Classification Knowledge Discovery In Teaching Information

A large amount of education and teaching information is stored in university databank in higher education information construction. This kind of information includes specialty setup, courses, teachers, students, student families, student health, student score each year etc. Just for these large amounts of information, most of these data cannot be utilized effectively. However, it is very important for the education advisers to obtain the valuable information for education-teaching innovation and administration behind this large amount of information.Many kinds of data mining techniques are studied in detail in this dissertation. The classification regulation is mainly studied. This dissertation explores the application of regulation discovering method in higher education information field. The mainly work includes the following aspects:(1) The development and the correlated theory knowledge of data mining technique are summarized. And the implementing scheme for the application of classification technique in higher education and teaching research is put forward.(2) Put forward the established method of student studying information database. The relativities among courses are mined by classify method and the score difference reasons are explored.(3) The classification arithmetic is studied deeply, and a new algorithm for decision tree base-on relativity attributes is proposed, and used to create the decision-making model of student score.(4) The research, as education rule, student grown-up rule, and student score variation rule are mined from a large amount of data, is developed.