Predicting Students' Degree Completion Using Decision Trees

Josan Dionisio Tamayo, MIT. Nilo V Francisco, Ph. D. Mary Eujene P Malonzo, Abigail P Bugay

Abstract


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. 


Keywords


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

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References


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