Research And Application Of Big Data Technology In Student Performance Analysis

Nowadays, the era of data explosion has come, traditional data processing technology has been unable to meet the needs of massive data processing. With the development of information technology, big data processing technologies led by Hadoop are widely applied on the Internet, business, medical, financial, and industrial and others, however its application in education is indeed rare. Therefore, this paper applies Hadoop cluster platform to analyze and process the student achievement data,and proposes a kind of Map Reduce-based association rules algorithm which improving the efficiency of the association rules algorithm as well as mining the association rules between courses.The work is mainly divided into the following two parts:Firstly, describe the core architectures in Hadoop platform—HDFS and Map Reduce, use HDFS to store education data. After the thorough understanding to the Map Reduce programming model, we implement the Apriori algorithm on the Hadoop platform successfully according to the Apriori algorithm of association rules,and the feasibility of the algorithm is verified by example. By changing the size of the data, min Sup and min Conf, we give the contrast analysis of the two algorithms,experiments show that the performance of Map Reduce Apriori algorithm is better than the traditional Apriori algorithm in all aspects.Secondly, the paper builds the Hadoop cluster platform, and the improved algorithm is applied to the real student data, meanwhile mine the correlation hidden in courses. This paper also makes some statistical analysis work.The main innovations of this paper are:(1) Improve the traditional Apriori association rules algorithm according to the characteristics of Map Reduce programming model, and the improved algorithm not only discovers the frequentitemsets, but also mines the strong association rules according to the frequent itemsets;(2) This paper sets up the Hadoop cluster platform to analyze and deal the real student achievement data through the studying of big data technology and the analysis of student data.A lot of information presents around us all the time in the campus, meanwhile,the potential value hidden in educational data endless and deserves us to explore. How to effectively invert the huge amount of data into fruitful results of educational research for applying to improve the decision-making and practice of education is of great significance for education research. We hope that the result can provide a direction for future researchers, also provide guidance for educators in the later teaching management.