Prediction of Online Students Performance by Means of Genetic Programming
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 grad...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-11-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2018.1508839 |
| _version_ | 1797684894731599872 |
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| author | Rosa Leonor Ulloa-Cazarez Cuauhtémoc López-Martín Alain Abran Cornelio Yáñez-Márquez |
| author_facet | Rosa Leonor Ulloa-Cazarez Cuauhtémoc López-Martín Alain Abran Cornelio Yáñez-Márquez |
| author_sort | Rosa Leonor Ulloa-Cazarez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:37:21Z |
| format | Article |
| id | doaj.art-9b9e2cc9ebe34a46a88e14fc2554bf5f |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:37:21Z |
| publishDate | 2018-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-9b9e2cc9ebe34a46a88e14fc2554bf5f2023-09-15T09:33:56ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452018-11-01329-1085888110.1080/08839514.2018.15088391508839Prediction of Online Students Performance by Means of Genetic ProgrammingRosa Leonor Ulloa-Cazarez0Cuauhtémoc López-Martín1Alain Abran2Cornelio Yáñez-Márquez3Sistema de Universidad Virtual-Universidad de Guadalajarade GuadalajaraDepartment of Software and Information Technologies Engineering, École de Technologie Supérieure-Université du QuébecCentro de Investigación en Computación - Instituto Politécnico NacionalProblem: 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.http://dx.doi.org/10.1080/08839514.2018.1508839 |
| spellingShingle | Rosa Leonor Ulloa-Cazarez Cuauhtémoc López-Martín Alain Abran Cornelio Yáñez-Márquez Prediction of Online Students Performance by Means of Genetic Programming Applied Artificial Intelligence |
| title | Prediction of Online Students Performance by Means of Genetic Programming |
| title_full | Prediction of Online Students Performance by Means of Genetic Programming |
| title_fullStr | Prediction of Online Students Performance by Means of Genetic Programming |
| title_full_unstemmed | Prediction of Online Students Performance by Means of Genetic Programming |
| title_short | Prediction of Online Students Performance by Means of Genetic Programming |
| title_sort | prediction of online students performance by means of genetic programming |
| url | http://dx.doi.org/10.1080/08839514.2018.1508839 |
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