Options
Varying impacts: The role of student self-evaluation in navigating learning analytics
Citation
Li, Q., Zhou, X., Xu, D., Baker, R. B., & Holton, A. J. (2024). Varying impacts: The role of student self-evaluation in navigating learning analytics. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (pp. 535-538). Association for Computing Machinery. https://doi.org/10.1145/3657604.3664715
Abstract
The enthusiasm for student-facing analytics as tools for supporting student self-regulation is overshadowed by uncertainties about their actual impact on student outcomes. This study aims to fill the gap in experimental evidence concerning student-facing analytics by implementing a randomized control trial. Specifically, we investigated the effects of data visualizations that display student level of content mastery in comparison to their peers, alongside recommendations for learning strategies. The preliminary results reveal that the intervention impacts student attribution and motivation in varying ways, based on their self-evaluation of their current course performance. Further analysis, including coding students' interpretation of the data visualization, will be conducted to uncover the diverse ways students might interpret the analytics.
Date Issued
2024
ISBN
9798400706332 (online)
Publisher
Association for Computing Machinery
Grant ID
FG20825
Funding Agency
National Science Foundation, United States of America