Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials

Research output: Contribution to journalArticlepeer-review

Abstract

In this study, we propose an integrated system to support learners' reviews. In the proposed system, the review dashboard is used to recommend review contents that are adaptive to the individual learner's level of understanding and to present other information that is useful for review. The pages of the digital learning materials that are estimated to be insufficiently understood by each learner and the web pages related to those pages are recommended. As a method for estimating such pages, we consider extracting the pages related to the questions that were answered incorrectly. We examined the accuracy of matching each question with the pages of the learning materials. We also conducted an experiment to verify the usefulness of the system and its effect on learning using a review dashboard. In the experiment, the evaluation of the review dashboard indicated that at least half of the participants found it useful for most types of feedback. In addition, the rate of change in quiz scores was significantly higher in the group using the review dashboard, which indicates that using the review dashboard has the effect of improving learning.

Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Learning Technologies
DOIs
Publication statusAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Education
  • Engineering(all)
  • Computer Science Applications

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