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Assessing the validity of a learning analytics expectation instrument: A multinational study

  • Alex Whitelock‐Wainwright*
  • , Dragan Gasevic
  • , Yi-Shan Tsai
  • , H.J. Drachsler
  • , M. Scheffel
  • , Pedro J. Muñoz-Merino
  • , Kairit Tammets
  • , Carlos Delgado Kloos
  • *Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    6   Link opens in a new tab Citations (Web of Science)

    Abstract

    To assist higher education institutions in meeting the challenge of limited student engagement in the implementation of Learning Analytics services, the Questionnaire for Student Expectations of Learning Analytics (SELAQ) was developed. This instrument contains 12 items, which are explained by a purported two-factor structure of “Ethical and Privacy Expectations” and “Service Feature Expectations.” As it stands, however, the SELAQ has only been validated with students from UK university, which is problematic on account of the interest in Learning Analytics extending beyond this context. Thus, the aim of the current work was to assess whether the translated SELAQ can be validated in three contexts (an Estonian, a Spanish, and a Dutch University). The findings show that the model provided acceptable fits in both the Spanish and Dutch samples, but was not supported in the Estonian student sample. In addition, an assessment of local fit is undertaken for each sample, which provides important points that need to be considered in future work. Finally, a general comparison of expectations across contexts is undertaken, which are discussed in relation to the General Data Protection Regulation (2018).

    Original languageEnglish
    Pages (from-to)209-240
    Number of pages32
    JournalJournal of Computer Assisted Learning
    Volume36
    Issue number2
    DOIs
    Publication statusPublished - Apr 2020

    Keywords

    • COVARIANCE STRUCTURE-ANALYSIS
    • PRINCIPLES
    • STRUCTURAL EQUATION MODELS
    • STUDENT PERCEPTIONS
    • SUPPORT
    • Student expectations
    • learning analytics
    • multinational
    • questionnaire

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