1. Prediction of Online Students Performance by Means of Genetic Programming.
- Author
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Ulloa-Cazarez, Rosa Leonor, López-Martín, Cuauhtémoc, Abran, Alain, and Yáñez-Márquez, Cornelio
- Subjects
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GENETIC programming , *LEARNING management , *INDEPENDENT variables , *PERFORMANCE evaluation , *DECISION making , *PREDICTION theory - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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