A Multimodal Alerting System for Online Class Quality Assurance
This work addresses quality assurance for online education platforms in China, where teaching resources are scarce, by providing a practical monitoring solution.
The paper tackles the problem of ensuring education quality in online one-on-one classes by developing a multimodal alerting system that monitors instructors using banned word detection and class quality prediction, achieving 74.3% alerting accuracy in a production environment.
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.