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• W2893991014 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 variableMethod: 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." @default.
• W2893991014 created "2018-10-05" @default.
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• W2893991014 date "2018-09-25" @default.
• W2893991014 modified "2023-10-13" @default.
• W2893991014 title "Prediction of Online Students Performance by Means of Genetic Programming" @default.
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• W2893991014 doi "https://doi.org/10.1080/08839514.2018.1508839" @default.
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