Analysing the Key Enablers of Students' Readiness for Online Learning: An Interpretive Structural Modeling Approach

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  • Author(s): Bhatt, Shahir; Bhatt, Amola; Thanki, Shashank
  • Language:
    English
  • Source:
    International Journal of Education and Development using Information and Communication Technology. 2021 17(4):105-130.
  • Publication Date:
    2021
  • Document Type:
    Journal Articles
    Reports - Research
  • Additional Information
    • Availability:
      International Journal of Education and Development using Information and Communication Technology. University of the West Indies Open Campus, Cavehill, Bridgetown, Barbados, BB11000, West Indies. e-mail: [email protected]; Web site: http://ijedict.dec.uwi.edu/
    • Peer Reviewed:
      Y
    • Source:
      26
    • Education Level:
      Higher Education
      Postsecondary Education
    • Subject Terms:
    • Subject Terms:
    • ISSN:
      1814-0556
    • Abstract:
      Education has undergone a paradigm shift due to the COVID-19 outbreak declared as a pandemic by the WHO in 2020. The current research attempts to identify the key factors which enable students' readiness for online learning (SROL). The Interpretive Structural Modelling (ISM) approach is used to draw contextual relationships among the enablers of SROL and these are further clustered using Matrix of Cross-impact Multiplications (MICMAC) analysis. Personality traits like 'Open to experience', 'Agreeableness' and 'Extraversion' emerged as driving enablers while; several factors like academic performance, prior exposure to online classes, self-efficacy in online settings, learner control, self-directed learning and motivation for learning emerged as linkage enablers which would ultimately affect the 'willingness for future exposure to online classes'. The understanding of these enablers can help instructors to customize their online course delivery and counsel students based on their levels of readiness for online learning.
    • Abstract:
      As Provided
    • Publication Date:
      2022
    • Accession Number:
      EJ1335754