TY - JOUR
T1 - Prediction of Online Students Performance by Means of Genetic Programming
AU - Ulloa-Cazarez, Rosa Leonor
AU - López-Martín, Cuauhtémoc
AU - Abran, Alain
AU - Yáñez-Márquez, Cornelio
N1 - Publisher Copyright:
© 2018, © 2018 Taylor & Francis.
PY - 2018/11/26
Y1 - 2018/11/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85053877982&partnerID=8YFLogxK
U2 - 10.1080/08839514.2018.1508839
DO - 10.1080/08839514.2018.1508839
M3 - Artículo
SN - 0883-9514
VL - 32
SP - 858
EP - 881
JO - Applied Artificial Intelligence
JF - Applied Artificial Intelligence
IS - 9-10
ER -