A Multimodal Alerting System for Online Class Quality Assurance

TL;DR

This paper presents a multimodal alerting system for online 1-on-1 classes that monitors instructor quality using banned word detection and class quality prediction, achieving 74.3% accuracy in real-world settings.

Contribution

It introduces a novel multimodal monitoring system combining banned word detection and quality prediction for online education quality assurance.

Findings

Achieved 74.3% alerting accuracy in real-world online courses.

Built a platform enabling college students to serve as part-time instructors.

Demonstrated effectiveness of the system both offline and online.

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.