Research on a quantification model of online learning cognitive load based on eye-tracking technology

被引：0

作者：

Xue Y. ^{[1
,2
]}

Zhu F. ^{[1
]}

Li J. ^{[1
]}

机构：

[1] Department of Educational Information Technology and Shanghai Engineering Research Center of Digital Educational Equipment, East China Normal University, Shanghai

[2] Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, East China Normal University, Shanghai

来源：

Multimedia Tools and Applications | 2025年 / 84卷 / 18期

基金：

上海市自然科学基金;

关键词：

Cognitive load; Eye-tracking technology; Online learning; Quantification model;

D O I：

10.1007/s11042-024-19814-4

中图分类号：

学科分类号：

摘要：

Online learning is characterized by a high degree of complexity and a wealth of information when compared to traditional classroom learning. This can have an adverse influence on the learning outcomes of online learners. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. The paper builds a quantification model of online learning cognitive load based on non-invasive eye-tracking technology by combining three eye-movement indicators: fixation time, fixation count, and pupil diameter. This is based on the analysis of cognitive load and eye-tracking technology. The study then uses a significant amount of eye movement experimental data in conjunction with the cognitive load test that students take in an online learning environment to confirm the viability and effectiveness of the quantification methodology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

引用

页码：18993 / 19007

页数：14

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