Predicting On-time Graduation based on Student Performance in Core Introductory Computing Courses using Decision Tree Algorithm - Co, Jeffrey; Casillano, Niel Francis (Jurnal Pendidikan Progresif, 2021)
Objectives: This study primarily aimed at developing a model that will predict whether a student will graduate on time based on their academic performance in their respective core introductory computing courses. Methods: The educational data mining process was employed in the conduct of this research. The process commenced with the collection of educational data and culminated with the evaluation of the developed model. This research utilized the decision tree algorithm. Findings: The model evaluation resulted to an 88.9% classification accuracy where the total number of actual “Yes” (students who graduated on-time) is 52.49 were classified correctly and 3 were misclassified as “No” in the prediction and the total number of actual “No” (students who did not graduated on-time) is 20.15 of which were classified correctly and 5 were misclassified in the prediction. Conclusion: Results of the study can be used as inputs in the crafting of new resource materials and an improved curriculum that will help improve the performance of students in the database management course. The model can also be used as a tool to help students graduate on-time.Keywords: decision tree, prediction, on-time graduation.DOI: http://dx.doi.org/10.23960/jpp.v11.i3.202116