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Abstract Students who enrol ,in the undergraduate program on ,informatics at the ,Hellenic Open University (HOU) demonstrate significant difficulties in advancing ,beyond the introductory courses. We have embarked in an effort to analyse their academicperfor mance throughout the academic year, as measured by the homework assignments, and attempt to...
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... our work, the latest version of WEKA (Witten & Frank, 2000) was used to bring the experimental results up-to-date, in the sense that more conventional reporting techniques were used. For the sake of simplicity and completeness we conducted a 10-fold cross-validation experiment with the classifiers shown in Table 5, and reported the average accuracy as well as the accuracy on the training set. We also report the model size based on the full training data set (where applicable, i.e. ...
Context 2
... on our tutoring experience, success in the initial written assignments is a strong indicator of successful follow-up. This is also corroborated by the fact that such success motivates the student towards a more whole- 2 Predictably, the results in Table 4 and in Table 5 are not comparable. ...
Context 3
... that a trade-off between size and accuracy is explicitly stipulated in the fitness function of GATREE (Papagelis & Kalles, 2001). That the trade-off is reasonable, as set in the default parameters, can be argued by referring to the experimental results as reported in Table 5, error and cross-validation error is (relatively) small for GATREE, we only report re- classification error results in Table 8 and in Table 9. In Table 10, however, we also report the cross-validation error for the largest-scale experiment. ...
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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.
Full text link: https://www.tandfonline.com/eprint/YfZ8EDTSQ7s5fTSk2p2k/full
