Course Recommender Systems with Statistical Confidence

Zachary Warnes, Evgueni Smirnov

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

Selecting courses in an open-curriculum education program is a difficult task for students and academic advisors. Course recommendation systems nowadays can be used to reduce the complexity of this task. To control the recommendation error, we argue that course recommendations need to be provided together with statistical confidence. The latter can be used for computing a statistically valid set of recommended courses that contains courses a student is likely to take with a probability of at least 1-e for a user-specified significance level e. For that purpose, we introduce a generic algorithm for course recommendation based on the conformal prediction framework. The algorithm is used for implementing two conformal course recommender systems. Through experimentation, we show that these systems accurately suggest courses to students while maintaining statistically valid sets of courses recommended.
Original languageEnglish
Title of host publicationProceedings of the 13th International Conference on Educational Data Mining, EDM 2020, Fully virtual conference, July 10-13, 2020
EditorsAnna N. Rafferty, Jacob Whitehill, Cristobal Romero, Violetta Cavalli-Sforza
PublisherInternational Educational Data Mining Society
Pages509-515
Number of pages7
ISBN (Electronic)9781733673617
Publication statusPublished - 1 Jan 2020
Event13th International Conference on Educational Data Mining, EDM 2020 - Virtual, Montrea, Canada
Duration: 10 Jul 202013 Jul 2020
https://educationaldatamining.org/edm2020/

Conference

Conference13th International Conference on Educational Data Mining, EDM 2020
Abbreviated titleEDM2020
Country/TerritoryCanada
CityMontrea
Period10/07/2013/07/20
Internet address

Keywords

  • Conformal Prediction
  • Course Recommendation
  • Recommender Systems

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