Enhancing Online Learning: A Multimodal Approach for Cognitive Load Assessment

Abstract

Online learning has become increasingly popular in recent years, but the frequent occurrence of cognitive overload has been notably impacting both the learning experience and effectiveness. Therefore, based on optimizing online learning, this study proposes a research framework for cognitive load assessment of online learning based on three modal data: electroencephalography (EEG), eye tracking, and face. Following this framework, a neural network was used to construct a cognitive load assessment model for online learning that integrates multimodal data. After validation, the assessment accuracy of the model reaches 91.52%. In addition, the results based on multimodal data analysis can be used as a reference for the development of learning resources and the optimization of online courses in intelligent online learning platforms. The assessment model constructed in this study can also be applied to the online learning platform, which is expected to realize prescription-adaptive online learning based on cognitive load assessment. Due to current research limitations, only specific thematic learning models have been explored. Future research will focus on model fine-tuning, complex learning scenarios and themes designing and expansion of research scale to enhance the model’s generalization capabilities.

Keywords:

• Multimodal data
• online learning
• cognitive load
• eye tracking
• EEG signals
• facial action recognition

Acknowledgements

To the peers and reviewers who have provided suggestions on this paper!

Disclosure statement

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

Additional information

Funding

This work was supported by the Natural Science Foundation of Shanghai, China under [Grant 22ZR1421300].

Notes on contributors

Yaofeng Xue

Yaofeng Xue is an Associate Professor in Department of Education Information Technology, Vice Director of Shanghai Engineering Research Center of Digital Educational Equipment, East China Normal University. His research interests are artificial intelligence in education, human-computer interaction.

Kun Wang

Kun Wang is a master’s degree candidate in department of education information technology at East China Normal University. His research interests include human-computer interaction, learning experience optimization, and artificial intelligence education.

Yisheng Qiu

Yisheng Qiu is a master’s degree candidate in department of education information technology at East China Normal University. His research interests include smart instructional systems and AI education, with a specific focus on exploring the application of AI algorithms to enhance student learning through effective instruction