Using multimodal analytics to systemically investigate online collaborative problem-solving

Abstract

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.

Keywords:

• collaborative problem-solving (CPS)
• attention
• multimodal learning analytics
• online
• hidden Markov model (HMM)

Disclosure statement

No potential conflict of interest was declared by the authors.

Funding information

This paper was supported by National Natural Science Foundation of China (61877027, 62177020) and CCNU Teaching Education Innovation Research Project (CCNUTEIII 2021-18).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Hengtao Tang, upon reasonable request.

Additional information

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, STEM 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.

Shuoqiu Yang

Shuoqiu Yang received a master’s degree in educational technology from the Nanjing Normal University, China, in 2020. She is currently pursuing a PhD with 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 environments 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 Center for Advanced Analytics and Business Intelligence, Texas Tech University; the Data Mining Laboratory, University of Central Florida; and the National Engineering Laboratory for Educational Big Data, Central China Normal University.

Hao Li

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