From: Predicting academic success in higher education: literature review and best practices
Ref | Algorithms Used | Model | Sample Size | Best Accuracy | Software |
---|---|---|---|---|---|
(Hamoud et al., 2018) | J48; REPTree; RT | [C] | 161 | REPTree-62.3% | WEKA |
(Al-barrak & Al-razgan, 2016) | J48 | [C] | 236 | – | WEKA |
(Putpuek et al., 2018) | ID3; C4.5; KNN; NB | [C] | – | NB - 43.18% | RapidMiner |
(Asif et al., 2015) | NB; KNN; NN; DT; RI | [C] | 347 | NB - 83.65% | RapidMiner |
(Oshodi et al., 2018) | LR; SVM | [C][R] | 101 | SVM − 76.67% | R |
(Adekitan & Salau, 2019) | PNN; RF; DT; NB; TE; LR | [C][R] | 1841 | LR - 89.15% | KNIME-MATLAB |
(Asif et al., 2017) | NB; K-NN; RF; NN; DT; RI; X-means | [C] [CC] | 210 | NB-83.65% | RapidMiner |