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.)
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).