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With the goal of supporting real-time AI-based agents to facilitate student collaboration, as well as to enable educational data-mining of group discussions, multimodal classroom analytics, and social network analysis, we investigate how to identify who-is-where-when in classroom videos. We take a person re-identification ( re-id ) approach, and we explore different methods of improving re-id accuracy in the challenging environments of school classrooms. Our results on a multi-grade classroom (MGC) dataset suggest that (1) fine-tuning off-the-shelf person re-id models such as AGW can deliver sizable accuracy gains (from 70.4\\% to 76.7\\% accuracy); (2) clustering, rather than nearest-neighbor identification, can yield accuracy improvements (76.7\\% to 79.4\\%) of identifying each detected person, especially when structural constraints are imposed; and (3) there is a strong benefit to re-id accuracy in obtaining multiple enrollment images from each student.