Assessing english Language teachers' pedagogical effectiveness using convolutional neural networks optimized by modified virus colony search algorithm.

Effective teacher performance evaluation is important for enhancing the quality of educational systems. This study presents a novel approach that integrates deep learning and metaheuristics to assess the pedagogical quality of English as a foreign language (EFL) instruction in a classroom setting. A comprehensive index framework is developed, comprising five primary dimensions: instructional design, instructional materials, teaching methods and approaches, teaching effectiveness, and classroom management. Each dimension is further divided into secondary indicators that capture specific aspects of teaching quality, including pronunciation, content coverage, lesson objectives, and student engagement. The proposed approach uses a convolutional neural network (CNN) architecture optimized by a modified virus colony search (VCS) algorithm to analyze audio and video recordings of classroom interactions. The results demonstrate that the VCS/CNN algorithm can accurately evaluate EFL instruction based on multiple criteria and indicators, outperforming existing methods in terms of accuracy, robustness, flexibility, and efficiency. This study contributes to the development of a reliable and efficient teacher evaluation framework that can provide timely feedback, identify teacher strengths and weaknesses, and inform areas for professional development. The proposed approach has the potential to improve the quality of EFL instruction and administration by enhancing teacher performance and student learning outcomes.