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The Mixed Teaching Quality Evaluation Model of Applied Mathematics Courses in Higher Vocational Education Based on Artificial Intelligence

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Advanced Hybrid Information Processing (ADHIP 2021)

Abstract

With the deepening of research on the mixed teaching of Applied Mathematics Course in higher vocational colleges, and the requirements for teaching quality are constantly improved, so it is necessary to conduct a comprehensive evaluation on it. However, due to the backward processing technology of evaluation indicators in traditional evaluation model, the calculation error of evaluation results is relatively large. Therefore, this paper designs the evaluation model of mixed teaching quality of Applied Mathematics Course in Higher Vocational Colleges Based on artificial intelligence. This paper constructs the evaluation index system of mixed teaching quality of Applied Mathematics Course in higher vocational colleges, and completes the consistency calculation. According to the results of the index selection, the neural network of artificial intelligence technology combined with fuzzy evaluation method is used to construct the hybrid teaching quality evaluation model. After the evaluation results are obtained, the evaluation results are classified. At this point, the design of the mixed teaching quality evaluation model for higher vocational applied mathematics courses based on artificial intelligence is completed. Constructing the experimental results, by comparing the experimental indicators, we can see that the evaluation error of this model is lower than that of the traditional model. It can be seen that this model works best.

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Chen, Hy. (2022). The Mixed Teaching Quality Evaluation Model of Applied Mathematics Courses in Higher Vocational Education Based on Artificial Intelligence. In: Liu, S., Ma, X. (eds) Advanced Hybrid Information Processing. ADHIP 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-94554-1_35

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  • DOI: https://doi.org/10.1007/978-3-030-94554-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94553-4

  • Online ISBN: 978-3-030-94554-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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