Measuring and classifying students' cognitive load in pen-based mobile learning using handwriting, touch gestural and eye-tracking data

Li, Qingchuan1; Luximon, Yan2; Zhang, Jiaxin3; Song, Yao4,5

2023-09-01

10.1111/bjet.13394

BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY   Impact Factor & Quartile

0007-1013

1467-8535

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.

eye-tracking handwriting multimodal learning analytics pen-based mobile learning touch gestural

SSCI

English

Others

This research is supported by the National Natural Science Foundation of China under grant [grant number 62207008] and the Shenzhen Educational Science Planning Project under grant [grant number zdfz20015].[62207008] ; National Natural Science Foundation of China[zdfz20015]

Education & Educational Research

Education & Educational Research

WOS:001075783400001

WILEY

SOCIAL SCIENCES, GENERAL

Web of Science

1.Harbin Inst Technol, Sch Humanities & Social Sci, Design Apartment, Shenzhen, Peoples R China
2.Hong Kong Polytech Univ, Sch Design, Hung Hom, Hong Kong, Peoples R China
3.Southern Univ Sci & Technol, Sch Syst Design & Intelligent Mfg, Shenzhen, Peoples R China
4.Sichuan Univ, Coll Literature & Journalism, Chengdu, Peoples R China
5.Sichuan Univ, Digital Convergence Lab Chinese Cultural Inheritan, Chengdu, Peoples R China

Li, Qingchuan,Luximon, Yan,Zhang, Jiaxin,et al. Measuring and classifying students' cognitive load in pen-based mobile learning using handwriting, touch gestural and eye-tracking data[J]. BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY,2023.

Li, Qingchuan,Luximon, Yan,Zhang, Jiaxin,&Song, Yao.(2023).Measuring and classifying students' cognitive load in pen-based mobile learning using handwriting, touch gestural and eye-tracking data.BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY.

Li, Qingchuan,et al."Measuring and classifying students' cognitive load in pen-based mobile learning using handwriting, touch gestural and eye-tracking data".BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY (2023).