A Comparative Study of Different Information Entropy Index in Personalized Exercise Recommendation

Qiulei Zheng, Huifan Gao, Fan Yang, Yinghui Pan, Yifeng Zeng*

*Corresponding author for this work

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

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Abstract

It is meaningful to recommend exercises to students in an online education system with a large amount of learning resource. Many recommendation methods usually rely on strategy in the recommendation system in order to predict an exercise score. In this paper, we compare different information entropy index in the exercise recommendation. These index consider how well exercise matches the student’s knowledge ability. In order to compare different methods, we introduce an interactive platform for dynamic exercise recommendation. We conduct a set of exercise recommendation experiments, compare the effect of different index and find the optimal index in the experiment.
Original languageEnglish
Title of host publicationICCSE 2021
Subtitle of host publicationIEEE: The 16th International Conference on Computer Science and Education
Place of PublicationPiscataway
PublisherIEEE
Pages1030-1034
Number of pages5
ISBN (Electronic)9781665414685, 9781665414678
ISBN (Print)9781665447546
DOIs
Publication statusPublished - 17 Aug 2021
EventICCSE 2021: The 16th International Conference on Computer Science and Education - Lancaster University, Lancaster, United Kingdom
Duration: 17 Aug 202121 Aug 2021
http://www.ieee-iccse.org/?_v=1625228873586

Publication series

NameProceedings of the International Conference on Computer Science & Education (ICCSE)
PublisherIEEE
ISSN (Print)2471-6146
ISSN (Electronic)2473-9464

Conference

ConferenceICCSE 2021
Country/TerritoryUnited Kingdom
CityLancaster
Period17/08/2121/08/21
Internet address

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