Research On The Construction Of User Portrait Of Distance Education Based On Data Mining

In recent years,with the development of Internet technology,the distance education industry has also been booming,and more and more people have begun to plunge into the wave of distance education.In distance education learning,users can choose the content of learning,the time of learning and the place of learning.This type of education breaks the boundaries of time and space and brings great convenience to learners,but there are also a series of problems.The distance education teachers cannot have a clear understanding of their own students,and thus cannot provide individualized guidance to learners.In addition,the problem of high dropout rates in distance education has become more and more serious due to differences in the educational background of students and lack of autonomy.At the same time,it is necessary to characterize the current characteristics of education users and encourage more people to participate in distance education.This thesis takes the user portrait technology as the starting point and the users in the School of Networking and Continuing Education of Xidian University as the analysis targets,effectively analyzes and mines the massive data generated during the user learning process through data mining technology and constructs the individual portrait and group portrait of the user,and finally applies the user portrait technology to the distance education learning platform to help distance education teachers undertake personalized teaching,timely detect users with a tendency to drop out and encourage more people to participate in distance education.The main work of the thesis is as followed:1.This thesis introduces the research status of distance education,data mining and user portrait technology at home and abroad,and expounds the research background of this thesis,and discusses the practical significance and use of user portrait technology in distance education learning platform.2.The data mining technology and user portrait technology are studied,and a distance education user portrait labeling system is built from two different perspectives.3.The logistic regression model in the classification algorithm is deeply studied and a model of distance education user dropout based on Logistic regression is constructed,and the correctness of the model is verified.4.The K-Means algorithm is studied.At the same time,an improved K-Means algorithm is proposed for the shortcomings in the K-Means algorithm.Through the experimental comparison of the two algorithms,the results show that the improved K-Means algorithm has better accuracy.5.The user portrait technology is applied to the learning platform of distance education,and a learning system including user portrait function of remote education is designed and implemented.Including the design and development of the learning platform,collecting and storing user's basic data and learning behavior data,preprocessing the data,and constructing individual and group user portraits for distance education user based on the Logistic regression model and the improved K-Means algorithm.