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Examining and Predicting Teacher Professional Development by Machine Learning Methods

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Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

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Abstract

Core quality and ability development analysis is important for evaluating and deepening the professional development of teachers. It is also useful for improving the quality of education and promotes the growth of the next generation. This research tries to investigate how prominent teachers regulate their professional development. The research also attempts to provide suggestions about current professional status of teachers. The answer to these questions will help teachers to better comprehend how teachers develop. To perform factor analysis for teacher professional development, we develop a set of questionnaire scheme, and collected a certain number of samples through the survey. Professional development is transformed to a classification problem. This study applies machine learning (ML) methods to identify significant attributes that prominent teachers shown in class education, and predict the level of teacher professional development. Eight ML methods are taken to classify the samples. Hyperparameter optimization is performed to improve prediction accuracy. The simulation results show that the ensemble method, support vector machine and artificial neural network are the top three ML methods for the problem. Hyperparameter optimization does not show great impact on the performance of the ensemble and support vector machine methods. The accuracy can reach above 85% by tuning the artificial neural network method through hyperparameter optimization. This study provides an important basis for the future intelligent analysis of teacher professional development.

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Acknowledgments

This paper was supported in part by the National Natural Science Foundation of China (Project No. 61901301), in part by the key project of the National Social Science Foundation of China (Project No. AFA70008), and in part by the Tianjin Higher Education Creative Team Funds Program.

Author information

Authors and Affiliations

  1. Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, 300387, China

    Xin Zhang

  2. Faculty of Education, Tianjin Normal University, Tianjin, 300387, China

    Yueyuan Kang

Authors
  1. Xin Zhang
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  2. Yueyuan Kang
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Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Zhang, X., Kang, Y. (2021). Examining and Predicting Teacher Professional Development by Machine Learning Methods. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_19

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5187-8

  • Online ISBN: 978-981-16-5188-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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