[Retracted] Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern
Table 5
Performance measure of classification methods for course “carrying out and writing a research” with different feature numbers.
Features
Methods
Accuracy
Precision
F1
Recall
Jaccard
Fbeta
12
NN
0.6250
0.3813
0.3888
0.4326
0.3297
0.3793
L-SVM
0.6643
0.3382
0.3825
0.4583
0.3382
0.3537
R-SVM
0.6786
0.3771
0.4089
0.4750
0.3605
0.3867
GP
0.6786
0.4339
0.4445
0.4844
0.3993
0.4340
DT
0.9071
0.8142
0.7943
0.7906
0.7656
0.8039
RF
0.7304
0.4934
0.4947
0.5367
0.4434
0.4876
NeuralN
0.7161
0.3917
0.4167
0.4667
0.3750
0.3990
AB
0.9464
0.8829
0.8835
0.8933
0.8663
0.8822
NB
0.4375
0.3733
0.3154
0.3780
0.2222
0.3340
15
NN
0.6946
0.4601
0.4572
0.4888
0.3996
0.4545
L-SVM
0.6643
0.3382
0.3825
0.4583
0.3382
0.3537
R-SVM
0.6911
0.4011
0.4210
0.4833
0.3678
0.4037
GP
0.6929
0.4597
0.4732
0.5233
0.4228
0.4601
DT
0.9339
0.8496
0.8435
0.8489
0.8218
0.8458
RF
0.6768
0.3481
0.3850
0.4450
0.3418
0.3610
NeuralN
0.7036
0.3996
0.4229
0.4750
0.3746
0.4059
AB
0.9464
0.8829
0.8835
0.8933
0.8663
0.8822
NB
0.7089
0.4706
0.4637
0.4866
0.4008
0.4634
18
NN
0.6768
0.3833
0.4080
0.4667
0.3607
0.3892
L-SVM
0.6643
0.3382
0.3825
0.4583
0.3382
0.3537
R-SVM
0.6643
0.3382
0.3825
0.4583
0.3382
0.3537
GP
0.6786
0.3789
0.4106
0.4750
0.3623
0.3885
DT
0.9196
0.8188
0.8058
0.8139
0.7743
0.8108
RF
0.6500
0.3947
0.4052
0.4619
0.3463
0.3924
NeuralN
0.7161
0.4021
0.4300
0.4833
0.3854
0.4105
AB
0.9464
0.8829
0.8835
0.8933
0.8663
0.8822
NB
0.4268
0.3708
0.3085
0.3637
0.2171
0.3286
21
NN
0.6375
0.3107
0.3334
0.3921
0.2783
0.3156
L-SVM
0.6643
0.3382
0.3825
0.4583
0.3382
0.3537
R-SVM
0.6643
0.3382
0.3825
0.4583
0.3382
0.3537
GP
0.6643
0.3400
0.3841
0.4583
0.3400
0.3555
DT
0.9339
0.8854
0.8852
0.9000
0.8604
0.8838
RF
0.6643
0.3736
0.4026
0.4667
0.3509
0.3820
NeuralN
0.6786
0.3374
0.3547
0.3938
0.3078
0.3419
AB
0.9464
0.8829
0.8836
0.8933
0.8663
0.8822
NB
0.4429
0.3704
0.3275
0.3730
0.2451
0.3408
NN denotes Nearest Neighbors, L-SVM stands for Linear SVM, R-SVM denotes RBF SVM, GP stands for Gaussian Process, DT denotes Decision Tree, RF stands for Random Forest, NeuralN stands for Neural Networks, AB denotes AdaBoost, and NB stands for Naive Bayes.