Application of Teaching Administrating Informatization in Universities Based on the Era of Big Data

Abstract

The arrival of the era of big data has brought a great impact on China's education industry and prompted the education industry to launch a series of educational reforms. Further use of information tools is an inevitable requirement of higher education reform, and many colleges and universities regard information construction as the foundation of higher education reform. In the process of informatization construction, there are bound to be various problems. By introducing the big data analysis system, this paper analyzes and summarizes the data collected by information systems such as the teaching resource database and the square educational administration system. K-MEANS clustering algorithm and association rule mining algorithm are designed. Aiming at the low time efficiency of Apriori algorithm of association rules, an improved I++ algorithm is proposed, and the association rules are applied to students' behavior analysis, and experimental analysis is carried out. Experiments show that the running time of the improved algorithm is obviously shortened, and the time complexity is small. Because the algorithm uses Boolean matrix and stores Boolean values "0"or "1,"the space complexity is also small. Therefore, the improved Apriori algorithm I++ is faster and more efficient.