If you are preparing a video or high-res slides for this topic, I recommend including: of the data processing pipeline.
Which teaching behaviors (e.g., frequent Q&A, use of multimedia) correlate most strongly with high student achievement.
Comparisons between manual evaluations and ML predictions (e.g., 85-90% alignment).
Summary of how ML enhances the objectivity of foreign language teaching evaluations.
The digitalization of higher education and the increasing need for standardized yet flexible foreign language assessment.
Ensuring the model doesn't penalize non-traditional but effective teaching methods. 6. Conclusion
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.