Abstract
Teaching evaluation is a crucial measure to improve the quality of education. It is essential to formulate a scientific and reasonable evaluation system for teaching in colleges and universities. Teaching is a dynamic process, which makes it challenging to evaluate it comprehensively. However, as a new technology, the artificial neural network has the characteristics of nonlinear processing, adaptive learning, and high fault tolerance for various evaluation problems. This article first analyzes the current status of physical education and proposes solutions that comprehensively improve the quality of physical education in colleges and other institutions. It also suggests that a scientific evaluation of teaching in colleges provides a better feedback, incentives, and guidance, respectively. A model for the quality evaluation of physical education in colleges is established using artificial neural network. This model quantifies the concept of teaching evaluation index into actual data as its input, and teaching effect as output. The use of artificial neural network framework maps the input with output using an internally configured decision support system. Furthermore, it uses relevant software to conduct empirical research. The results show that this model enhances the quality of teaching evaluation, which not only overcomes the subjective factors of experts in the evaluation process but also produces satisfactory evaluation result.
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Research Project on Education and Teaching Reform of Huzhou University in 2018 (NO.:JGBA1813).
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Han, Q. Using neural network for the evaluation of physical education teaching in colleges and universities. Soft Comput 26, 10699–10705 (2022). https://doi.org/10.1007/s00500-022-06848-9
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DOI: https://doi.org/10.1007/s00500-022-06848-9