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Drop-out in programming courses – prediction and prevention

Anja Hawlitschek (Hochschule Magdeburg-Stendal, Magdeburg, Germany)
Veit Köppen (Otto-von-Guericke-Universität, Magdeburg, Germany)
André Dietrich (Otto-von-Guericke-Universität, Magdeburg, Germany)
Sebastian Zug (Technische Universität Bergakademie Freiberg, Freiberg, Germany)

Journal of Applied Research in Higher Education

ISSN: 2050-7003

Article publication date: 31 July 2019

Issue publication date: 17 January 2020



An ideal learning analytics tool for programming exercises performs the role of a lecturer who monitors the code development, provides customized support and identifies students at risk to drop out. But a reliable prediction and prevention of drop-out is difficult, due to the huge problem space in programming tasks and variety of solutions and programming strategies. The purpose of this paper is to tackle this problem by, first, identifying activity patterns that indicate students at risk; and, second, finding reasons behind specific activity pattern, for identification of instructional interventions that prevent drop-out.


The authors combine two investigation strategies: first, learning analytic techniques (decision trees) are applied on features gathered from students, while completing programming exercises, in order to classify predictors for drop-outs. Second, the authors determine cognitive, motivational and demographic learner characteristics based on a questionnaire. Finally, both parts are related with a correlation analysis.


It was possible to identify generic variables that could predict early and later drop-outs. For students who drop out early, the most relevant variable is the delay time between availability of the assignment and the first login. The correlation analysis indicates a relation with prior programming experience in years and job occupation per week. For students who drop out later in the course, the number of errors within the first assignment is the most relevant predictor, which correlates with prior programming skills.


The findings indicate a relation between activity patterns and learner characteristics. Based on the results, the authors deduce instructional interventions to support students and to prevent drop-outs.



This work was embedded in the “Industrial-eLab” project and it is partially funded by the German Federal Ministry of Education and Research (Funding number 16DHL1033).


Hawlitschek, A., Köppen, V., Dietrich, A. and Zug, S. (2020), "Drop-out in programming courses – prediction and prevention", Journal of Applied Research in Higher Education, Vol. 12 No. 1, pp. 124-136.



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