Compensated Fuzzy Neural Network-Based Music Teaching Ability Assessment Model

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Music Pedagogy

Background:

• Effective assessment of music teaching ability is crucial in higher education.
• Existing methods may lack the precision needed for nuanced evaluation.
• Neural networks and deep learning show potential for educational assessment.

Purpose of the Study:

• To design and validate a novel assessment model for college music teaching ability.
• To explore the application of compensated fuzzy neural networks in this domain.
• To address and correct abnormal outputs in the assessment model.

Main Methods:

• Development of an assessment model utilizing a compensated fuzzy neural network algorithm.
• Analysis of model accuracy and identification of abnormal output causes.
• Experimental verification of the model's reliability and feasibility through teaching practice.

Main Results:

• The compensated fuzzy neural network algorithm demonstrated feasibility for music teaching ability assessment.
• Abnormal output causes were identified and addressed through proposed corrections.
• Experimental validation confirmed the model's practical applicability and reliability.

Conclusions:

• The compensated fuzzy neural network is a viable tool for assessing music teaching ability in colleges.
• This approach offers significant potential for improving the quality of music instruction in higher education.
• The study provides a foundation for further research in AI-driven educational assessment.