Siamese Neural Networks for Class Activity Detection

• URL: http://arxiv.org/abs/2005.07549v1
• Date: Fri, 15 May 2020 14:03:35 GMT
• Title: Siamese Neural Networks for Class Activity Detection
• Authors: Hang Li, Zhiwei Wang, Jiliang Tang, Wenbiao Ding, Zitao Liu
• Abstract summary: We build a Siamese neural framework to automatically identify teacher and student utterances from classroom recordings. The proposed model is evaluated on real-world educational datasets.
• Score: 49.320548570516124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on their pedagogical instructions. However, CAD is very challenging because (1) classroom conversations contain many conversational turn-taking overlaps between teachers and students; (2) the CAD model needs to be generalized well enough for different teachers and students; and (3) classroom recordings may be very noisy and low-quality. In this work, we address the above challenges by building a Siamese neural framework to automatically identify teacher and student utterances from classroom recordings. The proposed model is evaluated on real-world educational datasets. The results demonstrate that (1) our approach is superior on the prediction tasks for both online and offline classroom environments; and (2) our framework exhibits robustness and generalization ability on new teachers (i.e., teachers never appear in training data).

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