The Applied Research Of Data Mining In The Socres Prediction Analysis Of Postgraduate Entrance Examination Of Colleges And Universities

In recent years, third-grade colleges have faced increasingly fierce competition, and the rate of postgraduate entrance examination has been one of the key indicators to measure the teaching quality of third-grade colleges. Therefore, how to offer a better guide to students in their postgraduate entrance examination is a prior issue in the teaching management of third-grade colleges. Because of the lack of scientific guidance, the counselors can only determine whether their students are willing to take part in postgraduate college entrance examination or not and whether their students have the ability to take the exams by their own subjective consciousness. The application of data mining in teaching management could play a vital role in scientifically enhancing the register rate and acceptance rate of postgraduate entrance examination of third-grade colleges. People should give priority to the usage of data mining in the teaching management of third-grade colleges.With the development of data mining, data mining has enjoyed increasing application in the teaching management of institutions of higher learning. This dissertation not only introduces the theoretical knowledge, algorithm and software application of data mining, but also describes the application of data mining in Weka. Actually, it could predict the postgraduate entrance examination rate of universities and colleges in Weka. Data can provides theoretical foundation for the guidance of postgraduate entrance examination and solve some practical problems in institutions higher learning since they lack scientific guidance in postgraduate entrance examination.With the help of the managers of college educational administration system, the researcher use Weka to analyze the scores of all students of2008and2009in computer science and electronic technology department, and to predict their acceptance rate of postgraduate entrance examination. This dissertation gives priority to the detailed introduction of the application of following three data mining technology in Weka, including bayesian classification, LIBSVM classification and linear regression classification. The optimal model, obtained from the comparison among these three data mining methods, is applied in analyzing the influence of college students’ scores during their school time on their acceptance rate of postgraduate entrance examination. Those sample students are from the students in computer science and electronic technology department. The results could provide a better data support for counselors’ guidance in students’ postgraduate entrance examination, therefore, our register rate and acceptance rate of postgraduate entrance examination could be greatly enhanced.