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Comparative Analysis of Eye Tracking study on Mental Stress in Online Learning.
https://doi.org/10.34020/2073-6495-2026-2-065-081
Abstract
This paper addresses the problem of detecting cognitive fatigue in online learning environments using eye-tracking data. The proposed approach combines statistical analysis (Pearson correlation and one-way ANOVA) with machine learning classification. Based on a publicly available dataset containing 2,784 observations with 41 eye-tracking parameters (including pupil diameter, fixation duration, time to first fixation, and peak saccade velocity), the study classifies fatigue levels into three categories: low, moderate, and high. Three classifiers are compared: Naïve Bayes, k -Nearest Neighbors, and Random Forest. The results show that Random Forest achieves the highest performance with 87 % accuracy, 86.6 % recall, and 86.9 % F1-score. A strong negative correlation ( r = –0.74) is revealed between pupil diameter and fatigue level, indicating pupil constriction as a reliable fatigue marker. Limitations associated with remote webcam-based eye-tracking (lower sampling rate and reduced accuracy compared to lab-grade equipment) are discussed. The findings confirm the feasibility of real-time cognitive fatigue monitoring for adaptive e-learning systems that adjust instructional content based on the learner’s mental state.
About the Author
I. H. Ajayi
IТMO University
Russian Federation
Russian Federation
Ajayi Irety Hope - Postgraduate, Faculty of Software Engineering and Computer Systems
Saint Petersburg
References
1. Baragash R.S., & Al Samarraie H. Blended learning: Investigating the influence of engagement in multiple learning delivery modes on students’ performance. Telematics and Informatics. 2018. 35 (7). P. 2082–2098.
2. Jarodzka H., Janssen N., Kirschner P.A., & Erkens G. Avoiding split attention in com-puter-based testing: Is neglecting additional information facilitative? British journal of educational technology. 2015. 46 (4). P. 803–817.
3. Reed H.C. E-learning fatigue and the cognitive, educational, and emotional impacts on communication sciences and disorders students during COVID-19. Perspectives of the ASHA Special Interest Groups. 2022. 7 (6). P. 1885–1902.
4. Mayer R.E. Incorporating motivation into multimedia learning. Learning and instruction. 2014. 29. P. 171–173.
5. Rayner K. The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search. Quarterly journal of experimental psychology. 2009. 62 (8). P. 1457–1506.
6. Ajayi I.H., & Avksentyeva E. Machine Learning Methods for Predicting Cognitive Load in eLearning Environments using Eye Tracking Data. Pakistan Journal of Life & Social Sciences. 2024. 22 (2).
7. El Haddioui I. Eye Tracking Applications for E-Learning Purposes: An Overview and Perspectives. Cognitive Computing in Technology-Enhanced Learning. 2019. P. 151–174.
8. Bisogni C., Nappi M., Tortora G., & Del Bimbo A. Gaze Analysis: A survey on its applications. Image and Vision Computing. 2024. 144. P. 104961.
9. Liu P.L., Chen C.J., & Chang Y.J. Effects of a computer-assisted concept mapping learning strategy on EFL college students’ English reading comprehension. Computers & Education. 2010. 54 (2). P. 436–445.
10. Van Gog T., Paas F., Marcus N., Ayres P., & Sweller J. The mirror neuron system and observational learning: Implications for the effectiveness of dynamic visualizations. Educational Psychology Review. 2009. 21. P. 21–30.
11. Benedetto S., Drai-Zerbib V., Pedrotti M., Tissier G., & Baccino T. E-readers and visual fatigue. PloS one. 2013. 8 (12). P. e83676.
12. Bertrand J.K. The Utility of Webcam Eye-Tracking: Lessons Learned and Practical Applications in Decision-Making and Digital Human-Computer Interactions. 2023.
13. Anwyl-Irvine A.L., Massonnié J., Flitton A., Kirkham N., & Evershed J.K. Gorilla in our midst: An online behavioral experiment builder. Behavior research methods. 2020. 52. P. 388–407.
14. Joseph A.W., Jeevitha Shree D.V., Saluja K.P.S., Mukhopadhyay A., Murugesh R., & Biswas P. (2021, January). Eye tracking to understand impact of aging on mobile phone applications. In International Conference on Research into Design (pp. 315–326). Singapore: Springer Singapore.
15. Seeber I., Bittner E., Briggs R.O., De Vreede T., De Vreede G.J., Elkins A., ... & Söllner M. Machines as teammates: A research agenda on AI in team collaboration. Information & management. 2020. 57 (2). P. 103174.
16. Marelli L., Testa G., & Van Hoyweghen I. Big Tech platforms in health research: Re-purposing big data governance in light of the General Data Protection Regulation’s research exemption. Big Data & Society. 2021. 8 (1), 20539517211018783.
17. Wu C., Cha J., Sulek J., Zhou T., Sundaram C.P., Wachs J., & Yu D. Eye-tracking metrics predict perceived workload in robotic surgical skills training. Human factors. 2020. 62 (8). P. 1365–1386.
18. Beatty J. Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychological bulletin. 1982. 91 (2). P. 276.
19. Hess E.H., & Polt J.M. Pupil size in relation to mental activity during simple problem-solving. Science. 1964. 143 (3611). P. 1190–1192.
20. Yang X., & Krajbich I. Webcam-based online eye-tracking for behavioral research. Judgment and Decision making. 2021. 16 (6). P. 1485–1505.
21. Fernández-Delgado M., Cernadas E., Barro S., & Amorim D. Do we need hundreds of classifiers to solve real world classification problems? The journal of machine learning research. 2014. 15 (1). P. 3133–3181.
22. Sweller J. Cognitive load during problem solving: Effects on learning. Cognitive science. 1988. 12 (2). P. 257–285.
23. Zargari Marandi R., Fjelsted C.A., Hrustanovic I., Dan Olesen R., & Gazerani P. Eye movements in response to pain-related feelings in the presence of low and high cognitive loads. Behavioral Sciences. 2020. 10 (5). P. 92.
24. Adeshina Y.O., Deeds E.J., & Karanicolas J. Machine learning classification can reduce false positives in structure-based virtual screening. Proceedings of the National Academy of Sciences. 2020. 117 (31). P. 18477–18488.
25. Jiao Y., Chen X., Sun Z., Fu L., Jiang C., Wen C., & Zhang X. Data-driven detection and assessment for urban railway transit driver fatigue in real work conditions. Transportation research record. 2023. 2677 (1). P. 1367–1375.
26. Graf S., Viola S.R., Leo T., & Kinshuk In-depth analysis of the Felder-Silverman learning style dimensions. Journal of Research on Technology in Education. 2007.40 (1). P. 79–93.
27. Holmqvist K., Nyström M., Andersson R., Dewhurst R., Jarodzka H., & Van de Weijer J. Eye tracking: A comprehensive guide to methods and measures. oup Oxford. 2011.
28. Li Z., Wang Y., Zhang N., Zhang Y., Zhao Z., Xu D., ... & Gao Y. Deep learning-based object detection techniques for remote sensing images: A survey. Remote Sensing. 2022. 14 (10). P. 2385.
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For citations:
Ajayi I.H. Comparative Analysis of Eye Tracking study on Mental Stress in Online Learning. Vestnik NSUEM. 2026;(2):65-81. https://doi.org/10.34020/2073-6495-2026-2-065-081
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