Data Mining System Of Higher Vocational Education Research And Design

In recent years, higher vocational education has been developed rapidly. It has occupied about half of the country in scale, but however, it is not far from forming in the structure. From the quality and efficiency point of view, more than 50% of the higher vocational colleges have just been established. Higher vocational education is still in the exploratory stage. Therefore, higher vocational education system is not far from completed. Meanwhile, with the enrollment of higher vocational schools and development; some schools have accumulated a lot of data, such as student status management, performance management, employment management, etc. However, due to lack of information awareness and technology, the administrative personnel can only get some superficial information through the simple functions such as statistics and arrangement, but the information hidden in these data has not been applied. It is a considering issue for many vocational schools to face that how do make these data reuse and transform these existing data into the availability of knowledge in order to improve school management decision-making, management and educational quality. We can use Data mining technology to solve this problem. Many remarkable achievements have been achieved and successfully applied to in many fields no matter in our country and abroad but not in the education widely.The aim of the subjects is to set a system based on researching and studied of data mining system of higher vocational education. It is based on using rich educational experience and a lot of very valuable data of Beijing Information Technology College and focus on two centers of vocational education and employment, using data mining techniques to explore vocational education law about information.The system consists of four functional modules:Part one: students feature analysis module, the module presented in this paper analysis in a systematic way clustering of students as well as the results of clustering based on the use of genetic adaptive fuzzy C-means clustering algorithm (GaFcm-Genetic Algorithms Fuzzy C-Means) analysis. Part two:the talent factor analysis module, the module excavates the courses which students are taught the factors of employment have a greater impact by using C4.5 decision tree algorithm to establish the employment impact of factor mode. Part three:the student employment forecast module, the module built the decision tree based on principal component analysis of employment forecasting model, using the algorithm can predict the student performance information on whether the student employment and professional-related situations; Part four:the biogenic quality analysis module, it establishes a model which concludes student information and performance prediction by using genetic clustering algorithm and support vector machine classification method.