Mobile learning provides more flexibility and holds considerable promise for improving the learning process and promoting lifelong learning. In order to reduce the sense of isolation felt by the learners, this research integrates mobile learning in the blended synchronous learning environment (BSLE). This study proposed a mobile learning model in BSLE at Shanghai Open University, and 51 students' satisfaction and engagement surveys were examined. The results showed that student satisfaction with instruction and with interaction can significantly predict behavioral engagement, while satisfaction with instruction and with technology can significantly predict psychological engagement. The findings prove that the mobile learning mode in the blended synchronous learning environments is effective and contributes to the predictors of student engagement. Thus, it can provide some insights to construct a more flexible and effective learning space.

Teachers’ engagement in online learning is a key factor in improving the effectiveness of online teacher training. This paper introduces a multimodal learning analytics approach that uses data on brain waves, 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 at 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 promoting online teacher professional development.

The affiliation for Zhujun Jiang given in this article as originally published was incorrect. That affiliation has been replaced with the following affiliation: