Personalized and Explainable Course Recommendations for Students at Risk of Dropping out

This paper presents a course recommender system designed to support students who are struggling in their first semesters of university and who are at risk of dropping out. Considering the needs expressed by our students, we recommend a set of courses that have been passed by the majority of their nearest neighbors who have successfully graduated. We describe this recommender system, which is based on the explainable k-Nearest Neighbors algorithm, and evaluate the 2nd and 3rd semester recommendations using historical data. The evaluation reveals that the recommendations correspond to the actual courses passed by students who graduated, whereas the rec-ommendations and actually passed courses differ for students who dropped out. The recommendations show to struggling students a different, ambitious, but hopefully feasible way through the study program. Furthermore, a dropout prediction confirms that students are less likely to drop out when they pass the courses recommended to them