A Multimodal Alerting System for Online Class Quality Assurance
September 01, 2019 Β· Declared Dead Β· π International Conference on Artificial Intelligence in Education
"No code URL or promise found in abstract"
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Authors
Jiahao Chen, Hang Li, Wenxin Wang, Wenbiao Ding, Gale Yan Huang, Zitao Liu
arXiv ID
1909.11765
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI
Citations
13
Venue
International Conference on Artificial Intelligence in Education
Last Checked
4 months ago
Abstract
Online 1 on 1 class is created for more personalized learning experience. It demands a large number of teaching resources, which are scarce in China. To alleviate this problem, we build a platform (marketplace), i.e., \emph{Dahai} to allow college students from top Chinese universities to register as part-time instructors for the online 1 on 1 classes. To warn the unqualified instructors and ensure the overall education quality, we build a monitoring and alerting system by utilizing multimodal information from the online environment. Our system mainly consists of two key components: banned word detector and class quality predictor. The system performance is demonstrated both offline and online. By conducting experimental evaluation of real-world online courses, we are able to achieve 74.3\% alerting accuracy in our production environment.
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