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Data-Driven Teaching and Learning Effect Evaluation in Online Education

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  • Ling Wang

    (Yulin Normal University, China)

  • Guochu Liang

    (Yulin Normal University, China)

Abstract

The rapid development of online education has underscored the necessity of data-driven teaching functions for enhancing teaching quality and efficiency. This paper investigates the role of data-driven approaches in online education, with a particular focus on the practical application of data for evaluating learning outcomes. It highlights the importance of integrating diverse evaluation methods to provide a comprehensive understanding of student performance. Additionally, the paper emphasizes the need for effective data visualization and interpretation to support informed decision-making in educational settings. It also addresses the challenges encountered in implementing data-driven teaching, such as data privacy concerns, technological limitations, and teachers' data literacy, proposing targeted countermeasures to overcome these obstacles. The findings aim to provide valuable insights and practical guidance for the advancement of data-driven teaching practices in online education.

Suggested Citation

  • Ling Wang & Guochu Liang, 2025. "Data-Driven Teaching and Learning Effect Evaluation in Online Education," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global Scientific Publishing, vol. 20(1), pages 1-24, January.
    • Handle: RePEc:igg:jwltt0:v:20:y:2025:i:1:p:1-24
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