Effects of Fatigue Detection With Adaptive Feedback on Sustained Alertness and Learning Outcomes in Video‐Based Learning.
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 scal...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 6; pp. 1 - 16 |
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| Autores principales: | , , |
| Formato: | computer program equations & formulas pictorial research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2025
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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=189524194&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189524194 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Dec2025 vid: 41 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 189524194 189524194 189524194 10.1111/jcal.70133 189524194 ppf: 1 ppct: 15 formats: tig: atl: Effects of Fatigue Detection With Adaptive Feedback on Sustained Alertness and Learning Outcomes in Video‐Based Learning. aug: au: Hong, Zeng‐Wei Liang, Che‐Lun Liu, Ming‐Chi affil: Department of Information Engineering and Computer Science, Feng Chia University, Taichung City, Taiwan sug: subj: Videorecording Learning Methods Fatigue Diagnosis Biometrics Biofeedback Outcomes of Education Evaluation Attention Evaluation Validity Human Student Attitudes Fatigue Prevention and Control Experimental Studies Male Female Students, Undergraduate Psychosocial Factors Random Assignment Randomized Controlled Trials Surveys Pretest-Posttest Design Interviews Kruskal-Wallis Test Post Hoc Analysis Analysis of Covariance Mann-Whitney U Test Pearson's Correlation Coefficient Mental Fatigue Risk Factors Funding Source Questionnaires Male Female ab: 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. pubtype: Academic Journal doctype: computer program equations & formulas pictorial research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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