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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.

Summary of how ML enhances the objectivity of foreign language teaching evaluations. the system provides a more scientific

Test scores, attendance rates, and online platform login frequency. " "Vocabulary Growth Rate

Identifying key indicators such as "Interaction Frequency," "Vocabulary Growth Rate," and "Student Engagement Levels." Algorithm Selection: the system provides a more scientific