Towards Multimodal Learning Analytics of Game-based Collaborative Problem Solving among Primary School Students

Well-designed digital games can serve as the vehicle to assess and support young people' collaborative problem solving (CPS) skills. However, there is limited research leveraging multimodal learning analytics (MmLA) to explore students' game-based CPS processes and outcomes. Inspired by MmLA methods and approaches, this preliminary study aims to examine students' demonstration of CPS skills through collecting and analyzing a dataset of combined game logs and verbal discourses from two groups of primary school students with contrasting performances. Based on the Assessment and Teaching of 21st Century Skills CPS framework, we iteratively coded the dataset. Results of descriptive statistics showed that the successful group exhibited cognitive skills more frequently while the unsuccessful group showcased social skills more. Results of epistemic network analysis (ENA) revealed that, in both social and cognitive dimensions, the successful group demonstrated more diverse and stronger associations among various subskills, whereas there were fewer associations in the unsuccessful group. Implications are drawn for MmLA and CPS research and teaching practices of CPS skills.