A Course Recommender System Built on Success to Support Students at Risk in Higher Education

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Published Jun 27, 2024
Kerstin Wagner Agathe Merceron Petra Sauer Niels Pinkwart

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.

How to Cite

Wagner, K., Merceron, A., Sauer, P., & Pinkwart, N. (2024). A Course Recommender System Built on Success to Support Students at Risk in Higher Education. Journal of Educational Data Mining, 16(1), 330–364. https://doi.org/10.5281/zenodo.11384083
Abstract 34 | HTML Downloads 11 PDF Downloads 37

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Keywords

course recommender system, course set recommendation, student success, nearest neighbors, user-centered design, dropout prediction, two-step dropout risk prediction, university records

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Section
Extended Articles from the EDM 2023 Conference

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