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

Jeffrey Co(1,Mail), Niel Francis Casillano(2)

(1) Eastern Samar State University, Philippines
(2) Eastern Samar State University, Philippines

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Copyright (c) 2021 Jeffrey Co, Niel Francis Casillano
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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.

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


References

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.

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.

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

Alturki, R. A. (2016). Measuring and improving student performance in an introductory programming course. Informatics in Education-An International Journal, 15(2), 183-204.

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

Casillano, N. F. (2019). Unraveling Views of Students Towards Computer Programming A Sentiment Analysis and Latent Semantic Indexing Application. IJRTE, 8(1) 543 456.

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

Corney, M., Teague, D., Thomas, R.N. (2010). Engaging students in programming. In: Twelfth Australasian Conference on Computing Education – Volume 103. 63 72.

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.

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

Han, J. Kamber, M. (2008). Data Mining: concepts and techniques. 2nd Edition, Morgan Kaufmann publishers. Hand, D. J., & Adams, N. M. (2014). Data mining. Wiley StatsRef: Statistics Reference Online, 1-7.

Hoda, R., Andreae, P. (2014). It’s not them, It’s us! Why computer science fails to impress many first years. In: 16th Australasian Computing Education Conference. 159 162.

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

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.

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.

Maulana, A. (2021, February). Prediction of student graduation accuracy using decision tree with application of genetic algorithms. In IOP Conference Series: Materials Science and Engineering (Vol. 1073, No. 1, p. 012055). IOP Publishing.

Nikula, U., Gotel, O., Kasurinen, J. (2011). A motivation guided holistic rehabilitation of the first programming course. ACM Transactions on Computing Education. DOI: 10.1145/2048931.2048935

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 Modern Education and Computer Science, 11(7), 1.

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

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.

Riyanto, V., Hamid, A., & Ridwansyah, R. (2019). Prediction of Student Graduation Time Using the Best Algorithm. Indonesian Journal of Artificial Intelligence and Data Mining, 2(1), 1-9.

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

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.

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.

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


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