ABSTRACT Background Online learning has become increasingly prominent in education, necessitating a deeper understanding of its impact on students' cognitive load (CL). Existing studies often focus on single data sources and overlook multimodal assessments, limiting a deeper understanding of how CL and emotional states manifest and interact during online learning. Objectives This study examines the relationship between CL and emotional responses in asynchronous online learning environments to address existing research gaps. Using a multimodal approach, it aims to develop and establish the convergent validity of a composite index for estimating experienced cognitive strain during online learning. Method We analysed the CL of 24 students during online learning sessions on regression analysis and logistic regression. Data sources included false task rate, emotional responses from voice and video data, and brain signal analysis. The comprehensive Cognitive Load Index (CLI) was developed using these measures, with the Analytic Hierarchy Process (AHP) assigning weightings to each data source. Results and Conclusions Our findings indicate significant correlations between CL, emotional responses, and performance metrics. There were strong positive correlations with NASA Task Load Index (NASA‐TLX) scores, providing convergent validity evidence for the proposed index, and with false task rate, indicating that higher cognitive strain is associated with poorer task performance. Notably, emotional responses, particularly negative emotions, served as the strongest modality‐level contributors to the overall CLI. These multimodal data were effectively integrated by CLI, offering a convergent‐valid composite estimate of experienced cognitive workload during online learning. The study provides a methodological foundation for passive, observer‐based CL assessment in online learning environments for educators and instructional designers to enhance online learning experiences.
Rahimi et al. (Fri,) studied this question.