Publication
A Course Recommender System Built on Success to Support Students at Risk in Higher Education.
Kerstin Wagner; Agathe Merceron; Petra Sauer; Niels Pinkwart
In: Journal of Educational Data Mining (JEDM), Vol. 16, No. 1, Pages 330-364, International Educational Data Mining, 2024.
Abstract
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, is based on the explainable k-nearest neighbor algorithm and recommends a set of courses that have been passed by the majority of successful neighbors, that is, students who graduated from the study program. In terms of the number of recommended courses, we found a discrepancy between the number of courses that struggling students are recommended to take and the actual number of courses they take. This indicates that there may be an alternative path that these students could consider. However, the recommended courses align well with the courses taken by students who successfully graduated. This suggests that even students who are performing well could still benefit from the course recommender system designed for at-risk students. In the present work, we investigate a second type of success—a specific minimum number of courses passed—and compare the results with our first approach from previous work. With the second type, the information about success might be already available after one semester instead of after graduation which allows faster growth of the database and faster response to curricular changes. The evaluation of three different study programs in terms of dropout risk reduction and recommendation quality suggests that course recommendations based on students passing at least three courses in the following semester can be an alternative to guide students on a successful path. The aggregated result data and results explorations are available at: https://kwbln.github.io/jedm23.