A study on predicting students’ grades for ideological and political courses with decision tree generation rules

Predicting students’ course grades is an essential element in teaching. This paper used decision tree generation rules to study the prediction of students’ ideological and political course grades. Firstly, ID3 and C4.5 algorithms were briefly introduced; then, an improved C4.5 algorithm with higher computational efficiency was put forward. The formula of the C4.5 algorithm was optimized using theories such as the Taylor series. Finally, experiments were performed on the UCI dataset and students’ ideological and political course datasets. The results suggested that the average classification accuracy and computation time of the improved C4.5 algorithm was 79.37% and 74.1 ms, respectively, on the UCI dataset, which was better than the traditional C4.5 algorithm. Then, the experiment predicting students’ course grades demonstrated that the average quiz grade and the number of video views had the greatest impact on the final grades. The prediction accuracy of the improved C4.5 algorithm reached 93.46%, and the average computation time was 54.8 ms, which was 19.17% less than the C4.5 algorithm. The experimental results verify the effectiveness of the generation rule of the improved C4.5 algorithm in predicting students’ ideological and political course grades. This algorithm can be applied in the actual grade prediction.