Evaluation And Influence Analysis Of Continuing Education Students Based On Bayesian Network

Modern distance education which extensively apply with the development of Internet technology is an important form of continuing education.Students obtain knowledge by using courses from the online teaching platform.The research on modern distance education is an important aspect of continuing education.In the study of modern distance education,the establishment of student evaluation system which adjust the reality is major content of research educators.Bayesian network is one of the method in data mining,which has characteristics of incremental learning,integrated priority information and abundant probability expression ability.Utilizing Bayesian network to analyze the modern distance education students achievement,and improving the existing student evaluation program is helpful for modern distance educationThree main contributions of this article about the modern distance education student data analysis technology are listed as follows:(1)In the aspect of curriculum assessment,a structure diagram of students' achievemen evaluation based on curriculum organization is proposed.Firstly,the corresponding Bayesian network model is built by the following four main factors:quiz,assignment,course forum and final.Then,the data is analyzed by EXCEL and MATLAB,and the weights of the four factors is adjusted to improve the model and conduct experiments.Experiment results and model evaluations show that the improvement model can eval-uate students achievement objectively and comprehensively,and achieve more rational evaluation.(2)In the aspect of students' class performance,a structure diagram of students'achievement evaluation based on the learning state is proposed.First of all,the cor-responding relationship between grade evaluation and performance degree is found to establish the hierarchical diagram of students' achievement evaluation system.Then,according to the Bayesian network established by the causal relationship between factors,the students' achievement are estimated and verified by an actual example.Experiment results show that the proposed model can well evaluate students' achievement by stu-dents' learning state.(3)The main six factors are analyzed by two models combination.Then performance analysis system is built.A Bayesian network is constructed by the curriculum organi-zation and learning state.Firstly,the Bayesian network topology is constructed,which based on the model of the first two chapters.Then,combining the prior knowledge,the parameters of the model are analyzed,and the ideal evaluation scheme is found by the characteristics of data.This article used the MSBNX tool to create and evaluate Bayesian networks.The results show that the proposed student evaluation model improves the existing evaluation program.