Evaluation analysis of college Chinese teaching based on random simulation algorithm

This paper proposes a random simulation-based evaluation model for college Chinese teaching, aiming at accurately evaluating teaching effect and students’ learning progress through comprehensive and detailed evaluation index system, diversified data integration method and advanced Monte Carlo simulation technology. The evaluation system revolves around three dimensions: knowledge mastery, skill improvement, and emotional attitude to ensure the depth and breadth of evaluation. Data collection covers multiple sources of information such as academic performance, classroom interaction, and self-reflection, and undergoes rigorous preprocessing to improve the accuracy and reliability of the analysis. Based on the assumption that the teaching process is regarded as a dynamic random system, the model uses probability distribution and complex function relationship to quantify the dynamic change of students’ state, especially the improvement of key skills such as writing ability. In the experimental evaluation part, the validity of the model was verified by detailed data analysis, reliability and validity tests. Finally, the model showed high accuracy and recall in predicting students’ learning performance, proving its applicability and practicality in complex teaching environments.