Glancee: An Adaptable System for Instructors to Grasp Student Learning Status in Synchronous Online Classes

要旨

Synchronous online learning has become a trend in recent years. However, instructors often face the challenge of inferring audiences' reactions and learning status without seeing their faces in video feeds, which prevents instructors from establishing connections with students. To solve this problem, based on a need-finding survey with 67 college instructors, we propose Glancee, a real-time interactive system with adaptable configurations, sidebar-based visual displays, and comprehensive learning status detection algorithms. Then, we conduct a within-subject user study in which 18 college instructors deliver lectures online with Glancee and two baselines, EngageClass and ZoomOnly. Results show that Glancee can effectively support online teaching and is perceived to be significantly more helpful than the baselines. We further investigate how instructors' emotions, behaviors, attention, cognitive load, and trust are affected during the class. Finally, we offer design recommendations for future online teaching assistant systems.

著者
Shuai Ma
The Hong Kong University of Science and Technology, Hong Kong, China
Taichang Zhou
The Hong Kong University of Science and Technology, Hong Kong, China
Fei Nie
The Hong Kong University of Science and Technology, Hong Kong, China
Xiaojuan Ma
The Hong Kong University of Science and Technology, Hong Kong, China
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517482

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Technology for Classrooms

386
5 件の発表
2022-05-05 18:00:00
2022-05-05 19:15:00