Mapping primary students’ mobile collaborative inquiry-based learning behaviours in science collaborative problem solving via learning analytics

Yanjie SONG, Jiaxin CAO, Yin YANG, Chee-Kit LOOI

Research output: Contribution to journalArticlespeer-review

Abstract

This article reports on a study focusing on understanding how primary students conducted collaborative inquiry-based learning (CIBL) supported by a mobile app during the COVID-19 pandemic when all lessons were conducted online. Learning analytics (LA) were used to map students’ behaviours in CIBL activities. One class with 35 students in Grade 4 participated in this study. Log data was collected and analysed using learning analytics with process mining techniques to understand groups’ CIBL behaviours in a mobile learning environment. The findings revealed high- and low-performance groups’ common and different features of CIBL behaviours. The research findings can help inform both teachers of making pedagogical refinement in the CIBL activity design, and researchers of developing scaffolding tools at different phases of CIBL on the mobile learning app to enhance students’ collaborative problem-solving skills. Copyright © 2022 Elsevier Ltd. All rights reserved.

Original languageEnglish
Article number101992
JournalInternational Journal of Educational Research
Volume114
Early online dateMay 2022
DOIs
Publication statusPublished - 2022

Citation

Song, Y., Cao, J., Yang, Y., & Looi, C.-K. (2022). Mapping primary students’ mobile collaborative inquiry-based learning behaviours in science collaborative problem solving via learning analytics. International Journal of Educational Research, 114. Retrieved from https://doi.org/10.1016/j.ijer.2022.101992

Keywords

  • Collaborative inquiry-based learning (CIBL)
  • Science
  • Learning analytics
  • High- and low-performance groups
  • M-orchestrate app
  • Collaborative problem-solving
  • PG student publication

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