Scholars walk: A Markov chain framework for course recommendation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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 languageEnglish (US)
Title of host publicationEDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining
EditorsCollin F. Lynch, Agathe Merceron, Michel Desmarais, Roger Nkambou
PublisherInternational Educational Data Mining Society
Pages396-401
Number of pages6
ISBN (Electronic)9781733673600
StatePublished - 2019
Externally publishedYes
Event12th International Conference on Educational Data Mining, EDM 2019 - Montreal, Canada
Duration: Jul 2 2019Jul 5 2019

Publication series

NameEDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining

Conference

Conference12th International Conference on Educational Data Mining, EDM 2019
CountryCanada
CityMontreal
Period7/2/197/5/19

Bibliographical note

Funding 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.

Publisher Copyright:
© EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining. All rights reserved.

Keywords

  • Course recommendation
  • Higher education
  • Markov chains
  • Random walks
  • Sequential recommendation

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