Making Machine Learning Findings Accessible to Teachers in Blended Classrooms

Managing blended learning environments, which combine traditional face-to-face and online learning, can be challenging for teachers as it requires adapting and orchestrating both components effectively. Learning analytics dashboards (LAD) can provide teachers with insights into students’ self-studying habits in the online component. While recent advances in machine learning (ML) enable the identification of meaningful behavioral patterns, existing LADs mostly focus on providing aggregated information. Reasons for this are manifold, including the potential lack of trust in ML processes and the intricate nature of their visualizations. In this work, we investigate how to make ML findings accessible to teachers using a teacher-centered, mixed-method approach. We first design multiple visualizations and assess their perceived clarity, appeal, and actionability in a user study with 100 teachers. We then implement these visualizations in a dashboard to monitor students’ self-regulated learning behavior and adapt it to two different learning contexts: Reflective Writing and Flipped Classrooms. We evaluate the effectiveness and applicability of our dashboard through semi-structured interviews with 19 teachers. Our findings suggest that the visualization preferences, requirements, uses, and concerns of LADs differ considerably between the two contexts. Our study contributes to understanding teachers’ design preferences in LADs and the integration of ML-derived insights into classrooms.