A contextual bandits framework for personalized learning action selection

Andrew S. Lan, Richard G. Baraniuk

Research output: Contribution to conferencePaper

16 Scopus citations

Abstract

Recent developments in machine learning have the potential to revolutionize education by providing an optimized, personalized learning experience for each student. We study the problem of selecting the best personalized learning action that each student should take next given their learning history; possible actions could include reading a textbook section, watching a lecture video, interacting with a simulation or lab, solving a practice question, and so on. We first estimate each student’s knowledge profile from their binary-valued graded responses to questions in their previous assessments using the SPARFA framework. We then employ these knowledge profiles as contexts in the contextual (multi-armed) bandits framework to learn a policy that selects the personalized learning actions that maximize each student’s immediate success, i.e., their performance on their next assessment. We develop two algorithms for personalized learning action selection. While one is mainly of theoretical interest, we experimentally validate the other using a real-world educational dataset. Our experimental results demonstrate that our approach achieves superior or comparable performance as compared to existing algorithms in terms of maximizing the students’ immediate success.

Original languageEnglish (US)
Pages424-429
Number of pages6
StatePublished - Jan 1 2016
Event9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States
Duration: Jun 29 2016Jul 2 2016

Other

Other9th International Conference on Educational Data Mining, EDM 2016
CountryUnited States
CityRaleigh
Period6/29/167/2/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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