Model for predicting academic performance through artificial intelligence
Date
2020
2020
Author
Silva, Jesús
Romero, Ligia
solano, darwin
Fernández, Claudia
Pineda, Omar
Rojas, Karina
Metadata
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Enlace externo del documento: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090094518&doi=10.1007%2f978-981-15-6876-3_41&partnerID=40&md5=8195fed2ff7082bc4a3977ff9b05616b
Abstract
During the transit of students in the acquisition of competencies that allow them a good future development of their profession, they face the constant challenge of overcoming academic subjects. According to the learning theory, the probability of success of his studies is a multifactorial problem, with learning-teaching interaction being a transcendental element (Muñoz-Repiso and Gómez-Pablos in Edutec. Revista Electrónica de Tecnología Educativa 52: a291–a291 (2015), [1]. This research describes a predicative model of academic performance using neural network techniques on a real data set.
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