Recommendation System for Adaptive Learning

Yunxiao Chen, Xiaoou Li, Jingchen Liu, Zhiliang Ying

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each st...
Original languageEnglish (US)
Pages (from-to)014662161769795
JournalApplied Psychological Measurement
DOIs
StatePublished - 2017

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Learning
learning
Vanilla
Psychometrics
psychometrics
video
Students
instruction
Technology
Costs and Cost Analysis
classroom
costs
experience
student

Keywords

  • Gittins index
  • Markov decision process
  • adaptive learning
  • c-μ rule
  • hidden Markov model
  • multiarmed bandit problem
  • stochastic scheduling

PubMed: MeSH publication types

  • Journal Article

Cite this

Recommendation System for Adaptive Learning. / Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang.

In: Applied Psychological Measurement, 2017, p. 014662161769795.

Research output: Contribution to journalArticle

Chen, Yunxiao ; Li, Xiaoou ; Liu, Jingchen ; Ying, Zhiliang. / Recommendation System for Adaptive Learning. In: Applied Psychological Measurement. 2017 ; pp. 014662161769795.
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