What can multimodal data tell us about online synchronous training: Learning outcomes and engagement of in-service teachers

Frontiers in Psychology

article

Jun Xiao¹  Zhujun Jiang²  Lamei Wang¹  Tianzhen Yu¹ 

[1] Shanghai Engineering Research Center of Open Distance Education, Shanghai Open University;Department of Educational Technology, School of Education, Shanghai Normal University

关键词: engagement;    Eye-tracking;    Facial Expression;    EEG;    In-service teacher training;   

DOI  :  10.3389/fpsyg.2022.1092848

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

This paper introduces a multimodal learning analytics approach that uses data on brain wave, eye movements and facial expressions to predict in-service teachers’ engagement and learning outcomes in online synchronous training. This study analyzed to what extent the unimodal and multimodal data obtained from the in-service teachers (n=53) predict their learning outcomes and engagement. The results show that models using facial expressions and eye movements data had the best predictive performance on learning outcomes. The performance varied on teachers’ engagement: the multimodal model (integrating eye movements, facial expressions, and brain wave data) was best at predicting cognitive engagement and emotional engagement, while the one (integrating eye movements and facial expressions data) performed best in predicting behavioral engagement. At last, we applied the models to the four stages of online synchronous training and discussed changes in the level of teacher engagement. The work helps understand the value of multimodal data for predicting teachers’ online learning process and promote online teacher professional development.

【 授权许可】

CC BY