Assessing english Language teachers' pedagogical effectiveness using convolutional neural networks optimized by...

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Applied Linguistics

Background:

• Effective teacher performance evaluation is crucial for improving educational systems.
• Assessing pedagogical quality in English as a Foreign Language (EFL) instruction presents unique challenges.
• Existing evaluation methods may lack the comprehensive analysis required for nuanced feedback.

Purpose of the Study:

• To develop and validate a novel approach for assessing EFL pedagogical quality using deep learning and metaheuristics.
• To create a comprehensive index framework for evaluating multiple dimensions of teaching quality.
• To enhance the accuracy, robustness, and efficiency of teacher performance evaluation systems.

Main Methods:

• Development of a five-dimensional index framework (instructional design, materials, methods, effectiveness, management) with secondary indicators.
• Utilizing a Convolutional Neural Network (CNN) architecture for analyzing audio-video classroom recordings.
• Optimization of the CNN model using a modified Virus Colony Search (VCS) algorithm.

Main Results:

• The VCS/CNN algorithm demonstrated high accuracy in evaluating EFL instruction based on multiple criteria.
• The proposed method outperformed existing approaches in terms of accuracy, robustness, flexibility, and efficiency.
• The framework successfully identified specific aspects of teaching quality, including pronunciation, content coverage, and student engagement.

Conclusions:

• The study presents a reliable and efficient AI-powered framework for evaluating EFL teacher performance.
• This approach provides timely feedback, identifies strengths/weaknesses, and informs professional development.
• The findings have the potential to significantly improve EFL instruction quality and student learning outcomes.