IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p9987-d1177752.html
   My bibliography  Save this article

Research on the Evaluation Model of School Management Quality in the Compulsory Education Stage Based on Big Data Technology

Author

Listed:
  • Guanghui Min

    (College of Education, Fujian Normal University, Fuzhou 350007, China)

  • Muhui Lin

    (College of Education, Fujian Normal University, Fuzhou 350007, China)

  • Ying Liu

    (College of Education, Fujian Normal University, Fuzhou 350007, China)

  • Ning Yang

    (College of Education, Fujian Normal University, Fuzhou 350007, China)

  • Zhe Li

    (College of Education, Fujian Normal University, Fuzhou 350007, China)

Abstract

With the spread of compulsory education emerged continuous school management problems, and the quality of school management in compulsory education has attracted a great deal of attention in China. However, the application of information technology in the field is not yet detailed and wide, resulting in problems concerning heavy workloads and high difficulty in the whole evaluation process. As such, we have utilized big data technologies, including Apache Spark, Apache Hive, and SPSS, to effectively carry out data cleaning, correlation analysis, dynamic factor analysis, principal component analysis, and visual display on a sample of 1760 data points from 40 primary and secondary schools located in the Q Province of China. This has enabled us to construct a model for evaluating school management quality in the compulsory education stage, reducing the previous 22 management tasks required for evaluation down to just 5. Such streamlining has greatly reduced the workload and difficulty previously associated with evaluation, providing a more efficient and effective solution for assessing quality management in schools. It has improved the efficiency and accuracy of evaluation and further promoted the simultaneous development of education and education equity in the compulsory education stage.

Suggested Citation

  • Guanghui Min & Muhui Lin & Ying Liu & Ning Yang & Zhe Li, 2023. "Research on the Evaluation Model of School Management Quality in the Compulsory Education Stage Based on Big Data Technology," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9987-:d:1177752
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/9987/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/13/9987/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bai, Peng, 2009. "Sphericity test in a GMANOVA-MANOVA model with normal error," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2305-2312, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vargas, Tiago M. & Ferrari, Silvia L.P. & Lemonte, Artur J., 2014. "Improved likelihood inference in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 110-124.
    2. Marton Gosztonyi, 2023. "Comparative Analysis of X-Y-Z Generation Entrepreneurs in a Semi-Peripheral EU Member Country: Insights from Regularized Regression Techniques," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 191-217.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:9987-:d:1177752. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.