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Existing ML models in educational assessment (e.g., neural networks, decision trees). Data Collection:

Traditional foreign language teaching evaluation relies heavily on subjective student surveys and manual peer reviews, which often lack real-time accuracy and objectivity. This paper proposes a modern evaluation framework that utilizes machine learning (ML) to analyze multi-dimensional data—including classroom interaction, student performance, and sentiment analysis. By applying algorithms such as Random Forest and Support Vector Machines (SVM), the system provides a more scientific, data-driven approach to improving pedagogical outcomes in higher education. Existing ML models in educational assessment (e

Which teaching behaviors (e.g., frequent Q&A, use of multimedia) correlate most strongly with high student achievement. the system provides a more scientific

showing the error rates of different ML algorithms. Existing ML models in educational assessment (e