  Flipped Classrooms (FC) are a promising teaching strategy, where students
engage with the learning material before attending face-to-face sessions. While
pre-class activities are critical for course success, many students struggle to
engage effectively in them due to inadequate of self-regulated learning (SRL)
skills. Thus, tools enabling teachers to monitor students' SRL and provide
personalized guidance have the potential to improve learning outcomes. However,
existing dashboards mostly focus on aggregated information, disregarding recent
work leveraging machine learning (ML) approaches that have identified
comprehensive, multi-dimensional SRL behaviors. Unfortunately, the complexity
of 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 findings
accessible to teachers. We design and implement FlippED, a dashboard for
monitoring students' SRL behavior. We evaluate the usability and actionability
of the tool in semi-structured interviews with ten university teachers. We find
that communicating ML-based profiles spark a range of potential interventions
for students and course modifications.
