Student:
Student number:
Instructor:
Course:
Date:
Karel d’Hond
344473
S. Kapoor
Bachelor Thesis Economics of Markets and Organizations
4-7-2014
1

Many college instructors emphasize the importance of attendance to their students, explaining them that higher attendance is likely to improve their grades. This effect of attendance has been an interesting research point over the last two decades. On this topic Moore (2003) finds a positive correlation of .55 between the percentage of classes attended and the height of their grade as a percentage of the highest possible grade. Since this only indicates a correlation and no causality,
Moore did another experiment in two sections of the same course. In the first section he stressed the importance of the attendance, while he said nothing in the other section. So with the only difference that the students in the stressed group where shown the correlation between attendance and student grades, they went to 10% more of the lectures. The result of this extra attendance was remarkable, the students in the stressed group scored on average more than 15% higher on their test than their control group.
The positive effect of attendance on students’ performance is also proved by studies of Devadoss and Foltz (1996) and Marburger (2001). Devadoss and Foltz did a large scale research into the effect of a dozen different variables on class attendance and the performance of the students. In their study they provide evidence of a positive influence of class attendance on student performance. In
Marburger’s research he looks at the effect of absence during a lecture on a multiple choice question about the subject treated in that specific lecture. In his study he finds that students have a significant lower chance of giving a correct answer on a multiple-choice question that corresponds with the subject treated in a lecture they missed.
Finally one of the largest scaled student attendance researches up till now (Credé, Roch and
Kieszczynka, 2010) finds that attendance is the single best predictor of both the course grades and the average grade (GPA) of students. Their result is especially interesting since they use the data of more than 20 000 students out of 55 prior publication and 18 unpublished databases.
So there is a strong positive correlation between attendance and student grades. Furthermore the first 3 researches also found a causal effect of attendance on student grades. These findings make it easy to assume that obliging attendance improves student results since it will increase attendance.
However obligatory attendance could negatively influence the motivation and attitude of the students. On this topic Romer (1993) stated that research into mandatory tutorials would be valuable for educational researchers.
Other researches on the topic of mandatory tutorials are done by Levine (1992) and by Dobkin, Gill and Marion (2010). Dobkin et al. used a regression discontinuity design to research the effect of lectures on student grades. The discontinuity in his regression came from a policy where students who scored below the median on their midterm were obliged to go to the rest of the lectures of the course. The results of the research were that a mandatory attendance policy increased attendance by 10% and results of the students with mandatory tutorials were indeed higher.
Levine didn’t use a regression discontinuity design, but she compared three student groups, in the first group attendance was required, in the second group attendance wasn’t required although this wasn’t told explicitly to the students. In the last group the attendance wasn’t required either but here it was told to the students in the first lecture. In this study the mandatory attendance policy didn’t have a significant effect. There is no single reason why the results of both studies differ, but the most likely one is that the mandatory class policy in the study of Dobkin et al. only required the
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students to follow one 70 minutes during lecture per week, which is unlikely to create a negative attitude. Other possible reasons for a different result could be the subjects of the courses or the quality of the lecturers.
Since both researches didn’t come to a congruous result and the research of Dobkin et al. is likely to underestimate the effect of a negative attitude since the obligation takes only 70 minutes a week, there still is no good estimate of the effect of mandatory classes. This paper tries to find such an estimate with the use of a regression discontinuity design and data about the students of the
Erasmus School of Economics.
The effect of making tutorials mandatory on education results is interesting both from a scientific point of view and from an economic point of view. From economic point of view the mandatory tutorials are interesting because of the question whether it is an effective measurement to improve education and because of the question whether it is an efficient use of resources. In example, it is possible that the time and money used to give the tutorials could be spent more efficiently on research. The subject is interesting from scientific point of view because the result of this research, although based on a very specific setting, could be used as a stepping stone for similar research in other settings or for other research into educational effectiveness. Similar research could be done everywhere where there are policies of mandatory attendance, for example first aid and safety trainings or letting employees come to the office instead of allowing them to work at home. Other research in educational effectiveness that could be induced is research into the effectiveness in other attendance policies or research into other obligations. For instance giving a bonus to the grade of students with high attendance levels or the obligation to make assignments and hand them in.
The Data
The data used for the research comes from the Erasmus School of Economics and regards the study
Economics and Business Economics. Specifically the data is about all the students that started their study in 2012 and finished their first year in one year, which are 474 students in total. Of the students is known which grades they received for their first year courses and the grades of the four second year courses: International Economics, Fiscal Economics, Finance and Statistics 2. The grades of Statistics 2 however have been deleted from the research since the buildup of the statistics course is different from the other courses. The difference in the statistics course is that a part (20%) of the final grade for the course is determined by assignments that the students make during the tutorials.
Therefore the attendance of the students during the tutorials affects the grade of the students in two ways, which makes it impossible to give a good estimate of the mandatory tutorials effect. If the grades for the assignments and the exam are known, it is possible to use the grade of the exam instead of the grade for the entire course. Distributed over the three remaining courses and the 474 students in total there have been obtained 1332 grades. The grades vary from 1.0 to 10.0 where 10.0 is the highest and 5.5 is the lowest grade that makes the student pass the course. The distributions of the grades per course can be found in Appendix A1. The grades are approximately normal distributed with a mean of 6.4 and a standard deviation of 1.4 (Appendix A2).
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The Courses
For all the courses lectures where given for seven consecutive weeks and an exam was conducted in the eighth week. Both Fiscal and International Economics were given in the months September and
October, while Finance was given in November and December together with another course not treated in this research. Both Finance and International Economics are a course worth eight ECTS 1 which stands for a course load of 224 hours divided over 42 hours of lectures, 28 hours of tutorials and 154 hours of self-study. The lectures last two hours and are given three times a week to all the students together. The tutorials are given two times a week to groups of 20-25 students and also take two hours each. The other course, Fiscal Economics, is worth four ECTS and therefore has a course load of 112 hours. For Fiscal economics the students have two hours of lecture twice a week and in total they have four times two hours of tutorials in the course.
Mandatory Tutorials
If a student has passed all first-year courses and had an average grade of 7.0 or higher, the tutorials of the second year are optional. However if a student didn’t fulfill these conditions he is obliged to follow at least 70 percent of the tutorials per course. If a student attends less than 70 percent of the tutorials he will automatically fail the course and therefore will have to do the course again in the next year. Of all 474 students that passed the first year roughly 62 percent scored below a 7.0 on average in their first year (Appendix A3) and therefore they have mandatory tutorials in the second year. On average grade of all students for their first year was a 6.9 and the range of all students personnal averages was form 5.55 to 9.20 (Appendix A4). Of the 1332 grades received for second year courses, in 61 percent of the times the students had mandatory tutorials (Appendix A5).
This research is about the effect of making tutorials mandatory on student grades. Therefore the treatment will be; being obliged to go to tutorials, opposed to having the choice of going to tutorials.
Whether a student is obliged to go to the tutorials is denoted by the dummy variable T i
where T i
= 0 means the tutorials are not mandatory for student i and T i
= 1 means the tutorials are mandatory for student i. The effect of the treatment is measured as a difference in the grades the students receive for their courses. The grade obtained by student i for course c will be denoted as G ic
. To estimate the effect of the mandatory tutorials on the student results we need to find out the 𝛽
1
in the following regression:
(1) 𝐺 𝑖𝑐
= 𝛽
0
+ 𝛽
1
𝑇 𝑖
+ 𝛽
2
𝑋 𝑖
+ 𝜀 𝑖 where 𝛽
0
is the constant, 𝜀 𝑖
the error term and 𝑋 𝑖
is a composite variable for all personnel characteristics of a student that are of influence on his grade. The problem however is that it is impossible to measure all components of 𝑋 𝑖
. A possible way to solve this problem is to compare students with more or less the same characteristics but with a different treatment. The policy that students will have to finish their first year with at least a 7.0 on average to avoid mandatory tutorials gives us such a unique opportunity. Since the average grade for the first year is built up out of the grades of 10 different courses which should in total represent a full year of work it is plausible to assume that students just below the threshold line of a 7.0 are very close in characteristics to the
1 European Credit Transfer System. ECTS is a system that accounts points to courses in order to make them compareable between universities and countries. 60 ECTS is a full year workload.
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students just above the threshold. Another reason why using the average grade of the first year is very convenient is because the same characteristics (attitude, intelligence etc.) that determine the average grade in the first year are also relevant for the grades in the second year. So comparing the second year grades of students that scored just below the 7.0 for their first year with students that scored just above the 7.0 should give a rough estimate of the effect of the mandatory tutorials.
This rough estimate however isn’t statistical proof of any effect. In order to search for a more precise estimate of the effect that can be statistically proven, it is possible to use a regression discontinuity design in this case. A regression discontinuity design can be used if the assignment to a treatment group is determined by a running variable on either side of the threshold (the cutoff point between the students that are designated to the treatment group and the students designated to the control group). The running variable has to be correlated with the outcome variable in order to create a regression. In this research the average grade a student obtained in his first year will be the running variable and is expressed as A i
. The threshold lies at a 7.0 since this is the point from where the students may choose whether to follow the tutorials or not.
To check whether the treatment has an effect on the outcome, the regression from the running variable on the outcome variable below the threshold will be compared to the regression above it. If the outcome variable takes a jump at the threshold in order to switch from regressions, then there is a discontinuity in the regression and so there is a treatment effect. This means that if the mandatory classes have a positive effect, students with an average grade of 7.00 for their first year will generally have a lower average grade for their second year as students with an average grade of 6.99.
In terms of the potential outcome framework what the regression discontinuity design calculates as effect of mandatory classes on the average grade in the second year is E[G i
(1)|A i
=6.99] –
E[G i
(0)|A i
=7.00]. Combined with the assumption that students with an average grade of 6.99 for their first year are equal to students with an 7.00 for their first year, E[G i
(0)|A i
=6.99] = E[G i
(0)|A i
=7.00], the calculation gives the treatment effect. With 𝛽
0
as a constant and ɛ i
as the error term the associated regression will be:
(2) 𝐺 𝑖𝑐
= 𝛽
0
+ 𝛽
1
𝐴 𝑖
+ 𝛽
2
𝑇 𝑖
+ 𝜀
1
The coefficient β
2
shows the effect of mandatory classes on the student’s grade and T i
is as in the first regression the dummy variable. The variable A i
, the average grade student i scored in his first year, represents the X i
of the first regression. A plausible substitution since the same set of characteristics that influence the average grade of your first year are also of influence on your grades in the second year. To find the best estimate of the coefficient for mandatory tutorials the squared average of the first year grade will also be used in the regression. The new regression will be:
(3) 𝐺 𝑖𝑐
= 𝛽
0
+ 𝛽
1
𝐴 𝑖
+ 𝛽
2
𝐴 𝑖
2
+ 𝛽
3
𝑇 𝑖
+ 𝜀
1
In order to prevent a bias because students are relatively better or worse in the subject of the course than they are on average, it is important to use the grades from several second year courses. By doing this the new outcome variable will be G ic
where G is dependent on the student i and the course
c. Since differences in grades could now be due to the fact that one course is easier than the other or because some lecturers are better than other it is important to add a dummy variable for each course in the regression. These variables will be C IE c
, C FE c
and C Fi c
with:
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𝐶 𝑐
𝐼𝐸 = {
1, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑠 𝑐𝑜𝑢𝑟𝑠𝑒
0, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑛𝑜𝑡 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑠 𝑐𝑜𝑢𝑟𝑠𝑒
𝐶 𝑐
𝐹𝐸 = {
1, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐹𝑖𝑠𝑐𝑎𝑙 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑠 𝑐𝑜𝑢𝑟𝑠𝑒
0, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑛𝑜𝑡 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐹𝑖𝑠𝑐𝑎𝑙 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑠 𝑐𝑜𝑢𝑟𝑠𝑒
𝐶 𝑐
𝐹𝑖
= {
1, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐹𝑖𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑢𝑟𝑠𝑒
0, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑛𝑜𝑡 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐹𝑖𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑢𝑟𝑠𝑒
The new regression will be:
(4) 𝐺 𝑖𝑐
= 𝛽
0
+ 𝛽
1
𝐴 𝑖
+ 𝛽
2
𝐴 𝑖
2
+ 𝛽
3
𝑇 𝑖
+ 𝛽
4
𝐶
𝐼𝐸 𝑐
+ 𝛽
5
𝐶 𝑐
𝐹𝐸
+ 𝛽
6
𝐶
𝐹𝑖 𝑐
+ 𝜀 𝑖
Another possibility with the use of grades from several different courses is that the effect of the mandatory tutorials will be different per course. To sort out these effects per course from the general effect of the mandatory tutorials, an extra dummy for each course will be inserted to function as an interaction term. These variables will be called I
IE ic
, I
FE ic
and I
Fi ic
with as values:
𝐼
𝐼𝐸 𝑖𝑐
1, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑠 𝑐𝑜𝑢𝑟𝑠𝑒 𝑎𝑛𝑑 𝑡ℎ𝑒 𝑠𝑡𝑢𝑑𝑒𝑛𝑡
= { ℎ𝑎𝑑 𝑚𝑎𝑛𝑑𝑎𝑡𝑜𝑟𝑦 𝑡𝑢𝑡𝑜𝑟𝑖𝑎𝑙𝑠
0, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑛𝑜𝑡 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑠 𝑐𝑜𝑢𝑟𝑠𝑒 𝑎𝑛𝑑 𝑜𝑟 𝑡ℎ𝑒
𝑠𝑡𝑢𝑑𝑒𝑛𝑡 ℎ𝑎𝑑 𝑛𝑜 𝑚𝑎𝑛𝑑𝑎𝑡𝑜𝑟𝑦 𝑡𝑢𝑡𝑜𝑟𝑖𝑎𝑙𝑠
𝐼
𝐹𝐸 𝑖𝑐
1, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐹𝑖𝑠𝑐𝑎𝑙 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑠 𝑐𝑜𝑢𝑟𝑠𝑒 𝑎𝑛𝑑 𝑡ℎ𝑒 𝑠𝑡𝑢𝑑𝑒𝑛𝑡 ℎ𝑎𝑑
= { 𝑚𝑎𝑛𝑑𝑎𝑡𝑜𝑟𝑦 𝑡𝑢𝑡𝑜𝑟𝑖𝑎𝑙𝑠
0, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑛𝑜𝑡 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐹𝑖𝑠𝑐𝑎𝑙 𝐸𝑐𝑜𝑛𝑜𝑚𝑖𝑐𝑠 𝑐𝑜𝑢𝑟𝑠𝑒 𝑎𝑛𝑑 𝑜𝑟 𝑡ℎ𝑒 𝑠𝑡𝑢𝑑𝑒𝑛𝑡
ℎ𝑎𝑑 𝑛𝑜 𝑚𝑎𝑛𝑑𝑎𝑡𝑜𝑟𝑦 𝑡𝑢𝑡𝑜𝑟𝑖𝑎𝑙𝑠
𝐼
𝐹𝑖 𝑖𝑐
1, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐹𝑖𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑢𝑟𝑠𝑒 𝑎𝑛𝑑 𝑡ℎ𝑒 𝑠𝑡𝑢𝑑𝑒𝑛𝑡 ℎ𝑎𝑑 𝑚𝑎𝑛𝑑𝑎𝑡𝑜𝑟𝑦
= { 𝑡𝑢𝑡𝑜𝑟𝑖𝑎𝑙𝑠
0, 𝑇ℎ𝑒 𝑔𝑟𝑎𝑑𝑒 𝑖𝑠 𝑛𝑜𝑡 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑖𝑛 𝑡ℎ𝑒 𝐹𝑖𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑢𝑟𝑠𝑒 𝑎𝑛𝑑 𝑜𝑟 𝑡ℎ𝑒 𝑠𝑡𝑢𝑑𝑒𝑛𝑡 ℎ𝑎𝑑 𝑛𝑜 𝑚𝑎𝑛𝑑𝑎𝑡𝑜𝑟𝑦 𝑡𝑢𝑡𝑜𝑟𝑖𝑎𝑙𝑠
The final regression will be:
(5) 𝐺 𝑖𝑐
= 𝛽
0
+ 𝛽
1
𝐴 𝑖
+ 𝛽
2
𝐴 𝑖
2
+ 𝛽
3
𝑇 𝑖
+ 𝛽
4
𝐶
𝐼𝐸 𝑐
+ 𝛽
5
𝐶 𝑐
𝐹𝐸
+ 𝛽
6
𝐶
𝐹𝑖 𝑐
+ 𝛽
7
𝐼
𝐼𝐸 𝑖𝑐
+ 𝛽
8
𝐼
𝐹𝐸 𝑖𝑐
+ 𝛽
9
𝐼
𝐹𝑖 𝑖𝑐
+𝜀 𝑖
Comparing the students close to the threshold, the students with an average grade between a 6.9 and a 7.1 for their first year, shows us a negative correlation between mandatory tutorials and the students’ grades. In this group there were 72 students with mandatory tutorials and 60 students whose tutorials where optional (Appendix B1.1 and B1.2). On average the students with mandatory tutorials had a .29 points lower grade than the “free” students (Appendix B1.3). This is surprising since attendance in general is supposed to have a positive effect on students’ grades while this mandatory tutorials policy, which should increase attendance, has a negative effect.
Using the regression discontinuity design shows us that this negative effect isn’t just a local effect but also holds up for the entire sample size. Table 1 shows us the first two regressions. The independent variables are stated in the left column, the coefficient is stated as the upper number and the significance level as the lower number between bars. Regression A shows that with only the average
6

8,5
8
7,5
7
6,5 grade a student got in his first year and the mandatory tutorials dummy as explanatory variables, there already is a significant negative correlation between mandatory tutorials and the grades. In
Regression B the squared average grade for the first year is implemented as a second estimator for the grade. We can see that the effect still is negative and significant.
Table 1
Constant
Regression A
1.10
(.068)
.79
Regression B
7.33
(.009)
-.91
Average grade 1 st year
(Average grade 1 st year) 2
(.000) (.228)
.12
Student has Mandatory
Tutorials
R squared
-.25
(.042)
.251
(.024)
-.33
(.010)
.254
a.
The dependent variable is the students’ grades b.
The regression with the use of robust standard errors can be found in Appendix B2 c.
Graphical displays of the regressions can be found in Figure 1 and Appendix B3
According to the regressions A and B where the grade of the students is dependent on the average grade in the first year and whether a student has mandatory tutorials, the coefficient for the tutorials should be close to -.25 and -.33. This result is unexpected since the general assumption is that attendance increases your grades. Another point of interest is the height of the effect. Based on these two regressions the effect probably lays close to -0.3, a difference of about 10% of the standard deviation and a difference that would let close to 5% of all students more pass a course.
9
Grade
6
5,5
5
7

7,0
6,5
6,0
5,5
5,0
8,5
8,0
7,5
The results of the regressions with coefficients for the different courses are listed in Table 2. Using the course dummy has a very small influence on mandatory tutorials coefficient. The difference between Regression A and C is that the mandatory tutorials coefficient becomes .001 stronger and
.001 less significant. Between Regression B and D the significance stays the same and the coefficient becomes .002 weaker. Finally the R squared increases with .008. Although adding the course dummy only makes a small difference, it does show that the mandatory tutorials effect isn’t caused because the grades are obtained in different courses.
Constant
Regression C
1.27
(.035)
.79
Regression D
7.46
(.008)
-.90 Average grade 1 st year
(Average grade 1 st year) 2
(.000)
-.29
(.231)
.11
(.024)
-.29 Course = International
Economics
Course = Fiscal Economics
(.000)
-.22
(.000)
-.22
Student has Mandatory
Tutorials
R squared
(.007)
-.25
(.043)
.259
(.007)
-.32
(.010)
.262 a.
The dependent variable is the students’ grades b.
The grades for the Finance course are used as the benchmark for the course dummy c.
The regression with the use of robust standard errors can be found in Appendix B4 d.
Graphical displays of the regressions can be found in Figure 2 and Appendix B5
Figure 2
9,0
International economics
Fiscal
Economics
Finance
8

Constant
Average grade 1
(Average grade 1 st year st year)
Course = International
Economics
2
Regression E
1.22
(.044)
.79
(.000)
-.20
(.126)
Regression F
7.41
(.009)
-.90
(.232)
.11
(.024)
-.20
(.126)
Course = Fiscal Economics -.18
(.161)
-.18
(.161)
Course = International
Economics and Student has
Mandatory Tutorials
Course = Fiscal Economics and student has Mandatory
Tutorials
Student has Mandatory
Tutorials
-.15
(.385)
-.06
(.709)
-.18
-.14
(.388)
-.06
(.705)
-.25
R squared
(.256)
.259
(.111)
.262 a.
The dependent variable is the students’ grades b.
The grades for the Finance course are used as the benchmark for the course dummy c.
The regression with the use of robust standard errors can be found in Appendix B4
Another possibility is that there is interaction between the coefficient of the courses and the coefficient of the mandatory tutorials. This would mean that the effect of making tutorials mandatory differs per course. Regressions E and F check whether this is the case which results are stated in Table 3. The table shows that there is no increase in the R squared, so the new regressions don’t estimate the students’ grades any better than the old ones. However all the coefficients in the new regression are less significant. Therefor it is safe to state that the interaction terms have no effect and that the effect of mandatory tutorials is the same among all the courses.
Possible improvements
External validity
The current research only uses a limited dataset restricted to 475 students and 4 courses. Although it was enough to find a significant and big enough result, the result only reflects the economics students in Rotterdam that started in the year 2012. The consequence of this is that the external validity isn’t very high. An easy way to increase the external validity is using more years of data and a year dummy in the regression. This way the chance that the effect is affected by externalities specific to the year of research decreases. Another relatively simple improvement would be the addition of the other 6 second year courses. Using only three or four courses gives a small selection bias since
9

not every topic of the study is treated. Using these other courses would remove this bias.
Furthermore using more courses gives the chance of redoing the test on interaction terms between the courses and whether a student has mandatory tutorials or not. Other more difficult ways to improve the external validity of the project are using data from different faculties or even different universities. Using other faculties and universities would give two big opportunities. The first opportunity is that if the same results are found on other universities the effect of mandatory tutorials becomes undeniable. The second opportunity is to broaden the research and to compare the effects of different types of tutorial attendance policies.
Difference in attendance
Having access to data on the individual presence of the students would give a better insight in how the effect of mandatory tutorials works. If the data shows that both students with and without tutorials would come equally often to the tutorials, the negative effect of mandatory tutorials is probably caused because students with mandatory tutorials gain a negative attitude towards studying. Therefor they won’t like studying and spend less time doing it resulting in lower grades. The mandatory tutorials could also work as a signaling effect towards the students. Telling students that they won’t have to follow the tutorials obligatory because they are the group with the higher grades would let them know they are the better group which might motivate them to do even better.
Another interesting point of research could be the effect of mandatory tutorials on tutorial attendance. The students with mandatory tutorials have to go to at least 70% of the tutorials which could induce that the obliged students will only come to 70% or 75% of the tutorials while the voluntary students will come 85% or 90% of the time.
However maybe the most interesting and valuable research is whether attendance does have a positive effect on grades in this sample group. In this research the positive effect of attendance on grades is assumed based on previous papers. Although these papers clearly found and described proof for this effect, if the effect is also found in this data it would make the effect of mandatory tutorials even more interesting. To find the effect of attendance on grades regressing students’ grades on attendance is not possible since most of the students have mandatory tutorials. Possible ways to estimate the effect are to either regress the first year grades on the first year attendance or to regress the grades of the second year students without mandatory tutorials on their grades. The problem with the first year students is that every student has mandatory tutorials, so the spread of attendance lies between the 70% and 100% which might not be enough to proof an effect. The problem with the second year students is that all the students are capable of performing above average since they scored higher than a 7.0 on average for their first year. This would cause a selection bias for the effect.
Different Tutorial groups
Another way to improve the research is to look in which tutorial group each student is. The tutorials are given to groups of 20-25 students for two consecutive hours. With a total of 450 students per course this makes it impossible to use one teaching assistant or to give all the tutorials at the same time. However, using different teaching assistants and timeframes per tutorial might be of influence on the effect of mandatory tutorials. Data of in which tutorial group each student is and of on what time and by which TA the tutorials are given would make it possible to control for these factors.
Another possibility with these data is to check for spillover of the mandatory tutorials policy. The students with mandatory tutorials and students for whom the tutorials are optional are together in
10

the tutorials in an undetermined proportion. In case of spillover effects, a tutorial group where half of the students are obliged to come would perform significantly better or worse than a tutorial group where for example 80% of the students are obliged to come.
Further research
Costs of going to tutorials
In the current regression the effect of mandatory tutorials on students’ grades is calculated. However there might be some factors that have a big influence on the effect of mandatory tutorials. One of the biggest and most obvious factors is the costs a student has to make to go to the tutorials. These costs can both be financial costs or the costs of time. When it costs the student more to go to the tutorials it becomes more likely that he will stay at home. Therefor having mandatory tutorials opposed to optional tutorials will have a bigger impact on students with higher costs to go to tutorials.
Interesting research on this topic could be to check whether effect of mandatory tutorials is bigger for students with higher costs of going to tutorials. If this is the case, the effect of mandatory tutorials is probably due to the students having to spend time and resources on going to tutorials while they could spend it more effectively on their own. If the effect of mandatory tutorials is not bigger for students with higher costs of going to tutorials, than the effect of the mandatory tutorials is probably due to the attitude and beliefs of the students.
The main difficulty in this kind of research is that it is impossible to measure the total costs for a student to go to a tutorial. However it is possible to use one or more variables that represent at least some of the costs. Two possible variables could be the travel distance and time of a student to the university and whether a student a job or not. If a student lives far away from the university this will mean that it costs him more time to go to a tutorial and it will cost the student money to pay for his transport. If a student has a job, this will mean that while a student goes to a tutorial he won’t be able to work and therefor induce opportunity costs.
Third year courses
In the current research we look at the effect of mandatory tutorials during the first courses of the second year and with some previously described data it is possible to look at the entire second year.
But this is still only data about the results when the policy is active. In the third year however, there is no difference in attendance policy among the two student groups. Interesting research would be to look at some third year courses and to compare the students that did have mandatory tutorials and the students who didn’t to determine whether there is a lasting effect.
Mandatory attendance in other settings
If mandatory attendance decreases the results of students, this raises the question whether this effect also applies to other settings with mandatory attendance. A common example is that companies require employees to come to an office for their work. Up to some extent this is an attendance policy since it might be able that the employees could work at home. Interesting research that lies in the line of this paper would be to look whether the employees working at home are more productive than the office workers.
11

Other attendance policies
Besides making attendance mandatory there are a lot of other ways to induce the students’ attendance. Possibilities are giving bonus points for attendance, letting students make assignments for grades during the tutorials or treating a question that will come on the exam. Comparing the effect of the different policies could give insights into which policy works and whether any policy works at all.
Prior literature indicated that attendance has a positive effect on the results of university students.
The question raised by Romer (1993) related to this effect was whether classes should be mandatory.
To answer this question it is vital not only to know the effect of increased attendance on student results, but also to know the effect of a mandatory attendance policy. This mandatory attendance effect was estimated by comparing the grades of comparable students with and without mandatory tutorials.
Using a regression discontinuity design and the data of the Erasmus School of Economics, the main result found was that the effect of mandatory tutorials on the students’ grades proved to be negative. On the grades with a scale from 1 to 10 the effect was -.32. In order to determine whether this effect differed per course interaction terms between the tutorials and the individual courses were used but didn’t proof anything.
The outcome of this paper is interesting for policymakers since it proves that mandatory tutorials impede students to reach their full potential. The most interesting topics for follow-up research are using third year courses and the effect of higher costs to go to tutorials. Third year courses could prove whether the effect of mandatory tutorials is only active when the students actually have the tutorials or if there is some lasting effect. Taking the students’ costs of going to tutorials into account could be interesting since it will give some insight into how the effect of the mandatory tutorials works. Furthermore it would be interesting and helpful to know whether the same result can be found in other settings.
12

Credé, Roch, & Kieszczynka. (2010). Class Attendance in College: A Meta-Analytic Review of the
Relationship of Class Attendance With Grades and Student Characteristics. Review of
Educational Research, 272–295.
Devadoss, & Foltz. (1996). Evaluation of Factors Influencing Student Class Attendance and
Performance. American Journal of Agricultural Economics, 499-507.
Dobkin, Gill, & Marion. (2010). Skipping class in college and exam performance: Evidence from a regression discontinuity classroom experiment. Economics of Education Review, 566–575.
Levine. (1992, April). The effect of Different Attendance Policies on Student Attendance and
Achievement. Retrieved 6 23, 2014, from ERIC: http://eric.ed.gov/?id=ED348762
Marburger. (2001). Absenteeism and Undergraduate Exam Performance. The Journal of Economic
Education, 99-109.
Moore, R. (2003). The Importance of Class Attendance for Academic Success in Introductory Science
Courses. The American Biology Teacher, 325-329.
Romer. (1993). Do Sttudents Go to Class? Should They? The Journalof Economic Perspectives, 167-
174.
13

1
2.1
14

2.2
Grade
Valid N (listwise)
N
1332
1332
Mean
6,381
Std. Deviation
1,4019
3
Valid
Average grade 1 st year >= 7
Average grade 1 st year < 7
Total
Frequency
178
296
474
4
N
Percent
37,6
62,4
100,0
Minimum Maximum Mean Std. Deviation av1
5
474
Valid
Optional Tutorials
Mandatory Tutorials
Total
5,55 9,20 6,8907
Frequency
519
813
1332
Percent
39,0
61,0
100,0
,76425
15

Appendix B
1.1
Descriptive Statistics for the students with an average grade between 6.9 and 7.0
N Minimum Maximum Mean Std. Deviation
Grade 72 3,5 8,6 6,501 1,1312
1.2
Descriptive Statistics for the students with an average grade between 7.0 and 7.1
N Minimum Maximum Mean Std. Deviation
Grade 60 3,1 9,5 6,793 1,0229
1.3
2.1
Model
(Constant) av1
Mantut
2.2
Model
(Constant) av1 av1 2
Mantut
Coefficient
1.102
.785
-.248
Robust
Standard Error
.587
.076
.114
Significance 95% Confidence Interval
Lower Upper
.061
.000
.029
-.050
.636
-.471
2.254
.935
-.025
Coefficient
7.333
-.912
.115
-.326
Robust
Standard Error
2.964
.786
.052
.121
Significance 95% Confidence Interval
Lower Upper
.013
.247
.029
.007
1.518
-2.454
.012
-.564
13.148
.631
.217
-.088
16

3
Regression A
9
8,5
8
7,5
7
6,5
6
5,5
5
Grade
4.1
Model
(Constant) av1
Dinterec
Dfiscec
Mantut
4.2
Model
(Constant) av1 av1 2
Dinterec
Dfiscec
Mantut
Coefficient
1.269
.786
-.287
-.220
-.247
Robust
Standard Error
.589
.076
.081
.081
.113
Significance 95% Confidence Interval
Lower Upper
.031
.000
.000
.007
.030
.114
.636
-.447
-.379
-.470
2.425
.935
-.128
-.061
-.024
Coefficient
7.463
-.901
.114
-.286
-.221
-.324
Robust
Standard Error
2.964
.787
.052
.081
.081
.121
Significance 95% Confidence Interval
Lower Upper
.012
.252
.030
.000
.006
.008
1.648
-2.445
.011
-.446
-.380
-.562
13.278
.645
.217
-.127
-.062
-.086
17

5
Regression C
9
7
6,5
6
5,5
5
8,5
8
7,5
International
Economics
Fiscal
Economics
Finance
6.1
Model
(Constant) av1
Dinterec
Dfiscec
MTint
MTfis
Mantut
6.2
Model
(Constant) av1 av1 2
Dinterec
Dfiscec
MTint
MTfis
Mantut
Coefficient
1.224
-.786
-.199
-.182
-.145
-.062
-.177
Robust
Standard Error
.594
.076
.110
.114
.158
.159
.149
Significance 95% Confidence Interval
Lower Upper
.039
.000
.071
.109
.361
.696
.236
.059
.636
-.416
-.406
-.455
-.375
-.469
Coefficient
7.411
-.900
.114
-.199
-.183
-.144
-.063
-.254
Robust
Standard Error
2.969
.787
.052
.110
.114
.158
.159
.155
Significance 95% Confidence Interval
Lower Upper
.013
.254
.030
.071
.109
.363
.692
.102
1.586
-2.444
.011
-.414
-.406
-.454
-.375
-.559
13.237
.645
.217
.017
.041
.166
.249
.051
2.389
.936
.017
.041
.166
.250
.116
18