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

Document Type : Original Article

Authors

1 Department of Language and Literature,Hakim Sabzevari University

2 Department of Humanity, Faculty of English Language and Literature, Hakim Sabzevari University, Sabzevar, Iran

3 Department of English Teaching and Literature, Faculty of Humanities, Hakim Sabzevari University, Sabzevar, Iran.

4 English Department, Hakim Sabzevari University

Abstract

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. 

Keywords

Main Subjects

References

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Technology Assisted Language Education
Volume 2, Issue 4
December 2024
Pages 1-21

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