Predicting On-time Graduation based on Student Performance in Core Introductory Computing Courses using Decision Tree Algorithm

Jeffrey Co, Niel Francis Casillano

Abstract


Abstract: 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.

Abstrak: Tujuan: Studi ini ditujukan untuk mengembangkan model yang akan memprediksi apakah seorang siswa akan lulus tepat waktu berdasarkan performa akademik mereka dalam mata kuliah pengantar komputasi. Metode: Proses data mining pendidikan digunakan dalam penelitian ini. Prosesnya dimulai dengan pengumpulan data pendidikan dan diakhiri dengan evaluasi model yang dikembangkan. Penelitian ini menggunakan decision tree algorithm. Temuan: Evaluasi model menghasilkan akurasi pengklasifikasian hingga 88,9% di mana jumlah total jawaban "Ya" (siswa yang lulus tepat waktu) adalah 52,49 yang diklasifikasikan dengan benar dan 3 salah diklasifikasikan sebagai "Tidak" dalam prediksi dan jumlah total jawaban “Tidak” (siswa yang tidak lulus tepat waktu) adalah 20,15 di antaranya diklasifikasikan dengan benar dan 5 salah diklasifikasikan dalam prediksi. Kesimpulan: Hasil penelitian dapat digunakan sebagai masukan dalam penyusunan bahan ajar baru dan perbaikan kurikulum yang akan membantu meningkatkan kinerja mahasiswa pada mata kuliah manajemen basis data. Model juga dapat digunakan sebagai alat untuk membantu mahasiswa lulus tepat waktu.

Kata kunci: decision tree, prediksi, lulus tepat waktu.


DOI: http://dx.doi.org/10.23960/jpp.v11.i3.202116


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References


Kantardzic, M. (2003). Data mining : concepts, models, methods, and algorithms. Wiley Interscience. Retrieved from https:// ieeexplore-ieeeorg.library.iau.edu.sa/book/5265979.

Aleryani, A., Wang, W., De, B., & Iglesia, L. (2018). Dealing with missing data and uncertainty in the context of data mining. In International Conference on Hybrid Artificial Intelligence Systems.

Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3.

Ali, R. (2020, September 23). Predictive Modeling: Types, Benefits, and Algorithms. Retrieved from Oracle Netsuite: https://www.netsuite.com/portal/resource/articles/financial-management/predictive-modeling.shtml

Chaurasia, P. (2020). CONFUSION MATRIX. Retrieved from MGCUB: http://www.mgcub.ac.in/pdf/material/20200429020322e5dac20f58.pdf

Gupta, P. (2017, May 18). Decision Trees in Machine Learning. Retrieved from Towards Data Science: https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052

Hand, D. J., & Adams, N. M. (2014). Data mining. Wiley StatsRef: Statistics Reference Online, 1-7.

Martins, M. P. G., Miguéis, V. L., Fonseca, D. S. B., & Alves, A. (2019). A data mining approach for predicting academic success – A case study, (pp. 45–56). Cham: Springer.

Xing, W. (2019). Exploring the influences of MOOC design features on student performance and persistence. Distance Education, 40(1), 98–113

Liñán, L. C., & Pérez, Á. A. J. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. International Journal of Educational Technology in Higher Education, 12(3), 98-112.

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

Han, J. Kamber, M. (2008). Data Mining: concepts and techniques. 2nd Edition, Morgan Kaufmann publishers.

Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3.

Orion, H. C., Forosuelo, E. J. D., & Cavalida, J. M. (2014). Factors affecting students' decision to drop out of school. SLONGAN, 2(1), 16-16.

Tampakas, V., Livieris, I. E., Pintelas, E., Karacapilidis, N., & Pintelas, P. (2018, June). Prediction of students’ graduation time using a two-level classification algorithm. In International Conference on Technology and Innovation in Learning, Teaching and Education (pp. 553-565). Springer, Cham.

Aiken, J. M., De Bin, R., Hjorth-Jensen, M., & Caballero, M. D. (2020). Predicting time to graduation at a large enrollment American university. Plos one, 15(11), e0242334.

Xu, J., Moon, K. H., & Van Der Schaar, M. (2017). A machine learning approach for tracking and predicting student performance in degree programs. IEEE Journal of Selected Topics in Signal Processing, 11(5), 742-753.

Orange. (2015). Orange Visual Programming . Retrieved from orange3.readthedocs.io/:https://orange3.readthedocs.io/projects/orange-visual-programming/en/latest/widgets/

Demsar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, Mozina M, Polajnar M, Toplak M, Staric A, Stajdohar M, Umek L, Zagar L, Zbontar J, Zitnik M, Zupan B (2013) Orange: Data Mining Toolbox in Python, Journal of Machine Learning Research 14(Aug): 2349−2353.

Nurafifah, M. S., Abdul-Rahman, S., Mutalib, S., Hamid, N. H. A., & Ab Malik, A. M. (2019). Review on predicting students’ graduation time using machine learning algorithms. International Journal of Mode


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