Analysis Of College Student Achievement Based On Data Mining Technology

With the continuous deepening of the reform of ordinary higher education,the number of college students is on the rise year by year.Due to the development of society,the factors affecting students' performance have become more and more complex.As the core department in charge of teaching,academic Affairs Office is responsible for curriculum arrangement,teaching quality monitoring,scientific research and student performance management.With the rapid development of information technology,higher requirements are put forward for the management of educational administration system.Nowadays,data mining technology has been widely used in fields such as communication,business,education and finance,which has greatly changed people's cognition of data mining.However,there are relatively few applications in the field of education,especially in the educational administration management system of some private universities,whose functions mainly focus on daily business processing.The massive student scores only realize the basic storage function,but do not give play to their hidden value behind the data.Data mining technology can dig out the correlation between student achievement and curriculum from the massive student achievement,and provide data support for teachers to improve teaching methods and students to improve learning efficiency.This paper uses data mining technology to analyze the student scores of The Wenhua College of Yunnan Arts University,and finds out the correlation between student scores and courses,as well as the internal connection between courses,so as to make better teaching plans according to the curriculum setting.This paper mainly makes the following research work:(1)First of all,this paper briefly analyzes the utilization of student performance data in The Wenhua College of Yunnan University of the Arts,studies relevant theories at home and abroad and summarizes them,systematically learns data technology,association rule algorithm and other relevant theories,and puts forward improvement measures.(2)Secondly,the Apriori algorithm,FP-Growth algorithm,cluster analysis and other methods are used to carry out data mining on the grades of students from 2016 to 2018 in Wenhua College to find out the internal correlation between them and the internal relation between core curriculum setting,so as to provide data support for improving teachers' teaching work and students' learning efficiency.(3)Finally,through association rule algorithm and other technologies,the internal rules between student scores and courses are discovered,and the results of data analysis are effectively used to provide decision-making basis for educational administration departments in curriculum setting,professional construction,teaching management and talent training program.