Research on Russian language teaching quality evaluation based on the KNN algorithm

With the growing demand for Russian language education worldwide, the effective evaluation of teaching quality has become a critical area of study. Traditional methods of evaluating Russian language teaching often rely on subjective assessments, leading to inconsistencies and limited accuracy. This paper proposes the use of the K-Nearest Neighbors (KNN) algorithm, a widely applied machine learning technique, to develop an objective and data-driven model for evaluating the quality of Russian language teaching. The KNN algorithm classifies teaching quality based on various factors such as student performance, teacher feedback, and classroom engagement. By using historical teaching data, the algorithm identifies patterns in these features and classifies new teaching instances into predefined quality categories. The study uses a dataset comprising performance metrics, feedback from students and teachers, and engagement levels to train and test the model. The results demonstrate that the KNN-based evaluation model outperforms traditional subjective evaluation methods in terms of accuracy, consistency, and reliability. This approach offers a more comprehensive and objective means of assessing Russian language teaching quality, providing valuable insights for educators and administrators to improve teaching practices. The research highlights the potential of machine learning techniques, such as the KNN algorithm, in transforming educational quality assessment and offers a framework for future research in language education evaluation.