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Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

Author

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  • Bo Jiang

    (School of Educational Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Yanbai He

    (School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Rui Chen

    (School of Overseas Education, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Chuanyan Hao

    (School of Educational Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Sijiang Liu

    (School of Educational Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Gangyao Zhang

    (School of Educational Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

Abstract

Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.

Suggested Citation

  • Bo Jiang & Yanbai He & Rui Chen & Chuanyan Hao & Sijiang Liu & Gangyao Zhang, 2020. "Progressive Teaching Improvement For Small Scale Learning: A Case Study in China," Future Internet, MDPI, vol. 12(8), pages 1-15, August.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:8:p:137-:d:399939
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    References listed on IDEAS

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    Cited by:

    1. Chuanyan Hao & Anqi Zheng & Yuqi Wang & Bo Jiang, 2021. "Experiment Information System Based on an Online Virtual Laboratory," Future Internet, MDPI, vol. 13(2), pages 1-19, January.

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