Academic risk forecast model for undergraduate students of the National University of Engineering

• Autores: Garrafa Aragón, Hernán, Soto Rodríguez, Iván
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Nacional de Ingeniería
• Repositorio: Revistas - Universidad Nacional de Ingeniería
• Lenguaje: español; inglés
• OAI Identifier: oai:oai:revistas.uni.edu.pe:article/1073
• Enlace del recurso: https://revistas.uni.edu.pe/index.php/iecos/article/view/1073
• Nivel de acceso: acceso abierto
• Materia: Machine learning; integración de datos; predicción; machine learning; data integration; prediction

The present work, uses unstructured information in order to predict the academic risk of a student, making use of Machine Learning techniques. Phases: Construction of the datamart: The data from the different sources will be integrated to build the objective data repository, which will be divided into two: Training data and test data, Training of the model: which consists in elaborating the training model based on data from thedatamart, applying vectorial support machine. Validation of the model: It consists of evaluating the model obtained previously, using the test data from the datamart.