Year: 2024
Type: article
Abstract: The academic performance of students in Mexico has a great impact on the social and economic development of the country. Early detection of students at academic risk is necessary to improve educational quality and reduce school dropouts. This work presents a proposal that uses a predictive model based on Logistic Regression to identify students at high risk of academic failure and its usefulness to provide proactive and personalized support to those who need it. In addition, an overview of the impact of Artificial Intelligence and Machine Learning in education is presented, especially in predicting student dropout and supporting academic performance, allowing us to take an important step towards a more promising and successful educational future for students. students.
Authors: Antonio Álvarez Núñez, María del Carmen Santiago Díaz, Ana Claudia Zenteno Vázquez, Judith Pérez Marcial, Gustano Trinidad Rubín Linares
Institution: Benemérita Universidad Autónoma de Puebla
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4404839138