Paper Info
Title
Prediction of Online Students Performance by Means of Genetic Programming.
Abstract
Problem: Online higher education (OHE) failure rates reach 40% worldwide. Prediction of student performance at early stages of the course calendar has been proposed as strategy to prevent student failure. Objective: To investigate the application of genetic programming (GP) to predict the final grades (FGs) of online students using grades from an early stage of the course as the independent variable Method: Data were obtained from the learning management system; we performed statistical analyses over FGs as dependent variable and 11 independent variables; two statistical and one GP models were generated; the prediction accuracies of the models were compared by means of a statistical test. Results: GP model was better than statistical models with confidence levels of 90% and 99% for the training testing data sets respectively. These results suggest that GP could be implemented for supporting decision making process in OHE for early student failure prediction.
Year
DOI
Venue
2018
10.1080/08839514.2018.1508839
APPLIED ARTIFICIAL INTELLIGENCE
Field
DocType
Volume
Computer science,Genetic programming,Artificial intelligence,Higher education,Machine learning
Journal
32.0
Issue
ISSN
Citations 
9-10
0883-9514
0
PageRank 
References 
Authors
0.34
31
4
Name
Order
Citations
PageRank
Rosa Leonor Ulloa-Cazarez100.34
Cuauhtémoc López Martín293.20
Alain Abran3996204.62
Cornelio Yáñez-Márquez415326.34