- author = "Rosa Leonor Ulloa-Cazarez and Cuauhtemoc Lopez-Martin and Alain Abran and Cornelio Yanez-Marquez",
- title = "Prediction of Online Students Performance by Means of Genetic Programming",
- journal = "Applied Artificial Intelligence",
- year = "2018",
- volume = "32",
- number = "9-10",
- pages = "858--881",
- keywords = "genetic algorithms, genetic programming",
- ISSN = "0883-9514",
- publisher = "Taylor and Francis",
-
DOI = "
doi:10.1080/08839514.2018.1508839",
- size = "24 pages",
-
abstract = "Problem: Online higher education (OHE) failure rates
reach 40percent 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 on-line 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 90percent and 99 percent 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.",
-
notes = "Department of Information Systems, Universidad de
Guadalajara, Zapopan, Mexico
Also known as \cite{journals/aai/Ulloa-CazarezMA18},",
