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The widespread shift in higher education (HE) from in-person instruction to pre-recorded video lectures means that many instructors have lost access to real-time student feedback for the duration of any given lecture (a ‘sea of faces’ that express struggle, comprehension, etc.). We hypothesized that this feedback could be partially restored by analyzing student facial movement data gathered during recorded lecture viewing and visualizing it on a common lecture timeline. Our approach builds on computer vision research on engagement and affect in facial expression, and education research on student struggle. Here, we focus on individual student struggle (the effortful attempt to grasp new concepts and ideas) and its group-level visualization as student feedback to support human instructors. Research suggests that instructor supported student struggle can help students develop conceptual understanding, while …