Enhancing Students’ Online Self-Regulation through Learning Analytics: Students' Expectations

This study aimed to enhance the online self-regulation skills of TEFL students by incorporating insights from highly self-regulated learners—a significantly informative yet neglected cohort in LA literature—into the design of Learning Analytics (LA) to promote effective online self-regulated learning (SRL). Current LA research often emphasises technical aspects over pedagogical insights. To address this gap, we included three distinctive features to move beyond the current literature: a pedagogical lens, a retrospective design with a pragmatism framework for interpreting the results, and the purposive sampling of highly self-regulated students. Semi-structured interviews and reflective journals were used as the instruments, and a thematic analysis was conducted on the data through Nvivo. The analysis yielded three main themes: technology integration and dashboard customisation, human intervention and collaboration, personalised learning, feedback, and recommendations. The findings highlight implications for educational practices, policies, and LA design, emphasising the need to view students as active, research-oriented participants.

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Keywords: Learning Analytics ، Self-regulation ، Students’ Expectations ، Online Learning