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Exploring Relationships Between Distractibility and Eye Tracking During Online Learning

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Advances in Neuroergonomics and Cognitive Engineering (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 259))

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Abstract

More than half of students think their attention is easily shifted when they’re learning online. Distractibility, to a certain extent caused by visual stimuli is the main impact to decrease their academic performance. In addition, eye-tracking technology has been widely applied to explore distractibility in many “look” tasks, such as reading, viewing advertisements, and watching online videos as well as measure the efficiency of visual cognition. Therefore, this paper aimed to discuss the relationship between distractibility with eye movement indices and academic performance. Fifty high school students (30 girls) were recruited to complete experiment that was divided into two groups, which are the experimental group with distractions and controls with no one. The result showed that three of traditional eye movement indices were significantly correlated with distractibility (\(p < 0.05\)). Then we introduced the network accessibility model and the gaze transformation entropy to create two composite indexes according to the complexity and directivity of distractibility characteristics. The result revealed that the two composite indexes are significantly correlated with distractibility (\(p < 0.05\)). Finally, we constructed the mapping model about eye movement metrics about distractibility and online learning performance with a machine learning algorithm. The result ration was \(R^{2} = 0.799,\) and the error was \(Re < 0.1\), which proved the model was feasible and accessible. The research from the perspective of distractibility can provide valuable support for physiological indicators testing tools of academic performance and highlights the applications of eye movement dynamics.

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Notes

  1. 1.

    Ministry of education of the people’s Republic of China, “suspended class, ongoing learning” [EB/OL]. (2020-01-29) [2020-03-30]. http://www.moe.gov.cn/jybxwfb/gzdtgzdt/s5987/202001/t20200129416993.html.

  2. 2.

    Iresearch. 2020Q1&2020Q2eChina online education market data release report [EB/OL]. (2020-06-29) [2020-06-29]. http://report.iresearch.cn/report_pdf.aspx?id=3599.

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Authors and Affiliations

  1. School of Degisn Art and Media, Nanjing University of Science and Technology, Xiaoling 200#, Xuanwu District, Nanjing, Jiangsu, China

    Shanshan Chen, Yiqian Zhao, Tianyu Wu & Yajun Li

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  1. Shanshan Chen
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  2. Yiqian Zhao
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  3. Tianyu Wu
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  4. Yajun Li
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Correspondence to Shanshan Chen .

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Editors and Affiliations

  1. School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA

    Hasan Ayaz

  2. School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Umer Asgher

  3. DIGITAL – Institute for Information and Communication Technologies, Graz, Austria

    Lucas Paletta

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Chen, S., Zhao, Y., Wu, T., Li, Y. (2021). Exploring Relationships Between Distractibility and Eye Tracking During Online Learning. In: Ayaz, H., Asgher, U., Paletta, L. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 259. Springer, Cham. https://doi.org/10.1007/978-3-030-80285-1_31

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  • DOI: https://doi.org/10.1007/978-3-030-80285-1_31

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  • Print ISBN: 978-3-030-80284-4

  • Online ISBN: 978-3-030-80285-1

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