Self-regulated learning strategies and student video engagement trajectory in a video-based asynchronous online course: a Bayesian latent growth modeling approach

Dongho Kim, Il Hyun Jo, Donggil Song, Hua Zheng, Jingwei Li, Jiawen Zhu, Xing Huang, Wei Yan, Zhen Xu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The self-paced nature of asynchronous online learning (AOL) is recognized as an obstacle that disrupts student success in the learning environment. Without on-time interventions provided by instructors, students find it challenging to use learning strategies tailored to the learning environment, and their use of self-regulated learning (SRL) strategies has been regarded one of the key indicators of success in AOL. To examine how student SRL strategies are associated with their video engagement trajectory and learning outcomes, we used student video engagement data collected at multiple time points. Participants were 159 students who were taking a self-paced asynchronous online statistics course. Results revealed that student video engagement was found to increase over time and student management strategies contributed to the upward change. We also found that the growth of engagement predicted student achievement in the course. Our findings shed light on instructional strategies to support students in AOL contexts.

Original languageEnglish
Pages (from-to)305-317
Number of pages13
JournalAsia Pacific Education Review
Volume22
Issue number2
DOIs
StatePublished - Jun 2021

Keywords

  • Asynchronous online learning
  • Bayesian latent growth modeling
  • Self-regulated learning
  • Video-based learning

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