Applying learning analytics to explore the influence of online learners' motivation on their online learning behavioral patterns

Chih-Yuan Sun, Che Tsun Lin, Chien Chou

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

This study aims to apply sequential analysis to explore the effect of learning motivation on online learning behavioral patterns. The study participants were 160 graduate students, who were classified into three group types: low reading duration with low motivation, low reading duration with high motivation, and high reading duration based on a second-order cluster analysis. After performing a sequential analysis, this study revealed that highly motivated learners exhibited a relatively serious learning pattern in a multi-tasking learning environment, and that online reading duration was a significant indicator of seriousness in taking an online course. Finally, relevant recommendations were made to instructors and researchers based on the results of the present study.

Original languageEnglish
Pages (from-to)377-380
Number of pages4
JournalProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
DOIs
StatePublished - 31 Aug 2016
Event5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
Duration: 10 Jul 201614 Jul 2016

Keywords

  • Behavioral pattern
  • Learning analytics
  • Motivation
  • Online learning
  • Sequential analysis

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