Analysis of academic performance using machine learning techniques with assembly methods
Main Article Content
Keywords
Boosting, Educational data analytics, Ensemble, Machine learning, Student academic performance
Abstract
In recent years, the educational field has been permeated by data analysis models and algorithms that aim to search for knowledge from data to improve academic performance and other indicators. The main objective of this research is to predict the academic performance of students through machine learning techniques. Through feature selection methods are analyzed 324 variables, in order to determine the influential variables. The university academic performance prediction model is evaluated by means of supervised algorithms (KNN, SVC, Naive Bayes and decision tree), which are optimized using Python language. In addition, assembly algorithms are implemented that allow improving the accuracy of the previous classifiers. Bagging (CART, Random Forest; ExtraTreesClassifier), Boosting (AdaBoost, GBM, XGBoost, CatBoost, Light Boost) and Voting (Blending, Stacking) methods are implemented. The results show that the Stacking and Blending algorithms with accuracy values in each semester that oscillate around 85% and 75% for training and testing, respectively, yield the best results.
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Jalota, C., & Agrawal, R. (2019). Analysis of Educational Data Mining using Classification. Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, 243–247. https://doi.org/10.1109/ COMITCon.2019.8862214
Joshika, P. and Rajeshwari. (2019). STUDENTS ’ PERFORMANCE ANALYSIS USING SIMPLE K-MEANS. 21(14), 990–995.
Kaunang, F. J., & Rotikan, R. (2018). Students’ academic performance prediction using data mining. Proceedings of the 3rd International Conference on Informatics and Computing, ICIC 2018, 1–10. https:// doi.org/10.1109/IAC.2018.8780547
Kostopoulos, G., Kotsiantis, S., Pierrakeas, C., Koutsonikos, G., & Gravvanis, G. A. (2018). Forecasting students’ success in an open university. International Journal of Learning Technology, 13(1), 26–43. https:// doi.org/10.1504/IJLT.2018.091630
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Analysis of academic performance using machine learning techniques with assembly methods. bol.redipe [Internet]. 2021 Dec. 31 [cited 2025 Jan. 30];10(13):171-90. Available from: https://revista.redipe.org/index.php/1/article/view/1737
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Author Biographies
Leonardo Emiro Contreras Bravo, University Francisco Jose de Calda, Bogota D.C., Colombia
Engineer, Ph.D. student in engineering, Plant teacher. Faculty of Engineering. Francisco José de Caldas District University (Colombia).
Héctor Javier Fuentes López, University Francisco Jose de Calda, Bogota D.C., Colombia
Economist, PhD student in Economics. Plant teacher. Faculty of Engineering. Francisco José de Caldas District University (Colombia).
Edwin Rivas Trujilllo, University Francisco Jose de Calda, Bogota D.C., Colombia
Engineer, Doctor of Engineering. Plant teacher. Faculty of Engineering. Francisco José de Caldas District University (Colombia)
How to Cite
1.
Analysis of academic performance using machine learning techniques with assembly methods. bol.redipe [Internet]. 2021 Dec. 31 [cited 2025 Jan. 30];10(13):171-90. Available from: https://revista.redipe.org/index.php/1/article/view/1737