Relations between student online learning behavior and academic achievement in higher education: A learning analytics approach

Il Hyun Jo, Taeho Yu, Hyeyun Lee, Yeonjoo Kim

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

The purpose of this study is to suggest more meaningful components for learning analytics in order to help learners to improve their learning achievement continuously through an educational technology approach. 41 undergraduate students in a women’s university in South Korea participated in this study. The seven-predictor model was able to account for 99.3% of the variance in the final grade, F(8, 32) = 547.424, p <. 001, R2 =.993. Total login frequency in LMS, (ir)regularity of learning interval in LMS, and total assignments and assessment composites had a significant (p <.05) correlation with final grades. However, total studying time in LMS (β =.038, t =.868, p >.05), interactions with content (β = −.004, t = −.240, p >.05), interactions with peers (β =.015, t =.766, p >.05), and interactions with instructor (β =.009, t =.354, p >.05) did not predict final grades. The results provide a rationale for the treatment for student time management effort.

Original languageEnglish
Pages (from-to)275-287
Number of pages13
JournalLecture Notes in Educational Technology
Issue number9783662441879
DOIs
StatePublished - 2015

Keywords

  • E-learning
  • Educational technology
  • Higher education
  • Learning analytics

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