Investigating Students' Online Learning Behavior with a Learning Analytic Approach: Field Dependence/Independence vs. Holism/Serialism

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  • Author(s): Yang, Tzu-Chi; Chen, Sherry Y. (ORCID Chen, Sherry Y. (ORCID 0000-0002-3321-9362)
  • Language:
    English
  • Source:
    Interactive Learning Environments. 2023 31(2):1041-1059.
  • Publication Date:
    2023
  • Document Type:
    Journal Articles
    Reports - Research
  • Additional Information
    • Availability:
      Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
    • Peer Reviewed:
      Y
    • Source:
      19
    • Education Level:
      High Schools
      Secondary Education
    • Subject Terms:
    • Subject Terms:
    • Accession Number:
      10.1080/10494820.2020.1817759
    • ISSN:
      1049-4820
      1744-5191
    • Abstract:
      Individual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners' learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin's field dependence/independence and Pask's Holism/Serialism share some similarities. To this end, it is necessary to develop a framework to show overlapped behavior between these two cognitive style dimensions. To address this issue, this study used the Lag Sequential Analysis to examine the overlaps between these two cognitive style dimensions from the aspect of online learning behavior. The results from this study indicated that the overlaps mainly appear in comprehensive/local and dynamic/fixed approaches. Based on the findings of this study, we develop a framework that can support the improvement of instruction design so that the needs of different cognitive style groups can be accommodated. Accordingly, this study is an interdisciplinary work, which makes scientific contributions to three communities, i.e. human-computer interaction, digital learning and learning analytic.
    • Abstract:
      As Provided
    • Publication Date:
      2023
    • Accession Number:
      EJ1382122