A Model for Evaluating the Effect of Moral Education Intervention Measures Based on Decision Tree

With the deepening of education reform, moral education has gradually become an important part of students’ all-round development and social adaptability. In view of the strong subjectivity and inaccurate data analysis in the current effect evaluation of moral education intervention measures, an effect evaluation model based on decision tree is introduced, aiming to evaluate the effect of moral education intervention in a scientific and quantitative way, and provide data support and decision-making basis for subsequent moral education improvement. First, various data related to moral education intervention are collected, including students’ moral education assessment scores, behavioral change records, academic performance, etc. Then, the decision tree algorithm is used to train and model these data, and the information gain, Gini index and other indicators are used to select the optimal features to construct a decision tree model that affects the effect of moral education. Finally, combined with the output results of the decision tree model, the actual effect of moral education intervention measures is quantitatively evaluated to provide data support for the optimization of future moral education programs. The test results of the model show that the evaluation method based on decision tree can effectively reduce the subjectivity in the evaluation process and improve the accuracy and operability of intervention effect evaluation. The decision tree model constructed takes Pre Behavior as the root node. Low-behavior students (≤50) are more likely to improve through intervention, among which "classroom education" and "team activities" have better effects. The intervention effect of high-behavior students (>50) is stable. The moral education intervention effect evaluation model based on the decision tree can provide a more scientific and objective evaluation tool for moral education work, and improve the predictive ability of intervention effects and the adjustment space of intervention measures.