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Citation: Chen, B.; Wang, Y.; Wang, L.
The Effects of Virtual
Reality-Assisted Language Learning:
A Meta-Analysis. Sustainability 2022,
14, 3147. https://doi.org/10.3390/
su14063147
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sustainability
Review
The Effects of Virtual Reality-Assisted Language Learning:
A Meta-Analysis
Bing Chen , Yunqing Wang and Lianghui Wang *
Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal
University, Jinhua 321004, China; chen_bing@zjnu.edu.cn (B.C.); 202125200558@zjnu.edu.cn (Y.W.)
*Correspondence: wlh@zjnu.cn; Tel.: +86-13566997797
Abstract:
Existing literature reflects that VR technology is widely used in language learning settings.
Although many studies have identified the multiple benefits and affordance of Virtual Reality (VR)
technologies in language learning, most studies are qualitative studies that do not provide substantial
evidence to investigate the impact of this technology on language learning. To this end, this study
conducted a meta-analysis of 21 quantitative studies with 1144 participants published between
2010 and 2021. The study’s main purpose was to examine the effects of VR on students’ language
learning academic performance, including linguistic gains and affective gains. The results indicated
that VR-assisted language learning had a medium effect on the linguistic gains (Hedges’ g = 0.662,
95% CI [0.398–0.925]
,p< 0.001) and affective gains (Hedges’ g = 0.570, 95% CI [0.309–0.831], p< 0.001)
of students compared to non-VR conditions, respectively. Furthermore, the study further analyzed
the impact of several moderator variables such as education levels, hardware types, language skills,
target language, and L1/L2 on language learning gains. The research indicates that VR technology has
a great potential to improve language learning as an educational resource and provides suggestions
for further research and practice on the use of VR-assisted language learning.
Keywords: meta-analysis; virtual reality; language learning; VR-assisted language learning
1. Introduction
Language serves as a bridge between humans and society to communicate. With
the growing trend of globalization, language education has also developed rapidly. For
example, the recognized international language, English, has become a compulsory subject
in many countries and regions [
1
]. However, language learning is a time-consuming
and challenging process for many students as far as the current situation is concerned.
The main reason for language learning difficulties is the lack of an authentic language
environment, and learners cannot personally contact relevant contexts to use the target
language to achieve learning goals [
2
]. That is, it is essential that language learners are
provided an authentic learning environment and meaningful tasks [
3
]. The advancement of
computer technology as a learning tool has provided new methods and created real-world
environments to improve language learning [
4
]. As a novel technology, virtual reality (VR)
has provided numerous alternative learning opportunities to language learners in the past
decade [5].
VR technology and device appeared in the early 1960s, and many studies on VR and
its applications have already been carried out in recent years [
6
]. Virtual Reality (VR)
refers to a three-dimensional (3D) environment generated by computer technology, which
can provide a context similar to visual simulation and other senses [
7
]. It allows users to
communicate with people, machines, and other entities in the virtual environment by using
computers and various devices [
8
,
9
]. There are two main types of virtual reality devices,
namely immersive VR and non-immersive VR. Immersive VR involves high immersion
and costs, such as cave automatic virtual environment (CAVE) and headset VR devices.
Sustainability 2022,14, 3147. https://doi.org/10.3390/su14063147 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 3147 2 of 18
Non-immersive VR commonly refers to the desktop VR that interacts with computers
through the mouse, control handle, and other devices [
7
,
10
]. The dramatic reduction in the
cost of devices and technology has driven a rapid growth of VR applications in educational
fields such as medicine, science, and mathematics in recent years that has been proven to
be positive [
11
–
13
]. Learners feel the actual situation through sensory organs, which can
help them improve their motivation, participation, and learning ability [
5
]. Moreover, VR
has also been applied to language learning and has shown the importance and potential of
applications to support language learning.
1.1. Applications of VR in Language Learning
In recent years, a large number of studies have documented the application of
technology-enhanced language learning. VR has also received a great deal of attention in
this field. VR technology is characteristics of immersion, interaction, and involvement [
14
].
It breaks through the limitations of traditional media, provides language learners with a
realistic simulated language learning environment, and effectively supports their language
learning. For example, Wehner, Gump, and Downey [
15
] investigated the effect of Second
Life on the motivation of English-speaking undergraduates to learn Spanish. They found
that Second Life increased participants’ motivation. Huang, Hwang, and Chang [
7
] devel-
oped a Chinese writing SVVR learning system based on spherical video-based virtual reality
(SVVR) and compared the effects of this method with traditional technology-supported
learning methods in high school writing classes. It was found that, compared to a control
group that participated in traditional technology-supported learning classes, the SVVR
writing improved students’ writing performance and their self-efficacy while also reduc-
ing their cognitive load. For example, Tai and Chen [
16
] randomly assigned 72 middle
school students to the experimental group (mobile headset virtual device) and control
group (traditional multimedia) to investigate the effectiveness of mobile headset virtual
devices for learners’ English listening comprehension compared with video learning. The
results showed that mobile headset virtual devices were significantly higher than video. In
summary, these studies indicate that VR can promote language skills and foster motivation
and self-efficacy.
1.2. Previous Review Studies on VR-Assisted Language Learning
As far as we know, most of the existing literature [
10
,
17
–
20
] has described the system-
atic reviews of VR application in language learning through qualitative variables. These
studies have shown that VR application in language learning is steadily growing and
effectively used in language learning settings. Lin and Lan [
17
] conducted a content anal-
ysis of VR applications in language learning from 2004 to 2013, and they found that the
research topics on VR-enhanced language learning were mainly interactive communication,
behavior, affections, and task-based instruction. Solak and Erdem [
18
] also conducted a
content analysis on 40 papers published from 1995 to 2015 on foreign language learning
and teaching through virtual reality and identified that the data collection tool preferred
was document analysis, and that the effectiveness of VR and game-based learning were
the two prominent topics in this field. Huang, Zou, Cheng, and Xie [
20
] analyzed the VR
on language learning from five perspectives: ways of integrating VR tools in language
learning; primary users; major research findings; why it can effectively promote language
learning’s impact. Qiu et al. [
10
] conducted a systematic literature review that included
150 articles on VR/AR in language learning from 2008 to 2019. The study found that
VR was most applied in higher education and that the most reported advantages of VR
in language learning were learners’ learning behaviors, learning attitudes, and learning
performance. It also showed that task-based learning and game-based learning were the
most common learning strategies.
Furthermore, one meta-analysis measuring the impact of VR on language learning
has been published in journals. Wang et al. [
21
] conducted a meta-analysis to examine the
effect size of 3D virtual worlds applied to language learning in the past two decades. The
Sustainability 2022,14, 3147 3 of 18
research included 13 primary studies and found that overall effect sizes of linguistic and
affective gains were 0.832 and 0.531, respectively.
However, the aforementioned review studies present certain limitations. In existing
literature reviews, we find that most of the studies, through a qualitative approach, describe
and summarize the findings of VR technology in language learning and do not consider
quantitative methods with which to measure the impact of this technology on language
learning. It is not easy to use qualitative approaches to evaluate the actual overall effec-
tiveness of VR application on language learning and how moderator variables affect the
effects. Therefore, the present research has important research implications. We conducted
a meta-analysis to examine and quantify studies on the effectiveness of VR-assisted lan-
guage learning and to determine which moderator variables influence the effectiveness
of virtual reality to better guide the future application of virtual reality technology in
language learning.
1.3. Purpose of This Meta-Analysis
This meta-analysis used the PICO framework [
22
] as the research framework. The
PICO framework includes four major components: (1) Population. It refers to students
of different education levels who may be involved in language learning through VR
applications. (2) Intervention. It is considered as the treatments (e.g., the hardware types,
target language, and L1/L2) of studies on VR-assisted language learning. (3) Comparison.
It refers to the teaching strategy implemented in the control groups, including traditional
teaching methods and other educational technology resources. (4) Outcomes. It focuses
on the student’s learning performance of VR applications on language learning, such as
acquiring language skills and affective gains.
Therefore, the study proposed three purposes to provide a summary on the status
quo of the application of virtual reality technology in language learning, to determine the
overall effect size of virtual reality applications on students’ language learning outcomings,
and to identify whether moderator variables influence the impact of virtual reality on
students’ language learning gains. As a result, the research questions were formulated
as follows:
1.
What is the research status of VR technology-assisted language learning? Specifically,
what education levels, hardware types, language skills, target language, and L1/L2
were involved in the existing studies about this field?
2.
What is the overall effectiveness of virtual reality applications on students’ language
learning achievement?
3.
What kinds of moderator variables influence the effectiveness of virtual reality on
students’ language learning outcomes?
2. Method
We followed the procedure of meta-analysis proposed by Glass, MacGaw, and
Smith [
23
]. The procedures included (1) collecting studies, (2) coding the features of
studies, (3) calculating the effect size of each study, and (4) investigating the moderating
effects of the study’s characteristics.
2.1. Data Sources and Search Strategy
The literature search procedure of this study rigorously followed the PRISMA guide-
lines [
24
] to collect literature related to VR-supported language learning. In order to obtain
relevant sufficient and scientific papers, this study searched journal articles published from
2010 to 2021. The major databases used for the searches were the Web of Science Core Col-
lection, Springer Link, Wiley Online Library, and Scopus. Two sets of search keywords were
used to search: (1) VR technology-related keywords, including VR,Virtual Reality,Virtual
Environment, and Virtual Worlds; and (2) language-related keywords, including Language,
Second Language, Foreign Language, Second Language Acquisition, EFL, Listening, Speaking,
Reading, Writing, and Vocabulary. These two sets of keywords were used in incorporation
Sustainability 2022,14, 3147 4 of 18
with Boolean operators [
25
]. Moreover, the major journals of educational technology (e.g.,
Computers & Education, Computer Assisted Language Learning, Journal of Computer
Assisted Learning, British Journal of Educational Technology, Language Learning & Tech-
nology, Interactive Learning Environments, and ReCALL) and the list of collected papers’
references were perused as a supplement.
2.2. Inclusion and Exclusion Criteria
We followed the inclusion and exclusion criteria in Table 1to select the literature to
meet the meta-analysis requirements. All the papers had to be written in English and
published between 2010 and 2021. The studies had to be empirical and had to have adopted
either an experimental or quasi-experimental research design in which the experimental
group used VR-assisted students’ language learning compared to control groups that used
other learning methods. Furthermore, the studies had to provide sufficient information
to calculate effect sizes, such as standard deviations, means, and sample sizes. Finally,
experimental results included learning achievement (e.g., different language skills) or
affective gains, and the experimental and control groups’ learning contents had to be
the same.
Table 1. Inclusion and exclusion criteria.
Criteria Inclusion Exclusion
Time Published between 2010 and 2021 Not in this time period
Language English Non-English
Study type Empirical study Theoretical study
Intervention VR technology Non-VR
Research design
An experimental or
quasi-experimental design that
VR-assisted language learning
compared to other learning methods
Non-experimental designs
Participants Kindergarten, Elementary school,
Middle school, and College Adult
Content The same learning content Learning content are not the same
Results
Sufficient information to calculate the
effect size
Lacked any statistical results
or inadequate
2.3. Search Results
The process of literature identification, screening, eligibility, and eventual inclusion in
this meta-analysis is shown in Figure 1. We found 561 journal articles related to VR-assisted
language learning published from 2010 to 2021 and 8 articles from other sources during the
first phase of literature selection. After removing 117 duplicates, two researchers read the
title and abstract of each article and then identified whether or not the article was relevant
to VR-assisted language learning. Ultimately, a total of 82 full-text articles were selected
during the identification and screening process.
In the second phase, the included articles of the first stage were further screened. Only
experimental and quasi-experimental studies involving VR-assisted language learning were
carefully examined to identify whether the VR technology and other teaching/learning
methods were included. At this stage, theoretical studies, qualitative research, and survey
research were all excluded. After the second screening stage, 29 articles remained for
consideration in the meta-analysis.
In the final stage, we examined the experimental data reported in the study. Studies
that did not provide sufficient data to calculate effect sizes (e.g., mean, standard deviation,
sample size) were excluded. After this stage, only 21 articles were included in the further
meta-analysis. Of the 21 articles, only two were affective gains, and six included both
linguistic and affective gains.
Sustainability 2022,14, 3147 5 of 18
Sustainability 2022, 14, x FOR PEER REVIEW 5 of 19
ing/learning methods were included. At this stage, theoretical studies, qualitative re-
search, and survey research were all excluded. After the second screening stage, 29 articles
remained for consideration in the meta-analysis.
Figure 1. PRISMA flowchart of article selection.
In the final stage, we examined the experimental data reported in the study. Studies
that did not provide sufficient data to calculate effect sizes (e.g., mean, standard deviation,
sample size) were excluded. After this stage, only 21 articles were included in the further
meta-analysis. Of the 21 articles, only two were affective gains, and six included both lin-
guistic and affective gains.
2.4. Coding Scheme
The coding scheme of the present study was composed of four main categories: basic
research information, research participants’ level, control treatment, research treatments,
and learning outcomes. In addition to the basic research information, the other four items
and their moderator variables correspond to the components of the PICO framework. The
coding scheme was described in detail as follows:
2.4.1. Basic Research Information
This referred to the author’s last name, year of publication, region, and article’s title.
2.4.2. Research Participants’ Level
The research participants’ level corresponded to the “Population” of the PICO frame-
work and was coded by their educational levels, including elementary school, middle
school (junior or senior), and college.
Figure 1. PRISMA flowchart of article selection.
2.4. Coding Scheme
The coding scheme of the present study was composed of four main categories: basic
research information, research participants’ level, control treatment, research treatments,
and learning outcomes. In addition to the basic research information, the other four items
and their moderator variables correspond to the components of the PICO framework. The
coding scheme was described in detail as follows:
2.4.1. Basic Research Information
This referred to the author’s last name, year of publication, region, and article’s title.
2.4.2. Research Participants’ Level
The research participants’ level corresponded to the “Population” of the PICO frame-
work and was coded by their educational levels, including elementary school, middle
school (junior or senior), and college.
2.4.3. Control Treatment
The control treatment of the included studies corresponded to the “Comparison”,
referring to the different control group treatments. Previous meta-analyses had consid-
ered “control treatments” as moderator variables to compare experimental treatments
with different control treatments [
26
,
27
]. Garzón and Acevedo [
27
] divided the control
treatments into three types: multimedia, traditional lectures, and traditional pedagogical
tools. In this meta-analysis, the control group treatments were classified into two categories:
(1) traditional, which refers to traditional lectures and traditional pedagogical tools, such
as curriculum, conventional teacher introduction, or textbooks, or (2) multimedia, which
refers to educational resources accessed using videos, images, animation, or computer-
assisted instruction.
Sustainability 2022,14, 3147 6 of 18
2.4.4. Treatments
In the meta-analysis, for all the included studies, the treatments corresponded to the
PICO framework’s “Intervention”, which was considered as the hardware types, language
skills, target language, and L1/L2. The specific descriptions for these treatments were
as follows:
1.
Hardware types. According to Qiu et al.’s [
10
] classification of the immersion level of
the VR devices, VR can be divided into non-immersive and immersive devices. The
non-immersive devices included desktop VR, smartphones, and tablet computers.
Cave VR and head mount display were classified as immersive devices, such as
Samsung Gear VR, phone cardboard, and cave-like VR.
2.
Language skills. According to Hwang and Fu [
4
], Qiu et al. [
10
], and Sung et al. [
28
],
the language skills were divided into vocabulary acquisition, listening, speaking,
reading, writing, and mixed thereof.
3.
Target languages. This study classified the coding variables of different target lan-
guages into English, Chinese, Spanish, German, and Korean.
4.
L1/L2. According to Hwang and Fu’s [
4
] review study on mobile language learn-
ing applications, the L1 referred to the native and first language that the learners
learned from birth. The L2 usually referred to the learner’s subsequent language after
acquiring their first language.
2.4.5. Learning Outcomes
The learning outcomes corresponded to the PICO framework’s “Outcome”. It referred
to two categories of learning performance, including linguistic gains (e.g., language skills
and knowledge or content learning) and affective gains (e.g., learning attitudes, motivation,
and self-efficacy) [4,21].
The two researchers coded 21 articles according to the abovementioned coding rules.
The coding process was performed in a consistent process, and whenever there was a
question about coding and reaching consensus, the researchers held discussions to ensure
that the coding of the article was consistent. If two researchers had different coding
opinions, the article would be equally allocated to a third researcher for coding until they
agree on all the code. In the end, coding was reviewed again to ensure it was accurate
(Table A1).
2.5. Data Analysis
2.5.1. Computing Effect Sizes
Comprehensive meta-analysis software was used to conduct the meta-analysis and
compute the effect sizes. To compare the effect sizes, we chose to use Hedges’ g as a
standardized measure of effect sizes for computation in the present study. Hedges’ g was
the adjusted Cohen’s d that was a measurement based on the mean difference between
the two groups by the pooled standard deviation, and it was helpful for small sample size
bias [29]. The calculation formula for Cohen’s d was as follows:
Cohen’s d =ME−MC
r(NE−1)S2
E+(NC−1)S2
C
(NE−1)+(NC−1)
where
ME
and
MC
were the estimated means of the experimental and control groups,
respectively, with
NE
and
NC
being the sample sizes of both groups, and
S2
E
and
S2
C
the
respective standard deviation. Hedges’ g and Cohen’s d were similar for large sample
sizes, but Hedges’ g performed best for small samples when Cohen’s d was multiplied by a
correction factor J(which can be adjusted for small sample bias):
J=1−3
4(N−2)−1
Sustainability 2022,14, 3147 7 of 18
where Nwas the total sample size.
Hedges’g =J×Cohen’s d
Only one effect size was calculated for each article in the present study because a study
contributing more than one effect size might lead to the biased overall effect size [
30
]. If a
study had multiple effect sizes, they were combined into one value [
29
]. Simultaneously,
the I
2
test was used as heterogeneity examined, where I
2
< 25% indicated low; 25–75%
indicated moderate heterogeneity;
≥
75% was considered substantial heterogeneity [
31
].
When I
2
statistic > 50%, heterogeneity was considered significant. If there was heterogeneity,
the random-effects model was used; otherwise, the fixed-effect model was chosen.
2.5.2. Analyses of Publication Bias
Publication bias occurred when researchers published only favorable results [
29
]. To
examine publication bias in this study, it was evaluated with the funnel plot and the fail-safe
N test. We first assessed by visually observing the shapes of the funnel plot. If there was no
publication bias, the plot resembles a symmetrical inverted funnel. Otherwise, the plot was
asymmetric [
32
]. In addition to the funnel plot, the fail-safe N had been calculated. The
fail-safe N refers to the number of unpublished studies required to reduce the effect size to
an insignificant level [
33
]. There was no publication bias if the fail-safe N was larger than
5n + 10 (where n was the number of effect sizes).
3. Results
3.1. Descriptive Information
Twenty-one articles were included by the literature search, screening, coding, and
extracting process in this meta-analysis. Table 2describes the basic information of the 21
articles included in this study. These articles were published between 2010 and 2021, and
the studies were quasi-experimental designs. Different regions had researched VR-assisted
language learning, and Taiwan had the most relevant studies. The sample sizes ranged
from 10 to 106 participants in both the treatment and control group, and the total sample
size was 1144 (treatment group = 563 and control group = 581). The learning outcome
included linguistic gain, affective gain, and both.
Table 3shows the different moderator variables and their corresponding percentages.
Regarding control treatment, multimedia teaching was the largest proportion (57.1%). The
largest proportion of studies in the educational levels included college students (47.6%),
and the second largest group was middle school students (38.1%). As for hardware types,
immersive (57.1%) devices had a wider usage than non-immersive (42.9%). In terms of
language skills, the most frequently studied were vocabulary (26.3%), writing (26.3%),
and speaking (21.1%), followed by mixed (15.8%). Finally, the main target learning was
mostly English (66.6%), followed by Chinese (19.0%), and the most studied (85.7%) was L2
learning, with the rest being L1 learning (14.3%).
Sustainability 2022,14, 3147 8 of 18
Table 2. Basic Information of the included studies.
Study (Year) Region Sample Size (E/C) Learning Outcome
Acar and Cavas (2020) [34] Turkey 26 (15/11) linguistic gain
Alfadil (2020) [35] American 64 (32/32) linguistic gain
Chen and Hwang (2020) [36] Taiwan 93 (54/39) linguistic gain
affective gain
Chen and Liao (2021) [37] Taiwan 106 (53/53) linguistic gai
affective gain
Chen et al. (2021) [38] Taiwan 84 (42/42) linguistic gain
affective gain
Dolgunsöz et al. (2018) [39] Turkey 48 (24/24) linguistic gain
Ebadi and Ebadijalal (2020) [40] Iran 20 (10/10) linguistic gain
affective gain
Huang et al. (2020) [7] Taiwan 65 (30/35) linguistic gain
affective gain
Lan et al. (2018) [41] Taiwan 44 (22/22) linguistic gain
Lan et al. (2019) [42] Singapore 60 (26/34) linguistic gain
Neville (2015) [43] American 32 (13/19) linguistic gain
Nicolaidou et al. (2021) [44] Cyprus 40 (20/20) linguistic gain
Tai et al. (2020) [45] Taiwan 49 (24/25) linguistic gain
Tai and Chen (2021) [16] Taiwan 72 (36/36) linguistic gain
Urun et al. (2017) [46] Turkey 72 (36/36) linguistic gain
Wang et al. (2012) [47] American 55 (20/35) linguistic gain
Wehner et al. (2011) [15] American 40 (20/20) affective gain
Xu et al. (2011) [48] Korea 64 (32/32) affective gain
Xie et al. (2019) [49] American 10 (4/6) linguistic gain
Yang et al. (2010) [50] Taiwan 60 (30/30) linguistic gain
affective gain
Yang et al. (2020) [51] China 40 (20/20) linguistic gain
Note: E, experimental group; C, control group.
Table 3. Categories of the 21 included articles.
Variable Category Number of Studies (N) Proportion of Studies (%)
Control treatment Traditional 9 0.429
Multimedia 12 0.571
Education level
Elementary school 3 0.143
Middle school 8 0.381
College 10 0.476
Hardware type Immersive 12 0.571
Non-immersive 9 0.429
Language skill
Vocabulary 5 0.263
Listening 2 0.105
Speaking 4 0.211
Reading 0 0.000
Writing 5 0.263
Mixed 3 0.158
Target language
English 14 0.666
Chinese 4 0.190
Spanish 1 0.048
German 1 0.048
Korean 1 0.048
L1/L2 L1 3 0.143
L2 18 0.857
Sustainability 2022,14, 3147 9 of 18
3.2. Overall Effect Size
Table 4shows the overall effect sizes of linguistic gains and affective gains. Because
the Q statistics and I
2
statistics revealed that the effect sizes in linguistic gains (Q = 73.476,
I
2
= 75.502, p< 0.001) and affective gains (Q = 14.955, I
2
= 53.317, p< 0.05) were of moderate
heterogeneity, the random-effects model was used to pool the data [
30
]. Furthermore,
this indicated that one or more moderators were attributable to this heterogeneity other
than sampling error [
52
], and further moderator analysis was required, as described in
Section 3.3.
Table 4. The effect sizes of linguistic- and affective-gain in random-effects model.
Category Effect Size 95% CI Heterogeneity
Dependent Variable k g SE Q-Value df I2
Linguistic 19 0.662 0.134 *** [0.398–0.925] 73.476 *** 18 75.502
Affective 8 0.570 0.133 *** [0.309–0.831] 14.955 * 7 53.317
Note: k, the number of effect sizes; g, Hedges’ g; SE, standard error; Q-value, Q value of the heterogeneity test
between the subgroups; CI, confidence interval; * p< 0.05; *** p< 0.001.
Using a random-effects model to pool the effect sizes, the overall effect sizes showed a
statistically significant difference in linguistic gains (g = 0.662, CI [0.398–0.925],
p< 0.001
)
and affective gains (g = 0.570, CI [0.309–0.831], p< 0.001) on VR-assisted language learning
compared to other learning methods. According to Cohen [
53
], when the effect size
≤
0.2, it
indicated a small effect, while when it was between 0.2 and 0.8, it was a moderate effect, and
≥
0.8 was classified at a large effect. Moreover, this suggested that VR-assisted language
learning had a moderate impact on students’ linguistic gains and affect in language learning
classrooms. In other words, language learning conditions using VR technologies had
significantly better learning outcomes than non-VR conditions in language learning. These
findings provided solid evidence for the advantages of VR devices in language learning and
complemented previous qualitative review research on VR-assisted language learning [
20
].
3.3. The Effect Sizes of Moderator Variables on Linguistic Gains
To learn more about the moderating effects of linguistic gains, this study analyzed
the impact of moderator variables on linguistic gains. Table 5depicts the effect sizes for
moderator variables.
Table 5. Descriptive for the moderator analysis on the linguistic gains.
Category k g SE 95% CI QBdf
Control
treatment 1.469 1
Traditional 9 0.840 0.199 *** [0.450–1.229]
Multimedia 10 0.514 0.181 ** [0.158–0.869]
Education level 1.322 2
Elementary
school 3 0.468 0.350 [−0.219–1.154]
Middle school 8 0.845 0.213 *** [0.429–1.262]
College 8 0.546 0.223 * [0.109–0.982]
Hardware type 8.178 ** 1
Immersive 12 0.409 0.142 ** [0.131–0.688]
Non-immersive 7 1.091 0.191 *** [0.716–1.466]
Language skill 3.620 4
Vocabulary 5 0.761 0.258 ** [0.256–1.266]
Listening 2 0.850 0.407 * [0.052–1.648]
Speaking 4 1.032 0.318 ** [0.409–1.655]
Sustainability 2022,14, 3147 10 of 18
Table 5. Cont.
Category k g SE 95% CI QBdf
Writing 5 0.319 0.260 [−0.191–0.830]
Mixed 3 0.522 0.339 [−0.142–1.186]
Target language 0.754 2
English 14 0.731 0.162 *** [0.414–1.049]
Chinese 4 0.497 0.315 [−0.121–1.115]
German 1 0.316 0.635 [−0.928–1.561]
L1/L2 0.605 1
L1 2 0.361 0.412 [−0.446–1.168]
L2 17 0.701 0.145 *** [0.416–0.986]
Note: k, the number of effect sizes; g, Hedges’ g; SE, standard error; Q-value, Q value of the heterogeneity test
between the subgroups; CI, confidence interval; * p< 0.05; ** p< 0.01; *** p< 0.001.
3.3.1. Control Treatments
Table 5indicates that VR had a moderate impact on language learning. However, it
was necessary to verify that VR contributed to this effect. In other words, it was important
to establish that this effect was not the result of other interventions but the result of
intervention with VR. To make this clear, the effect of VR treatment compared to the
control treatment was examined. The results shown in Table 5suggested that using VR
technologies was more effective than using other teaching resources, including traditional
(g = 0.840, p< 0.001) and multimedia (g = 0.514, p< 0.01) teaching resources. These findings
showed that the improvement in language learning appeared to be related to the use of
VR and not just the intervention, even if there was no significant difference (Q
B
= 1.469,
p= 0.226) between the various categories of control treatment.
3.3.2. Educational Levels
With respect to educational levels, middle school had largest effect on linguistic gains
(g = 0.845, p< 0.001), followed by the college (g = 0.546, p< 0.05). The educational levels of
elementary school (g = 0.468, p= 0.182) did not reach significant effect sizes. Q
B
also did
not reach the 0.05 significance level (Q
B
= 1.322, p= 0.516), suggesting that there was no
significant difference between the different categories of educational levels.
3.3.3. Hardware Types
There was a significant difference between the categories of hardware types (
QB= 8.178,
p< 0.01). The non-immersive devices had the largest effect size (g = 1.091, p< 0.001) but
the immersive devices obtained a moderate effect size (g = 0.409, p< 0.01).
3.3.4. Language Skills
There was no significant difference among the various categories of language skills
(Q
B
= 3.620, p= 0.460). Except for writing skills (g = 0.319, p= 0.220) and mixed (g = 0.522,
p= 0.124), which had no significant effect size, other language skills showed a significant
effect size. The effect size was largest for speaking skills (g = 1.032, p< 0.01), followed
by listening (g = 0.850, p< 0.05) and vocabulary (g = 0.761, p< 0.01). VR can enhance the
learning performance of language representations and comprehension in a short period of
time, such as speaking learning. For example, Chen and Hwang [
36
] found that the use of
VR can enhance participants’ move structures and levels of learning motivation in their
speaking learning compared with conventional multimedia learning.
3.3.5. Target Languages
English obtained a moderate-to-high effect size (g = 0.731, p< 0.001), while there was
no significant effect size for Chinese (g = 0.497, p= 0.115) and German (g = 0.316, p= 0.618).
However, Q
B
also did not reach the 0.05 significance level (Q
B
= 0.754, p= 0.686), which
indicated that there was no significant difference. In summary, the results showed that
Sustainability 2022,14, 3147 11 of 18
VR applications for English learning were highly effective. Chinese and German did not
achieve a significant effect, probably due to the small sample size.
3.3.6. L1/L2
The Q
B
statistics also did not reach the 0.05 significance level, indicating that there was
no significant difference (Q
B
= 0.605, p= 0.437). The effect size for L2 achieved a large level
(g = 0.701, p< 0.001), while there was no significant effect size for L1 (g = 0.361, p= 0.380).
3.4. The Effect Sizes of Moderator Variables on Affective Gains
Regarding the moderating effects of affective gains, this study analyzed the impact of
moderator variables on affective gains. Table 6shows the results of descriptive statistics for
the moderator analysis on the affective gains.
Table 6. Descriptive for the moderator analysis on the affective gains.
Category k g SE 95% CI QBdf
Control treatment 0.448 1
Traditional 2 0.421 0.274 [−0.116–0.959]
Multimedia 6 0.636 0.168 *** [0.307–0.966]
Education level 1.603 2
Elementary school
1 0.339 0.383 [−0.411–1.090]
Middle school 2 0.380 0.256 [−0.121–0.882]
College 5 0.719 0.179 *** [0.367–1.071]
Hardware type 2.513 1
Immersive 4 0.394 0.159 * [0.082–0.706]
Non-immersive 4 0.780 0.184 *** [0.419–1.140]
Target language 0.486 3
English 5 0.521 0.198 ** [0.132–0.909]
Chinese 1 0.834 0.438 [−0.026–1.693]
Spanish 1 0.700 0.478 [−0.237–1.637]
Korean 1 0.570 0.436 [−0.284–1.425]
L1/L2 0.300 1
L1 2 0.701 0.271 * [0.171–1.231]
L2 6 0.529 0.160 ** [0.215–0.842]
Note: k, the number of effect sizes; g, Hedges’ g; SE, standard error; Q-value, Q value of the heterogeneity test
between the subgroups; CI, confidence interval; * p< 0.05; ** p< 0.01; *** p< 0.001.
3.4.1. Control Treatments
There was a no significant difference between the different control treatments
(Q
B
= 0.448, p= 0.503). The multimedia had a moderate effect size (g = 0.636, p< 0.001),
while there was no significant effect size for traditional (g = 0.421, p= 0.125).
3.4.2. Educational Levels
Table 6showed that QB did not reach the 0.05 significance level (Q
B
= 1.603,
p= 0.449
),
indicating no significant difference among the different educational levels. College usage
had a moderate-to-high effect size on affective gains (g = 0.719, p< 0.001), while the
educational levels of elementary school (g = 0.339, p= 0.376) and middle school (g = 0.380,
p= 0.137
) did not obtain significant effect sizes. Djigunovic (2014) reported that, due to
a lack of contact with the language and world knowledge, children relied on teachers’
guidance, which may reduce children’s motivation to learn L2 in a situational linguistic
learning environment. The reduction in affective gain was influenced by self-assessment
rather than language experience, which was similar to the findings of Wang et al. [
21
].
Therefore, college students’ affective gain was significantly larger than elementary and
middle school.
Sustainability 2022,14, 3147 12 of 18
3.4.3. Hardware Types
There was a no significant difference between the categories of hardware types
(
QB= 2.513
,p= 0.113). The no-immersive devices obtained a large effect size (g = 0.780,
p< 0.001), followed by the immersive devices (g = 0.394, p< 0.05).
3.4.4. Target Languages
As shown in Table 6, English obtained a moderate effect size (g = 0.521, p< 0.01), while
there was no significant effect size for Chinese (g = 0.834, p= 0.057), Spanish (
g = 0.700,
p= 0.143
), and Korean (g = 0.570, p= 0.191). However, Q
B
also did not reach the significance
level (QB= 0.486, p= 0.922), which indicated that there was no significant difference.
3.4.5. L1/L2
The Q
B
statistics also did not reach the significance level, suggesting that there was
no significant difference (Q
B
= 0.300, p= 0.584). The effect size for L2 obtained a moderate
effect size (g = 0.701, p< 0.01), and L1 also obtained a moderate effect size (g = 0.529,
p< 0.05).
3.5. Evaluation of the Publication Bias
The funnel plot and classic fail-safe N were adopted in this study to examine whether
the included studies were affected by publication bias. If there was no publication bias,
the funnel plot was similar to a symmetrical inverted funnel [
32
]. The visual analysis in
Figures 2and 3showed that the funnel chart was centralized and symmetrical, indicating
no publication bias. To further confirm whether there was a publication bias, we computed
the classic fail-safe N. The results of the classic fail-safe N showed that a total of 444 studies
with invalid results were required to invalidate the effect size for linguistic gains, and a total
of 77 studies were needed to invalidate the effect size for affective gains. To summarize, we
concluded that publication bias was absent, which did not inflate the effect sizes.
Sustainability 2022, 14, x FOR PEER REVIEW 13 of 19
Figure 2. Funnel plot assessing publication bias on linguistic gains.
Figure 3. Funnel plot assessing publication bias on affective gains.
4. Discussion
Many studies have suggested the potential of VR-assisted language learning. How-
ever, there is little consensus on whether it can help improve language learning. Through
integrating the finding of published empirical studies of VR-assisted language learning,
our research provided concrete evidence on the overall effect sizes of VR-assisted lan-
guage learning on students’ linguistic and affective gain and how moderator variables
influenced its effectiveness.
4.1. The Effectiveness of VR-Assisted Language Learning
Based on the results of 21 articles (N = 1144) in this meta-analysis, we find evidence
for the overall effectiveness of VR applications on students’ language learning achieve-
ment, and we also find an overall positive effect size of 0.662 and 0.570 on students’ lin-
guistic and affective gain, respectively, suggesting that VR can enhance students’ lan-
guage learning achievement compared to non-VR conditions. The overall effect size can
be considered a medium effect [53], which is consistent with the findings of Wang et al.
[21] that VR-assisted language learning can facilitate language knowledge acquisition and
enhance affection. The positive findings related to VR-assisted language learning may be
attributed to several features of virtual reality: (1) immersive learning can effectively pro-
mote language learning [49]; (2) improve language skills through interaction between VR
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Standard Error
Hedges's g
Funnel Plot of Standard Error by Hedges's g
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
0.0
0.1
0.2
0.3
0.4
0.5
Standard Error
Hedges's g
Funnel Plot of Standard Error by Hedges's g
Figure 2. Funnel plot assessing publication bias on linguistic gains.
Sustainability 2022,14, 3147 13 of 18
Sustainability 2022, 14, x FOR PEER REVIEW 13 of 19
Figure 2. Funnel plot assessing publication bias on linguistic gains.
Figure 3. Funnel plot assessing publication bias on affective gains.
4. Discussion
Many studies have suggested the potential of VR-assisted language learning. How-
ever, there is little consensus on whether it can help improve language learning. Through
integrating the finding of published empirical studies of VR-assisted language learning,
our research provided concrete evidence on the overall effect sizes of VR-assisted lan-
guage learning on students’ linguistic and affective gain and how moderator variables
influenced its effectiveness.
4.1. The Effectiveness of VR-Assisted Language Learning
Based on the results of 21 articles (N = 1144) in this meta-analysis, we find evidence
for the overall effectiveness of VR applications on students’ language learning achieve-
ment, and we also find an overall positive effect size of 0.662 and 0.570 on students’ lin-
guistic and affective gain, respectively, suggesting that VR can enhance students’ lan-
guage learning achievement compared to non-VR conditions. The overall effect size can
be considered a medium effect [53], which is consistent with the findings of Wang et al.
[21] that VR-assisted language learning can facilitate language knowledge acquisition and
enhance affection. The positive findings related to VR-assisted language learning may be
attributed to several features of virtual reality: (1) immersive learning can effectively pro-
mote language learning [49]; (2) improve language skills through interaction between VR
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Standard Error
Hedges's g
Funnel Plot of Standard Error by Hedges's g
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
0.0
0.1
0.2
0.3
0.4
0.5
Standard Error
Hedges's g
Funnel Plot of Standard Error by Hedges's g
Figure 3. Funnel plot assessing publication bias on affective gains.
4. Discussion
Many studies have suggested the potential of VR-assisted language learning. However,
there is little consensus on whether it can help improve language learning. Through
integrating the finding of published empirical studies of VR-assisted language learning,
our research provided concrete evidence on the overall effect sizes of VR-assisted language
learning on students’ linguistic and affective gain and how moderator variables influenced
its effectiveness.
4.1. The Effectiveness of VR-Assisted Language Learning
Based on the results of 21 articles (N = 1144) in this meta-analysis, we find evidence for
the overall effectiveness of VR applications on students’ language learning achievement,
and we also find an overall positive effect size of 0.662 and 0.570 on students’ linguistic and
affective gain, respectively, suggesting that VR can enhance students’ language learning
achievement compared to non-VR conditions. The overall effect size can be considered a
medium effect [
53
], which is consistent with the findings of Wang et al. [
21
] that VR-assisted
language learning can facilitate language knowledge acquisition and enhance affection.
The positive findings related to VR-assisted language learning may be attributed to several
features of virtual reality: (1) immersive learning can effectively promote language learn-
ing [
49
]; (2) improve language skills through interaction between VR and learners [
5
,
54
];
(3) effectively filter by authentic language learning settings (e.g., learning anxiety) [
7
,
49
].
Our meta-analysis reveals the potential of using VR applications in language learning and
provides teachers with options to support their teaching.
4.2. The Moderator Analyses of VR-Assisted Language Learning
In this study, we identify six moderator variables and investigate how the design of
the intervention influenced the effectiveness of VR-supported language learning. Although
the overall results are positive, we should consider that individual studies’ results may
vary by factors, such as control treatment, education level, hardware type, target language,
language skill, L1/L2. However, the findings of our moderator analysis indicate that
only one moderator (hardware type) is significant at p< 0.01. Compared to immersive
devices, non-immersive devices have the most significant effect on linguistic gains. The
reason for this is that long-term use of immersive devices affects learners’ senses, such as
dizziness [
10
]. Furthermore, students might only focus on the interesting content rather
than the learning content, failing to achieve learning goals [
21
]. It also shows that the effect
of immersive and non-immersive devices on learners’ language learning can be compared
in future research.
Sustainability 2022,14, 3147 14 of 18
Regarding the control treatment, we compared VR applications as a teaching resource
with other types of teaching resources, including multimedia and traditional resources. We
do not find significant differences among the control treatment in this study and suggest
that VR applications promote language learners’ linguistic and affective gain compared to
multimedia and traditional resources. Moreover, higher-education students are the main
research subjects, consistent with some research on VR-assisted language learning [
10
,
21
].
The reason for this may be that most of the researchers who support language learning
with VR come from colleges and may find college students easier than collecting relevant
data in K-12 schools. While VR has a large effect on the linguistic gains of middle education
and a medium-to-large effect on the affective gains of higher education students, these
differences were not statistically significant in the different educational levels. Overall,
the language learning of the different educational levels was positively influenced by VR
applications, which is consistent with previous research results [21].
The findings of our moderator analysis indicated a large effect size for speaking than
other language skills in terms of language domains. However, these differences were not
statistically significant for different language skills. It should be noted that research on
reading is lacking, which should be addressed in future research. At the same time, the
result related to the target language and L1/L2 revealed that most studies on VR-assisted
language learning have focused on English as an L2. This may be because learning an L2 is
far more challenging than learning L1 [
17
]. Our results regarding this moderator suggest
that VR technology could be used in both L1 and L2 learning, indicating that these have a
good potential as an educational resource.
4.3. Limitations of the Study
As a limitation of our meta-analysis, only journal articles published from 2010 to 2021
in the two major databases and major journals were included in this study. Although it was
found that publication bias was unlikely to be an issue for this meta-analysis, the findings
of linguistic and affective gains might be influenced by studies with insignificant results
that were rejected due to the small sample of the study. Moreover, articles excluded by the
eligibility criteria may also alter the meta-analysis results. More studies were needed to
assess the actual value of large effect sizes with small samples. Therefore, future research
should include journal articles, conference papers, doctoral theses, reviews, and articles in
languages other than English should also be considered. Moreover, other databases (e.g.,
ProQuest or JSTOR) and longer time periods should be considered when searching for
relevant articles.
5. Conclusions
Based on the above discussion, several suggestions for VR-assisted language learning
are put forward as follows:
First, future research should enrich the diversity of applications of VR-assisted lan-
guage learning. The essence of VR-assisted language learning is VR technology designed
to improve the effectiveness of language teaching/learning methods. Existing research
should not be limited to higher education but should focus more on kindergarten and K-12
education. For example, Cerezo, Calderón, and Romero [
55
] adopted VR-assist language
learning to help preschool children practice the pronunciation of basic English vocabulary.
The results showed that VR-assisted language learning significantly impacted the children’s
motivation and improved their performance compared to traditional methods. Further-
more, most of the studies focused on vocabulary-related knowledge (e.g., vocabulary and
writing) and a lack of grammar knowledge and reading skills. Language learning programs
mainly involve English as a foreign language, with few studies having focused on learning
other languages as a second or foreign language, and there has been little research related
to native language teaching. Thus, future research should consider different languages
with different skills to expand the diversity of VR-assisted language learning.
Sustainability 2022,14, 3147 15 of 18
Second, future research should examine the effects of different VR devices on language
learning. The results of our moderator analysis show that only the different device types
had significant differences in this meta-analysis. Non-immersive devices performed better
for students’ linguistic gain than immersive devices. The existing studies have mainly
compared VR with traditional teaching on language learning, and there is a lack of research
on the effect of different VR devices in language learning. Therefore, it is worthwhile to
examine the effects of different VR devices on language learning in future research.
Third, future research should not only focus on language knowledge acquisition and
affective enhancement, but also focus on the development of higher-order thinking in the
process of language acquisition. From these studies included in the meta-analysis, it can be
found that the results of language gains are mainly obtained through post-intervention tests,
and the analysis of emotional gain is mainly obtained through questionnaires. However,
there is a lack of a deeper mechanism exploration of VR-assisted language learning, such
as higher-order cognition or behavior evaluation. With the development of emerging
technologies such as artificial intelligence and big data, more innovative approaches, such
as learning behavior analysis, should be used to understand the nature of VR-supported
language learning. For example, there has been evidence from neuroscience research
to support immersion learning for L2 acquisition [
56
]. Future research should not only
consider learning benefits, but also examine the development or changes in higher-order
thinking or abilities.
Author Contributions:
Conceptualization, B.C., Y.W. and L.W.; methodology, B.C., Y.W. and L.W.;
software, B.C.; validation, B.C. and Y.W.; formal analysis, B.C., Y.W. and L.W.; investigation, B.C.
and Y.W.; resources, B.C. and Y.W.; data curation, B.C. and Y.W.; writing—original draft preparation,
B.C., Y.W. and L.W.; writing—review and editing, B.C., Y.W. and L.W.; visualization, B.C. and Y.W.;
supervision, L.W.; project administration, B.C. and L.W.; funding acquisition, L.W. All authors have
read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Leading Talent Project of Philosophy and Social Science
Planning of Zhejiang Province grant number 22YJRC02ZD-1YB.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. The coded papers.
Authors Title Journal Reference
Acar and Cavas (2020)
The Effect of Virtual Reality Enhanced Learning
Environment on the 7th-Grade Students’ reading
and Writing Skills in English
Malaysian Online Journal of
Educational Sciences [34]
Alfadil (2020) Effectiveness of virtual reality game in foreign
language vocabulary acquisition Computers & Education [35]
Chen and Hwang (2020)
Effects of experiencing authentic contexts on
English speaking performances, anxiety and
motivation of EFL students with different
cognitive styles
Interactive
Learning Environments [36]
Chen and Liao (2021)
Effects of panoramic image virtual reality on the
workplace English learning performance of
vocational high school students
Journal of Educational
Computing Research [37]
Sustainability 2022,14, 3147 16 of 18
Table A1. Cont.
Authors Title Journal Reference
Chen et al. (2021)
Virtual reality in problem-based learning
contexts: effects on the problem-solving
performance, vocabulary acquisition and
motivation of English language learners
Journal of Computer Assisted
Learning [38]
Dolgunsöz et al. (2018) The effect of virtual reality on EFL writing
performance
Journal of Language and
Linguistic Studies [39]
Ebadi and Ebadijalal (2020)
The effect of Google Expeditions virtual reality
on EFL learners’ willingness to communicate
and oral proficiency
Computer Assisted Language
Learning [40]
Huang et al. (2020)
Learning to be a writer: a spherical video-based
virtual reality approach to supporting
descriptive article writing in high school Chinese
courses
British Journal of Educational
Technology [7]
Lan et al. (2018)
Real body versus 3D avatar: the effects of
different embodied learning types on EFL
listening comprehension
Educational Technology
Research & Development [41]
Lan et al. (2019) Does a 3D immersive experience enhance
Mandarin writing by CSL students?
Language Learning &
Technology [42]
Neville (2015)
The story in the mind: the effect of 3D gameplay
on the structuring of written L2 narratives ReCALL [43]
Nicolaidou et al. (2021)
Comparing immersive virtual reality to mobile
applications in foreign language learning in
higher education: a quasi-experiment
Interactive Learning
Environments [44]
Tai et al. (2020)
The impact of a virtual reality app on adolescent
EFL learners’ vocabulary learning
Computer Assisted Language
Learning [45]
Tai and Chen (2021) The impact of immersive virtual reality on EFL
learners’ listening comprehension
Journal of Educational
Computing Research [16]
Urun et al. (2017) Supporting Foreign Language Vocabulary
Learning Through Kinect-Based Gaming
Journal of Game-Based
Learning [46]
Wang et al. (2012)
Learning effects of an experimental EFL program
in second life
Educational Technology
Research & Development [47]
Wehner et al. (2011)
The effects of Second Life on the motivation of
undergraduate students learning a foreign
language
Computer Assisted Language
Learning [15]
Xu et al. (2011)
A New Approach Toward Digital Storytelling:
An Activity Focused on Writing Self-efficacy in a
Virtual Learning Environment
Educational Technology
& Society [48]
Xie et al. (2019) Effects of using mobile-based virtual reality on
Chinese L2 students’ oral proficiency
Computer Assisted
Language Learning [49]
Yang et al. (2010)
Integrating video-capture virtual reality
technology into a physically interactive learning
environment for English learning
Computers & Education [50]
Yang et al. (2020)
From experiencing to expressing: a virtual reality
approach to facilitating pupils’ descriptive paper
writing performance and learning
behavior engagement
British Journal of
Educational Technology [51]
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