"

Identification Of Factors That Affect The Academic Performance Of High School Students In Peru Through A Machine Learning Algorithm

Published in: Prospective and trends in technology and skills for sustainable social development. Leveraging emerging technologies to construct the future: Proceedings of the 19th LACCEI International Multi-Conference for Engineering, Education and Technology
Date of Conference: July 19-23, 2021
Location of Conference: Virtual
Authors: Lady Denisse Infante Acosta (Pontificia Universidad Católica del Perú, PE)
Jonatán Edward Rojas Polo (Pontificia Universidad Católica del Perú, PE)
Full Paper: #68

Abstract:

The Peruvian Ministry of Education annually conducts the Student Census Evaluation (ECE) to evaluate the level of learning achievement in the subjects of mathematics, reading and science and technology, both in public and private schools. The results obtained through these evaluations are classified as Before beginning, Beginning, In process or Satisfactory. According to the results of the ECE 2019, it is observed that the academic performance achieved in the area of mathematics presents the highest percentage of students at the Satisfactory level (17.7%); however, in turn, said field of study is also the one that groups the highest percentage of students at the Before beginning level (33.0%). For these reasons, the present research aims to identify those variables that affect the learning achievements in mathematics of high school students. In order to achieve this analysis, a classification model was constructed for each of the mentioned levels, through an ensemble machine learning algorithm that uses the gradient boosting method. As a result of the modeling, the importance of the variables analyzed was obtained, which finally identified those that have a greater relevance in the prediction of the classification of each level of learning achievement.