Study of comprehensive evaluation method of undergraduates based on data mining

With expansion of university enrollment, the number of students increases every year, and information of their courses, scores and normal performance records also shows rapid expansion. More and more data of undergraduates have to be stored in database or data warehouse with the capacity increasing. It is difficult for teachers to finish comprehensive evaluation of over ten thousand students at the same time with the traditional methods of average scores, weighted average marks or Grade Point Average. The traditional ways are of some disadvantages of large error propagation, close results, small discrimination, low efficiency and poor scalability when facing mass assess data. Therefore, a new comprehensive evaluation method based on data mining of database or data warehouse of undergraduates is proposed. The method is carried out by decision tree algorithms. The results of case reveals that the decision tree algorithms of data mining technology can distinguish between the merits of the level of university students and realize the classification comprehensive evaluation, and the problem that the traditional methods are not fit for the student assessment of too much records and mass data is solved. The new method is of greater efficiency. It´s an effective complement to traditional evaluation methods.