A study on prediction of academic performance based on current learning records of a language class using blended learning

Byron Sanchez, Xiumin Zhao, Takashi Mitsuishi, Terumasa Aoki

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

Abstract

In this paper, we describe a classification method that does not rely on historic data to predict changes in student academic performance, and therefore predict if a student will fail a class or not. By classifying students into groups given their grades, and extracting the common features in between them, it is possible to use those common features to predict if other students that share common characteristics will fall into the same classification groups. As well, those same common features can be used to help students improve their academic performance.

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings
EditorsAhmad Fauzi Mohd Ayub, Antonija Mitrovic, Jie-Chi Yang, Su Luan Wong, Wenli Chen
PublisherAsia-Pacific Society for Computers in Education
Pages493-495
Number of pages3
ISBN (Print)9789869401265
Publication statusPublished - 2017 Jan 1
Event25th International Conference on Computers in Education, ICCE 2017 - Christchurch, New Zealand
Duration: 2017 Dec 42017 Dec 8

Publication series

NameProceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings

Other

Other25th International Conference on Computers in Education, ICCE 2017
CountryNew Zealand
CityChristchurch
Period17/12/417/12/8

Keywords

  • Feature selection
  • K-Means clustering
  • Learning analytics
  • Student performance prediction
  • Unsupervised learning

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Information Systems
  • Hardware and Architecture
  • Education

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