The Relationship Between Teaching Presence Types and Learning Effectiveness in Virtual Reality Contexts: An Empirical Study Based on Latent Profile and Network Analysis

doi:10.16382/j.cnki.1000-5560.2026.06.004

Abstract

Abstract:

Learners’ perceived learning effectiveness in VR environments is influenced by teaching presence and need for cognition; however, the mechanisms underlying these relationships remain insufficiently understood. Guided by the Community of Inquiry framework, this study surveyed a sample of 1,347 university students to identify latent profiles of teaching presence in VR-based instruction using latent profile analysis. In addition, network analysis was employed to examine the structural characteristics and core components of each profile, and the effects of different profiles on perceived learning effectiveness were tested while also assessing the moderating role of need for cognition. The results revealed that: (1) teaching presence in VR instruction can be categorized into three profiles—“Basic Construction Type,” “Innovative Leading Type,” and “Balanced Progressive Type”—which differed significantly across the dimensions of instructional design, facilitation, and direct instruction; (2) network analysis indicated distinct differences among the three profiles in network density, core nodes, and cross-dimensional interaction levels, with the “Innovative Leading Type” exhibiting the highest level of integration and systemic coordination; and (3) the “Innovative Leading Type” achieved the highest score in perceived learning effectiveness, with need for cognition significantly moderating the differences between the “Basic Construction Type” and the other two profiles. These findings illuminate the latent profiles of teaching presence, their network structures, and their differential impacts on perceived learning effectiveness in VR contexts, offering theoretical insights and practical implications for teacher professional development and personalized instructional support.

Key words: teaching presence, perceived learning effectiveness, need for cognition, latent profile analysis, network analysis

Jing Ma, Yue Wang. The Relationship Between Teaching Presence Types and Learning Effectiveness in Virtual Reality Contexts: An Empirical Study Based on Latent Profile and Network Analysis[J]. Journal of East China Normal University(Educationa, 2026, 44(6): 56-77.

Figures/Tables 19

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模型	AIC	BIC	ABIC	Entropy	LMRT	BLRT	类别概率
1类	8495.267	8526.501	8507.442
2类	5517.726	5569.783	5538.017	0.953	0.0000	0.0000	0.5657 0.4343
3类	441.195	514.074	469.602	0.997	0.0000	0.0000	0.391 0.207 0.402
4类	−220.997	−127.295	−184.473	0.988	0.1081	0.0000	0.353 0.207 0.079 0.361

类别	归属概率
类别	C1	C2	C3
C1	1	0	0
C2	0	1	0
C3	0.002	0.001	0.997

	教学临场感类型			F	事后检验
	1.基础构建型（n=527）	2.创新引领型（n=279）	3.平衡渐进型（n=541）	F	事后检验
教学设计	（3.08±.28）	（4.94±.21）	（3.95±.27）	4645.85***	1<3<2
促进对话	（3.05±.20）	（4.97±.13）	（3.95±.21）	9308.32***	1<3<2
直接指导	（3.01±.15）	（4.98±.76）	（3.97±.15）	18164.83***	1<3<2

	C1vsC2					C1vsC3
	β	SE	OR	95%CI	P	β	SE	OR	95%CI	P
性别（男性为参照）
女	0.205	0.146	1.277	(0.921,1.634)	0.161	−0.403	0.165	0.668	(0.483,0.923)	0.014
生源地（乡镇为参照）
城市	−0.075	0.146	0.928	（0.698,1.235）	0.608	0.066	0.172	1.068	(0.762,1.496)	0.703
年级（大三为参照）
大一	−0.432	0.437	0.649	(0.275,1.529)	0.323	−0.787	0.541	0.455	(0.158,1.315)	0.146
大二	−0.415	0.439	0.661	(0.279,1.562)	0.345	−0.536	0.541	0.585	(0.203,1.688)	0.321
院校（X校为参照）
H校	1.466	0.463	4.330	(1.748,10.727)	0.002	−0.877	0.289	0.416	(0.236,0.733)	0.002
J校	1.301	0.519	3.672	(1.327,10.161)	0.012	−1.041	0.427	0.353	(0.153,0.814)	0.015
S校	1.411	0.608	4.101	(1.245,13.513)	0.020	−1.786	0.699	0.168	(0.043,0.660)	0.011

变量	调节变量模型（因变量：感知学习效果）
变量	β	SE	t	P	LLCI	ULCI	F	R²
	“基础构建型”（C1）vs“创新引领型”（C2）
常数	3.871	0.034	113.25	<0.001	3.804	3.938	214.12	0.445
TP	0.681	0.070	9.737	<0.001	0.534	0.818
NFC	0.595	0.055	10.859	<0.001	0.487	0.703
TP types*NFC	−0.341	0.093	−3.68	0.003	−0.523	−0.159
	“基础构建型”（C1）vs“平衡渐进型”（C3）
常数	3.617	0.023	154.40	<0.001	3.571	3.663	85.10	0.194
TP	0.222	0.047	4.740	<0.001	0.130	0.314
NFC	0.576	0.047	12.298	<0.001	0.484	0.667
TP types*NFC	−0.272	0.094	−2.888	0.004	−0.456	−0.088
	“创新引领型”（C2）vs“平衡渐进型”（C3）
常数	4.08	0.029	139.29	<0.001	4.024	4.138	100.88	0.271
TP	−0.492	0.063	−7.87	<0.001	−0.614	−0.369
NFC	0.418	0.041	10.182	<0.001	0.337	0.498
TP types*NFC	0.070	0.075	0.924	0.356	−0.078	0.217