Mining Multidimensional And Multilevel Association Rules In Educational Data

With the rapid development of science and technology,major universities as the cradle of technical talent,many of the future changes in society is quietly born here.Most of the colleges and universities already have their own education platform,in such a platform,a large number of data generated every day,a reasonable mining of these data brings very important significance to teachers and students even education guidance.As a very important part of educational data,the performance of students can reflect the student learning situation and teaching quality intuitively to a certain degree.This paper applies the association rule technology in data mining to analyze and extract the teaching achievement data,and use the meaningful results as the basis of teaching guidance and students' training.The association rule is a kind of algorithm in data mining technology,The main purpose is to analyze the association between the found data.The purpose of this paper is to discover the association between the courses by mining the teaching achievement,so the association rule is adopted as the main method of this experiment.In this paper,the association rules are analyzed deeply,and the development history of association rules is sorted out.An algorithm for mining multidimensional and multilevel association rules called MMSP is proposed.This paper analyzes the student achievement with the actual data.We follow the process of data mining,collect the data required for the experiment,and then we preprocess the data and get the data model for the experiment.Then we use the relevant algorithm to mine the data.The first is to use the FP-growth algorithm to process each semester course;Second we use the MMSP algorithm to mine the courses in different terms;Then,we add other reference dimensions such as gender and college entrance examination results to the experiment.Finally,we show some meaningful results,and were analyzed and summarized.This paper focuses on the actual situation of the performance data,as the score data has a lot of valuable hierarchical structure,and there are many other attributes related to the results.Our experiments are more concerned with the association between courses in different semesters,adding time constraints,and adding other reference dimensions,thereby enriching the mining of information and finding more meaningful knowledge.Experiments show that the application of MMSP algorithm in the achievement data is very successful.The knowledge obtained by mining can be found to be valuable by analysis.The conclusion can be used as a data basis for the teaching staff to be used as a teaching task arrangement,but also to help teachers to teach in accordance with their aptitude,and it can also help to guide students to learn more effectively.