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Development and Implementation Path of Kindergarten Stem Educational Activities Based on Data Mining.

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This study explores a data mining approach for developing and implementing localized STEM (Science, Technology, Engineering, and Mathematics) education in Chinese kindergartens. It aims to improve audit methods and provide new research avenues for STEM teaching activities.

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Area of Science:

  • Early Childhood Education
  • STEM Education
  • Data Mining Applications

Background:

  • China's early childhood education sector actively studies STEM education, incorporating international research.
  • There is a need to adapt STEM education to the specific realities of Chinese kindergartens.

Purpose of the Study:

  • To investigate the localization implementation path of STEM education in kindergartens.
  • To develop and improve a STEM education monitoring index system using data mining.
  • To determine the function and mode of data mining algorithms in kindergarten STEM education.

Main Methods:

  • Utilizing data mining techniques to analyze the development and implementation of kindergarten STEM education activities.
  • Applying data mining algorithms to refine the STEM education monitoring index system.
  • Employing data technology for continuous auditing of STEM educational activities.

Main Results:

  • A data mining-based approach for developing and implementing kindergarten STEM educational activities was investigated.
  • The study analyzed the improvement of a STEM education monitoring index system through data mining algorithms.
  • The function path and mode of data mining algorithms in kindergarten STEM education were determined.

Conclusions:

  • The data mining algorithm provides a continuous audit mechanism for kindergarten STEM educational activities.
  • This approach enhances audit scope and means, offering innovative research ideas for STEM education.
  • The study contributes to building a path model for the development and implementation of kindergarten STEM educational activities.