Laboratory experience is critical to foster college students’ collaborative problem-solving (CPS) abilities, but whether students stay cognitively engaged in CPS tasks during online laboratory sessions remains unknown. This study applied multimodal data analysis to examine college students’ (N = 36) cognitive engagement in CPS during their online experimentation experience. Groups of three collaborated on CPS tasks via shared worksheets and computer-based simulations on videoconferences. Portable electroencephalogram instruments were used to determine students’ levels of cognitive engagement in CPS activities. The multimodal data analysis (e.g., electroencephalogram, surveys, and artifacts) results showed a significant difference in students’ cognitive engagement between different phases of CPS. The students’ cognitive engagement significantly differed between groups who did and did not complete the task. Additionally, intrinsic motivation predicted students’ cognitive engagement in the completion groups while self-efficacy was the primary predictor of cognitive engagement for the groups who did not complete the task.

The purpose of this research was to apply multimodal learning analytics in order to systemically investigate college students’ attention states during their collaborative problem-solving (CPS) in online settings. Existing research on CPS relies on self-reported data, which limits the validity of the findings. This study looked at data in a systemic manner by collecting and analyzing multimodal data including electroencephalogram data, knowledge tests and video recordings. The study found students’ attention was positively correlated to their knowledge gains. Also, students’ attention varied across different conditions of collaborative patterns as the highest attention level was recorded in the centralized condition. A hidden Markov model was then applied to explain the difference across various conditions by identifying both the hidden states and the transitions among the states during CPS. The findings of this research advanced theoretical insights and provided practical implications on understanding and supporting CPS in online college-level courses.

Blended learning has been widely integrated in college-level computer science education. Despite evidence about benefits of blended learning, students’ in-class activities remain underexplored. To afford effective blended learning experience, supporting students in both modalities is essential. This study thus took an initial step to fill the gap by investigating college students’ in-class activities in a blended course from the perspective of attention. Using non-intrusive electroencephalography (EEG) instruments to collect attentional data, this study found students’ attention in in-class activities positively correlated with their learning gains. Students’ attention also varied across in-class activities, reaching a higher level in group discussions than in pre-tests and lectures. Linear regression analysis indicated students’ length of time spent viewing online resources and their pre-test scores significantly predicted their in-class attention. The findings of the study provide insight into course design and facilitation for effective blended computer science courses.