Application of decision trees for the identification of adaptability of students in online education

• Autores: Araoz Valencia, Luis Emanuel, Huaracha Condori, Walter, Quispe Quicaña, Víctor Raúl, Turpo Coila, Alex Ronaldo
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/113
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/113; https://doi.org/10.48168/innosoft.s12.a113; https://purl.org/42411/s12/a113; https://n2t.net/ark:/42411/s12/a113
• Nivel de acceso: acceso abierto
• Materia: Artificial Intelligence; Machine Learning; decision trees; Python; classification; online education; Inteligencia artificial; aprendizaje automático; árboles de decisión; clasificación; educación en línea

Due to the global pandemic by Covid-19, online education was established in student learning. However, the effectiveness of this modality, as well as the adaptability of the students, is something that may depend on some factors. In this sense, this research article presents a description of the use of decision trees to determine the adaptability of students in online education, using a dataset of 1205 records with data such as the type of connection and internet, device, condition. financial, among other important data. Likewise, tools such as Google Colab, Python and popular libraries were used in similar works of Artificial Intelligence and Machine Learning.