Effects of Fatigue Detection With Adaptive Feedback on Sustained Alertness and Learning Outcomes in Video-Based Learning

IF 4.6 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2025-10-09 DOI:10.1111/jcal.70133

Zeng-Wei Hong, Che-Lun Liang, Ming-Chi Liu

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Abstract

Background

Online video-based learning often leads to fatigue, which detracts from engagement and learning outcomes. Previous studies have examined monitoring mental states like attention through electroencephalography (EEG) headsets, but limitations such as high costs, discomfort, and limited scalability persist.

Objectives

This study evaluates the effectiveness of facial recognition technology in detecting fatigue levels during video-based learning. By using eyelid closure (PERCLOS) and mouth opening percentage (POM) indicators, it aims to provide adaptive feedback that supports engagement and reduces fatigue. Key research questions address the impact on learning outcomes, feedback accuracy, and technology acceptance across different learner groups.

Methods

Three groups were established in an experimental design: an experimental group receiving fatigue-responsive feedback, a control group with random feedback, and a second control group with no feedback. Post-experiment assessments measured learning outcomes, feedback accuracy, and technology acceptance.

Results

Findings reveal that adaptive, fatigue-based feedback significantly enhances engagement and learning outcomes compared to random or no feedback. The experimental group maintained higher alertness in learning, reflected in both quantitative data and learner feedback.

Conclusions

Facial recognition technology offers a scalable and non-intrusive solution to address fatigue in video-based learning. Adaptive feedback based on real-time fatigue detection improves learners' sustained focus, suggesting practical applications for future online education initiatives. Further research is recommended to optimise feedback mechanisms and explore long-term impacts on learning efficacy.

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自适应反馈疲劳检测对视频学习中持续警觉性和学习效果的影响

基于视频的在线学习往往会导致疲劳，从而降低参与度和学习效果。之前的研究已经通过脑电图（EEG）耳机检测了注意力等精神状态，但成本高、不舒服、可扩展性有限等局限性仍然存在。目的本研究评估面部识别技术在视频学习过程中检测疲劳程度的有效性。通过使用眼睑闭合（PERCLOS）和张嘴百分比（POM）指标，旨在提供自适应反馈，支持参与并减少疲劳。关键的研究问题解决了不同学习者群体对学习成果、反馈准确性和技术接受程度的影响。方法采用随机反馈组、随机反馈组和无反馈组，按实验设计分为三组。实验后评估测量了学习成果、反馈准确性和技术接受度。研究结果显示，与随机或无反馈相比，适应性的、基于疲劳的反馈显著提高了参与度和学习效果。实验组在学习中保持了较高的警觉性，这体现在定量数据和学习者反馈两方面。面部识别技术为解决视频学习中的疲劳问题提供了一种可扩展且非侵入性的解决方案。基于实时疲劳检测的自适应反馈提高了学习者的持续注意力，为未来的在线教育计划提供了实际应用。建议进一步研究以优化反馈机制并探索对学习效能的长期影响。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope