Enhancing English Language Teaching Assessment Through Random Forest Algorithms

This study overcomes shortcomings in conventional English teaching assessments through the creation of a machine learning system for objective quality evaluation. The system evaluates multidimensional data—student achievement, teacher evaluations, classroom interaction, and learner profiles—via Random Forest algorithms for achievement prediction and clustering algorithms for teaching style analysis. Essential metrics such as exam scores, participation levels, and satisfaction ratings train the models to produce accurate, data-based insights. Outcomes show higher reliability than subjective techniques, with 97.63% accuracy, 96.62% precision, 95.32% recall, and a 97.23% F1 score. The AI-based method delivers scalable, empirical assessment of instructor effectiveness, supporting targeted instructional change. By merging predictive analytics with control-based techniques, the approach increases assessment adaptability while retaining high standards. This research sheds light on machine learning's educational transformative value, presenting schools with an automated means of optimizing English language teaching through measurable feedback loops.