Decision Tree Based On Data Mining Technology To Score Analysis

The expansion of enrollments has caused the soaring number of students in universities, which exerts some negative effects on the quality of higher education. Teachers need to accumulate a large quantity of data in teaching.For example, the processing of students' test score is merely to sum up the inspective number of students who got A, B, C or D, which fails to figure out the reasons hidden behind their various academic performances.This essay puts forward a significant program that uses decision tree to find the valuable information hidden behind every student's test score.Data mining is a kind of decision support tool and a method to analysis data information to a deep level. Undoubtedly, it is beneficial to apply this data mining to our teaching work, for it can analyze the correlation between test score and other various factors thoroughly and it has a capacity of transforming masses of data into classification rules. Thus it guarantees the upgrading of data procession and improvement of our education quality.Decision tree is a vital technology in data mining and data forecasting and also a kind of taxonomy in form of decision tree reasoned out from a group of disordered and irregular cases. this essay will develop the method of applying decision tree to the test score processing.In this essay, the author illustrates the related conceptions of data mining, the basic theory and the learning process of ID3 algorithms, focusing on the application of data mining in test score processing and the method to use ID3 algorithms to construct decision tree. Also, it gives an example of using decision tree to analyze a group of students' test score and offers a model of test score processing based on decision tree.