Artificial neural network model of the results of college learning based on rubrics

  • Daniel Carbonel-Olazabal Universidad Nacional de Ingeniería
  • Judith Betetta-Gomez Universidad Nacional de Ingeniería
  • Laura Esponda-Versace Universidad Inca Garcilaso de la Vega
  • Cornelio Gonzales-Torres Universidad Inca Garcilaso de la Vega

Resumen

In the formative evaluation of the college student, applying rubric-based techniques is significant in obtaining relevant learning achievements. So, this study addresses modeling through the use of Artificial Neural Networks (ANN) of the results rubrics, corresponding to a sample of students from the Inca Garcilaso de la Vega University. The successful prediction results make the model a useful tool for the teacher and students for predicting the outcomes and targeting learning weaknesses. Furthermore, the reason for using ANN lies in working with strongly non-linear variables, so it would be a more effective model than those developed using convergent approximation polynomials, as will explain then. Also, it is essential to have worked on the Google Collaborative platform, as it is simple and accessible for students and teachers because it does not require specialized computing resources or neural network software on the personal computer. In this sense, all processes are of the cloud computing type.

Publicado
2020-12-10
##submission.howToCite##
CARBONEL-OLAZABAL, Daniel et al. Artificial neural network model of the results of college learning based on rubrics. Éxegesis, [S.l.], v. 12, n. 1, p. 5, dic. 2020. ISSN 2077-012X. Disponible en: <http://revistas.uigv.edu.pe/index.php/exegesis/article/view/708>. Fecha de acceso: 16 ene. 2021
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