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Psychological Analysis Using Artificial Intelligence Algorithms of Online Course Learning of College Students During COVID-19

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  • Haiyan Ji

    (Nantong University)

Abstract

COVID-19 is the worst disease caused by the coronavirus initially located in Wuhan, China in December 2019 as declared by WHO with a high transmission rate associated with serious illness. The sensitive situation of the COVID-19 educational systems shifted to online learning environments that negatively affected students’ psychological health. The prediction accuracy in diagnosis and non-infectious disease screening is improved using artificial intelligence algorithms based on clinical data for psychological symptoms with medical history of students and families. Using statistical models implemented for collecting data relevant to effects on factors relative to students such as disturbance in learning, loss of jobs, and obligations expansion for students. The analysis considers factors such as sleeping hours, social interaction, psychological state, and academic performance of students who have adopted online learning. Clustering analysis is adopted for factors affecting students in online learning platforms and data mining for data sets of students from college databases. The survey is performed with 75 teachers and 110 students connected with different online platforms for online learning and analyzing the psychological impact of implementing artificial intelligence algorithms. The novelty of the study lies in innovatively applying AI algorithms like MLP, ANN, and other different machine learning models to evaluate the psychological dimensions of online learning during COVID-19.

Suggested Citation

  • Haiyan Ji, 2025. "Psychological Analysis Using Artificial Intelligence Algorithms of Online Course Learning of College Students During COVID-19," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 3996-4018, March.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-01965-2
    DOI: 10.1007/s13132-024-01965-2
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    References listed on IDEAS

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    1. Aixia Li & A. Y. M. Atiquil Islam & Xiaoqing Gu, 2021. "Factors Engaging College Students in Online Learning: An Investigation of Learning Stickiness," SAGE Open, , vol. 11(4), pages 21582440211, November.
    2. Ma, Zhenjun & Yan, Rui & Nord, Natasa, 2017. "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings," Energy, Elsevier, vol. 134(C), pages 90-102.
    3. Hanlin You & Mengjun Li & Jiang Jiang & Bingfeng Ge & Xueting Zhang, 2017. "Evolution monitoring for innovation sources using patent cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 693-715, May.
    4. Mostafa Rezapour & Scott K Elmshaeuser, 2022. "Artificial intelligence-based analytics for impacts of COVID-19 and online learning on college students’ mental health," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-31, November.
    5. Samuel Adu Gyamfi & Patrick Ohemeng Gyaase, 2017. "Virtualization of University Education: The Impact of ICT-Mediated Learning Environment on Students' Performance," International Journal of E-Services and Mobile Applications (IJESMA), IGI Global, vol. 9(4), pages 24-40, October.
    6. Indy Man Kit Ho & Kai Yuen Cheong & Anthony Weldon, 2021. "Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-27, April.
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