虚拟现实情境下教学临场感类型与学习效果的关系——基于潜在剖面与网络分析的实证研究

doi:10.16382/j.cnki.1000-5560.2026.06.004

摘要/Abstract

摘要：

学习者在虚拟现实教学情境下的学习效果关乎技术赋能教育的实践成效，深受学习者内部差异与外部支持协同作用的影响。本研究聚焦于认知需求与教学临场感，旨在掲示二者对学习效果的影响路径。基于探究共同体理论，本研究以1347名高校学习者为研究对象，采用潜在剖面分析方法识别虚拟现实教学情境下的教学临场感类型，并结合网络分析探究各类型的结构特征，进而检验不同类型的教学临场感对学习效果的影响及认知需求在其中所起的调节作用。结果表明：（1）虚拟现实教学情境下的教学临场感可分为“基础构建型”“创新引领型”和“平衡渐进型”三类。不同类型在教学设计、促进对话和直接指导三个维度上存在显著差异。（2）网络分析结果显示，不同类型的教学临场感在网络紧密度、核心节点及跨维度互动水平上呈现差异，其中“创新引领型”网络呈现出更高的聚合度。（3）在学习效果上，“创新引领型”得分最高，且认知需求显著调节“基础构建型”与其他两类在学习效果上的差异。本研究揭示了虚拟现实教学中教学临场感的潜在类型、网络结构及其效应特征，为教师发展和个性化教学支持提供了理论依据与实践参考。

关键词: 教学临场感, 感知学习效果, 认知需求, 潜在剖面分析, 网络分析

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

马婧, 王玥. 虚拟现实情境下教学临场感类型与学习效果的关系——基于潜在剖面与网络分析的实证研究[J]. 华东师范大学学报(教育科学版), 2026, 44(6): 56-77.

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.

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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