基于HM-OLS逐步回归模型的学生网络学习成绩关联因素分析

doi:10.13229/j.cnki.jdxbgxb.20231537

Abstract

Abstract:

To address the problem of existing regression analysis models being prone to estimation distortion or difficult to estimate accurately， an HM-OLS stepwise regression analysis model was proposed. First， the data was compressed and reduced in dimension using principal component analysis （PCA） to solve the problem of multicollinearity. Second， the processed data was used to construct a matrix， and the model parameters are estimated using least squares method， completing the model fitting， and conducting heteroscedasticity test and multicollinearity detection. Finally， the AIC value was used as a reference， and the forward stepwise regression method is used to select influencing factors， re-fitting the model， and completing the correlation analysis. The experimental results show that the HM-OLS stepwise regression analysis model proposed in this paper effectively eliminates the problems of scale differences and multicollinearity， and its stability and fitting effect are significantly better than those of traditional OLS and ridge regression analysis models. It can also accurately analyze the influencing factors with strong correlation to student academic performance in the network learning space.

Key words: HM-OLS stepwise regression model, network learning space data, student grades, correlation analysis

CLC Number:

TP391.1

Jun-jie LIU,Jia-yi Dong,Yong YANG,Dan LIU,Fu-heng QU,Yan-chang LYU. Analysis of factors associated with online learning performance of students based on HM-OLS stepwise regression model[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3755-3762.

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References 13

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变量	最小值	下四分位数	中位数	上四分位数	最大值	平均值
CRR	0.732	0.914	0.94	0.969	0.99	0.926
APR	0.091	0.286	0.506	0.718	0.965	0.512
PCR	0.308	0.644	0.804	0.909	0.994	0.765
NVR	0.003	0.568	0.708	0.813	0.962	0.684
ITR	2	1756	2 926	10 839	5 184 078	386 021
TS	1	25.25	64	78.0	6 865	195.018
BCR	11	124.75	693.5	3442	4 323 638	156 512
DPR	1	5.25	8	13	57	10.703
TRD	0	0.253	0.446	0.689	1	0.474
TM	0	0.066	0.123	0.388	1	0.297
CAS	0	0.089	0.292	0.559	1	0.355
EER	0	0.362	0.387	0.563	1	0.473
EIR	0	0.005	0.375	0.743	1	0.391
EHR	0	0	0.279	0.804	1	0.425

	coef	p	std err	［0.025	0.975］
APR	35.4399	0.001	7.136	21.076	49.804
PCR	52.3951	0.001	6.158	10.001	64.790
CAS	14.1343	0.048	6.970	0.105	28.164
EER	34.1781	0.001	4.682	24.754	43.602

Analysis of factors associated with online learning performance of students based on HM-OLS stepwise regression model

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