Artificial Neural Network Model to Predict Academic Results in Mathematics II; [Modelo de rede neural artificial para prever resultados acadêmicos em Matemática II]

Incio-Flores F.A.; Capuñay-Sanchez D.L.; Estela-Urbina R.O. · Revista Electronica Educare (2023) · DOI: 10.15359/ree.27-1.14516

Resumen

Objective. This article shows the design and training of an artificial neural network (ANN) to predict academic results of Civil Engineering students of the Fabiola Salazar Leguía National Intercultural University, from Bagua-Peru, in the subject of Mathematics II. Method. The CRISP-DM methodology was used, surveys were conducted to collect the data, and the RNA model was implemented in the Matlab software using the nnstart command and two learning algorithms: Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM). The performance of the model was evaluated through the mean square error and the correlation coefficient. Conclusions. The LM algorithm achieved better prediction effectiveness.

Instituciones: Universidad César Vallejo; Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua