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Examination in Accordance with Aptitude: Selection and Optimization of Curriculum Assessment Methods in Higher Education Adapted to the Teacher–Student Game Behaviors

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  • Ying Qu

    (School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018, China
    Data Science and Intelligent Computing Research Center, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Si Chen

    (School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018, China
    Data Science and Intelligent Computing Research Center, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Shugui Cao

    (School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018, China)

Abstract

The relationship between teachers and students in higher education has now developed into a game of two-way interaction, with both sides often clashing over the choice of curriculum assessment methods. Curriculum examination is a crucial factor in evaluating the quality of higher education. Additionally, it significantly impacts the fairness of education and students’ motivation to learn. To resolve such conflicts, we analyze the teacher–student game psychology using the conflict analysis method. Then, based on the overall stability of the situation, we have come to the conclusion that we need to adopt “innovative examinations”. Specific recommendations were made to teachers and students through the analysis of the results, and then the integration of the influencing factors proposed optimization strategies aimed at ensuring the fairness of the examination, based on the choice of the type of course, with reference to the reality of teachers and students, and oriented to the educational and teaching environment. We provide practical guidance for selecting and optimizing assessment methods to improve their appropriateness for individualized teacher and student needs. This promotes the process of teaching reform and helps achieve sustainable development in education.

Suggested Citation

  • Ying Qu & Si Chen & Shugui Cao, 2023. "Examination in Accordance with Aptitude: Selection and Optimization of Curriculum Assessment Methods in Higher Education Adapted to the Teacher–Student Game Behaviors," Sustainability, MDPI, vol. 15(19), pages 1-26, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14121-:d:1246527
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    References listed on IDEAS

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    1. Xue Bai & Yongguo Bai & Lianhui Li, 2022. "Fuzzy Neural Network for the Online Course Quality Assessment System," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, November.
    2. Correa, Hector & Gruver, Gene W., 1987. "Teacher-student interaction: A game theoretic extension of the economic theory of education," Mathematical Social Sciences, Elsevier, vol. 13(1), pages 19-47, February.
    3. Sihua Li & Ying Li & Haohan Lin, 2023. "Research on Sustainable Teaching Models of New Business—Take Chinese University Business School as an Example," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
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