Evaluation analysis of college Chinese teaching based on random simulation algorithm

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Bibliographic Details
Title: Evaluation analysis of college Chinese teaching based on random simulation algorithm
Authors: Jiayu Wei
Source: Journal of Computational Methods in Sciences and Engineering.
Publisher Information: SAGE Publications, 2025.
Publication Year: 2025
Description: 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.
Document Type: Article
Language: English
ISSN: 1875-8983
1472-7978
DOI: 10.1177/14727978251369169
Rights: URL: https://journals.sagepub.com/page/policies/text-and-data-mining-license
Accession Number: edsair.doi...........f1f79e54d1112e88e66f9a443c20a6db
Database: OpenAIRE
Description
Abstract: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.
ISSN:18758983
14727978
DOI:10.1177/14727978251369169