About: Siamese Neural Networks for Class Activity Detection

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Siamese Neural Networks for Class Activity Detection
Creator	Li, Hang Wang, Zhiwei Tang, Jiliang Ding, Wenbiao Liu, Zitao
Source	PMC
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).
has issue date	2020-06-10(xsd:dateTime)
bibo:doi	10.1007/978-3-030-52240-7_30
has license	no-cc
sha1sum (hex)	5220a6f03549570bba5a0b8f69c87196684a8dd3
schema:url	https://doi.org/10.1007/978-3-030-52240-7_30
resource representing a document's title	Siamese Neural Networks for Class Activity Detection
has PubMed Central identifier	PMC7334716
schema:publication	Artificial Intelligence in Education
resource representing a document's body	covid:5220a6f03549570bba5a0b8f69c87196684a8dd3#body_text
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