Data-Informed Decision Making with Learning Analytics
This project explores data-informed decision-making in education, specifically focusing on how teachers and students interact with learning analytics to reflect on and adjust their teaching and learning activities. Considering aspects of integration, agency, reference frame and dialogue, the research investigates the cognitive, psychological and sociocultural factors affecting how students and teachers work with data-based tools and the implications of these activities for analytics design. Through careful investigation of how teachers and students approach, make sense of, and (sometimes) act on the information provided by analytic dashboards to inform their teaching and learning, key factors affecting the process are identified. Process models are also used to identify leverage points for tool design and situational enactment that can foster rich use of data-based tools to inform educational decision-making.
Learning Analytics PI
External Collaborators
Yeonji Jung, University of Memphis
Core Publications
Jung, Y., & Wise, A. F. (2024). Probing actionability in learning analytics: The role of routines, timing, and pathways. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 871-877). New York: ACM.
Li, Q., Jung, Y., d’Anjou, B., & Wise, A. F. (2022). Unpacking Instructors’ analytics use: two distinct profiles for informing teaching. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 528-534). New York: ACM.
Wise, A. F., & Jung, Y. (2019). Teaching with analytics: Towards a situated model of instructional decision-making. Journal of Learning Analytics, 6(2), 53-69.