Skip to main content

Table 2 Summary of results of research seeking degree level prediction

From: Predicting academic success in higher education: literature review and best practices

RefAlgorithms UsedModelSample SizeBest AccuracySoftware
(Hamoud et al., 2018)J48; REPTree; RT[C]161REPTree-62.3%WEKA
(Al-barrak & Al-razgan, 2016)J48[C]236WEKA
(Putpuek et al., 2018)ID3; C4.5; KNN; NB[C]NB - 43.18%RapidMiner
(Asif et al., 2015)NB; KNN; NN; DT; RI[C]347NB - 83.65%RapidMiner
(Oshodi et al., 2018)LR; SVM[C][R]101SVM − 76.67%R
(Adekitan & Salau, 2019)PNN; RF; DT; NB; TE; LR[C][R]1841LR - 89.15%KNIME-MATLAB
(Asif et al., 2017)NB; K-NN; RF; NN; DT; RI; X-means[C] [CC]210NB-83.65%RapidMiner
  1. [C] for classification; [R] for regression; [CC] for clustering; BN Bayes net, DT decision tree, KNN k-nearest neighbors, LR logistic regression, NB naive Bayes, (P)NN (probabilistic) neural network, RB rule based, RI rule induction, RF random forest, RT random tree, NN neural network, TE tree ensemble; −: information not available