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Feedback in University Education
Initial registration date
July 17, 2019
June 26, 2020 3:29 AM EDT
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University of Erlangen-Nuremberg
Other Primary Investigator(s)
Additional Trial Information
It is of public interest that students achieve high returns from public education and perform well in exams. In a field experiment, I study the impact of randomized pre-exam test feedback on exam performance in an undergraduate macroeconomic class. Before and after the test participants reveal beliefs regarding their individual effort-performance relationship. Only students in the treatment group receive feedback on their performance in the test before the second belief elicitation. In addition, I elicit students’ self-confidence regarding their performance in the test. I can measure each student’s consecutively exerted study effort by tracking her activity on the e-study platform. Finally, I link the experiment and platform data to the students’ exam grades. In doing so, I can examine to what extent the exogenous feedback treatment shifted students’ believed effort-performance relationship, their exerted study effort and their final exam performance.
I provide randomized feedback on a pre-exam test to undergraduate university students. I compare shifts in beliefs about expected exam performance (for a constant level of effort), actual exerted study effort and actual exam performance between students that received feedback on the pre-exam test to those that did not receive any feedback. The feedback has a debiasing character regarding participants’ believed individual effort-performance relationship and/or their self-confidence and should therefore influence their study decisions (i.e. exerted effort) and outcomes (i.e. performance in the exam).
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Belief elicitation of each student’s self-confidence.
Elicitation of beliefs about individual effort-performance relationship and shifts therein.
Difference in actually exerted study effort on the platform (between treatment and control group).
Difference in actual exam performance (between treatment and control group).
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Heterogeneity analyses (with respect to personality traits, A-levels GPA etc.)
Secondary Outcomes (explanation)
All students that signed up for an undergraduate macroeconomics exam are invited to visit a newly implemented e-study-platform with exam preparation material. In order to access the platform, students need to participate in a short test on exam relevant material. Before that test, students reveal beliefs about their assumed individual effort-performance relationship, e.g. how much they believe they need to study in order to achieve their desired grade.
After the test, students guess how well they performed in it. Next, only students in the treatment group receive feedback on their test performance before once more beliefs on individual effort-performance relationship are elicited. Next, students get access to the e-study platform. The platform consists of an overview page with some instructions and hyperlinks to the lessons 1 to 11. Higher lessons are only unlocked if all respectively lower lessons have been completed by providing answers to all the containing exercises. I monitor each student’s activity on the platform closely. In doing so, not only the number of completed lessons but also the intensity of the use of the platform can serve as an objective measure of study effort in the analyses. Finally, I link the experiment and platform data to the students’ exam grades. I can therefore examine to what extent the feedback treatment shifted students’ believed effort-performance relationship, their exerted study effort and their final exam performance. In addition, I can determine students’ self-confidence regarding test performance.
Experimental Design Details
Randomization done in office using Stata. Randomization into treatment and control takes place before students arrive on the platform.
Leading up to the exam, students have the option to hand in solved exercises and receive feedback afterwards. I make sure that the number of students who handed in material is balanced between treatment and control group. Furthermore, I also stratify on students’ A-levels grade point average.
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
Symmetric sample sizes between treatment and control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
It was not possible to run pilots. Therefore, standard deviations of outcome variables could not be estimated. Consequently, no power calculation is possible.
INSTITUTIONAL REVIEW BOARDS (IRBs)
Ethics Commission of the School of Business and Economics of University of Erlangen-Nuremberg
IRB Approval Date