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Evaluation Method of English Teaching Quality Based on SOFM Neural Network

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Application of Big Data, Blockchain, and Internet of Things for Education Informatization (BigIoT-EDU 2022)

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Abstract

With the globalization of world economy, culture, science and technology, international cooperation and exchanges are becoming more and more frequent, and English will occupy a more and more important position. As a public compulsory basic course for Non-English Majors in Colleges and universities in China, College English plays a very important role in Expanding College Students’ knowledge, improving foreign language comprehensive quality and cultivating language application ability. Therefore, the quality of College English teaching is also included in one of the important indicators of college curriculum construction evaluation. This paper analyzes four problems existing in College English teaching, and points out that emphasizing summative assessment and neglecting formative assessment are the main reasons affecting the quality of English teaching. Therefore, this paper proposes an English teaching quality evaluation method based on SOFM neural network to strengthen the monitoring of the formation process of teaching quality, so as to improve teaching quality.

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References

  1. Dai, W.: Research on diversification of English teaching evaluation in Higher Vocational Colleges. Comp. Study Cult. Innov. 5, 115–116 (2017)

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Correspondence to Jing Sheng .

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Sheng, J. (2023). Evaluation Method of English Teaching Quality Based on SOFM Neural Network. In: Jan, M.A., Khan, F. (eds) Application of Big Data, Blockchain, and Internet of Things for Education Informatization. BigIoT-EDU 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 466. Springer, Cham. https://doi.org/10.1007/978-3-031-23947-2_53

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  • DOI: https://doi.org/10.1007/978-3-031-23947-2_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23946-5

  • Online ISBN: 978-3-031-23947-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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