[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.