Data Mining In College Achievement Analysis

Since the successive expand enrollment and the influence of the top-up program together with the rapid increase of the undergraduates, a great many schools are facing the condition such as the shortage of teachers and the lack of hardware resources, these difficulties pose a real challenge for the teaching management and quality of the university. Data mining technology can acquire useful knowledge from the massive information, which benefits for the decision-making of the manager. In this paper, the author is grounded on the data of the school-based student, trying to discover the valuable information so that can offer help for the scientific decisions of school management in the future.This paper explored three aspects:the association rule, the decision-tree rule and cluster analysis. Firstly it introduced the summary of the data mining as well as the theoretical knowledge, common algorithms and mining tools; and then used the SPSS Clementine software, combining with the Apriori algorithm, C5.0 algorithm, K-means algorithm and hierarchical clustering algorithms and other algorithms to have the data mining to achieve the analysis of collection among courses and the results of the teacher assessment. At last researched what factors affected student achievement and the distribution of achievement in each college and so on.In summary, this paper preliminarily explored the implementation of data mining technology in the management of universities and laid a solid foundation in the continuing research.