Design and implementation of a B/S architecture-based real-time teaching quality evaluation system for university English courses

The advancement of computer technologies and the emergence of smart education have revolutionized teaching quality evaluation by enabling the integration of Browser/Server (B/S) architectures and artificial intelligence (AI). This study proposes a real-time teaching quality evaluation system specifically designed for university English courses, utilizing the scalability and adaptability of the B/S architecture to integrate data from diverse sources such as student behavior, teacher feedback, and ideological education outcomes. A multidimensional evaluation framework is developed, incorporating machine learning algorithms, including classification, regression, and clustering, to analyze teaching effectiveness, student engagement, and curriculum alignment with ideological goals. The system also aligns with the principles of smart education by leveraging real-time feedback capabilities, data-driven insights, and personalized interventions, allowing educators to dynamically adjust teaching strategies to meet diverse learning needs. Experimental validation using a dataset of 1,200 students and 60 teachers demonstrates that the proposed system significantly outperforms traditional evaluation methods. System-generated scores exhibit stronger correlations with objective performance indicators, such as exam improvement rates, while also enhancing the assessment of ideological education outcomes. These results underscore the system’s ability to deliver precise, scalable, and actionable evaluations, providing a robust framework for integrating advanced technologies into smart education practices.