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Using Data Mining Approach for Student Satisfaction With Teaching Quality in High Vocation Education.

Bailin Chen1, Yi Liu2, Jinqiu Zheng3

  • 1Department of Student Affairs, Dongguan Polytechnic, Dongguan, China.

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|February 7, 2022
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Summary

This study uses data mining to analyze student satisfaction with vocational education teaching quality. Findings identify key factors influencing satisfaction, aiding in educational strategy and quality improvement.

Keywords:
artificial intelligencedata mining approachhigh vocation educationstudent satisfiedteaching quality

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Area of Science:

  • Educational Technology
  • Data Mining Applications
  • Vocational Education

Background:

  • High vocational education is crucial for China's skilled talent development.
  • Modern vocational education requires alignment with technological and industrial advancements.
  • Artificial intelligence offers potential solutions for enhancing teaching quality in this sector.

Purpose of the Study:

  • To investigate the application of data mining for improving student satisfaction with high vocational education teaching quality.
  • To identify key factors influencing student satisfaction in basic entrepreneurship curricula.
  • To provide insights for enhancing educational strategies and student management.

Main Methods:

  • A questionnaire was designed to assess student satisfaction with the teaching quality of basic entrepreneurship courses.
  • Survey data from vocational education students was collected and analyzed.
  • Data mining techniques and analysis software were employed to identify influential factors.

Main Results:

  • The study identified specific factors that significantly impact student satisfaction with the teaching quality of basic entrepreneurship.
  • Analysis revealed the current status of teaching quality in the selected curriculum.
  • The findings provide empirical evidence for understanding student perceptions.

Conclusions:

  • Data mining is an effective approach to analyze and improve student satisfaction in vocational education.
  • The results can inform targeted interventions to enhance teaching quality and student engagement.
  • Recommendations are provided for educational strategy, student management, and curriculum development.