Application of Decision Tree Algorithm in the Analysis and Evaluation of Quality Education Credits

The application of decision tree algorithm in the analysis and evaluation of quality education credits is to use the decision tree to judge whether students should obtain educational credits. Decision trees are used because they can easily be built from existing data sets collected for other purposes. The analysis here will be completed with the help of data from previous years, which will be used as input to this prediction model. The main purpose behind the use of this technology is to find out whether there is any positive or negative correlation between some factors such as age, gender, class and high-quality education credits. The purpose of such analysis is to determine students who may be eligible for educational credits based on their academic achievements, but their academic achievements do not meet the necessary criteria for obtaining educational credits. This type of analysis will help schools decide which students should receive credit and which students do not need credit. In order to explore the management mode of quality education and apply it to the quality education work in higher vocational colleges, this article introduces data mining technology, which divides and digitizes quality education, so as to achieve the analysis and evaluation of quality education credits, provide broader development space and institutional guarantee for students’ quality education, and make quality education an organic component of the education and teaching work in higher vocational colleges.