Predicting Students' Degree Completion Using Decision Trees


  • Josan Dionisio Tamayo Centro Escolar University, Km. 44 Longos, Malolos Bulacan 3000, Philippines
  • MIT. Nilo V Francisco Centro Escolar University, Km. 44 Longos, Malolos Bulacan 3000, Philippines
  • Ph. D. Mary Eujene P Malonzo Centro Escolar University, Km. 44 Longos, Malolos Bulacan 3000, Philippines
  • Abigail P Bugay Centro Escolar University, Km. 44 Longos, Malolos Bulacan 3000, Philippines


Decision Trees, Education Data Mining, Predictive Analytics, Data Mining Algorithm, Data Mining.


Educational Data Mining (EDM) helped institutions to improve students' performance by predicting student's future learning behavior. To benefit from this, the researchers conducted this study to predict the successful degree completion and provide early intervention as necessary. Decision Tree algorithm provided by WEKA is used to build the model using students' data such as Entrance Exam Results, gender, school type where they graduated high school and final grades from English 1, Algebra and major subjects. Students who entered the University from school years 2012-2013, 2013-2014, 2014-2015 and 2015-2016 were selected. RandomForest suited best for the model and desktop application was designed and evaluated as Outstanding in terms of Efficiency, Accuracy and User Friendliness. 


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How to Cite

Tamayo, J. D., V Francisco, M. N., Eujene P Malonzo, P. D. M., & P Bugay, A. (2018). Predicting Students’ Degree Completion Using Decision Trees. International Journal of Computer (IJC), 28(1), 75–89. Retrieved from