Uncovering Key Educational Predictors of GPA in Mathematics Education Undergraduates: A Decision Tree Classification Study
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(1) Department of Mathematics Education, Universitas Pendidikan Indonesia, Indonesia
(2) Department of Mathematics Education, Universitas Pendidikan Indonesia, Indonesia
Corresponding Author
Student academic achievement in the form of Grade Point Average (GPA) is one of the measures to assess the quality of learning. Not only are individual cognitive factors a determinant of students' high GPAs, but other non-cognitive factors can affect GPA achievement. Therefore, this study aims to examine the factors that influence academic achievement, as measured by GPA. This study was a descriptive quantitative study involving 63 mathematics education student respondents, to identify the factors that influence the GPA achievement of students in the mathematics education study program. The data was analyzed descriptively and tested using a decision tree. Based on the decision tree algorithm, the results showed that learning methods were the dominant factor in classifying student GPA achievements, specifically whether the GPA of 3.50 or higher or less than 3.50. Students with a GPA of 3.50 or higher tend to benefit positively from discussion-based learning methods, while those with a GPA of less than 3.50 tend to engage more with traditional learning approaches. In addition to learning methods, factors such as the reason for choosing the study program, parents’ educational background, and the duration of independent study time also contribute to strengthening or weakening students’ GPA achievements. In the model evaluation results, the decision tree algorithm showed good predictive performance, demonstrating its effectiveness in classifying students based on their GPA achievement levels. Therefore, the results of this study can serve as a reference for educators and educational institutions to design more effective pedagogical strategies that not only strengthen cognitive skills but also foster the holistic development of students in mathematics education programs.
Keywords: classification, decision tree, grade point average, mathematics education.
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