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

Comparisons between manual evaluations and ML predictions (e.g., 85-90% alignment). Existing ML models in educational assessment (e

Identifying key indicators such as "Interaction Frequency," "Vocabulary Growth Rate," and "Student Engagement Levels." Algorithm Selection: " "Vocabulary Growth Rate

Summary of how ML enhances the objectivity of foreign language teaching evaluations. Existing ML models in educational assessment (e

Subjectivity and lag-time in traditional evaluation methods.

Test scores, attendance rates, and online platform login frequency.

Which teaching behaviors (e.g., frequent Q&A, use of multimedia) correlate most strongly with high student achievement.