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

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...

Full description

Saved in:
Bibliographic Details
Published in:Revista IEEE América Latina 2021-01, Vol.19 (1), p.98-106
Main Authors: Ulloa-Cazarez, Rosa Leonor, Garcia-Diaz, Noel, Soriano-Equigua, Leonel
Format: Article
Language:English
Subjects:
Accuracy
Adaptation models
Adaptive systems
Artificial neural networks
Biological neural networks
CAI
Computational Intelligence
Computational modeling
Computer assisted instruction
Datasets
e-learning
Education
Fuzzy logic
Fuzzy Systems
General regression neural networks
Higher education
Hybrid Intelligent Systems
IEEE transactions
Inference
Multi-layer Adaptive Fuzzy Inference System
Multilayers
Neural Networks
Online education
Performance prediction
Predictive models
Radial basis function
Statistical analysis
Student performance prediction
Takagi-Sugeno model
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. The MANFIS was trained and tested using a dataset integrated by the scores obtained by students in four online Higher Education courses. The MANFIS prediction accuracy was compared against the accuracies of Multilayer neural network, Radial Basis Function Neural Network, and General Regression Neural Network. The accuracy of the MANFIS prediction statistically outperformed at least one neural network (out of three possible) in each dataset. The Results indicate that MANFIS is an alternative model to predict student performance in online Higher Education settings.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2021.9423852