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Flipped Classrooms (FC) are a promising teaching strategy, where studentsengage with the learning material before attending face-to-face sessions. Whilepre-class activities are critical for course success, many students struggle toengage effectively in them due to inadequate of self-regulated learning (SRL)skills. Thus, tools enabling teachers to monitor students' SRL and providepersonalized guidance have the potential to improve learning outcomes. However,existing dashboards mostly focus on aggregated information, disregarding recentwork leveraging machine learning (ML) approaches that have identifiedcomprehensive, multi-dimensional SRL behaviors. Unfortunately, the complexityof such findings makes them difficult to communicate and act on. In this paper,we follow a teacher-centered approach to study how to make thorough findingsaccessible to teachers. We design and implement FlippED, a dashboard formonitoring students' SRL behavior. We evaluate the usability and actionabilityof the tool in semi-structured interviews with ten university teachers. We findthat communicating ML-based profiles spark a range of potential interventionsfor students and course modifications.

Visualizing Self-Regulated Learner Profiles in Dashboards: Design Insights from Teachers