Construction and evaluation of AI students'comprehensive evaluation model driven by big data
With the rapid development of educational informatization and big data technology,constructing an AI-based comprehensive student evaluation model has become a crucial approach to enhancing educational quality and achieving personalized teaching.This article aims to explore how to utilize big data technology to build a comprehensive,objective,and dynamic student evaluation model,and to evaluate this model.Initially,a framework for constructing a comprehensive student evaluation model based on the BP network structure is proposed.This framework encompasses data collection,preprocessing,feature extraction,model training,evaluation result analysis,and feedback optimization.Subsequently,empirical research is conducted on the model using actual educational datasets.By comparing and analyzing the effectiveness of different evaluation models,the accuracy and effectiveness of the constructed model are verified.Lastly,the feasibility and potential impacts of the model in practical applications are discussed,providing new insights and methods for the reform of the educational evaluation system.
big dataAI technologystudents comprehensive evaluation modelconstruction and evaluation
NETL
NSTL
万方数据
