If you are preparing a video or high-res slides for this topic, I recommend including: of the data processing pipeline.
Abstract
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. If you are preparing a video or high-res
Existing ML models in educational assessment (e.g., neural networks, decision trees). Data Collection: the system provides a more scientific
Student feedback (text), classroom video analysis (feature extraction). classroom video analysis (feature extraction).