A study on the efficiency and accuracy of neural network model to optimize personalized recommendation of teaching content

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The statistics of datasets

Statistical term	ASSISTMents2015	EdNet
Number of students	4286	1200
Number of exercises	18026	13624
Knowledge points	131	195
Number of answer records	289653	1125341
Number of correctly answered exercises	201328	836725
Number of incorrect answers to exercises	88325	228616

Experimental data

Network node	5 knowledge points in each chapter
Data	15623 student history answer data
	KU relationship
	Recommended number of test questions n
	Number of question banks

Overall results on student performance prediction

Model	ASSISTMents2015			EdNet
	ACC↑	RMSE↓	AUC↑	ACC↑	RMSE↓	AUC↑
IRT	0.6555	0.5361	0.6092	0.6921	0.4654	0.7118
DINA	0.6659	0.5337	0.6788	0.6894	0.4871	0.6826
MIRT	0.7033	0.4695	0.7251	0.7047	0.4476	0.7273
NeuralCD	0.7406	0.4481	0.7605	0.7166	0.4304	0.7784
RCD	0.7421	0.4349	0.7938	0.7299	0.4239	0.7865
This article	0.7826	0.4271	0.8072	0.7497	0.4043	0.7907

Comparison of indicator data for test results of class C student

Algorithm/model	Precision	Recall	F1	MAE	CRR¯ std. deviation	KU
Random	0.6062	0.7651	0.6764	0.0285	0.0051	1,2,4,5
DT	0.7036	0.8247	0.7594	0.0275	0.0118	2,3,4
IRT	0.6796	0.8247	0.7452	0.0223	0.0138	1,2,4,3
PMF	0.7637	0.8839	0.8194	0.0182	0.0032	1,2,4
CUPMF	0.8046	0.8921	0.8461	0.0141	0.0029	1,2,4

Comparison of indicator data for test results of class A student

Algorithm/model	Precision	Recall	F1	MAE	CRR¯ std. deviation	KU
Random	0.5562	0.7544	0.6403	0.0525	0.0235	1,2,4
DT	0.6164	0.7613	0.6812	0.0387	0.0016	2,3,4
IRT	0.6599	0.5836	0.6194	0.0656	0.0421	1,2,4,5
PMF	0.8599	0.8774	0.8686	0.0308	0.0045	1,2,4
CUPMF	0.8953	0.9105	0.9028	0.0147	0.0018	1,2,4

Comparison of indicator data for test results of class B student

Algorithm/model	Precision	Recall	F1	MAE	CRR¯ std. deviation	KU
Random	0.6648	0.7534	0.7063	0.0289	0.0182	1,2,4,3
DT	0.7081	0.7521	0.7294	0.0226	0.0129	1,2,4
IRT	0.7541	0.8453	0.7971	0.0188	0.0141	1,2,4
PMF	0.8028	0.9052	0.8509	0.0111	0.0023	1,2,4
CUPMF	0.8517	0.9121	0.8809	0.0136	0.0019	1,2,4