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Can the Paths of Successful Students Help Other Students with Their Course Enrollments?

Kerstin Wagner; Agathe Merceron; Petra Sauer; Niels Pinkwart
In: 16th International Conference on Educational Data Mining. International Conference on Educational Data Mining (EDM), Pages 171-182, International Educational Data Mining Society, 2023.


In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design and which is based on the explainable k-nearest neighbor algorithm, recommends a set of courses that have been passed by the majority of the student’s nearest neighbors who have completed their studies. The present evaluation is based on the data of students from three different study programs. One result is that the recommendations do lower the dropout risk. We also discovered that while the recommended courses differed from those taken by students who dropped out, they matched quite well with courses taken by students who completed the degree program. Although the course recommender system targets primarily students at risk, students doing well could use it. Furthermore, we found that the number of recommended courses for struggling students is less than the number of courses they actually enrolled in. This suggests that the recommendations given indicate a different and hopefully feasible path through the study program for students at risk of dropping out.