Siamese Neural Networks for Class Activity Detection

Hang Li; Zhiwei Wang; Jiliang Tang; Wenbiao Ding; Zitao Liu

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arXiv · 2005.07549

Siamese Neural Networks for Class Activity Detection

Hang Li·Zhiwei Wang·Jiliang Tang·Wenbiao Ding·Zitao Liu

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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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Hang Li, Zhiwei Wang, Jiliang Tang, Wenbiao Ding, Zitao Liu. 2020-05-15. Siamese Neural Networks for Class Activity Detection. https://arxiv.org/abs/2005.07549

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