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Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial
Siyuan Li1, Chao Wang2, Ying Wang1
1Graduate School, Adamson University, 0900, Manila, Metro Manila, Philippines.
Scientific Reports
|February 27, 2024
Summary
This study introduces an AI-powered fuzzy evaluation model to improve physical education teaching assessments in colleges. The new model enhances efficiency and provides a robust framework for evaluating teaching methods.
Area of Science:
- Education
- Artificial Intelligence
- Fuzzy Logic
Background:
- Evaluating physical education teaching methods in higher education presents challenges due to numerous assessment factors and a lack of structured frameworks.
- Existing methods often struggle with comprehensive and objective evaluation of diverse teaching aspects.
Purpose of the Study:
- To develop and validate an artificial intelligence-based multi-feature fuzzy evaluation model for assessing physical education teaching methods in colleges and universities.
- To establish an efficient and structured framework that addresses the limitations of traditional evaluation approaches.
Main Methods:
- A multi-feature fuzzy evaluation model integrating natural language processing and fuzzy logic was developed.
- The enhanced cuckoo search optimization algorithm was employed for parameter optimization and assessment calculation.
- The model incorporated evaluation perspectives from management, instructors, and students, including a student mobility mechanism.
Main Results:
- The proposed AI model achieved high scores in key areas: 97.01% for average skill performance, 87.36% for learning progress, 93.49% for physical fitness, 95.04% for participation, 95.49% for student satisfaction, and 96.8% for teaching efficiency.
- The system demonstrated superior performance compared to traditional evaluation methods.
Conclusions:
- The developed AI-driven fuzzy evaluation model offers an effective and efficient framework for assessing physical education teaching quality in higher education.
- This research contributes to the advancement of pedagogical practices by providing a data-driven approach to enhance teaching methods and student outcomes.
