Predicting Students' Degree Completion Using Decision Trees

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


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. 


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

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Acharya, A and Sinha, D. (2014). Early Prediction of Students Performance using Machine Learning Techniques. International Journal of Computer Applications 107(1):37-43, December 2014.

Al-Razgan, A., Al-Khalifa S., and Al-Khalifa H. S.,(2013). Educational data mining: A systematic review of the published literature 2006-2013 in Proc. the 1st International Conference on Advanced Data and Information Engineering, 2013, pp. 711-719.

Al-Barrak A. and Al-Razgan, M. (2016). Predicting Students Final GPA Using Decision Trees: A Case Study Mashael

Baker, R.S. and Yacef, K (2009), The state of educational data mining in 2009: A review and future visions

Becker, J. Student Success and College Readiness: Translating Predictive Analytics Into Action, Providence Public Schools L. Shane Hall, Dallas Independent School District

Bydžovská, H. and Brandejs, M. (2014). Knowledge Discovery and Information Retrieval

Devasia, T., Vinushree, T. P. and Hegde, V. (2016. Prediction of students performance using Educational Data Mining, 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), Ernakulam, 2016, pp. 91-95.

Kovacic, Z (2010). Early prediction of student success: mining students’ enrolment data, in Information Science and IT Education (InSITE) Conference Proceedings, ed. I Science Institute, Informing Science Institute, Santa Rosa, pp.647-665

Levinger, B., Sims, A. and Whittington , A. (2013). Towards Student Success Prediction

Roccini, L. M. (2011). The impact of learning communities on first year students’ growth and development in college. Research in Higher Education, 52, 178-193.

Rokach and Maimon, Data Mining and Knowledge Discovery Handbook.

Sperry, R. A. Predicting First-Year Student Success in Learning Communities: The Power of Pre-College Variables. Learning Communities Research and Practice, 3(1), Article 2.

Tekin, A. (2014). Early prediction of students’ grade point averages at graduation: A data mining approach. Eurasian Journal of Educational Research 54, 207-226.

Predictive Analytics for Student Success: Developing Data-Driven Predictive Models of Student Success Final Report University of Maryland University College January 6, 2015

Prediction of Student Success Rate Using Naive Bayes (2017)

Online References

Maryland universities to use data to predict student success — or failure, Retrieved January 5, 2017


A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python), Retrieved December 17, 2016


Machine Learning Group at the University of Waikato, Retrieved December 15, 2016 from


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