| Título |
Prediction of Online Students Performance by Means of Genetic Programming |
| Tipo |
Revista |
| Sub-tipo |
JCR |
| Descripción |
Applied Artificial Intelligence |
| Resumen |
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. |
| Observaciones |
DOI 10.1080/08839514.2018.1508839 |
| Lugar |
Philadelphia, PA |
| País |
Estados Unidos |
| No. de páginas |
858-881 |
| Vol. / Cap. |
v. 32 no. 9-10 |
| Inicio |
2018-09-25 |
| Fin |
|
| ISBN/ISSN |
|
