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IJIET 2023 Vol.13(12): 1890-1898
doi: 10.18178/ijiet.2023.13.12.2002

Predicting Academic Performance Path Using Classification Algorithms

Edwar Abril Saire-Peralta* and Maria del Carmen Córdova-Martínez

Manuscript received June 5, 2023; revised July 17, 2023; accepted September 18, 2023.

Abstract—The objective of this research is to determine the academic performance route of students entering the Systems Engineering program. The academic performance route is defined by three courses, which develop sequentially in the first semesters, where students show difficulty to be approved. The population is represented by 827 students, the research was approached from a quantitative approach, the research design is non-experimental and the scope or level of research is correlational. The methodology implemented is CRISP-DM (Cross Industry Standard Process for Data Mining) using machine learning algorithms, through binary classification models using logistic regression algorithms, random forests and XGboost. The results have allowed predicting whether a student would pass or fail in each of the courses, determining their academic performance path. The classification models have been able to achieve an accuracy between 87% and 93%.

Index Terms—Classification algorithms, supervised learning, data mining, academic performance

The authors are with Universidad Nacional de San Agustin de Arequipa, Peru.
*Correspondence: esaire@unsa.edu.pe (E.A.S.P.)

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Cite: Edwar Abril Saire-Peralta* and Maria del Carmen Córdova-Martínez, "Predicting Academic Performance Path Using Classification Algorithms," International Journal of Information and Education Technology vol. 13, no. 12, pp. 1890-1898, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Managing Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net

 

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