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International Symposium on Emerging Technologies for Education

International Conference on Web-Based Learning

SETE 2020, ICWL 2020: Learning Technologies and Systems pp 103–114Cite as

Influencing Factors of Students’ Online Learning Satisfaction During the COVID-19 Outbreak: An Empirical Study Based on Random Forest Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12511))

Abstract

New coronavirus is wreaking havoc around the world and has a profound impact on the international community, especially on higher education. Online teaching provides an effective path for higher education to avoid the risk of cross-contagion in traditional classroom education under epidemic condition. In order to ensure the quality of online teaching during the epidemic, this study takes the students’ satisfaction of online education learning as a measurement object. 1120 online learners from 126 colleges and universities in 26 provinces were investigated through 50 questions survey in 10 dimensions. First, the chi-square test is used to pre-process all the characteristics of the factors, and 30 influencing factors with the highest feature correlation are selected. Random forest algorithm is used to establish a satisfaction classification model in the training set. The accuracy in the test set is 0.72. Through the ranking of feature contribution, the influential factors with higher weight are obtained. The results show that in online learning, the attractiveness of teachers’ teaching methods is the most influential factor, while curriculum arrangement and learning environment rank second and third. Finally, according to the research results, this paper puts forward some suggestions and countermeasures.

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Author information

Authors and Affiliations

  1. People’s Public Security University of China, Beijing, China

    Yuzhuo Zhang, Hongji Yang, Kangrui Zhao & Chunmei Han

  2. Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin, China

    Po Zhang

Authors
  1. Yuzhuo Zhang
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  2. Po Zhang
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  3. Hongji Yang
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  4. Kangrui Zhao
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  5. Chunmei Han
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Corresponding author

Correspondence to Chunmei Han .

Editor information

Editors and Affiliations

  1. Ningbo Institute of Industrial Technology, Ningbo, China

    Chaoyi Pang

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Monash University, Clayton, VIC, Australia

    Guanliang Chen

  4. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania

    Elvira Popescu

  5. Department of Physics, Zhejiang University, Hangzhou, China

    Lu Chen

  6. School of Computer Science, South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Zhejiang University, Ningbo, China

    Bailing Zhang

  8. Universidad Internacional De La Rioja, Logroño, Spain

    Silvia Margarita Baldiris Navarro

  9. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

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Zhang, Y., Zhang, P., Yang, H., Zhao, K., Han, C. (2021). Influencing Factors of Students’ Online Learning Satisfaction During the COVID-19 Outbreak: An Empirical Study Based on Random Forest Algorithm. In: Pang, C., et al. Learning Technologies and Systems. SETE ICWL 2020 2020. Lecture Notes in Computer Science(), vol 12511. Springer, Cham. https://doi.org/10.1007/978-3-030-66906-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-66906-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66905-8

  • Online ISBN: 978-3-030-66906-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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