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Higher Education Curriculum Evaluation Method Based on Deep Learning Model.

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This study uses deep learning networks to evaluate higher education curricula in Chongqing, finding key factors impacting results. A new teaching model aims to enhance educational quality and address curriculum deficiencies.

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Area of Science:

  • Education
  • Computer Science
  • Artificial Intelligence

Background:

  • Higher education is crucial for societal progress and individual development.
  • Effective evaluation of higher education curricula is essential for quality improvement.
  • Existing methods may not fully capture the complexities of curriculum effectiveness.

Purpose of the Study:

  • To analyze and construct a deep learning network and self-encoder system for evaluating higher education curricula.
  • To assess the quality of higher education curricula in Chongqing using a deep learning approach.
  • To identify factors influencing the evaluation results of higher education curricula.

Main Methods:

  • Development of a deep learning network and self-encoder system.
  • Application of the system to evaluate curricula from 50 universities in Chongqing.
  • Analysis of the impact of test objects, indicators, and hidden layers on evaluation outcomes.
  • Design of a classroom teaching model to improve higher education quality.

Main Results:

  • The number of test objects significantly influences curriculum evaluation results.
  • The selection of indicators is a critical factor in the accuracy of curriculum assessment.
  • The number of hidden layers in the deep learning network affects the evaluation outcomes.
  • A novel classroom teaching model was developed to address identified curriculum quality issues.

Conclusions:

  • Deep learning networks provide a robust framework for evaluating higher education curricula.
  • Curriculum evaluation is sensitive to specific parameters within the deep learning model.
  • The developed classroom teaching model offers a potential solution for improving higher education quality.