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Sustainable Construction of Higher Education MOOCs Using CNN Feature Extraction

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Listed:
  • Yongyan Zhao

    (Harbin University, China)

  • Jian Li

    (Harbin University, China)

Abstract

The attention time of students studying in MOOC (Massive Open Online Courses) classroom was analyzed to optimize and further improve their performance. On this basis, a student class model based on convolutional neural networks (CNN) feature extraction was proposed. Through Pr (Adobe Premiere) technology, students' class videos were processed by framing, and relevant features were extracted based on changes in students' eye movement trajectories. Then, 10 class videos of ten different experimenters were selected for comparative experiments. After comparing the results, it was found that the test scores of the experimental personnel using MOOC model for assisted learning were significantly different from those before using MOOC model. The final test scores of the students using MOOC model for learning increased to 5-10 points, which had a certain positive impact on the learning results. In the context of sustainable development of higher education, the construction and application of the MOOC model require more favorable promotion and practice.

Suggested Citation

  • Yongyan Zhao & Jian Li, 2024. "Sustainable Construction of Higher Education MOOCs Using CNN Feature Extraction," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 19(1), pages 1-13, January.
    • Handle: RePEc:igg:jwltt0:v:19:y:2024:i:1:p:1-13
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    1. Yaqoob, Ibrar & Hashem, Ibrahim Abaker Targio & Gani, Abdullah & Mokhtar, Salimah & Ahmed, Ejaz & Anuar, Nor Badrul & Vasilakos, Athanasios V., 2016. "Big data: From beginning to future," International Journal of Information Management, Elsevier, vol. 36(6), pages 1231-1247.
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