What can multimodal data tell us about online synchronous training: Learning outcomes and engagement of in-service teachers - Xiao, Jun; Jiang, Zhujun; Wang, Lamei; Yu, Tianzhen (Frontiers in Psychology, 2023)
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.