Understanding college students’ cognitive engagement in online collaborative problem-solving: A multimodal data analysis

Abstract

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.

Keywords:

• collaborative problem-solving
• multimodal analytics
• electroencephalogram
• cognitive engagement
• online
• post-pandemic

Disclosure statement

No potential conflict of interest was declared by the author(s).

Data availability statement

The data that support the findings of this study is available from Miao Dai and Xu Du upon reasonable request.

Additional information

Funding

This paper was supported by the National Key R&D Program of China (2021ZD0110702) and the National Science Foundation of China (61937001, 62177020) awarded to Xu Du.

Notes on contributors

Hengtao Tang

Hengtao Tang is an assistant professor in the Department of Educational Studies at the University of South Carolina. His research interests include learning analytics; self-regulated learning; science, technology, engineering, and mathematics education; and open educational resources.

Miao Dai

Miao Dai is a PhD candidate at Central China Normal University, China. Her research interests include machine learning, deep learning, and educational data mining.

Xu Du

Xu Du is currently a professor in the National Engineering Research Center for E-Learning at Central China Normal University, China. His research interests include smart environment and mobile learning, resource scheduling and recommendation, machine learning, and educational data mining

Jui-Long Hung

Jui-Long Hung is a professor in the Department of Educational Technology, Boise State University and a researcher in the National Engineering Laboratory for Educational Big Data, Central China Normal University. His research interests include educational data and text mining and learning analytics.

Hao Li

Hao Li is an associate professor in the National Engineering Research Center for E-Learning at Central China Normal University, China.