Predict Academic Performance at UNADECA through Classification Systems
DOI:
https://doi.org/10.35997/unaciencia.v16i31.738Keywords:
Educational Data Mining, Machine Learning, random forest, metricsAbstract
Predicting the academic performance of students is not only a task that attracts researchers but also the administrative staff of university faculty. Effective models can be created using specific algorithms for supervised and unsupervised educational data mining. Cleaning and coding techniques were applied to the data set. The execution of the algorithms and the comparison of their metrics made it possible to determine the courses that should be assisted with greater attention in the quest to improve students' academic performance. The data were divided into two groups, one for learning and the other for prediction. Algorithms in the Python language and a graphical tool, RapidMiner Studio, were used. No clustering was performed due to lack of consistent information in the original data. The classification algorithm that had the best metrics was Random Forest, exceeding 90% accurracy in the different cases. RapidMiner, on the other hand, the algorithm with the best results was Gradient Boosted Trees with an accuracy of 93.6%, with the specific prediction of the result of pass or fail. A comparison was made by schools, with very similar results for Nursing, Psychology and Theology, with an accuracy of approximately 93%.
Downloads
References
A Alférez, G. H., Esteban, O. A., Clausen, B. L., & Ardila, A. M. M. (2022). Automated machine learning pipeline for geochemical analysis. Earth Science Informatics, 15(3), 1683-1698. https://doi.org/10.1007/s12145-022-00821-8
Almasri, A., Alkhawaldeh, R. S., & Çelebi, E. (2020). Clustering-Based EMT Model for Predicting Student Performance. Arabian Journal for Science and Engineering, 45(12), 10067–10078. https://doi.org/10.1007/s13369-020-04578-4
Alsariera, Y. A., Baashar, Y., Alkawsi, G., Mustafa, A., Alkahtani, A. A., & Ali, N. (2022). Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance. Computational Intelligence and Neuroscience, 2022, 1–11. https://doi.org/10.1155/2022/4151487
Altabrawee, H., Ali, O. A. J., & Ajmi, S. Q. (2019). Predicting Students’ Performance Using Machine Learning Techniques. JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences, 27(1), 194–205. https://doi.org/10.29196/jubpas.v27i1.2108
Babu, C., & Varghese, R. (2020). Predicting Student’ s Performance Using Educational Data Mining. 9(1).
Burman, I., & Som, S. (2019). Predicting Students Academic Performance Using Support Vector Machine. Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 756–759. https://doi.org/10.1109/AICAI.2019.8701260
Dhilipan, J., Vijayalakshmi, N., Suriya, S., & Christopher, A. (2021). Prediction of Students Performance using Machine learning. IOP Conference Series: Materials Science and Engineering, 1055(1), 012122. https://doi.org/10.1088/1757-899x/1055/1/012122
Domingos, P. (2012). A few useful things to know about machine learning. Communications of ACM, 55(10), 78–87. https://doi.org/10.1145/2347736.2347755
Gull, H., Saqib, M., Iqbal, S. Z., & Saeed, S. (2020). Improving Learning Experience of Students by Early Prediction of Student Performance using Machine Learning. 2020 IEEE International Conference for Innovation in Technology, INOCON 2020, 1–4. https://doi.org/10.1109/INOCON50539.2020.9298266
Hasan, H. M. R., Shahariar, A., & Rabby, A. (n.d.). Hasan, H. M. R., Rabby, A. S. A., Islam, M. T., & Hossain, S. A. (2019). Machine Learning Algorithm for Student’s Performance Prediction. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). Kanpur, India. https://doi.org/10.1109/ICCCNT45670.2019.8944629
Hasterok, D., Gard, M., Bishop, C. M. B., & Kelsey, D. (2019). Chemical identification of metamorphic protoliths using machine learning methods. Computers and Geosciences, 132(March), 56–68. https://doi.org/10.1016/j.cageo.2019.07.004
Jawad, S. M. D. (2021). Student Performance Analysis System Using Machine Learning. Journal of Resource Management and Technology, 12(84), 84–90.
Kamel, H., Abdulah, D., & Al-Tuwaijari, J. M. (2019). Cancer Classification Using Gaussian Naive Bayes Algorithm. Proceedings of the 5th International Engineering Conference, IEC 2019, 165–170. https://doi.org/10.1109/IEC47844.2019.8950650
Marius-Constantin, P., Balas, V. E., Perescu-Popescu, L., & Mastorakis, N. (2009). Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems, 8(7), 579–588.
Mierswa, Igno, and K. (2022). RapidMiner. RapidMiner Studio. https://rapidminer.com/
Osman, A. S. (2019). Data mining techniques: Review. International Journal of Data Science Research, 2(1), 1–4.
Priya, S., Ankit, T., & Divyansh, D. (2021). Student performance prediction using machine learning. Advances in Parallel Computing, 39(03), 167–174. https://doi.org/10.3233/APC210137
Rimadana, M. R., Kusumawardani, S. S., Santosa, P. I., & Erwianda, M. S. F. (2019). Predicting Student Academic Performance using Machine Learning and Time Management Skill Data. 2019 2nd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2019, 511–515. https://doi.org/10.1109/ISRITI48646.2019.9034585
Vasani, V. P., & Gawali, R. D. (2013). Classification Performance Evaluation. Encyclopedia of Systems Biology, 3(3), 411–411. https://doi.org/10.1007/978-1-4419-9863-7_100213
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Unaciencia Revista de Estudios e Investigaciones
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
