Exploring online students’ self-regulated learning with self-reported surveys and log files: a data mining approach

Moon-Heum Cho, Jin Soung Yoo

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Many researchers who are interested in studying students’ online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students’ SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students’ online SRL patterns with the use of data mining techniques. We examined both self-reported self-regulation surveys and log files to predict online students’ achievements and found using log files was more powerful in predicting students’ achievements in an online course than self-reported survey data. Discussions to enhance teaching and learning practices with the use of data mining are provided.

Original languageEnglish (US)
Pages (from-to)970-982
Number of pages13
JournalInteractive Learning Environments
Volume25
Issue number8
DOIs
StatePublished - Nov 17 2017
Externally publishedYes

Keywords

  • Data mining
  • log files
  • online learning
  • self-regulated learning
  • self-reported surveys

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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