Personalized and Explainable Course Recommendations for Students at Risk of Dropping outKerstin Wagner; Agathe Merceron; Petra Sauer; Niels Pinkwart
In: Antonija Mitrovic; Nigel Bosch (Hrsg.). Proceedings of the 15th International Conference on Educational Data Mining. International Conference on Educational Data Mining (EDM), July 24-27, Dorham, United Kingdom, Pages 657-661, ISBN 978-1-7336736-3-1, International Educational Data Mining Society, 7/2022.
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 recommendations after the 1st and the 2nd semester using historical data. The evaluation reveals that the recommendations correspond to the actual courses passed by students who graduated, whereas the recommendations 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.