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<p>Learning analytics (LA) can improve student outcomes and support institutional decision-making in higher education. However, its adoption remains limited in developing countries due to concerns about trust, privacy, and institutional readiness. This study addresses this gap by examining the factors that influence LA adoption from a multi-stakeholder perspective. It combines elements from the SHEILA policy framework and the Unified Theory of Acceptance and Use of Technology (UTAUT). A survey of 488 students and faculty members from three Saudi universities was conducted. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test eight hypotheses and analyze direct and indirect relationships. The results show that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly affect behavioral intention and LA adoption. Behavioral intention also plays a mediating role. These findings offer practical insights for institutions aiming to improve adoption through aligned policies, technical infrastructure, and stakeholder engagement. The integrated SHEILA–UTAUT model provides a validated and adaptable framework for guiding LA implementation in similar settings.</p>

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Enhancing institutional readiness: A Multi-Stakeholder approach to learning analytics policy with the SHEILA-UTAUT framework using PLS-SEM

  • 期刊论文

  • 发表日期：

    2025年6月16日

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  • Riyaz Sheikh Abdullah,
  • Feisal Hadi Masmali,
  • Abdulrahman Alhazemi,
  • Choo Wou Onn,
  • Syed Md Faisal Ali Khan

摘要

Learning analytics (LA) can improve student outcomes and support institutional decision-making in higher education. However, its adoption remains limited in developing countries due to concerns about trust, privacy, and institutional readiness. This study addresses this gap by examining the factors that influence LA adoption from a multi-stakeholder perspective. It combines elements from the SHEILA policy framework and the Unified Theory of Acceptance and Use of Technology (UTAUT). A survey of 488 students and faculty members from three Saudi universities was conducted. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test eight hypotheses and analyze direct and indirect relationships. The results show that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly affect behavioral intention and LA adoption. Behavioral intention also plays a mediating role. These findings offer practical insights for institutions aiming to improve adoption through aligned policies, technical infrastructure, and stakeholder engagement. The integrated SHEILA–UTAUT model provides a validated and adaptable framework for guiding LA implementation in similar settings.