Measuring Cognitive Load Index in Online Learning: A Multi‐Modal Approach.
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 emot...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 3; pp. 1 - 19 |
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| Autores principales: | , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194050896&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194050896 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Jun2026 vid: 42 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 194050896 194050896 194050896 10.1002/jcal.70252 194050896 ppf: 1 ppct: 18 formats: tig: atl: Measuring Cognitive Load Index in Online Learning: A Multi‐Modal Approach. aug: au: Rahimi, Fatema Sadeghi‐Niaraki, Abolghasem Hussain, Jamil Song, Houbing Wang, Huihui Choi, Soo‐Mi affil: Department of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea sug: subj: Cognition Learning Methods Online Education Student Attitudes Evaluation Emotions Funding Source Human Male Female Adult Adolescence Descriptive Statistics Logistic Regression Scales Criterion-Related Validity Questionnaires Quantitative Studies Coefficient alpha Pearson's Correlation Coefficient Paired T-Tests Adult: 19-44 years Adolescent: 13-18 years Male Female ab: 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. Practitioner Notes: What is already known about this topic ○E‐learning is a prevalent educational method, but persistent challenges like low learner engagement and high dropout rates remain significant concerns.○Cognitive load (CL) and emotional states—specifically negative affective responses—are key factors that impact learning efficiency and outcomes.○Traditional assessment of CL often relies on single data sources or retrospective self‐reports, which may fail to capture the dynamic, multidimensional nature of mental effort.What this paper adds ○This study introduces a Comprehensive Cognitive Load Index (CLI) that integrates objective performance data (false task rate) with negative emotional responses derived from audio, video, and brain signals (EEG).○This research utilizes the Analytic Hierarchy Process (AHP) to assign expert‐informed, transparent weights to different data modalities.○Findings reveal that negative emotions are the strongest modality‐level contributors to the overall estimation of a learner's cognitive strain.○The CLI demonstrated strong convergent validity through significant correlations with established benchmarks like the NASA Task Load Index (NASA‐TLX).Implications for practice and/or policy ○Affect‐Aware Design: Educators and instructional designers should prioritize emotional engagement, as negative emotions compete for the same limited working memory resources required for learning.○Passive Monitoring: The study provides a methodological foundation for passive, observer‐based assessment, allowing for real‐time monitoring of cognitive strain without interrupting the learning process. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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