TY - GEN
T1 - Identification of factors that affect the academic performance of high school students in Peru through a machine learning algorithm
AU - Acosta, Lady Denisse Infante
AU - Polo, Jonatán Edward Rojas
N1 - Publisher Copyright:
© 2021 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2021
Y1 - 2021
N2 - The Peruvian Ministry of Education annually conducts the Student Census Evaluation (ECE, for its acronym in Spanish) 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 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%). Considering the aforementioned, this research aims to identify those variables that affect the learning achievements in mathematics of high school students. Thus, for the proposed analysis, a classification model was built 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 greater relevance in the prediction of the classification of each level of learning achievement.
AB - The Peruvian Ministry of Education annually conducts the Student Census Evaluation (ECE, for its acronym in Spanish) 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 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%). Considering the aforementioned, this research aims to identify those variables that affect the learning achievements in mathematics of high school students. Thus, for the proposed analysis, a classification model was built 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 greater relevance in the prediction of the classification of each level of learning achievement.
KW - Classification model
KW - Educational system
KW - Machine learning
KW - Student academic performance
UR - https://www.scopus.com/pages/publications/85122034048
U2 - 10.18687/LACCEI2021.1.1.68
DO - 10.18687/LACCEI2021.1.1.68
M3 - Conference contribution
AN - SCOPUS:85122034048
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - 19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
A2 - Larrondo Petrie, Maria M.
A2 - Zapata Rivera, Luis Felipe
A2 - Aranzazu-Suescun, Catalina
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Prospective and Trends in Technology and Skills for Sustainable Social Development" and "Leveraging Emerging Technologies to Construct the Future", LACCEI 2021
Y2 - 19 July 2021 through 23 July 2021
ER -