Academic Success Prediction based on Important Student Data Selected via Multi-objective Evolutionary Computation

Nobuhiko Kondo, Takeshi Matsuda, Yuji Hayashi, Hideya Matsukawa, Mio Tsubakimoto, Yuki Watanabe, Shinji Tateishi, Hideaki Yamashita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes an academic success prediction modeling approach that can be used for student advising, in which a multi-objective evolutionary computation approach is applied that automatically selects important explanatory variables suitable to predict academic success and construct multiple predictive models based on machine learning. Numerical experiments using actual student data suggest that it is possible to construct predictive models in considering the trade-off of prediction performance and model interpretability.

Original languageEnglish
Title of host publicationProceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
EditorsTokuro Matsuo, Kunihiko Takamatsu, Yuichi Ono, Sachio Hirokawa
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages370-373
Number of pages4
ISBN (Electronic)9781728173979
DOIs
Publication statusPublished - 2020 Sep
Event9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020 - Kitakyushu, Japan
Duration: 2020 Sep 12020 Sep 15

Publication series

NameProceedings - 2020 9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

Conference

Conference9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020
Country/TerritoryJapan
CityKitakyushu
Period20/9/120/9/15

Keywords

  • academic success
  • interpretability
  • multi-objective evolutionary computation
  • predictive model
  • variable selection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management

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