Class Attendance, PowerPoint Slides and Learning Outcomes
William Alpert
University of Connecticut
Archita Banik
University of Connecticut
Oskar R. Harmon
University of Connecticut
James Lambrinos
Union Graduate College
Richard Langlois
University of Connecticut
Working Paper 2012-42
December 2012
365 Fairfield Way, Unit 1063
Storrs, CT 06269-1063
Phone: (860) 486-3022
Fax: (860) 486-4463
http://www.econ.uconn.edu/
This working paper is indexed on RePEc, http://repec.org
1
Class Attendance, PowerPoint Slides and Learning Outcomes
By:
William Alpert, University of Connecticut, Associate Professor of Economics
Archita Banik, University of Connecticut, PhD Economics Graduate Student
Oskar Harmon, University of Connecticut, Associate Professor of Economics
James Lambrinos, Union Graduate College, Professor of Economics
Richard Langlois, University of Connecticut, Professor of Economics
ABSTRACT
At many large universities it is conventional to deliver undergraduate introductory economics courses in a large
lecture hall with a live lecturer. However, not surprisingly, casual empiricism suggests that rates of student
absenteeism are significantly greater in a large lecture format than in a smaller classroom setting. A
compounding factor is that numerous empirical studies have established a significant negative relation between
absenteeism and student performance.
Though many instructors employ the technology of PowerPoint presentation in the traditional lecture, there is
reluctance among some instructors to distribute the PowerPoint lecture notes to students. Their concern is that
if absenteeism is greater in large lecture classes and greater absenteeism leads to poorer performance, then
won’t distributing lecture presentations online will contribute to an increase in absenteeism and therefore lower
educational outcomes?
This study investigates the relation between student performance and the use of online lecture notes. The
findings confirm the usual finding that absence from the lecture reduces the probability of a correct response to
questions covered in the lecture. For students absent from class, however, studying from instructor provided
lecture notes increases the probability of a correct response. For students with a multi-modal learning style the
positive effect of lecture notes offsets the negative effect of absence, not so for students with mono-modal
learning styles.
Key Words: Learning Preference, Absenteeism, Lecture Notes
JEL Code: A2, A22
2
INTRODUCTION
Concern for attendance and the allure of the new technologies are pushing innovations into the classroom where
principles of economics is taught. At large universities the economics is plain – the instructional delivery of
choice for teaching Principles of Economics is the large lecture hall. Anecdotal evidence confirms what
common sense tells us -- rates of student absenteeism are higher for this format than the small lecture format.
Surveys of instructional methods (Becker and Watts: 1996; and Coates and Humphreys: 2003) report a growing
number of instructors use technology to augment the traditional chalk and talk method of delivery of principles
of economics. Though the cost of adopting new technology is high (e.g. a steep learning curve of new
software, and ongoing costs related to maintenance and upgrades) many instructors are inspired to adopt it
because of the promise of benefits (exceeding costs) of positive impacts on learning outcomes (Goffe and Sosin
2005; Sosin et al. 2008). For example, among the benefits of online delivery are eliminating distance
commuting, accommodation of schedule conflicts, and a variety of learning materials accommodating different
learning preferences. On the other hand, among the costs of online delivery are reduced student-to-student, and
instructor-to-student interactions, and possible miss match between the independent learning skills and the level
of maturity required of online and those possessed by the typical undergraduate student. The findings of several
empirical studies of adopting new technologies suggest a gradual approach (Gratton-Lavoie and Stanley 2009
provides an excellent summary).
A specific technology innovation adopted by some instructors is making available to students the lecture notes
of the PowerPoint used for the lecture presentation. These notes can assist a student with note-taking and can be
a substitute if a student does not attend a lecture. Note taking is a complex task that involves simultaneously the
skills of listening, organizing, and writing (Grabe 2005). Some students are more accomplished at the task than
are others. The instructor provided lecture notes can reduce the time spent on creating an organized outline and
free time up to spend on listening and understanding. For students with poor note-taking skills, this can make a
significant difference in whether they are able to create lecture notes suitable for reviewing for an exam. While
instructor provided lecture notes can encourage some students to skip class, but they can also provide a
substitute for students unable to attend class because of an unavoidable conflict. Ultimately whether instructor
provided lecture notes adversely effects learning outcomes depends on a complex set of factors including
student learning preferences, student skills, unpredictable scheduling conflicts, and the student’s goal for their
course grade.
3
REVIEW OF THE LITERATURE
The numerous empirical studies of the effect of attendance on student performance in principles of economics
classes uniformly report a negative and statistically significant relation. The early studies of principles of
economics classes (Romer 1993) used exam score and attendance records. More recent studies (Chen and Lin
2008a; and Marburger 2006, 2001) use panel data of individual exam questions and attendance at the lectures
where the material of these questions was covered. These studies estimate a model where the dependent
variable is an indicator variable for whether the question is answered correctly and the independent variables
consist of an indicator variable for absence, and variables to control for student characteristics. These studies
uniformly report a statistically significant negative effect of absenteeism on student performance. Most
recently, (Bethune 2010) takes a slightly different tack and compares the magnitude of the attendance effect
relative to other factors effecting student performance. He reports that absenteeism has a negative effect, but
that the strongest predictor is prior academic success as measured by GPA and SAT scores. He argues that his
results suggest attendance is over emphasized in prior studies, and suggests future research give more attention
to uncovering other factors, additional to attendance, that influence learning outcomes.
Another important variable influencing learning outcomes is note taking during lectures. There is a large
literature in the field of education and psychology that measures the effect of note taking and note reviewing on
student performance (Grabe 2005). For students with good note taking skills, note taking in class has positive
benefits associated with making a personalized copy. However, for students with poor note taking skills, note
taking in class has the disadvantage of leaving the student with an inadequate transcript of the lecture,
distracting the student from the full benefit of listening to a lecture, and curtailing the benefits from reviewing
the lecture notes (Grabe 2005). For a course in psychology (Grabe 2005) investigated the correlation between
student use of instructor provided online lecture notes, absenteeism and the effect on student performance. In
the introductory psychology class of 183 students, 48 students (26%) did not make use of the lecture notes. It is
reported that the mean exam score for lecture note users was significantly higher than for non lecture note users.
Of the students viewing the lecture notes, 75% had viewed them 6 or more times. When asked how frequently
they printed out the lecture notes and used them during lecture, 20% reported they did not use the lecture notes
in this way, and 80% responded they have used them in this way, of which 42% had done so 6 or more times.
Also examined was use of lecture notes as a replacement for class attendance. The students who viewed the
lecture notes 6 or more times were asked how frequently they used lecture notes as a replacement for attending
class. Twenty-one percent responded they never used the lecture notes to replace attending class, and 28%
responded they had used the notes for this purpose. And it was reported that the mean exam score of the two
groups was not significantly different. There are, at least, two interesting implications of these findings. When
instructor lecture notes are provided, students will opt to view them and many students will view them
frequently. And, when using the lecture notes frequently, the lecture notes can be an effective way for students
to make up the material when absent from class.
Chen and Lin (2008b) investigate the effect of lecture notes on learning outcomes for a principles of economics
class. Their sample consists of 126 students from an intermediate microeconomics course at an elite public
university in Taiwan. The instructor lectured from a PowerPoint presentation and the slides were made
available on a university server 3 to 5 days prior to each lecture. Their study examines whether the exam scores
of the students who down loaded the PowerPoint lecture notes prior to attending the live lecture differed from
the exam scores of students that did not download the slides prior to attending live lecture. In their findings they
report that the effect of downloading the lecture notes prior to lecture is positive, statistically significant, and
slightly larger than effect of attending the lecture without having previously downloaded the lecture notes.
4
What is the impact of differences in learning styles on lecture attendance and usage of instructor provided
lecture notes? This is an issue, to our knowledge, not yet researched in the economics of education literature. If
a student’s learning style is not well suited for the lecture environment, then the return to attendance may be
lower, and the return to lecture supplements may be higher than for the average student. There is an extensive
literature in the field of educational psychology that believes learning styles are a valid approach to
understanding how students absorb and process information. If learning styles are consistently correlated with
instructional methods and learning outcomes, then identifying and matching learning styles with instructional
methods will increase the efficiency of the learning process. An example of this line research is Hamadea and
Artailb (2010), which examines the correlation of learning styles with learning outcomes for a training course in
Computer Assisted Design (CAD). Using the Barsch (1996) learning style inventory, their study identifies the
learning styles of 44 students in the course. The authors track the time to construct, test, and improve four CAD
models constructed during the training period. They identify a learning style as correlated with efficiency in
construction of CAD models, and another learning style as correlated with degree of sophistication of the CAD
models. From this evidence they suggest that efficiencies in the CAD production process can be realized by the
assignment of engineers with activist learning styles to the parts and assemblies stage of producing CAD
models, and placing engineers with reflective, intuitive or global learning styles in the downstream stages of
altering and testing CAD models. A similar example in the field of accounting is (Wynn-Williams et al 2008).
Several empirical studies illustrate the potential of learning styles research for improving learning outcomes in
the instruction of principles of economics courses. The (Boatman et al 2008) study used the VARK Index of
Learning Styles (Fleming 2012), and reported a positive correlation between student performance and a strong
preference for the visual learning style. Noting the reliance on diagrammatic explanations in widely used
introductory economics textbooks, they comment that students with a preference for visual presentation will
likely do well in economics courses. And conversely, supplementing diagrams with non-visual based
explanations of economic concepts may increase learning outcomes for students with learning styles that are not
predominated by a visual preference. Another example of the potential of learning styles research is implied in
the findings reported by (Brown and Leidholm 2002). This study compares learning outcomes of students in an
introductory economics course where one set receives instruction in the traditional lecture format and the other
receives instruction in an online format. Their findings are that the learning outcomes for students in the online
delivery format were handicapped relative to those for students in the traditional delivery format. The authors
speculate that the outcome may be explained by the students in the online section as not having the strong
independent learning skills needed to do well in an online environment. An implied potential benefit of the
Brown and Lidhomm findings is that, by identifying student learning styles, academic advisors could better
advice students on course selection where mode of instructional delivery is a variable in the mix (Boatman et
al.). Other studies show a consistent link that learning preferences consistently differ between online students
and traditional students in enrolled classes (Halsne and Gatta 2002; and Diaz and Cartnal 1999), and a
consistent link between learning preferences and learning outcomes (Charkins et al 1985, Hall 1982).
In the literature on instruction in principles of economics classes, there have been numerous studies of the
effect of lecture attendance on exam performance and one study of the effect of using PowerPoint lecture notes
to assist with note-taking during a lecture and exam performance (Chen and Lin 2008b). This study is slightly
different than from (Chen and Lin 2008b) in the examination of the usage of PowerPoint lecture notes. We
investigate whether using PowerPoint lecture notes are an effective substitute for students that are absent from
the live lecture. Their study looked at whether the instructor provided lectures notes assisted in-class learning,
we are looking at whether these notes assist out-of-class learning. It is an important distinction because absence
from class is not always a result of undervaluation of education; it often is a result of illness, or difficulty in
scheduling, or occurrence of unexpected events, or conflicting pressing athletic /job demands, or a mismatch
between the lecture format and the student learning style. Lecture notes are potentially a work around for
infrequent absence. This study addresses two issues. Can instructor provide lecture notes mitigate to some
5
extent the adverse impact of absence from live lecture? And do student learning styles influence class absence
and usage of instructor provided lecture notes?
DATA
The data in this study were collected from an undergraduate introductory economics course with an enrollment
of 125 students. The course is taught from the approach of an informed citizens’ guide to the economy and
economic ideas. Students passing the course may still take Principles of Microeconomics and/or
Macroeconomics for course credit. It is primarily taken by non majors and taught in a traditional large lecture
in person lecture format. Class meets twice weekly. The notes are in the format of PowerPoint slides with
visual images, and links to outside material to increase student engagement in the subject.
The data come from three sources. Clickers were used to record attendance and attendance was taken in 16 of
24 (66%) lectures. The second source was from responses to a survey questionnaire on learning style
preferences. Sixty-seven students (54%) returned completed questionnaires. The third source was student
transcripts that provided data on academic characteristics. (The administration of the questionnaire and consent
forms was done in accordance with University protocols for use of human subjects in research.)
Descriptive statistics for the sample of survey respondents are reported in Table 1. The variable abs_total is
the number of classes skipped and is taken from the clicker data. It shows that on average students’ skipped 4.7
classes out of the 16 lectures in which attendance was taken. The variable D_online_lectures is one if the
student used the online lecture notes when absent from class and zero otherwise. Approximately half of the
students (55%) skipping class report using the online lectures notes to make up the absence. The average grade
point average (GPA) at the beginning of the semester is 2.90. The average number of credits carried during the
semester (credits_enrolled) is about 15, which is the usual load to graduate in four years. Fifty-five percent of
the class are freshmen (D-Fresh), and 21% are sophomores (D_Soph). The overwhelming majority, 75%, live
on campus (D_on_campus), 34% have a job (D_job), and 27% are female (Female).
6
Table 1 Descriptive Statistics of Sample Respondents
Variable
N
Mean
Std Dev
Minimum
Maximum
Exam
67
67.85
10.64
32.00
89.00
abs_total
67
4.69
3.95
0.00
15.00
D_online_lectures
67
0.55
0.50
0.00
1.00
GPA
62
2.90
0.61
1.50
4.00
D_Econ_Eng
67
0.24
0.43
0.00
1.00
D_Fresh
67
0.55
0.50
0.00
1.00
D_Soph
67
0.21
0.41
0.00
1.00
D_Junior
67
0.09
0.29
0.00
1.00
D_Senior
67
0.15
0.36
0.00
1.00
D_have_job
67
0.34
0.48
0.00
1.00
D_on_Campus
67
0.75
0.44
0.00
1.00
credits_enrolled
66
14.73
2.40
5.00
20.00
Female
67
0.27
0.45
0.00
1.00
LS_A
67
0.24
0.43
0.00
1.00
LS_V
67
0.31
0.47
0.00
1.00
LS_K
67
0.22
0.42
0.00
1.00
LS_M
67
0.22
0.42
0.00
1.00
7
A survey consisting of 16 questions designed to draw a composite profile of a student’s learning style was
administered at the end of the semester. The survey is produced by the University of South Dakota's, and is
available for free use at the web site: http://www.usd.edu/trio/tut/ts/stylest.html. The survey questions are
reported in an appendix. For each question the first response (A) indicates a “visual” learner, the second
response (B) an “auditory” learner, and the third response (C) a “kinesthetic” learner. Students had the option
to select more than one response for each question.
Each student is classified according to the highest total number of times selecting response A, B or C. For
example, if response A is selected 6 times, response B, 5 times, and response C, 5 times, then the student is
classified as having a predominate V (visual) learning style. If the response pattern was 6,6,4, then the student
is classified as having an “M” (multiple) learning style. Students selecting multiple responses to a question are
labeled as a “VAK” learning styles. A conventional brief description for each learning style is: Visual learners
prefer to learn by reading; Auditory learners prefer to learn by listening to lectures; and Kinesthetic learners
have difficulty taking notes and prefer to learn by doing. (Source of descriptions of learning preference: VARK
website: www.vark.com)
Table 2 shows the learning style ranked by the average number of classes skipped. The tabulation shows that
the students with a predominately auditory preference skip fewer classes than the other learning styles, whereas
students with a predominately visual preference skip relatively more classes. This pattern is consistent with the
expectation that auditory learners prefer listening to lectures, and visual prefer reading lecture notes.
TABLE 2 Mean of Class Absence by Learning Style
Predominate
Learning
Style
n
mean
std
Auditory
16
3.62500
3.59398
Kinesthetic
15
4.73333
3.75056
Multimodal
15
4.73333
3.86313
Visual
21
5.42857
4.47852
ALL
67
4.68657
3.95125
8
Additionally students were asked whether they had skipped any classes, and if they skipped class did they use
the instructor provided online lectures notes as a substitute? The question was worded as: “Did you use the
online lecture notes at the class website to make up the missed classes?” There were three possible responses: “I
did not miss a lecture”, “Yes”, and “No”. A frequency count shows that 28% responded they did not miss a
lecture, 55% responded they used the lecture notes and 17% responded they did not use the online lecture notes.
In Table 3 the Learning Preference Index is reported for three groups: those who attended all classes (refer to as
“No-Skip”), those who skipped classes and used the lecture notes (Skip-Use-Notes), and those who skipped
class and did not use the lecture notes (Skip-No-Use-Notes.
TABLE 3 Cross Tabulation of LS_Type by Whether Use Lecture Notes When Skipping Class
Predominate
Learning
Style
n
mean
std
Kinesthetic
13
0.53846
0.51887
Visual
13
0.76923
0.43853
Auditory
11
0.90909
0.30151
Multimodal
11
0.90909
0.30151
ALL
48
0.77083
0.42474
Table 3, for the group of students that skipped class, shows the learning style ranked by the percent of the
cohort that used the lecture notes to make up the class. The tabulation shows that for students skipping class,
students with a predominately kinesthetic preference have the lowest lecture note utilization rate 54%, and
whereas 91% of the students with a predominately auditory (91%) and multimodal (91%) have the highest
utilization rates.
Together Tables 2 and 3 provides some evidence regarding our first question: What is the profile of learning
styles associated with students that are absent from class and use the PowerPoint lecture notes to make the class
up? The tabulations suggest the following profiles. The students with a predominate (auditory) preference for
listening to lectures tend to skip the fewest classes and most (91%) of that cohort use the lecture notes to make
the class up. The students with a predominate (visual) preference for reading lectures tend to skip the most
classes and 77% of that cohort use the lecture notes to make the class up. The students with a kinesthetic
learning style tend to skip classes at the average rate, but only half (54%) of that cohort use the lecture notes to
make the class up.
EMPIRICAL MODEL
We are interested in whether absenteeism and student usage of the online lecture notes affected student
performance. A potential econometric problem that arises in data like ours is bias from unobserved student
characteristics. One approach to this econometric problem is to arrange the data as a panel of exam questions
with the dependent variable is an indicator variable y equal to 1 if the question correctly answered, and equal to
0 if incorrectly answered. Following Marburger,(2001, 2005); and Chen and Fang (2008a, 2008b ) we
associated the multiple choice questions with the lecture that covered the course content, and from the
attendance records a variable we constructed a variable to indicate whether the student attended the class during
9
which the content of the question was covered. Using this method, we merged a file of the student response to
each of 100 final exam questions with a file of student characteristics resulting in a sample of approximately
5,000 observations where the question is the unit of observation.
Following (Cameron 2010) and Sanca (2010) the panel data can be modeled as:
yit = β1 x1it + β2 x2it + εit , where i = 1,2,…N ; t = 1,2, ….T.
1.
1.
N is the total number of students, T is the total number of questions. The dependent variable yit is an indicator
of whether the response is correct (1) or not (0), where i is the ith student, t is the tth exam question. x1i is
academic input; x2i is student characteristics; and ԑ i is the idiosyncratic error term. For academic input
we use variables that measure lecture attendance and use of lecture notes. For student characteristics we use
variables that measure academic achievement and demographic characteristics.
If the variable in x2 is measured with error (i.e. it omits unobserved variables such as motivation, hour spent
studying etc.) then the logit estimates will not be unbiased. Let αi be the random individual-specific effects of
the excluded variables. An approach to get unbiased estimates is to assume that the effects of the omitted
variables are fixed for the individual, correlated with the individual’s observed characteristics, and independent
of the idiosyncratic error term. The resulting compound error term is written as: uit = αi + εit .
This is the “fixed effects” model and equation (1) then becomes:
(2) yit = β1 x1it + β2 x2it + αi + εit ,
We then estimate OLS on the mean difference transformed data:
(3) yit - y i = β2 (x2it - x 2i ) + ( εit - ԑ i )
The transformation eliminates the αi but it also eliminates the other time invariant characteristics, such as
academic achievement, because they are constant across the question responses for each individual.
The effects of the time invariant variables, however, can be estimated by the random effects model , which is
estimated by GLS and makes the stronger assumption that the unobserved effects are uncorrelated with the
observed variables.
RESULTS
The Logit estimation results are reported in Table 4. Three models are estimated. The dependent variable in all
models equals 1 if the question is answered correctly and equals 0 if the response is incorrect. In Model 1 the
specification includes only two repressors: absent, and used lecture notes. The regress or absent measures the
effect of absence from lecture on the probability of a correct response (hereafter the absent effect). The
estimated coefficient is negative and statistically significant at the .01 level, which is consistent with the
findings reported in previous studies. The regress or used lecture notes measures the effect of the behavior of
studying from the lecture notes when absent from the lecture, on the probability of a correct response (hereafter
the lecture notes effect). The estimated coefficient is positive and statistically significant at the .01 level. The
absolute value of the absent effect, is larger than the lecture note effect. Converting the estimated coefficients
into probabilities, the absent effect reduces the probability of a correct response by 11.5 percent, and the lecture
10
note effect increases the probability of correct response by 8 percent. (The marginal effects are reported in
Table 6.)
To test the sensitivity of the estimation results in Model 1 to different specifications we estimated two models
with additional controls. Model 2 adds controls for learning styles, and academic characteristics. Model 3 adds
the additional controls for the interaction of lecture notes and learning style to the controls in Model 2. In
Models 2 and 3 the coefficients of the controls for learning style (Auditory, Visual, and Kinesthetic) are
statistically insignificant at the .10 level. This result is contrary to our expectation but these controls have an
effect when interacted with lecture notes in Model 3. In Models 2 and 3 the coefficients of the variables for
GPA and Econ or Engineer Major are each positive and statistically significant at the .01 level. This result is
consistent with previous studies. In Models 2 and 3 the coefficients of the controls for class rank (sophomore,
Junior, and Senior), and whether have a job, were statistically insignificant at the .10 level. In Models 2 and 3
the coefficient of the variable for credits enrolled is negative positive and statistically significant at the .05 level.
This result is consistent with previous studies. The interpretation of the negative effect reflects a substitution
effect of dividing time between studies and work, but other studies also report a positive coefficient reflecting
the positive correlation of time spent at work and the level of serious commitment to studies.
The addition of the controls in Models 2 and 3 effects the estimates of the absent effect and the lecture notes
effect, but only marginally. In Model 2 the effect of the additional controls is to that the numerical magnitude
of the absolute value of the absent effect and the lecture notes effect are equal and the effects are offsetting. In
Model 3 the effect of the additional controls is to that the numerical value of the lecture effect is very sensitive
to the interaction with the learning style variables. In Model 3 the estimated coefficient of the lecture note effect
is 1.114 but the estimated values for the interaction with the three learning style variables range from -0.801 to 0.992 (each of which is statistically significant at the .01 level). The interpretation is that the lecture notes effect
for the multi-modal learning style (the excluded group in the learning style controls) is 1.114, but for the other
learning style groups it ranges from 0.124 to 0.313 with the Kinesthetic learning style having the smallest
positive effect.
11
TABLE 4 BINARY DEPENDENT VARIABLE: Correct
Model 1
Model 2
Model 3
Model 4: Random
Effects:
absent
-0.501***
(-5.52)
-0.397***
(-4.08)
-0.389***
(-4.00)
-0.267*
(-2.12)
used lecture notes
0.372**
(3.28)
0.399**
(3.22)
1.114***
(4.71)
1.002***
(3.77)
notes*Auditory
-0.858**
(-3.13)
-0.894**
(-2.97)
notes*Visual
-0.992***
(-3.58)
-0.817**
(-2.62)
notes*kinesthetic
-0.801**
(-2.75)
-0.881**
(-2.72)
-0.0384
0.0616
0.105
(-0.40)
(0.59)
(0.58)
0.037
0.146
0.117
(0.38)
(1.44)
(0.66)
-0.0324
0.0461
0.0734
(-0.32)
(0.43)
(0.4)
GPA
0.244***
(4.04)
0.233***
(3.8)
0.235*
(2.15)
Econ or Engineer Major
0.397***
(4.91)
0.401***
(4.96)
0.422**
(3.00)
Sophmore
0.122
0.117
0.108
(1.35)
(1.3)
(0.68)
-0.147
-0.155
-0.155
(-1.00)
(-1.04)
(-0.58)
-0.0714
-0.109
-0.0978
(-0.59)
(-0.88)
(-0.43)
0.0833
0.0948
0.105
(1.19)
(1.34)
(0.83)
0.0174
-0.00432
0.0113
(0.17)
(-0.04)
(0.06)
-0.0334*
(-2.15)
-0.0339*
(-2.10)
-0.0327
0.755
(22.03)
0.377
0.362
0.305
(1.44)
(1.35)
(0.65)
Observations
5245
4787
4787
4787
Wald Chi2
30.9756
80.6721
95.765
36.9414
Prob > chi2
0.0000
0.0000
0.0000
0.0021
Log lik.
-3335.4246
-3020.6011
-3012.8449
-2982.2884
t statistics in parentheses
*
Auditory
Visual
Kinesthetic
Junior
Senior
have job
live on campus
credits enrolled
Constant
***
p < 0.05,
**
p < 0.01,
***
p < 0.001
(-1.16)
12
Omitted Variable Bias
The goodness of fit measures for the logit models, which do not correct for omitted variable bias, are reported in
the bottom rows of the Table 4. The calculated Prob value for the Wald Chi2 test of the null hypothesis that
the estimated coefficients are not significantly different from zero, is rejected at the 0.01 level for all three Logit
estimations.
To test Model 3 for omitted variable bias, we estimate that the same specification using the random effects
model, and the results are reported in Table 4 in the column labeled Model 4. For the random effects models 4
the calculated value of the wald chi2(2) for the Breusch and Pagan Lagrangian multiplier (LM) test for random
effects is 5.29 which is significant at the 0.07 level. This LM test is for whether the variation of the individual
specific effects is sufficiently large to reject the null hypothesis of no individual specific effects. Based on the
calculated value of the LM statistic we cannot reject the Logit Model 3 in favor of the Random Effects Model 4
at the .05 level of significance.
Marginal Effects
The marginal effects of the model specifications are compared in Table 5. Without controlling for learning
style effects (Model 1) the absent effect is dominates the lecture notes effect. When learning styles are
controlled for (Model 3) the lecture notes effect dominates the lecture notes effect for multi-modal learning
style. For the remaining learning styles: Auditory, Visual, and Kinesthetic; the absent effect dominates the
lecture notes effect.
TABLE 6 Marginal Effects
absent
used lecture notes
notes*Auditory
notes*Visual
notes*Kinesthetic
Model 1 Model 2 Model 3
-0.115
-0.091
-0.089
0.079
0.084
1.002
-0.894
-0.817
-0.881
13
SUMMARY AND CONCLUSIONS
This study empirically investigates the influence of class absence, instructor provided lecture notes, and
learning styles on learning outcomes.
Can students offset the negative effect of class absence on exam performance by using PowerPoint lecture notes
as a substitute for attending the lecture? We report empirical estimates, when evaluated at the sample mean,
imply that that absence from class decreases the probably of a correct answer by 11.05% and usage of instructor
provided PowerPoint lecture notes increases the probably of a correct answer by 7.9%.
What is the role of learning styles on profile of learning preferences associated with students that are absent
from class and use the PowerPoint lecture notes? Our cross tabulation of learning preference index and lecture
note usage is suggestive of two observations. The students with an auditory preference are less likely to skip
lectures, and if they skip a lecture they are more likely to use the PowerPoint lecture notes. In contrast, the
students with a kinesthetic preference are more likely to skip lectures and are more likely to not use the
PowerPoint lectures when absent from class. Our estimates show that for students that skip class and study
from the PowerPoint lecture notes, student’s with multi-modal learning styles the net effect on the probability of
a correct answer is positive, but students with mono-modal learning styles (Auditory, Visual, and Kinesthetic)
are better off not skipping class.
In sum, the learning preferences associated with attending traditional lecture is auditory, and conversely the
learning preferences associated with absence from class is kinesthetic. What is the implication? Using
instructional innovations target the learning styles of students not attending class might help the learning
outcomes of students with these preferences. For example, are adding to the website hands on interactive
quizzes or crossword puzzles might induce the multimodal/kinesthetic to use these materials when absent from
class.
14
TABLE 4 Logistic Estimation Results: BINARY DEPENDENT VARIABLE: Correct
Model 1
Model 2
Model 3
***
***
absent
-0.501
(-5.52)
-0.397
(-4.08)
-0.389***
(-4.00)
used lecture notes
0.372**
-3.28
0.399**
-3.22
1.114***
-4.71
notes*Auditory
-0.858**
(-3.13)
notes*Visual
-0.992***
(-3.58)
notes*kinesthetic
-0.801**
(-2.75)
Auditory
Visual
kinesthetic
-0.0384
0.0616
(-0.40)
-0.59
0.037
0.146
-0.38
-1.44
-0.0324
0.0461
(-0.32)
-0.43
GPA
***
0.244
-4.04
0.233***
-3.8
Econ or Engineer Major
0.397***
-4.91
0.401***
-4.96
Sophmore
0.122
0.117
-1.35
-1.3
-0.147
-0.155
(-1.00)
(-1.04)
-0.0714
-0.109
(-0.59)
(-0.88)
0.0833
0.0948
-1.19
-1.34
0.0174
-0.00432
-0.17
(-0.04)
-0.0334*
(-2.15)
-0.0339*
(-2.10)
0.755***
-22.03
0.377
0.362
-1.44
-1.35
Observations
5245
4787
4787
Wald Chi2
30.9756
80.6721
95.765
Prob > chi2
0.0000
0.0000
0.0000
Log lik.
-3335.4246
-3020.6011
-3012.8449
t statistics in parentheses
*
Junior
Senior
have job
live on campus
credits enrolled
Constant
p < 0.05,
**
p < 0.01,
***
p < 0.001
15
TABLE 5 FIXED and RANDOM EFFECTS Estimation Results: BINARY DEPENDENT VARIABLE: Correct
Fixed Effects
Fixed Effects
Random Effects
-0.154
(-1.07)
-0.267*
(-2.12)
0.870**
-3.05
1.002***
-3.77
notes*Auditory
-0.889**
(-2.83)
-0.894**
(-2.97)
notes*Visual
-0.643
(-1.93)
-0.817**
(-2.62)
-0.924**
(-2.71)
-0.881**
(-2.72)
absent
-0.154
*
-0.268
(-2.12)
(-1.07)
used lecture notes
Random Effects
0.228
*
0.320
-2.02
-1.26
notes*kinesthetic
Auditory
Visual
Kinesthetic
-0.00878
0.105
(-0.05)
-0.58
0.0224
0.117
-0.13
-0.66
-0.0265
0.0734
(-0.15)
-0.4
GPA
0.260
-2.38
0.235*
-2.15
Econ or Engineer Major
0.419**
-2.95
0.422**
-3
Sophmore
0.12
0.108
-0.75
-0.68
-0.167
-0.155
(-0.62)
(-0.58)
-0.0812
-0.0978
(-0.36)
(-0.43)
0.0853
0.105
-0.67
-0.83
0.00912
0.0113
-0.05
-0.06
-0.0336
-0.0327
(-1.20)
(-1.16)
0.326
0.305
-0.69
-0.65
-1.964***
(-7.50)
-1.996***
(-7.50)
*
.Junior
Senior
have job
live on campus
credits enrolled
Constant
lnsig2u
Constant
Observations
4787
4787
4787
4787
Wald Chi2
1.5911
25.7087
11.8253
36.9414
Prob > chi2
0.4513
0.0186
0.0373
0.0021
Log lik.
-2771.5292
-2988.0059
-2766.4121
-2982.2884
Hausman Test Prob > chi2
t statistics in parentheses
0.2643
0.1173
*
p < 0.05,
**
p < 0.01,
***
p < 0.001
16
TABLE 6 Marginal Effects
absent
used lecture notes
notes*Auditory
notes*Visual
notes*Kinesthetic
Model 5 Model 7
Model 1 Model 2 Random Random
Logit
Logit
Effects
Effects
-0.115
-0.091
-0.268
-0.267
0.079
0.084
0.320
1.002
-0.894
-0.817
-0.881
17
REFERENCES
Barsch, J. (1996). Barsch learning style inventory (rev. ed.). Novato, CA: Academic Therapy Publications.
Becker,W. E., and M. Watts. 1996. Chalk and talk: A national survey on teaching undergraduate
economics.American Economic Review 86 (May): 448–53.
Bethune, John. J. 2010, Attendance, Grades and Learning in the Microeconomic Principles Class, Journal of
Economics and Education Research, Vol 11, Number 1,
Boatman, Kara, Courtney, Richard, and Lee, William, 2008, "See How They Learn": The Impact of Faculty and
Student Learning Styles on Student Performance in Introductory Economics, The American Economist Vol. 52,
No. 1 (Spring), 39-48.
Brown, B. W., and C. E. Liedholm. 2002. Can Web courses replace the classroom in principles of
microeconomics? American Economic Review 92 (May): 444–48.
———. 2004. Designing online course materials based on student preferences in using learning resources.
Presentation at the Allied Social Science Association, San Diego, January 5. <http://www.msu.
edu/~brownb/brown-liedholm_aea_2004.pdf> (accessed 2011).
Cox, S.R., 1974, Computer-Assisted Instruction and Student Performance in Macroeconomic Principles,
Journal of Economic Education Fall 29-37
Charkins, R. J., Dennis M. O'Toole,and James N. Wetzel, 1985, Linking Teacher and Student Learning Styles
with Student Achievement and Attitudes, Journal of Economic Education Spring, 111-120
Chen, Jennjou; Lin, Tsui-Fang. 2008a , Class Attendance and Exam Performance: A Randomized Experiment.
Journal of Economic Education, Summer, Vol. 39 Issue 3, p213-227
Chen, Jennjou; Lin, Tsui-Fang. 2008b , Does Downloading PowerPoint Slides Before the Lecture Lead to
Better Student Achievement?. International Review of Economic Education, , p9-18
Coates, D., and B. R. Humphreys. 2003. An inventory of learning at a distance in economics. Social Science
Computer Review 21 (Summer): 196–207.
Coates, D., B. R. Humphreys, J. Kane, and M. Vachris. 2004. “No significant distance” between faceto- face
and online instruction: Evidence from principles of economics. Economics of Education Review 23 (October):
533–46.
Diaz, D. P., & Cartnal, R. B. 1999,. Students' learning styles in two classes: Online distance learning and
equivalent on-campus. College Teaching 47(4), 130-135
Fleming, Neil “Vark Questionaire, A Guide to Learning Styles”. Retrieved 8/2/2012, from http://www.varklearn.com/english/page.asp?p=questionnaire.
Goffe, William L., and Kim Sosin, 2005,. "Teaching with Technology: May You Live in Interesting Times."
Journal of Economic Education 36.3: 278-91.
Grabe, M. 2005, Voluntary use of Online Lecture Notes: Correlates of Note Uand and Note Use as an
Alternative to Class Attendance, Computers and Education, 44, 409-421.
18
Gratton-Lavoie, C. and Stanley, D. 2009, Teaching and Learning Principles of Microeconomics Online: An
Empirical Assessment, Journal of Economic Education, Winter 3-24.
Hall, Chris, 1982, Giving More Choice to Students in Economic Education: Results and Evaluation, Journal of
Economic Education, Winter, 19-31.
Halsne, A.E., Gatta, L.A., 2002, Online versus Traditionally Delivered Instruction: A Descriptive Study of
Learner Characteristics in a Community College Setting, Online Journal of Distance Learning Education, vol V,
Number 1, Spring
Hamadea, R.F., and Artailb, H. A., 2010, A study of the influence of learning style of beginner computer-aided
design users on their performance, Journal of Engineering Design, 21: 5, 561 — 577
Lin, Tsui-Fang, and Jennjou Chen, 2006, Cumulative Class Attendance and Exam Performance, Applied
Economics Letters, Applied Economics Letters, Vol. 13, p937-942.
Marburger, Daniel R.. 2006, Does Mandatory Attendance Improve Student Performance? , Journal of
Economic Education, SpringVol. 37 Issue 2, p148-155
Marburger, Daniel R.. 2001, Absenteeism and Undergraduate Exam Performance Journal of Economic
Education, Spring Vol. 32 Issue 2, p99-109
Romer, D. 1993, Do Student Go to Class? Should They?, Journal of Economic Perspectives, vol 7, (3) 167-174.
Sosin, Kim; Lawson, Robert; Hall, Joshua; Mateer, G. Dirk. 2008,, From Abba to Zeppelin, Led: Using Music
to Teach Economics. Journal of Economic Education, Winter Vol. 39 Issue 1, p107-107, 1p
Terry, N., J. Lewer, and N. Macy. 2003. The efficacy of alternative instruction modes in economics. Journal of
Economics and Economic Education Review 4 (1): 23–34.
Wynn-Williams, K., Whiting, R. H. and Adler, A. 2008, The influence of business case studies on learning
styles; an empirical investigation, Accounting Education: an international journal, 17(1), pp. 227–247.
Lauren Dechant provided the Learning Preference Questionaire
19
APPENDIX
Top of Form
Survey Protocol Attendance
Started: March 31, 2010 5:14 PM
Questions: 28
1-5 Demographics
1.
(Points: 0)
I am
1. Female
2. Male
Save Answer
2.
(Points: 0)
How many years old are you?
1.
Save Answer
20
3.
(Points: 0)
About how long on average, in minutes, is (or would be) your commute (oneway) to campus? (Respond "0" if you live on campus, "999" if more than 120
minutes .)
1.
Save Answer
4.
(Points: 0)
Did you work at a job during this winter term?
1. Yes, I worked full-time
2. Yes, I worked part-time
3. No I did not work at a job
Save Answer
5.
(Points: 0)
On average, approximately how many hours per week did you work at a job
this inter-session? (Respond "0" if you did not work at a job this semester.)
1.
Save Answer
6-9 Academics
6.
21
(Points: 0)
Approximately what is your class level?
1. freshman
2. sophmore
3. junior
4. senior
5. graduate student
Save Answer
7.
(Points: 0)
Which of the following best describes your major?
a. Economics or Business major
b. Math, Science or Engineering major
c. College of Continuing Studies
d. Other Liberal Arts major
e. Other
Save Answer
8.
(Points: 0)
At the begining of the semester approximately what was your GPA?
1.
Save Answer
9.
22
(Points: 0)
How many credits did you enrolled in this semester?
1.
Save Answer
10-11 Attendance
10.
(Points: 0)
Approximately how many classes were you absent from?
1.
Save Answer
11.
(Points: 0)
Did you use the online lecture notes at the class website to make up the missed
classes?
1. I didn't miss any classes
2. Yes
3. No
Save Answer
12-27 Learning
Style Questions
12.
23
(Points: 0)
When you study for a test, would you rather
1. read notes, read headings in a book, and look at diagrams and illustrations.
2. have someone ask you questions, or repeat facts silently to yourself.
3. write things out on index cards and make models or diagrams.
Save Answer
13.
(Points: 0)
Which of these do you do when you listen to music?
1. daydream (see things that go with the music)
2. hum along
3. move with the music, tap your foot, etc.
Save Answer
14.
(Points: 0)
When you work at solving a problem do you
1. make a list, organize the steps, and check them off as they are done
2. make a few phone calls and talk to friends or experts
3. make a model of the problem or walk through all the steps in your mind
Save Answer
15.
24
(Points: 0)
When you read for fun, do you prefer
1. a travel book with a lot of pictures in it
2. a mystery book with a lot of conversation in it
3. a book where you answer questions and solve problems
Save Answer
16.
(Points: 0)
To learn how a computer works, would you rather
1. watch a movie about it
2. listen to someone explain it
3. take the computer apart and try to figure it out for yourself
Save Answer
17.
(Points: 0)
You have just entered a science museum, what will you do first?
1. look around and find a map showing the locations of the various exhibits
2. talk to a museum guide and ask about exhibits
3. go into the first exhibit that looks interesting, and read directions later
Save Answer
18.
25
(Points: 0)
What kind of restaurant would you rather NOT go to?
1. one with the lights too bright
2. one with the music too loud
3. one with uncomfortable chairs
Save Answer
19.
(Points: 0)
Would you rather go to
1. an art class
2. a music class
3. an exercise class
Save Answer
20.
(Points: 0)
Which are you most likely to do when you are happy?
1. grin
2. shout with joy
3. jump for joy
Save Answer
26
21.
(Points: 0)
If you were at a party, what would you be most likely to remember the next day?
1. the faces of the people there, but not the names
2. the names but not the faces
3. the things you did and said while you were there
Save Answer
22.
(Points: 0)
When you see the word "d - o - g", what do you do first?
1. think of a picture of a particular dog
2. say the word "dog" to yourself silently
3. sense the feeling of being with a dog (petting it, running with it, etc.)
Save Answer
23.
(Points: 0)
When you tell a story, would you rather
1. write it
2. tell it out loud
3. act it out
Save Answer
27
24.
(Points: 0)
What is most distracting for you when you are trying to concentrate?
1. visual distractions
2. noises
3. other sensations like, hunger, tight shoes, or worry
Save Answer
25.
(Points: 0)
When you aren't sure how to spell a word, which of these are you most likely to do?
1. write it out to see if it looks right
2. sound it out
3. write it out to see if it feels right
Save Answer
26.
(Points: 0)
What are you most likely to do when you are angry?
1. scowl
2. shout or "blow up"
3. stomp off and slam doors
Save Answer
28
27.
(Points: 0)
Which are you most likely to do when standing in a long line at the movies?
1. look at posters advertising other movies
2. talk to the person next to you
3. tap your foot or move around in some other way
Save Answer
28-28 Outcomes
28.
(Points: 0)
The website for this course has an electronic file of your work for this course.
Would you consent to have this information used for specific research purpose of
determining the correlation between the learning outcomes and course work?
1. YES, I grant consent for the use of the data for the specific research purpose
stated.
2. NO, I do not grant consent for the use of the data for the specific research
purpose stated.
Save Answer
29
NOTES/COMMENTS
Lauren:
(Actually, for many students note-taking does not support retention of material – so the statement here is not
true – also interaction CAN happen, but not necessarily -- in fact – many large lectures do NOT have much
student-instructor, student-student interaction. Also - be careful how you use the word “learning” since
interaction isn’t the same as learning.)
(Lets use learning “preferences” instead of “styles” – it is a more current term in the education world)
Online lecture notes are introduced to provide students with the benefit of a good set of lecture notes to assist
students in studying.