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Classroom activity detection (CAD) aims at accurately recognizing speakerroles (either teacher or student) in classrooms. A CAD solution helps teachersget instant feedback on their pedagogical instructions. However, CAD is verychallenging because (1) classroom conversations contain many conversationalturn-taking overlaps between teachers and students; (2) the CAD model needs tobe generalized well enough for different teachers and students; and (3)classroom recordings may be very noisy and low-quality. In this work, weaddress the above challenges by building a Siamese neural framework toautomatically identify teacher and student utterances from classroomrecordings. The proposed model is evaluated on real-world educational datasets.The results demonstrate that (1) our approach is superior on the predictiontasks for both online and offline classroom environments; and (2) our frameworkexhibits robustness and generalization ability on new teachers (i.e., teachersnever appear in training data).

Siamese Neural Networks for Class Activity Detection