Course selection is a crucial and challenging problem that students have to face while navigating through an undergraduate degree program. The decisions they make shape their future in ways that they cannot conceive in advance. Available departmental sample degree plans are not personalized for each student, and personal discussion time with an academic advisor is usually limited. Data-driven methods supporting decision making have gained importance to empower student choices and scale advice to large cohorts. We propose Scholars Walk, a random-walk-based approach that captures the sequential relationships between the different courses. Based on the "wisdom of the crowd" and the students' prior courses, we recommend a short list of courses for next semester. Our experimental evaluation illustrates that Scholars Walk outperforms other collaborative filtering and popularity-based approaches. At the same time, our framework is very efficient, easily interpretable, while also being able to take into consideration important aspects of the educational domain.
|Original language||English (US)|
|Title of host publication||EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining|
|Editors||Collin F. Lynch, Agathe Merceron, Michel Desmarais, Roger Nkambou|
|Publisher||International Educational Data Mining Society|
|Number of pages||6|
|State||Published - 2019|
|Event||12th International Conference on Educational Data Mining, EDM 2019 - Montreal, Canada|
Duration: Jul 2 2019 → Jul 5 2019
|Name||EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining|
|Conference||12th International Conference on Educational Data Mining, EDM 2019|
|Period||7/2/19 → 7/5/19|
Bibliographical noteFunding Information:
This work was supported in part by NSF (1447788, 1704074, 1757916, 1834251), Army Research Office (W911NF1810344), Intel Corp, and the Digital Technology Center at the University of Minnesota. Access to research and computing facilities was provided by the Digital Technology Center and the Minnesota Supercomputing Institute.
© EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining. All rights reserved.
Copyright 2020 Elsevier B.V., All rights reserved.
- Course recommendation
- Higher education
- Markov chains
- Random walks
- Sequential recommendation