The study aims to utilise multimodal learning analytics with artificial intelligence (AI) to investigate regulation in collaborative learning. This multidisciplinary study bridges learning sciences, affective computing, information systems, and AI research for developing novel methodologies and advancing understandings of regulatory processes in collaborative learning. AI deep learning models will be designed and applied on multimodal data consisting of video, audio, self-reports, and physiological data (electrodermal activities and heartrates). The findings of this study will establish critical foundations for extending the boundaries of theory building and testing in learning sciences, especially for learning regulation research. The outcomes will also help educational technologists and developers design effective learning analytics solutions and tools for teachers for supporting regulation in collaborative learning and facilitate multidisciplinary collaboration.

2022

2025