Research on the Storage and Analysis of Teaching State Data of the University

Min LI, Xiang-jun DU

Abstract


Teaching state data of the university has many characteristics like big data, such as volume, variety, value, velocity and so on. It is difficult to cope with data integration, multi-dimension, multi-granularity, systemic analysis and mining. This paper presents the storage model and analysis framework of teaching state data based on data warehouse, data mining and complex network theory, and collects teaching state data accumulated by Qingdao University. Further, a multi-level data mart is proposed to store the data, and OLAP, association rule mining, clustering analysis as well as complex network modelling tools are developed to analyze these data. The analysis of test result data and teaching quality evaluation index data show that the model and the framework are helpful for knowledge discovery of teaching state data.

Keywords


Teaching state data, Data mart, Data mining, Complex network


DOI
10.12783/dtssehs/aetms2017/15844

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