Abstract
The evaluation of teachers’ teaching ability is an important part of educational activities, and a reasonable evaluation method plays an important role in improving teachers’ ability. At present, most of the evaluation methods used by schools and educational institutions are manually formulated some evaluation indicators. These methods are usually influenced by the personal preferences and the implementation is time-consuming. According to these problems, this paper proposes a method to evaluate teachers’ teaching ability based on BP neural network. Through constructing the templates, we extract teachers’ information and establish the knowledge base. Then a BP neural network is used to teaching ability evaluation. Finally, the experimental results prove the proposed method is effective.
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Lin, Y.: On the constitution and cultivation of teachers’ teaching ability in colleges and universities. Educ. Occup. 9, 99–110 (2008)
Ma, Y.: Research on the Components of Chemistry Teachers’ Teaching Ability in Excel. Shandong Normal University, Jinan (2017)
Liu, J.: Model building and promotion strategy of the teaching ability of university and college teachers. J. Changsha Univ. Sci. Technol. (Soc. Sci.) 3, 133–137 (2015)
Chunlan, Yu.: International comparison of teachers’ performance evaluation system in higher vocational colleges and its enlightenment to China. Mod. Commun. 21, 32–34 (2018)
Gao, X.: The Research of the Evaluation Method of Teachers’ Teaching Ability Based on Emotion. Bohai University, Jinzhou (2017)
Wang, H., Li, Q.: Research on teaching ability evaluation of university teachers based on discrete hopfield neural network. Mod. Comput. 28, 20–23 (2018)
Wang, Q.: Research on the construction of teaching ability evaluation system for young teachers majoring in ideological and political education in Tibet. Teach. Educ. (High. Educ. Forum) 18, 37–39 (2018)
Zhang, B., Zhang, J., Huang, L.: Research on the application of computer modeling in teaching evaluation. Audiov. Educ. China 4, 103–109 (2013)
Zhao, W.: Evaluation of teaching ability of young teachers in local colleges and universities based on students’ evaluation of teaching data. J. Huaihai Inst. Technol. (Humanit. Soc. Sci. Ed.) 16(9), 129–131 (2018)
Yang, B., Zhang, L.: Teaching quality monitoring and evaluation method based on SVM. Comput. Inf. Technol. 26(2), 27–30 (2018)
Mengjun, H.: Research on Rule-based Extraction of Mongolian Character Attributes. Inner Mongolia University, Huhehaote (2018)
Yang, Y.: Research and Implementation of Key Technologies for Chinese Information Extraction. Beijing University of Posts and Telecommunications, Beijing (2008)
Li, X., Miao, Z.: Free text information extraction technology. Inf. Sci. 7, 815–821 (2004)
Zhao, L.: Research on Comment Information Extraction Based on Maximum Entropy Method. Shanghai Jiaotong University, Shanghai (2009)
Wang, S., et al.: Natural language semantic construction based on cloud database. Comput. Mater. Continua 57(3), 603–619 (2018)
Li, D.: Information Extraction Algorithm Based on the Template Matching in Traffic Standards. Chang’an University (2017)
Xiong, Z., Shen, Q., Wang, Y., Zhu, C.: Paragraph vector representation based on word to vector and CNN learning. Comput. Mater. Continua 55(2), 213–227 (2018)
Acknowledgments
This work is supported by National Nature Science Foundation (No. 61501529), National Language Committee Project (No. ZDI125-36).
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Niu, P., Sun, Y., Song, W., Zhang, S. (2019). Evaluation Method of Teachers’ Teaching Ability Based on BP Neural Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_3
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DOI: https://doi.org/10.1007/978-3-030-24274-9_3
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