Measuring and Classifying Students' Cognitive Load in Pen-Based Mobile Learning Using Handwriting, Touch Gestural and Eye-Tracking Data
In: British Journal of Educational Technology, Jg. 55 (2024), Heft 2, S. 625-653
Online
academicJournal
Zugriff:
Although the utilization of mobile technologies has recently emerged in various educational settings, limited research has focused on cognitive load detection in the pen-based learning process. This research conducted two experimental studies to investigate what and how multimodal data can be used to measure and classify learners' real-time cognitive load. The results found that it was a promising method to predict learners' cognitive load by analysing their handwriting, touch gestural and eye-tracking data individually and conjunctively. The machine learning approach used in this research achieved a prediction accuracy of 0.86 area under the receiver operating characteristic curve (AUC) and 0.85/0.86 sensitivity/specificity by only using handwriting data, 0.93 AUC and 0.93/0.94 sensitivity/specificity by only using touch gestural data, and 0.94 AUC and 0.94/0.95 sensitivity/specificity by using both the touch gestural and eye-tracking data. The results can contribute to the optimization of cognitive load and the development of adaptive learning systems for pen-based mobile learning.
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Measuring and Classifying Students' Cognitive Load in Pen-Based Mobile Learning Using Handwriting, Touch Gestural and Eye-Tracking Data
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Autor/in / Beteiligte Person: | Li, Qingchuan ; Luximon, Yan ; Zhang, Jiaxin ; Song, Yao |
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Zeitschrift: | British Journal of Educational Technology, Jg. 55 (2024), Heft 2, S. 625-653 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 0007-1013 (print) ; 1467-8535 (electronic) |
DOI: | 10.1111/bjet.13394 |
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