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Siamese Neural Networks for Class Activity Detection
- Publication Year :
- 2020
-
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).<br />Comment: The 21th International Conference on Artificial Intelligence in Education(AIED), 2020
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2005.07549
- Document Type :
- Working Paper