Multi-layer Adaptive Fuzzy Inference System for Predicting Student Performance in Online Higher Education

Authors

  • Rosa Leonor Ulloa-Cazarez Universidad de Guadalajara, México http://orcid.org/0000-0002-3868-0166
  • Noel García-Díaz
  • Leonel Soriano-Equigua Facultad de Ingeniería Mecánica y Eléctrica en la Universidad de Colima, México

Keywords:

Computational Intelligence, fuzzy system, hybrid intelligent systems, Artificial Neural Network, multi-layer adaptive fuzzy inference system, student performance, online education, e-learning

Abstract

Research on student performance prediction has evolved from the early application of statistical techniques to later use of computational techniques. Results in this field are varied thus, we have to take advantage of previous research results. This study proposes a Multi-layer Adaptive Neuro-Fuzzy Inference System (MANFIS) for student performance prediction in online Higher Education settings. To generate the MANFIS, we used a dataset integrated by the scores obtained by students in four online Higher Education courses. The MANFIS prediction accuracy was statistically compared against the accuracies of three neural networks. The results indicate that MANFIS is an alternative model to predict student performance in online Higher Education settings

Published

2020-09-21
Bookmark and Share