Application of educational data mining approach for student academic performance prediction using progressive temporal data

Ruangsak Trakunphutthirak, Vincent C.S. Lee

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)


Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting student academic performance. The key aims of this study are fourfold. First, is to fill the educational literature gap by applying machine learning techniques in educational data mining, making use of the Internet usage behaviour log files and LMS data. Second, LMS data and Internet usage log files were analysed with machine learning techniques for predicting at-risk-of-failure students, with greater explanation added by combining student demographic data. Third, the demographic features help to explain the prediction in understandable terms for educators. Fourth, the study used a range of Internet usage data, which were categorized according to type of usage data and type of web browsing data to increase prediction accuracy.

Original languageEnglish
Pages (from-to)742-776
Number of pages35
JournalJournal of Educational Computing Research
Issue number3
Publication statusPublished - 2022


  • at-risk students
  • educational data mining
  • log files
  • machine learning techniques

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