they experienced the quality of teaching, how well the
course was managed, and whether the workload was reasonable. On the basis of this
Faculty course feedback, the participants did not find the course exceptionally demanding
and they also appreciated the instructional procedures and the contribution of the teachers.
Participants and procedure
The participants were 213 (N = 213) [2005 n = 138, 2006 n = 75] first year elementary
teacher and kindergarten teacher students who participated in an introductory course in
educational psychology in two consecutive autumn terms in University of Helsinki, Finland. Only the voluntary students who attended the lectures participated. Students were not
rewarded for their co-operation in this study.
Ethical consideration
All participants were explained the purpose of the study. It was emphasized that the
participation was voluntary and that they could interrupt filling in the questionnaire at any
time. All participants also signed an informed consent form.
Materials
All the instruments described below were drawn from the MED NORD questionnaire
(Lonka et al. 2008) which is a collection of scales measuring a variety of aspects of student
learning. MED NORD scales are quite short, since the instrument was originally designed
in the context of medical education, and the purpose was to maximise participation
in situations where also other measurements were included.
Approaches to learning
In MED NORD, Students’ approaches to learning were assessed with 12 items, based on
previous inventories such as ASI (Entwistle and Ramsden 1983), ASSIST (Tait et al. 1998)
and ILS (Vermunt 1998), but formulated so as to describe what kinds of practices students
valued in studying. Originally, the items were hypothesized to reflect two types of
approaches to studying, a deep approach (e.g., ‘‘It is important to try to relate details to a
bigger whole’’) and a Surface Approach (e.g., ‘‘It is important to memorize new definitions
and scientific concepts as literally as possible’’). Based on our previous work (Heikkilä
et al. 2011), however, we hypothesized that the deep approach would divide into two
aspects in this population. Therefore three factors, reflecting Deep Understanding (e.g., ‘‘It
is important to try to relate details to a bigger whole’’), Critical Evaluation, and Surface
Approach (e.g., ‘‘It is important to memorize new definitions and scientific concepts as
literally as possible’’) would describe the data best. All statements were rated using a
Likert-scale ranging from 1 (totally disagree) to 6 (totally agree).
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Cognitive and attributional strategies
A shortened version of Strategy and Attribution Questionnaire (SAQ, Nurmi et al. 1995)
was used to assess students’ cognitive and attributional strategies. The short version of the
inventory includes 12 items describing three scales: Optimism, Task Avoidance, and Social
Optimism. Again, based on our previous study (Heikkilä et al. 2011), we used 10 items
from the inventory to reflect the given three types of strategies. The Likert scale ranged
from (1) totally disagree to (6) totally agree.
Problems with regulation of learning
Items concerning problems with regulation of learning were adopted from the Inventory of
Learning Styles (Vermunt and van Rijswijk 1988). These items have been widely used and
validated in earlier studies in Finland (Lonka and Lindblom-Ylänne 1996; Nieminen et al.
2004; Heikkilä and Lonka 2006). Two items from the original five-item scale were used for
assessing students’ self-evaluated problems with self-regulation from the original scale
Lack of Regulation (e.g., ‘‘I notice that I have trouble processing a large amount of subject
matter’’). A Likert-scale ranging from 1 (I seldom or never do this) to 6 (I almost always do
this) was used for rating each item.
Subjective well-being
Three separate scales were used for assessing students’ well-being. Stress was measured
with a single-item measure of stress symptoms (Elo et al. 2003). This measure has first a
definition of stress following a question and a rating scale: ‘‘Stress refers to a situation in
which a person feels tense, restless, nervous or anxious, or is unable to sleep at night
because his/her mind is troubled all the time. Do you feel this kind of stress these days?’’
The response was recorded on a 5-point scale varying from 1 (not at all) to 5 (very much).
For assessing exhaustion in relation to studying, a modified four-item version of the
Maslach and Jackson’s (1981) exhaustion scale was adopted. The frequency of symptoms
reflecting exhaustion (e.g., ‘‘I feel totally exhausted’’) were rated on a five-point Likertscale ranging from 1 (never) 5 (all the time). Students’ experienced Lack of Interest (e.g.,
‘‘The contents of my studies do not motivate me’’) was assessed with two items from the
Inventory of General Study Orientations (IGSO) (Mäkinen et al. 2004). This scale closely
resembles questions that measure cynicism in studies by Schaufeli et al. (2002).
Epistemological beliefs
Based on Schommer’s (1990) work on students’ epistemological beliefs, three items
referred to a view of preferring Certain Knowledge (‘‘Teaching should provide certain facts
about the issues that are being studied’’). Two items assessed how highly students’ valuing
of practical knowledge (‘‘It is important that the things I study have practical value’’). All
items were rated using a Likert-scale ranging from (1) totally disagree to (6) totally agree.
All items from the above set of scales (except for the one-item Stress-scale) were
subjected to a confirmatory factor analysis. For the present purposes, we specified an
11-factor measurement model in which all given items were set to load on the respective
factor only. The model was fitted to the data using maximum likelihood estimates as
implemented in the Mplus statistical program. The fit of the model was evaluated using the
fit indices CFI, SRMR and RMSEA along with the v2-statistic, as suggested by Hu and
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Bentler (1999). Cutoff values of C90, B.09 and B.06 were used for CFI, SRMR and
RMSEA, respectively. The estimated model fitted the data well, v2(471) = 640.24,
p = .000; CFI = 0.92; SRMR = .057; RMSEA = .041, thus confirming our hypothesized
structure. All items and factor loadings are reported in ‘‘Appendix’’. Based on the model,
we constructed composite scores for each scale, and the resulting variables were labelled as
(1) Optimism, (2) Deep Understanding, (3) Exhaustion, (4) Surface Approach, (5) Task
Avoidance, (6) Lack of Interest, (7) Social Optimism, (8) Lack of Regulation, (9) Deep
Critical, (10) Certain Knowledge, and (11) Practical Value. The Cronbach’s alphas for
each variable were .88, .75, .84, .67, .75, .71, .66, .69, .76, .72, .48, respectively (see
‘‘Appendix’’ for standardized factor loadings for the chosen measurement model).
Study success was measured with using the grade achieved in the course from which the
data were collected. The final grade was given on a Bologna scale 1–5 (which roughly
resembles the American scale from E to A).
Data analysis
Bivariate correlations were computed in order to examine the relationships between the
scales. Latent class clustering was used for clustering students into homogeneous groups,
and, finally, a series of ANOVAs was conducted to examine between-group differences
across the criterion variables.
Students with similar patterns of approaches to learning, regulation of learning and
cognitive and attributional strategies were identified through latent class cluster analysis
(LCCA; Vermunt and Magidson 2002). LCCA is a probabilistic or model-based variant of a
traditional cluster analysis (Vermunt and Magidson 2002), and aims to identify the smallest
number of latent classes or groups that adequately describe the associations among observed
continuous variables. Classes are added stepwise until the model optimally fits the data, and
statistical criteria such as Bayesian Information Criterion (BIC) are used to evaluate the
best-fitting model. Compared to traditional cluster analysis, the advantages of this procedure
include a less arbitrary choice of the cluster criterion, a possibility to operate with mixed
measurement levels (i.e., different scale types), and a possibility to impose restrictions to the
parameters. Note, that although recent simulation and method comparison studies provide
evidence supporting the use of LCCA instead of the more traditional clustering methods
(Bacher et al. 2004; Magidson and Vermunt 2002), justified criticism directed at the
Practical Value of some of the above advantages and the robustness of LCCA in general
have also been presented (Bartholomew et al. 2008; Marsh et al. 2009).
Variables reflecting adaptive and maladaptive components of learning activity within
each framework were used for the LCCA. That is, Deep Understanding, Critical Evaluation, and Surface Approach from the approaches to learning framework, Lack of Regulation from the regulation of learning framework, and Optimism and Task-Avoidance from
the cognitive strategies framework were included as clustering variables in the analysis.
The purpose of this was to examine the extent to which these variables contributed to
qualitatively different types of motivational-cognitive student profiles.
Results
Our first question concerned the relationship between approaches to learning, Lack of
Regulation, and cognitive and attributional strategies. In order to explore these relations,
bivariate correlations were calculated (Table 1). Deep Understanding correlated positively
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with Critical Evaluation and Optimism and negatively with Lack of Regulation. Critical
Evaluation had positive correlation with Optimism. Surface Approach correlated negatively with Optimism. Lack of Regulation correlated negatively with Optimism and positively with Task-Avoidance. There was a negative correlation between Task-Avoidance
and Optimism.
Profiles
The results from a series of LCCAs using Latent Gold statistical software (Vermunt and
Magdison 2002) suggested that a three-group solution described the data best. The BIC
values (smaller value implying better fit) for one- to four-group solutions were 4154.12,
4,101.13, 4,096.09, and 4,104.63 respectively. The results from ANOVAs on clustering
variables show the extent to which each variable differentiated the groups (see Table 2).
Figure 1, displaying standardized score mean profiles, illustrates the relative differences
between the three groups. The students were distributed in a following manner into the
three groups: 50% of the students in the first group (n = 106), 28% in the second (n = 60),
and 22% in the third group (n = 46).
The groups differed statistically significantly on all clustering variables with effect sizes
(g2p) ranging from .09 to .49 (see Table 2). Pairwise comparisons, however, suggested
Table 1 Pearson product moment correlations between approaches to learning and cognitive and
attributional strategies
1
2
3
4
5
1. Deep Understanding
2. Critical Evaluation
.45**
3. Surface Approach
-.03
-.28**
4. Lack of Regulation
-.19**
-.19**
5. Optimism
.20**
.31**
6. Task-Avoidance
.05
.10
-.18**
-.01
.04
-.39**
.38**
-.22**
* p \ .05, ** p \ .01
Table 2 Means, standard deviations, and ANOVA results for group differences on approaches to learning,
regulation of learning, and cognitive and attributional strategies
Variable
Non-regulative
n = 106
Self-directed
n = 60
Non-reflective
n = 46
M
SD
M
SD
M
SD
F(2,211)
g2p
p
Deep Understanding*
5.27a
.51
5.68
.29
5.13a
.57
2.99
\.0.001
.17
Critical Evaluation*
4.85a
.80
5.71
.39
4.58a
.69
43.44
\.0.001
.29
Surface Approach
2.78a
.73
2.31
.68
2.85a
.76
1.14
Lack of Regulation
3.60
.78
2.26a
.73
2.48a
.81
7.24
\.0.001
.40
Optimism*
4.00
.74
5.03
.47
4.45
.66
47.40
\.0.001
.31
Task-Avoidance*
3.64
.63
2.90
.77
2.08
.47
98.93
\.0.001
.49
\0.001
.09
Means within a row sharing the same subscripts are not significantly different at the p \ .05 level. Due to
unequal variances, Games-Howell correction instead of Bonferroni was applied for variables denoted
with an *
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variation in the patterns of differences across the groups. All groups differed significantly
from each other on Optimism and Task Avoidance while pairwise differences were
detected on all the other variables. Group 1 had a maladaptive profile with high scores on
Task-Avoidance and Lack of Regulation, low score on Optimism and average scores on
Deep Understanding, Critical Evaluation and Surface Approach. Group 2 had a very
adaptive profile altogether: students in this group scored high on Optimism, Deep
Understanding and Critical Evaluation and low on Task Avoidance, Surface Approach and
Lack of Regulation. Group 3 represented another kind of potentially maladaptive profile: it
had the lowest scores on Deep Understanding and Critical Evaluation, but interestingly,
also on Task Avoidance. The three groups were labelled according to the score mean
profiles as (1) non-regulating students (2) self-directed students, and (3) non-reflective
students (see Fig. 1).
Finally, we performed a series of ANOVAs with the cognitive-motivational profile as an
independent factor and all the other variables of interest as dependent variables (see
Table 3 for a summary of results). First, we examined group differences in relation to
Social Optimism, Lack of Interest, Stress and Exhaustion. The main effects were
Fig. 1 Cognitive-motivational profiles (standardized mean scores) of the groups
Table 3 Means, standard deviations and ANOVA results on stress, exhaustion, academic performance,
lack of interest and epistemological beliefs
Non-regulative
n = 106
Self-directed
n = 60
Non-reflective
n = 46
M
SD
M
SD
M
SD
F(2,211)
p
g2p
Stress
3.24
1.20
2.50a
1.02
2.61
1.13
9.93
.012
.09
Exhaustion
2.94
.94
2.36a
.74
2.54a
.87
9.45
.009
.08
Lack of Interest*
1.93a
.83
1.68ab
.68
1.55b
.63
4.82
.000
.04
Certain Knowledge
3.46a
.97
2.79
.88
3.55a
.89
12.45
.000
.11
Practical Value
4.65a
.93
4.31a
.96
4.40a
.87
2.89
.058
.03
Means within a row sharing the same subscripts are not significantly different at the p \ .05 level. Due to
unequal variances, Games-Howell correction instead of Bonferroni was applied for variables denoted with
an *
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465
significant for all variables with effect sizes ranging from .04 to .09. Pairwise comparisons
revealed that the self-directed students displayed highest level of social optimism. In
contrast, non-regulating students reported higher levels of stress and exhaustion than either
non-reflective or self-directed students, which, in turn, did not differ from each other.
Similarly, they also reported lacking interest significantly more than the other two groups.
Next, we examined whether there were differences between the groups in epistemological beliefs. The main effect was significant for Certain Knowledge and marginally
significant for Practical Value (see Table 3). Interestingly, both non-reflective students and
non-regulative students scored higher on Certain Knowledge than self-directed students,
but did not differ from each other. Regarding Practical Value, the non-regulating students
had highest scores, but none of the pairwise differences reached statistical significance.
A third aim was to examine whether there were differences between the groups in study
success. Study success was assessed by means of final examination of the course. The main
effect was significant for course grade, F(2, 211) = 6.67, p = 0.002, g2 = .06. Pairwise
comparison with Bonferroni’s correction revealed that self-directed students were more
successful in their studies (M = 3.81, SD = .71) than either non-regulative students
(M = 3.35, SD = .84) or non-reflective students (M = 3.51, SD = .74). The latter two
groups did not differ from each other.
Discussion
The aim of this study was first to explore how approaches to learning and cognitive and
attributional strategies are related to each other in first-year teacher students. The correlative results supported our previous findings with students from other fields (Heikkilä and
Lonka 2006; Heikkilä et al. 2011): Critical Evaluation and Deep Understanding were
positively related with Optimism and negatively with Lack of Regulation, which in turn,
had a strong positive correlation with Task-Avoidance. The second aim was to examine
what kinds of cognitive-motivational profiles can be identified among teacher students.
With this person-oriented approach we wanted to examine what kinds of groups of students
with different cognitive-motivational profiles exists among teacher students. Three groups
were identified: non-regulating students, self-directed students, and non-reflective students.
These groups differed from each other in well-being, in epistemological beliefs, and in
study success.
The first profile ‘non-regulating students’ (50%) was, quite surprisingly, the biggest
group of the three. Students in this group had the highest scores on Lack of Regulation and
Task-Avoidance and lowest on Optimism, while showing average scores on approaches to
learning scales. In other words, students in this group demonstrated both problems with
regulating their studies and a general tendency to avoid challenging goals and situations.
This group reported the most often stress, most exhaustion, and the least interest. They also
showed the strongest preference for certain and directly applicable knowledge. This group
highly resembled ‘helpless students’ group in our previous study (Heikkilä et al. 2011).
The characteristics of this group were similar to those in self-handicapping strategy in
university environment (Eronen et al. 1998; Nurmi et al. 2003), coupled with problems of
regulation of learning. A similar dysfunctional pattern was found in variable oriented
studies too: Lonka et al. (2008) found a dysfunctional orientation among medical students
by using a variable-oriented approach.
The size of the group calls for explanations. Why is this group as big as it is, even
though the student population is a highly selective one? One possible explanation for our
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finding is that there is a mismatch between the students and their learning environment.
Eccles and Midgley (1989) proposed that negative developmental changes may result if the
educational context does not provide a developmentally appropriate educational environment for students. Such a negative developmental fit may lead to alienation and cynicism.
In Finland, where making the transition to university is very demanding, it is possible that,
for some students, the gap between the demands of the academic environment and the level
of their competence is too big giving rise to problems. It is also possible that these students
have problems only in the academic environment since they may be highly work-life
oriented (Mäkinen et al. 2004): an interesting finding of Endedijk (2010) was that active
regulation among teacher students dominated in practice schools, while passive regulation
was relied on at the university. There is also a more positive explanation for the problems
of these novice students. The students may actually experience a constructive friction
(Vermunt and Verloop 1999). This means that the learning environment, where the control
is shared between the teacher and the students, challenges the first-year students to develop
their self-regulatory skills.
The profile of ‘Self-directed students’ (28%) was characterized by the highest levels of
Deep Understanding, Critical Evaluation, and Optimism, and lowest levels of Surface
Approach, and Lack of Regulation. This group was the least stressed and exhausted. In
their epistemological beliefs they showed the lowest emphasis on Certain Knowledge and
Practical Value. In our previous study with first-year art, law, and agriculture students, 35%
of the students had a highly similar profile (Heikkilä et al. 2011). The relationship between
a tendency to self-regulate and deep approach has been demonstrated before (e.g., Vermunt
1998; Nieminen et al. 2004) and this study further supports those findings. In the university
environment, an optimistic strategy, which was a part of the self-directive students’ profile,
has earlier been shown to be related to general well-being and study success (Eronen et al.
1998; Nurmi et al. 2003). In younger students, mastery-oriented students have been shown
to have relatively high levels of both academic achievement and subjective well-being:
mastery- or learning-oriented students suffer less from school burnout than the success
oriented ones do (Tuominen-Soini et al. 2008). Earlier studies with teacher students have
demonstrated very similar numbers of students expressing self-regulated learning: in a
study of Endedijk (2010) 29% showed a meaning-oriented learning conception. This kind
of profile was probably in congruence with the activating setting (Vermunt and Verloop
1999), since these students obtained the highest grades of the course.
‘Non-reflective students’ (22%) expressed lowest levels of Deep Understanding,
Critical Evaluation, and Task-Avoidance. In Surface Approach, Lack of Regulation, and
Optimism these students had average loadings. They did not seem to be distressed either,
receiving average scores on Stress, Exhaustion, and Lack of Interest. In our previous study,
we named a quite similar profile as non-academic students (Heikkilä et al. 2011), since
these students demonstrated little Critical Evaluation and Deep Understanding, which are
prominent aspects of the traditional academic meaning orientation. However, the present
study on professionally oriented teacher students leads us to think about this profile in a
new way: this profile resembles ‘‘cookbook orientation’’, found in another professionallyoriented group of students, namely medical students (Lonka et al. 2008). Cookbook
orientation includes a strong preference for certain, concrete, and practical, easily applicable-knowledge. Similar to the cookbook orientation, non-reflective students had a high
loading on Certain Knowledge, and the highest scores on Surface Approach. Students in
non-reflective group may be work-life oriented in a way that studies have a strong
instrumental value for them. We did not, unfortunately, measure this aspect of motivation
in the present study.
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467
Some methodological reflections
Teacher students were studied in a pre-service teacher education programme. In other
words, they were not different from traditional higher education students who follow a
course at the university, and they were not involved in teaching in practice at the time of
data collection. We cannot, therefore, say anything about their cognitive-motivational
profiles in practical teaching situations. It can be, as Endedijk (2010) showed, that teacher
students use much better self-regulatory processes in situations where students face reallife challenges as teachers.
The reliabilities of the scales, in general were satisfactory or good. The only exception
was the scale ‘‘practical value’’. This measurement had a moderate ceiling effect, since
most of the participants appeared to score high on this measurement. The population also
expressed high levels of deep approach. These results are in line with previous research on
Finnish university students (Mäkinen et al. 2004; Lonka and Lindblom-Ylänne 1996).
The final shortcoming was the correlative nature of the study. Longitudinal designs are
needed to better understand the true dynamics and developmental nature of the given
phenomena. It is quite understandable that young students coming directly from high
school may experience trouble in a course that challenges their previous mental models and
calls for active participation. In a sense, then, the present study provides a good starting
point; a snapshot of the onset of the development of these teacher students’ motivationalcognitive profiles in the context of university.
Conclusions
We applied a person-oriented approach in order to study what kinds of cognitive-motivational profiles can be identified among first-year teacher students. We were able to
validly extract groups of students sharing similar tendencies and to demonstrate some
important differences in these students’ well-being, study success, and epistemological
beliefs. Pintrich (2004) argued that students’ strategies of monitoring and regulating both
their cognition and motivation should be included in conceptual models, and in assessment
of student learning. Our results support this argument: when taking into account both
cognitive and motivational aspects of learning we were able to build a more comprehensive
picture of university students.
Our results showed that cognitive and motivational aspects are not only related to study
success but also connected with general well-being of the teacher students. This is an
important finding since school-related burnout is a problem in schools and higher education (Salmela-Aro et al. 2009). Salmela-Aro and Kunttu (2010) proposed that success in the
entrance examination might show in a high level of study engagement in the subjects
where only a small portion of the applicants are accepted. Our results support this idea:
students in this sample were quite optimistic, and not highly exhausted or stressed.
For teacher educators this study has an important message: only one third of the students
showed an adaptive cognitive-motivational profile. Even in a population of highly selective
teacher students, two thirds of the students expressed some kind of a maladaptive profile.
First-year teacher students may be, more than we believe, dependent on external regulation
and highly instrumentally oriented. Obstacles in early studies may result from a number of
issues: insufficient study skills, lack of appropriate goals, emotional exhaustion, and
concerns with one’s own competence. Teachers and teacher educators are considered key
factors in promoting active learning (Niemi 2002). This means that teachers working in
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new activating learning environments should learn how to actively regulate their own
learning. And not only that, beyond one’s own self-regulation, they should eventually
become able to support their own pupils learning and foster their self-regulatory skills.
Prior research has demonstrated that in order to become an expert teacher-instead of only
an experienced non-expert teacher—self-regulation skills are necessary (Kreber et al.
2005). In the light of our findings, teacher education should make a strong effort to activate
students’ regulatory strategies. In order to promote constructive frictions in learning,
activating courses should probably be more common. Longitudinal studies should show
whether this assumption really holds.
Previous research on teachers’ self-efficacy has shown that teachers with a strong sense
of efficacy tend to be more open to new ideas and more willing to experiment with new
methods to better meet the needs of their students (Stein and Wang 1988). Even though we
did not exactly measure self-efficacy in this study, our cognitive-motivational profiles
include similar aspects. We should, therefore, develop teacher education so as to provide
more constructive feedback to students, thus helping them to enhance their self-efficacy
beliefs, deep approaches to learning and self-regulation. The notion of agency describes a
person’s capability and motivation to make choices and to take intentional actions in the
direction of these choices in a way that makes a difference in one’s life and in the
community (Scardamalia 2002). Future teachers should be well prepared to serve as such
agents in their schools. If they are given an active role in their own studying, they will
probably be more likely to promote agency in their future pupils.
Acknowledgments This study was supported by the Academy of Finland (Grant numbers 1116847,
109193 and 111799) and Helsinki University Research Funds.
Appendix
See Table 4.
Table 4 Standardized factor loadings for the chosen measurement model
Optim
OPTIM1
0.653
OPTIM3
0.578
OPTIM12
0.739
OPTIM7
0.917
Deep
und.
DEEP UND39A
0.587
DEEP UND41A
0.605
DEEP UND43A
0.656
DEEP UND45A
0.606
DEEP UND47A
0.696
Exh.
EXH1
0.735
EXH2
0.828
EXH3
0.815
EXH4
0.629
Surface
SURF38A
0.642
SURF40A
0.626
123
Task
avoid.
Lack
of int.
Social
opt.
Lack
of reg.
Deep
crit.
Certain
know.
Practic.
Know.
High Educ (2012) 64:455–471
469
Table 4 continued
Optim
Deep
und.
Exh.
Surface
SURF42A
0.535
SURF44A
0.504
Task
avoid.
TASK AVOID2
0.817
TASK AVOID4
0.455
TASK AVOID5
0.871
Lack
of int.
LACKINT1
0.954
LACKINT2
0.576
SOCIAL OPT9
Social
opt.
Lack
of reg.
Deep
crit.
Certain
know.
Practic.
Know.
0.729
SOCIAL OPT10
-0.539
SOCIAL OPT11
0.620
LACKREG1
0.782
LACKREG2
0.678
DEEP CRIT46A
0.755
DEEP CRIT49A
0.787
CERT1
0.577
CERT2
0.730
CERT3
0.721
PRACT1
0.565
PRACT2
0.591
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