Related Data for Thesis/Dissertation: Examining the effectiveness of self-referenced and peer-referenced learning analytics dashboards in enhancing students' learning: taking individual differences into account

There is significant interest in harnessing learning analytics (LA) for providing students with personalised formative feedback. However, LA dashboards often employ a one-size-fits all approach instead of considering the learning needs of diverse learners. This study examines the effectiveness of self-referenced and peer-referenced LA dashboards in enhancing positive student outcomes, taking individual differences into account. A quasi-experiment with an embedded mixed methods approach was used with 209 Secondary Three students in Singapore. ANOVAs and ANCOVAs revealed no significant differences between self-referenced and peer-referenced dashboards. Multiple regression analysis revealed individual differences as important predictors of learning outcomes. Epistemic network analysis highlighted the importance of students’ perceptions of how helpful and motivating they found the dashboards to be for their learning. Findings highlight the theoretical and methodological importance of taking individual differences into account and have practical implications for designing more purposeful formative LA dashboards for enhancing students’ learning and well-being.