Interactive Learning Environments
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/nile20
Trends in VR/AR technology-supporting language
learning from 2008 to 2019: a research perspective
Xing-yue Qiu , Chuang-Kai Chiu , Lu-Lu Zhao , Cai-Feng Sun & Shu-jie Chen
To cite this article: Xing-yue Qiu , Chuang-Kai Chiu , Lu-Lu Zhao , Cai-Feng Sun & Shu-jie Chen
(2021): Trends in VR/AR technology-supporting language learning from 2008 to 2019: a research
perspective, Interactive Learning Environments, DOI: 10.1080/10494820.2021.1874999
To link to this article: https://doi.org/10.1080/10494820.2021.1874999
Published online: 28 Jan 2021.
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INTERACTIVE LEARNING ENVIRONMENTS
https://doi.org/10.1080/10494820.2021.1874999
REVIEW ARTICLE
Trends in VR/AR technology-supporting language learning from
2008 to 2019: a research perspective
Xing-yue Qiua, Chuang-Kai Chiu
b
, Lu-Lu Zhaob, Cai-Feng Sunb and Shu-jie Chenb
a
Department of Education Information Technology, East China Normal University, Shanghai, People’s Republic of
China; bCollege of Education, Wenzhou University, Wenzhou City, People’s Republic of China
ABSTRACT
ARTICLE HISTORY
This paper systematically reviews 150 (VR/AR)-supporting language
learning articles from 2008 to 2019 and summarizes the development
trends. The definition of VR/AR is given firstly, and then the model of
VR/AR supporting learning is proposed. This model’s language types,
research participants, learning devices, learning goals, research issues,
research methods, research domains and learning strategies act
as a coding scheme here. Meanwhile, through review and analysis,
empirical study has been proven to be in its peak period; the VR/AR is
increasingly applied, and larger numbers of immersive devices are
being used. Under such circumstances, higher education is the principal
of research, and speaking/expressing and words are still the most
significant learning goals. As for research issues, the first period (2008–
2013), which focuses more on learners’ opinions, learning behaviours,
learning attitudes and learning performance, is still the main scheme
even though the second period (2014–2019) has more research issues
and wider research scale. Right now, research about teaching and
learning has already drawn the most attention, and task-based learning,
situated learning, and game-based learning are the most commonly
used learning strategies. Finally, this paper analyses VR/AR used in
language learning through SWOT analysis to provide more profound
suggestions and further discussion.
Received 25 June 2019
Accepted 8 January 2021
KEYWORDS
Literature review; virtual
reality; augmented reality;
language learning;
development trend
Introduction
Influenced by behavioral theory, the early research on language learning tended to focus on the
language itself and on the method of instruction. However, accompanying the popularity of cognitive theory, researchers have gradually found that language learning is actually a process whereby
learners develop their own unique language system, and language learning is tightly linked to individuals. In this context, researchers have shifted their focus to the autonomy of language learners
(De Guerrero, 1999). Essentially, as the communication bridge between human beings and
society, language learning is a time-consuming and complicated process. Atkinson (2002) stresses
that language learning is a social behaviour, arguing that language can be regarded as a social practice. It is an achievement of a society and a tool for the society to use. Language is meaningless if it is
not based in a social environment. Donato and McCormick (1994) agreed that the best way for
foreign language learners to learn a language is to use the target language in an authentic language
environment. However, compared with the general language learning process, authentically using a
language is both complicated and unpredictable.
CONTACT Shu-jie Chen
340384272@qq.com, csj@wzu.edu.cn
© 2021 Informa UK Limited, trading as Taylor & Francis Group
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X.-Y. QIU ET AL.
In the process of language learning, the requirements of sociality and autonomy tend not to be
satisfied. Nowadays, the application of technologies in education is increasingly maturing. Promoting language learning with technology has become the main focus of research and has provided
new solutions to improve language learning methods. Technology should not be limited to being
used as an auxiliary tool but should be a driver to create a technological environment for language
learning. In the early twenty-first century, some researchers proposed that the role of information
technology being used in language learning will change greatly: from learning tools/partners
to a virtual environment where learners can interact and cooperate with native speakers (Schwienhorst, 2002). In the past years, the continuously developing virtual reality (VR) technology has
brought new hope to language learners.
VR technology was invented in the early 1960s, but did not attract attention for a long time.
Recently, VR has made tremendous technical progress and has gradually matured. VR now also
enjoys more technological types (Liu et al., 2017). This paper, referring to the “virtuality continuum”
concept proposed by Milgram and Kishino (1994), defines various technological types which are
used to improve experiences in different environments. The first one is the virtual environment,
an environment with a sort of reality generated by computer technology that can largely isolate
people from the real world. The virtual environment represents the traditional virtual reality technology which creates a multi-sensory learning environment that is highly similar to visual, auditory and
tactile sensations in real-life environments by using technologies such as dynamic environment
modeling, real-time 3D image generation, stereoscopic display and sensor tracking, environmental
development tool applications, system integration and others, thereby bringing vivid sensory stimulation to the user (Steuer, 1992). The second one is augmented reality (AR), which is the opposite of
the virtual environment. Under such a reality, we can better sense and experience the superimposed
virtual information displayed by computers and other technologies in an augmented way. This
environment, based on augmented reality technologies, can enable the real and virtual subjects
to coexist in a space where users can interact with virtual subjects naturally. Finally, mixed reality
(MR) technologies that are used in education nowadays pursue both augmented reality and virtuality to build an environment where the coexisting real and virtual subjects can interact with each
other. Hence, VR, AR and MR are all committed to proving and building an environment and
share too many functions, users’ feelings and experiences for them to be completely divided. Therefore, aiming to demonstrate a better literature review, this paper will use VR/AR to refer to virtuality
continuum technologies (no MR-applied cases were found).
VR/AR’s biggest contribution to improving language learning is creating an immersive environment for first/second language learners. Such improvement also demonstrates its distinct advantages over traditional multimedia. On one hand, it is supported by multiple technologies
including computer graphics, emotion computing, and sensor techniques, with which user experience is significantly improved (Wang et al., 1996). On the other hand, the media richness, social presence, and self-openness that VR/AR provides in social experience are much stronger than what
traditional media can offer. This is greatly applied in language learning activity requiring highquality interaction (Bowman & Mcmahan, 2007). With these merits, VR/AR’s will enable foreign
language learners to learn a language as if they were in a physical environment. As a newly introduced technology, VR/AR can effectively support language learning by breaking through the
limits of traditional media and giving users a better sense of immersion and presence. As a result,
it can take the place of virtual and physical environments to some extent.
As mentioned above, VR shares many similarities with AR in terms of belonging to the same technology type. Viewing things from a technical perspective, we should re-examine related literature
and moreover, pay more attention to how such new technologies are being used in education
from a research perspective in order to identify the strengths and weaknesses of VR/AR. However,
it is found that most of current literature reviews fail to achieve above-mentioned goals for following
reasons. Firstly, these papers limit research tools to one specific type of VR/AR, such as focusing on
how desktop VR (based on computers’ 3D learning environment, simulated situation) is applied in
INTERACTIVE LEARNING ENVIRONMENTS
3
language learning (Lin & Lan, 2015; Peterson, 2011). Then, a few papers review the current status and
trends from a research perspective. For example, Parmaxi (2020) reviewed the technologies used,
language learning settings and duration of educational activities during 2015–2018 and illustrated
VR’s merits and drawbacks. VR is considered to be a teaching tool in promoting language learning,
but this study failed to review research methods, research issues, research domains and so on, which
led to the lack of significant content of empirical research. Furthermore, some papers only reviewed
parts of research issues. For instance, they only focused on how technologies influence some certain
research issues, such as VR’s effect on improvement (Wang et al., 2020) or word-learning results
(Legault et al., 2019). Since current literature reviews have drawbacks from both a technical and
research perspective, they did not comprehensively reflect the role played by VR/AR or critical
research in language learning.
In order to further understand how VR/AR influences language learning from research perspective
in accordance with the latest technical trends, this paper will systematically review the studies on the
application of VR/AR technology in language learning. Compared with the previous research
methods, firstly, this study builds a model of VR/AR supporting learning based on the respective
characteristics and correlations of language learning and VR/AR technology. Furthermore, it provides
a detailed and systematic review of the development trend of the model elements over the past 12
years, including research participants, learning devices, learning goals, research issues, research
methods, research domains and learning strategies. Exploring issues through different dimensions,
this paper does not merely illustrate the status of research and development but also suggests a
valuable direction for future research ultimately.
Research methods
Data collection
Following the suggestion in the review studies by Hwang and Tsai (2011) and Fu and Hwang (2018),
this research focuses on a 12-year timeframe (2008–2019) which consists of two time periods, 2008–
2013 and 2014-2019, to compare the development trends. Literature researched here consists of
articles and reviews. To ensure the quality of the materials quoted and the relevance of the research
topics, all the resources are from the Scopus Database and the data retrieval steps are demonstrated in
Figure 1. The keywords expression included in the data retrieval is: (“virtual world” OR “virtual environment” OR “virtual reality” OR “augmented reality” OR “mixed reality”) AND (“Second language” OR
“language learning” OR “Language education” OR “language acquisition” OR “foreign language”).
Among these, “virtual world” and “virtual environment” were frequently referenced in related
studies since they are closely connected to VR/AR; hence, this study used “virtual world” and
Figure 1. Process and methods of data searching and collection.
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X.-Y. QIU ET AL.
“virtual environment” as keywords in order to cover the maximum amount of VR/AR-supporting
language learning literature (Melchor-Couto, 2018). But due to different understandings of virtual
environment and virtual world, some researchers studied learning environments which did not
show any features of VR/AR. For instance, they studied such environments as an e-learning environment which provided no VR/AR experience (Carrero et al., 2017), social media (Andújar-Vaca & CruzMartínez, 2017; Rahimi et al., 2015) and digital games. Therefore, among 228 results retrieved in the
first period, a total of 58 articles were deleted for the reason mentioned above. Then, by reviewing
the rest of the articles in details, another 20 articles which were unrelated to this research
were also excluded. Among these deleted articles, (1) VR/AR is mentioned but is not the main focus
(Reisoğlu et al., 2017; (2) a few studies focus on technology which supported staff such as voice-recognition, which are largely different from language learning. Finally, the content of 150 articles was
analyzed here.
Data distribution
Figure 2 demonstrates the quantity of published VR/AR-supporting language learning research
articles over the course of 2008–2019. In general, the quantity of research continued to increase
with an obviously increasing trend. However, in the first six years, there was no significant increase
in the quantity, and there was a total of 42 studies between 2008 and 2013 while 2013 only has one
more article than 2008 does. In the second period (the later six-years), however, the total number of
articles increased at a stable pace and reached 23, the highest quantity during the entire 12 years.
This trend is in accordance with expecting outcomes from the technologies’ development and also
reflects that researchers are increasingly interested in applying VR/AR in language learning.
In order to investigate technological trends in the research, this paper firstly classifies 122 empirical articles out of 150 in total, according to their learning devices, and then demonstrates their distribution. Learning devices here, referred to desktop VR, immersive VR (Cave and Headset VR) and
AR, and none researches, using mixed-reality devices, are found. As shown in Figure 3, although
the distribution of empirical researches is similar to Figure 2, these technologies show their
different development trends. Considering some important issues, we divide those 12 years into
three stages. Firstly, there is the technology exploration stage (2008–2013): during this stage,
there are 30 empirical studies in total. Among these, 83.3% studies, with slight growth, apply
desktop VR to improve language learning while AR (2) and immersive VR (3) receive less attention.
Secondly, there is the desktop VR development stage (2014–2016): this stage is marked by VR’s
recurrence in the Hype Cycle for Emerging Technologies (Jackie & Hung, 2013) provided by
Gartner (a world-famous consulting company). The number of desktop VR climbs visibly (from 5
Figure 2. Distribution status of VR/AR-supporting language learning studies in 2008–2019.
INTERACTIVE LEARNING ENVIRONMENTS
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Figure 3. Distribution status of VR/AR-supporting language learning studies in 2008–2019 (by technology type).
to 15) and reaches its highest point in 2016 with 34 articles in total while AR (6) and immersive VR (1)
still have a small quantity. The last stage is VR/AR promotion (2017–2019): in 2016, the Horizon
Report for Higher Education predicts that VR/AR will be promoted in 2 to 3 years; this is also
proven in the following figure (Johnson et al., 2016). During these three years, desktop VR starts
to decline in quantity and drops to 8 in 2019; however, AR and immersive VR have been applied
more widely with an increasing quantities and have gotten close to desktop VR with 6 and 7 articles
respectively. All these indicate that nowadays VR/AR-supporting language learning has transformed
in a technical way and AR and immersive VR will be applied in more researchers in the future.
Coding scheme
To further explain the research dynamics and the development trends of VR/AR-supporting language
learning, this study establishes its own coding scheme by referring to the literature reviews’ framework
Figure 4. The model of VR/AR-supporting learning.
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of technology-based learning model research conducted by Hsu et al. (2012) and Chang et al. (2018),
VR/AR’s features and the emphasis in language learning supported by technologies. As shown in
Figure 4, the model mainly includes research participants, learning devices and learning goals,
research issues, research methods, learning strategies and research domains. Besides that, language
types will also be included as a significant analysing index. What follows is a detailed illustration for
each dimension.
Language types
The coding scheme of the language types is taken from Hwang and Fu’s (2019) review study on technology-based language learning. In our study, language types are categorized into first language
(L1), second language (L2) and "none" according to the learners’ acquired sequence. The first
language is the native language, and the learners of a first language are children in most cases.
The learning of a second language often takes place after the acquisition of one’s first language,
and the learners tend to be teenagers or adults. Meanwhile, based on the target language, languages
are classified into English, Chinese, Spanish, German, French, Italian, Japanese, Russian, “others” and
“none”. Similarly, the languages listed above can also be categorized into first language or second
language. “None” means the languages that have not yet been acquired.
Research participants
The coding scheme of the research participants is based on Hwang and Tsai’s (2011) research. It
includes: preschool education, elementary school students, junior and senior high school students,
higher education, teachers, the public, working adults, “others” and “none”. If multiple types of participants are measured in a study, then the category of the corresponding participant will be counted
as 1 point. In this research, teachers should be treated differently from the other participants because
a teacher joins in the language learning process by teaching instead of learning (no matter whether
it is a first or a second language). This implies that students are also involved in the learning process.
Therefore, as long as teachers are involved in the research and are being measured, they will
be given 1 point. “None” means that one joins in no learning activities.
Learning devices
The learning devices are classified according to the immersive levels of the VR/AR devices. Immersion, a technical attribute of VR/AR, means that VR/AR users feel immersed in a virtual environment
and interact with it (Witmer & Singer, 1998). It is a total immersive virtual environment which can cut
off the relations between users and the real world, which enables them to fully immerse themselves
in the new reality (Jennett et al., 2008). Given this definition, VR/AR can be divided into non-immersive and immersive devices (as shown in Table 1).
The common non-immersive device is desktop VR. Desktop VR uses graphic display devices such
as computers as carriers, supported by control devices including a mouse, control handles and so
forth for operation in a virtual environment. Due to the low immersion, desktop VR systems have
little sense of reality, which results in poor user experience. Furthermore, some portable devices,
smartphones and tablet computers, which are mainly used to augment a real environment, are
Table 1. Learning devices.
Non-Immersive device
A: Terminal Equipment
Immersive device
B: Cave VR
C: VR Head Mount Display
D: AR Head Mount Display
Desktop VR
Smart phone
Tablet computer
Cave-like VR (less than 3 screens)
Cave VR(3 screens or more)
Headset VR (computer based)
Phone cardboard
AR Head Mount Display
INTERACTIVE LEARNING ENVIRONMENTS
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also classified as Terminal Equipment since they share the same functions and users’ experience as
desktop computers. According to the retrospective study by Freina and Ott (2015), Cave (CAVE Automatic Virtual Environment) VR and headset VR devices are able to provide an immersive environment. Cave VR systems project a virtual environment by presenting virtual scenes on the wall
panels of a closed space. Learners, equipped with devices like stereoscopic shutter glasses and
handles, can interact with the environment and feel highly immersed in such a closed environment
without interruptions from the outside world.
Due to the high cost and demand on space, cave VR is seldom employed in educational research.
However, researchers generally prefer to use partial functions of cave VR to build a less-costly cavelike VR environment where users experience is weakened due to the simplification of the functions.
In this research, a device with three or more screens is defined as a cave device, while ones whose
have fewer than three screens are defined as cave-like devices.
Compared with other devices, headset VR can cut off the connections between the users’ sense of
their body and the real world, allow them to fully perceive the digital world or immerse themselves in
the virtual environment. There are many types of headset VR devices with various functions, costs
and immersive levels. This research divides headset devices into: headset VR (computer-based)
and phone cardboard. Headset VR, including Oculus Rift, HTC Vive, is fully functional but also
fairly expensive. Phone cardboard, creating a virtual environment by smartphone, is the most affordable VR device where learners can feel immersed in the environment. Besides, headset AR also has
some similar products, such as Microsoft Holo Lens and Glass Enterprise Edition. AR headsets (AR
Head Mount Display)’s role in learning is similar to VR’s.
Learning goals
According to the research of Hwang and Fu (2019), the goals of language learning include grammar,
vocabulary, pronunciation/speaking, reading, writing, listening, others and none. Compared with
other technologies, learning activities supported by VR/AR emphasize sociality so it can examine
different aspects of the learners. In this case, pronunciation and speaking are regarded as one learning goal. Each learning goal will add 1 point if the empirical study includes more than one learning
goal.
Research issues
The research issues, with reference to the research of Hwang and Fu (2019), include five parts and 12
topics: the Affect aspect: technology acceptance/intention of use, attitudes/motivation/anticipation
of efforts, self-efficacy/confidence, satisfaction/interest, learning anxiety and opinion of learner/
learning perception (including interviews or open-ended questions); the Cognitive aspect: learning
performance, higher order skills and collaboration/communication; the Psychomotor aspect: correctness and fluency of operation or demonstration; the Learning behaviours; and the Causal analysis:
correlation/cause-and-effect analysis. In addition, sense of Presence and Immersion, which are key
indexes for reflecting the virtual environment, are also included in the research issues.
Research methods
There are four types of research methods: qualitative methods, quantitative methods, mixed
methods and others. Other’s sub-coding includes: literature review (systematic analysis), literature
review (meta-analysis), opinion article, discussion about theories and methodology, and the
outlook of the technologies’ application.
Research domains
Based on the research of Fu and Hwang (2018), we selected the research domains in the coding
scheme of this research. The research domain is defined by the content of a paper. It includes software development, instruction and learning, survey activities, and others. Software development is
an article that explains the design or development of a VR/AR-based language learning system. It
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includes system introduction, design principle, developing process used to test the learning system.
Instruction and learning only includes empirical studies which study the language learning activities
conducted in VR/AR environment and test research participants with related issues. If an article
includes the system design as well as the learning issues, its researching domain belongs to instruction and learning field. Survey activities include correlation analysis, causal analysis, analysis of the
user’s behaviour and the multimodal research that is unique to the VR/AR environment. Others
include discussion of viewpoints, theoretical introduction, literature review, learning experience,
and so forth.
Learning strategies
According to the review on the application of 3D virtual environments in education carried out
by Reisoğlu et al. (2017), this study includes 11 learning strategies: namely task-based learning,
situated learning, game-based learning, collaborative learning, inquiry-based learning, experiential learning, problem-based learning, role-playing, storytelling, project-based learning and
others.
After establishing the coding scheme, two researchers start coding according to the aforementioned rules and listed the results on an Excel sheet whose consistency analysis is higher than
88%. At the end, the differences were revised by discussing them with another expert.
Research results
In this study, there are 122 empirical studies in the 150 reviewed articles, including 20 AR empirical
studies. After analysis, we found that most of them were classified into one category with fewer
studies classified into multiple categories. For example, in an empirical study by Morton et al.
(2011), the participants learned both Japanese and Italian.
Distribution of language in the literature
As shown in Figure 5, in terms of the target languages, English is the most prominent language with
76 studies. Chinese follows with 15 studies. Spanish ranks third with 11 studies, followed by German,
Japanese, Italian, Korean, French, Portuguese and Russian. The “Others” category covers two studies
for Irish (L1), two for Czech (one as the L1 and the other as an L2), one for Mayo (L1) used in Northwestern Mexico, one for Basque (L2), one for Amazigh (L2) and one for Polish(L2). Besides, an article
applying VR to sign language learning shows that the virtual environment can also support nonverbal language expression. With the particularities of sign language, this study classifies its first
Figure 5. Distribution of language in the literature.
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and second language types as None. In addition, there are 3 studies conducting 2 languages’ learning activities.
Among learning activities conducted by empirical researches during 2008–2019, researchers
tended to apply VR/AR environments to second languages learning activities (118 activities on
record), but only 6 activities cover the first language’s learning type. There are two reasons
for the above-mentioned phenomenon: on one hand, applying VR/AR technologies can more
support second language development more by building a social environment where the learners
can fully apply the target language in such real and complicated situations and
experience a cross-cultural learning environment at the same time (Chiang et al., 2014; Lan et al.,
2013; Zheng et al., 2015). On the other hand, most of the first language learners are children who
find it really difficult to operate VR/AR devices. Moreover, whether VR/AR devices are suitable for children has been a long-lasting debate (Freina & Ott, 2015).
Trends of research participants
The development trend of research participants is discussed as follows. Comparing the number of
research participants in 2008–2013 and 2014–2019, 6 articles in total test teachers and students
at the same time (4 for higher education, 1 for primary school and 1 for pre-school). As shown in
Figure 6, over the course of 12 years, higher education was mostly focused on, with 77 studies, followed by research participants from elementary school students (13), teachers (13), the public (11)
junior and senior high school students (10), preschool education (3) and working adults (1). Compared with the first six years, the total number of participants in the second half grew by three
times. Besides, it is clear that researchers are also paying attention to a wider range of participants
as the total number of participants is growing. For example, articles focusing on non-higher-education participants grow from 9 to 42 during these two stages. Though 42 is still far fewer than
the number of articles focusing on higher education, it’s obvious that researchers have already
started to focus on applying VR/AR to basic education (21). In the second six years, more articles
discuss and analyse issues such as the results of applying VR/AR or realizing VR/AR teaching
mode in the virtual environment from a teacher’s (10) perspective. Besides, the public (10) also
having been a key research participants, are tested on psychological status, experience with
various technologies and other issues. Therefore, more attention should be paid to other participants, such as those in preschool, elementary and junior/high school education in order to
further promote the spreading of VR/AR technology and make it satisfy the requirements of
different types of learners.
Figure 6. Trends of research participants.
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Figure 7. The number of evaluated learning devices.
The number of evaluated learning devices
Counting the VR/AR devices used in the empirical studies from 2008 through 2019 (shown in Figure
7), we found that 101 VR and 21 AR devices are used in total. Due to different functions and experiences of these devices, this chapter will illustrate details of different devices types in separate
sections.
Desktop VR device
We find that desktop VR is the most widely used device in language learning, with the total number
reaching 87. To further understand how desktop VR is applied in these 87 articles, these papers find
that 11 types of software used are mainly existing ones while some are self-developed.
As shown in Figure 8, Second Life is the most used software accounting for 55% (48), and selfdeveloped software is 17% (15). It shows that the base for applying desktop VR has already
become relatively mature. With Second Life as its main base, desktop VR is the device used the
most, since it supports many language learning and research activities. Meanwhile, many self-developed system also make their own contribution in satisfying increasing learning needs and extending
Figure 8. Distribution of desktop VR software systems.
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the learning environment. As for the rest of the software systems, OpenSim is 12%, Quest Atlantis is
5%, online role-playing games (MMORPG), represented by World of Warcraft, are 3%. The others
(including AvayaLive™ Engage, and The Sims, Croquelandia, Moviestorm, meet-me, ImALeG and
so on) are 8%. Such figures shows that desktop VR, with its high availability and accessibility, can
enable researchers to find an environment fitting for their research and teaching. Currently, there
are many desktop VR-based language learning projects around the world. The representative projects include VILL@GE in the European Union, the 3D multi-user virtual Russian world in Russia
and so on (Milton, 2012).
Immersive VR device
Since headset VR can provide a virtual environment which represents the ceiling of the immersive
level of current VR technology, it is regarded as a key technology for improving the language learning environment (Chen & Hwang, 2020). Limited to its high application cost and strict technological
requirements for creating an environment and realizing interaction, headset VR is only being applied
narrowly with 11 articles (VR head-mounted display and Phone cardboard). However, to our
delighted, these 11 articles were mainly conducted in the second six years, and 9 (out of 11) were
published during 2018–2019. All of these indicate that immersive VR has made great progress in
recent years. This paper predicts that though non-immersive VR still be dominant in language learning studies in the near future, immersive VR will be applied in language learning studies much more
widely and deeply. Among these 11 articles, 7 VR head-mounted displays and 5 Phone cardboards
are used in total. Firstly, according to researches, in the virtual environment provided by headset VR,
learners will have chances to express themselves with embodied cognition and a sense of safety
(Gordon et al., 2019) and finally improve in all language skills under such immersive learning (Xie
et al., 2019). Some articles also demonstrate how its application integrates with other highly
advanced technologies such as in Hassani, Nahvi and Ahmadi’s (2013). This research
develops an intelligent agent using virtual reality in order to enable learners to
improve their spoken language by interacting with intelligent agent characters. Unfortunately, all
the aforementioned researches all failed to provide a long-time learning opportunity for learners
with headset VR. This is possibly because that headset VR is still in its primary stage without
a mature application software system or using it for long time may cause danger and physical
health problems.
A possible solution for such problems may be using Phone cardboard, which can be more easily
operated, to support language learning, but Phone cardboard sacrifices some sense of interaction
and experiences. Users can easily own a virtual environment by simply installing a related App to
their mobile phone and using it with Phone cardboard. Such a device is much more portable
than headset VR and desktop VR, and really suitable for class teaching. Firstly started during
2018–2019, even though it only has five articles which still successfully reveal another possible developing trend. This paper thinks that it’s likely to become a key teaching tool on future language
classes.
Cave VR only has 3 empirical studies. One study uses a cave-like environment, and the rest two
apply cave VR. Among them, Urun et al. (2017) use one digital screen and Kinect to establish a cavelike environment where students can master English words via game-based learning. These two cave
VR studies, only conducted in 2008–2009, may represent a decline of these immersive devices in the
first six years, possibly since cave VR is too expensive and cannot be used easily. In recent years,
the emergence of headset VR has seemed to find a new direction for immersive devices which
can be widely used in the future.
AR device
Applying AR-supporting devices is addressed by 21 articles in total where mobile devices have 19
articles with 16 for smart phones and 3 for tablets while AR headsets have 2 articles. Nowadays,
since AR is commonly used in mobile devices, this paper counts the AR software system (see
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Figure 9. Distribution of mobile AR software systems.
Figure 9) to check whether these AR devices have been used maturely or not. From Figure 9, in ARsupporting language learning researches, the self-developed software (7) still plays the dominant
role, following by Aurasma (4) which is an AR video-making software; the next is AR Flashcards (3)
which are generally only used in words-learning only. Finally, except for an unexplained software
(Vedadi et al., 2017), the remaining virtual environments are all from AR games. Such distribution
shows that AR mobile devices still lack of mature software systems to support language learning.
Furthermore, such software can only provide limited learning functions which barely support
the learning process, so it still has a long way to go in the future.
Meanwhile, 2 articles talk about applying AR headsets. One tries an immersive word learning and
proves that, this way will better promote delayed productive recall than the traditional way (Ibrahim
et al., 2018). Another develops a highly interactive virtual environment where users can
learn about cultures in the classroom. This study shows that not only are learning
results significantly improved, teacher–students communication is also enhanced (Yang & Liao,
2014).
Figure 10. Trends of learning goals.
INTERACTIVE LEARNING ENVIRONMENTS
13
Table 2. Trends of AR’s learning goals.
Grammar
Vocabulary
Pronunciation/Speaking
Reading
Writing
Listening
2008–2013
2014–2019
Total
0
0
2
0
0
1
1
13
6
2
4
3
1
13
8
2
4
4
Trends of learning goals
This section explains the development trend of learning goals regarding VR/AR-supporting language
learning research in the first period (2008–2013) and the second period (2014–2019). As shown in
Figure 10, the quantity of language learning goals changed greatly over the two periods, growing
from 47 in the first to 141 in the second. Among these goals, pronunciation/speaking is the most
important goal for VR-based language learning and 88 empirical studies dealing with oral language
improvement as its research goal. These advantages of VR/AR demonstrated here prove that VR/ARsupporting language learning focuses more on how to use languages practically. Moreover, vocabulary (45), listening (21) and writing (16) are also addressed many times. These three all increase significantly in the second period, especially the vocabulary, which grows from only 5 in the first period
to 40 in the second. This growth indicates that one of VR/AR’s development trends is satisfying the
needs to promote more language skills. In contrast, not as much attention is paid to grammar and
reading, with fewer than 10 and only slight growth. Finally, other includes the goal of accuracy of
sign-gestures’ movements (Martino et al., 2017). It should be stated that AR studies involves 32 learning goals in total with their distribution shown in Table 2. According to this table, basic vocabularylearning is the main goal for AR-supporting devices while such distribution also indicates that AR is
still mainly used to train basic language skills instead of being further applied in language learning.
Hence, other than reproducing the real social context of a target language, VR/AR technology should
also demonstrate how it creates an environment with its imaginative and innovative features in
order to provide the more visualized, imaginative and super-natural learning activities and learning
contents. Besides, AR still should take full advantages of its convenience to further develop learning
environments which can support the training of Pronunciation/Speaking, Grammar, Reading and
Writing skills.
Trends of research methods and research domains
As shown in Figure 11, in the first period of empirical studies, qualitative methods are used most
widely (16), followed by quantitative methods (7) and mixed methods (7); Others (non-experimental
methods) accounts for a large part with 12 articles. This indicates that research on VR/AR-supporting
language learning is still in its exploratory stage. In the second period, the quantity of all empirical
research methods increased by several times, especially the quantitative methods (from 7 to 39) and
mixed methods (from 7 to 30), and the number of qualitative methods also climbs a lot (from 16 to
23). On the other hand, the proportion of the “Others” type decreased from 28.6% to 14.8% even
though its quantity increased slightly. This reveals that after accumulation in the first period, an
increasing number of researchers better understand the integration of VR/AR and language learning.
VR/AR proved itself a reliable language learning environment at the time when its empirical studies
peaked although its research methods are still in transition. Finally, with large proportion, non-experimental methods are further classified according to their contents in order to provide a more detailed
understanding of their distribution (see Table 3).
Considering developing trends in research field, we can further confirm the opinion mentioned
above. According to Figure 12, instruction and learning (19 studies) is the top research domain in
the first period while its corresponding influence is not the same as its numerical proportion.
14
X.-Y. QIU ET AL.
Figure 11. Trends of research methods.
Meanwhile, in the second period, instruction and learning became the most focused research
domains with 68 articles in total. Hence, it is predicted that VR/AR-supporting language learning
research will keep improving with the input of continuous theories in the future and
will gradually form some complete learning modes to satisfy learners’ needs. Meanwhile, the
number of survey activities also increased while such growth is not obvious compared with that
of instruction and learning since they both grew in the second period; moreover, software development and others both have a small quantity of research. It’s should be noticed that among all nonempirical studies, 8 are classified into software development while 1 is instruction and learning
according to the articles’ detailed contents. This is also why the quantity of “Others” here is
different from that of research methods (Deutschmann & Panichi, 2013).
Trends of research issues
This section analyses the development trend of the research issues in the two periods. Research
issues here cover six aspects: the affect, cognitive, psychomotor, learning behaviours, causal analysis
and immersion and presence. As shown in Figure 13, the researches on the affect aspect accounts for
the most articles (108) which indicates that learners’ psychological and emotional experience in
virtual environments are hot researching topics. The research on the cognitive aspect ranks
second with 62 articles in total, increasing from 9 in the first period to 53 in the second, growing
by 488.9%. Such growth of the cognitive aspect also reflects that the focus of research domains
has gradually shifted to the effect of VR/AR-supporting language learning, and the number of
such studies is expected to grow in the future. Aside from that, the quantity of learning behaviours
ranks third (46), increase greatly from that of the first period. At the same time, psychomotor, causal
analysis and immersion and presence have few studies respectively with a slight increase in the
second period.
In Table 4, we summarize the development trend of all the research issues from all subjects. In the
first period (2008–2013), the top four issues are: learners’ opinions (14), learning behaviour (13),
Table 3. Distribution of “Others” methods.
Research subject
Literature review(systematic, meta-analysis)
Opinion article
Theories, Methods discussion
Outlook of technologies application
Technological tools introduction
2008–2013
3
0
2
2
5
2014–2019
3
1
1
8
3
Total
6
1
3
10
8
INTERACTIVE LEARNING ENVIRONMENTS
15
Figure 12. Trends of research domains.
learning attitudes, motivation and anticipation of effort (11) and learning performance (9) which, in
total, accounts for 83.9% of all issues in this period. One interesting thing should be noticed is that
these four issues are still the most focused ones in the second period (2014–2019), with 127 in total.
Among them, “Learning performance” increasing by 388.9%, is the hottest research issue with its
quantity (44) far exceeding that of the rest. This indicates that the research trend has shifted from
“VR/AR environment’s influences on learners” to “how to use VR/AR to improve learners’ learning
performance”. However, with obvious growth in quantity, these four issues only account for
69.4% in total, with a slight decrease, out of a total of 183 in the second period. Meanwhile, even
though researchers have been exploring them more, the rest of the issues in the same period
have been researched less than the four issues mentioned above in terms of quantity of studies.
In the cognitive aspect, the number of studies on higher order skills (3) and tendency of collaboration/communication (6) appeared in the second period, but the existing research is far from
enough. Actually VR/AR stands out not only in creating an imaginative and real learning atmosphere,
but also in providing a free and positive learning environment beyond time and space. It is beneficial
for improving learners’ abilities of reasoning, problem solving, critical thinking and forming creative
ways of thinking (Chen, 2016). For instance, some researchers have applied role-play games in
language teaching. According to the research results, language learning workshops based on VR
support knowledge co-construction, can encourage learning motivation and facilitate higherorder learning (Oksanen, 2013).
Figure 13. Trends of all research issue aspects.
16
X.-Y. QIU ET AL.
Table 4. Trends of research issues.
Research issues
Affect aspect
Cognitive aspect
Psychomotor aspect
Learning behaviors
Causal analysis
Immersion and
presence
1.Technology acceptance or intention of use
2.Attitudes, motivation and anticipation of effort
3.Self-efficacy, confidence
4.Satisfaction or interest
5.Learning anxiety
6.Opinion of learner or learning perception
7.Learning performance
8.Higher order skills
9.Collaboration/communication
10.Correctness and fluency of operation or
demonstration
11.Learning behavior
12.Correlation or cause-and-effect analysis
13.immersion and presence
14.Others
2008–
2013
2014–
2019
Growth
rate
Total
3
11
1
1
1
14
9
0
0
2
10
21
4
7
6
29
44
3
6
6
233.3%
90.9%
300.0%
600.0%
500.0%
107.1%
388.9%
NA
NA
200.0%
13
32
5
8
7
43
53
3
6
8
13
1
0
33
7
4
153.9%
600.0%
NA
46
8
4
0
3
NA
3
Hence, researchers should make full use of the imaginative aspect of VR/AR to make the environment construction, role participation and strategy application in language learning more expressive
and make the most of the value of VR/AR in higher-order skills training. Meanwhile, Immersion and
Presence has 4 articles (2 for desktop VR; 2 for immersive VR) in the second period. Immersion and
presence, as a key features of VR/AR, are directly involved in the interaction and participation experience under the virtual environment (Liaw, 2019). As such, the necessity of such researches
in the virtual environment designing process requires wider researches in the future to make
the virtual environment better support language learning. Among “Others”, 2 articles in total use
common data from multi-modal learning analysis. One uses MRI to observe neural activity
in the word-learning process in a virtual and real environment and applies an AR headset to
record eye movements in the same process (Ibrahim et al., 2018; Legault et al., 2019); the other
article analyses the complex adaptive systems of language learning which is based on a virtual
environment (Scholz & Schulze, 2017).
In conclusion, nowadays VR/AR has already been accepted by researchers and learners in plenty
of language learning studies. However, the content researchers focus on is still the same as before.
Firstly, research on the cognitive aspect is still in its preliminary stage with more on lower level cognitive learning including memory effect and the accuracy of expression, while researches on higherlevel cognitive aspects is still lacking and need to be addressed. Secondly, more aspects such as the
affect, immersion and presence should be introduced to further analyze the virtual environment’s
matching degree, merits and effectiveness in language learning.
Figure 14. Trends of language learning strategies.
INTERACTIVE LEARNING ENVIRONMENTS
17
Trends of language learning strategies
The term “strategy”, in broad sense, is goal-oriented and involves self-consciousness, as is the
language learning strategy (Holec, 1981). As such, both the process and the results of learning
should be valued. In language learning, the application of strategies is the key to its success. VR/
AR assures a stable social learning environment with continuous interaction. It also unleashes the
potential of strategy application by creating a technology-based language learning platform for
communication, cooperation, recording and analysis.
As shown in Figure 14, the top three applied strategies are situated learning (43), task-based
learning (39), and game-based learning (30). Situated learning’s articles climb from 9 in the first
period to 34 in the second. This growth proves that VR/AR can perfectly satisfy the needs of situated
learning since it creates an environment where reality and virtuality overlap with each other. In
such an environment, everything originally from the real world is virtualized to weaken the
emotional influences on how learners use languages. The number of task-based learning activities
increased from 6 in the first period to 33 in the second, with the highest growth rate among
all the methods implemented. Task-based language learning activities can make the dependencies
between learners much closer. In such learning environments, tailored task-based learning activities,
abundant social scenes, and immediate feedback and instruction from instructors are given. With
these advantages, learners can achieve complicated learning goals by interaction and cooperation
with other learners (Lan et al., 2013). This is how VR/AR helps task-based learning strategy play its
role in language learning. Moreover, VR/AR can be used as a game-based platform to create a creative and enjoyable learning environment for learners.
In the virtual environment, collaborative learning (15) is also well-supported. Different from
the traditional CSCL environment, learners can have real-time face-to-face interaction through characters and finish collaborative work with a team. Meanwhile, VR/AR also brings various environmental resources for experiential learning (9), role playing (7), storytelling (7) strategies,
even though less attention is being paid to this aspect. Besides, inquiry-based learning (5),
problem-based learning(2) and project-based learning (1) strategies have only been applied little
since these three strategies, limited to their features, can only be carried out smoothly with stricter
requirements for scenario design and functions realization. The four strategies included in the
“others” category are traditional teacher-oriented instruction or theme discussion.
Discussion and conclusions
This study reviews the development trend of research on VR/AR-supporting language learning
between 2008 and 2019 from multiple dimensions. The overall quantity of research has shown
rapid growth in the latest six years (2014–2019), which is largely due to the development of VR/
AR technology. By further analyzing learning devices, research participants, research methods,
research fields, research issues, learning strategies and other research dimensions, we can affirm
VR/AR technology ‘s high application potential and value in language learning. The following
section presents the results and concludes the SWOT of VR/AR-supporting language learning
research (see Table 5) to provide constructive suggestions for future research.
Strengths
The application value of VR/AR technology in language education has been confirmed. Desktop VR
technology is the most widely used technology in research since it is relatively mature and is supported by multiple software systems (such as Second Life, OpenSim, etc.) for language learning.
Proven by a large number of empirical studies, virtual environment can make language learning
more effective, compared with a traditional environment. In recent years, the research on VR/AR-supporting language learning has entered the peak period of instruction and learning research and
18
X.-Y. QIU ET AL.
Table 5. SWOT of VR/AR-supporting language learning research.
Strengths
.
.
.
.
.
It can establish multimodal and near-real-life language
learning scenarios
It supports the development of interactive language
learning activities and the implementation of multiple
learning strategies
It facilitates the transfer of language acquisition from virtual
to reality
It reduces the impact of emotional filters through virtual
avatars and motivates active self-expression
It helps students share resources and promotes equal
opportunities for interaction
Opportunities
The field of basic education has a strong demand for
technology to improve the learning environment
. VR technology can also meet the learning needs of
grammar, vocabulary and listening
. Immersive VR technology is increasingly mature, and its
application in language learning has just begun.
. It can fully record the learning behavior of learners and
facilitate research and analysis.
. It can fully perform non-verbal information (such as body
language, facial expressions, etc.).
.
Weaknesses
.
.
.
Compared with immersive devices, the environment for
the desktop VR, which is widely applied for research, is not
ideal.
VR/AR’s technical cost problem hasn’t solved yet, making it
difficult to be promoted promotion
Long-time use of VR devices may affect physical and
mental health
Threats
Insufficient discussion about key research issues;
. No unified industry standards for its application and
promotion
. Easily make learners get addicted to virtual reality and fail
to form a serious learning attitude
.
application. The most popular research issue is the learning effect of learners, and the most important learning target is the pronunciation and expression of foreign languages. When learning is virtualized, learners can avoid being exposed to actual application scenarios, and are less influenced by
internal and external factors of language interaction, thus being less affected by the emotional filter.
This enables them to be more motivated to learn with enthusiasm (Kreijns et al., 2002). Task-based
learning, situational learning and gamification learning have become the most widely used learning
strategies in empirical researches. Learning materials and content can be visually presented in
such an environment, which reflects the advantages of VR/AR technology in supporting language
learning and proves that the application of VR/AR technology can solve traditional learning
media’s problem of technical limitation. From the technical dimension, immersive devices (a kind
of VR/AR devices) are gradually maturing, such as inventions of devices for seamless wireless
connectivity that will enable technologies to be applied without being limited by space and time.
This can take full advantages of VR/AR under various learning scenarios. For instance, some researchers develop learning activities based on headset devices. VR/AR can provide a sufficient sense of
presence which cannot be provided by traditional classrooms in order to let learners have access
to participate in activities like situational learning, role playing and collaborative learning. According
to researches, headset devices can facilitate a strong sense of immersion and presence to learners,
distinguishing it from other multimedia technologies. It can create an ideal technical environment,
which is more conducive to the smooth transfer of knowledge from virtual to reality.
Weaknesses
The main weakness in current research is mainly in VR/AR’s practical application. Nowadays,
most of the learning devices used in the classroom are desktop VR, which can only provide
very limited interactive and immersive experiences—it is nearly impossible to reconstruct the
real-life context without multiple sensory experiences and interaction channels. Meanwhile,
non-immersive AR technologies, with their high portability, can support language learning conducted in many more scenarios. But AR-related teaching resources mainly focus on training
INTERACTIVE LEARNING ENVIRONMENTS
19
single skill such as words, so they cannot provide a learning environment with many resources.
Headset VR, though it the best at recreating a real language application environment, can only
be applied sparingly due to its high cost. Presently, the headset VR system is in its mature stage
of application and has immense space of application in language learning. But it still has a long
way to go before matching this complicated device with various language learning needs.
Meanwhile, some portable devices such as smartphones and Phone cardboards can also be
used as potential high-immersive VR tools. Although proven effective by some empirical
studies in recent years, these devices can also improve language learning(Chiang et al., 2014;
Hwang et al., 2016) but still lag far behind headset VR in both learning experience and interaction, so they are mainly used as supplementary tools in teaching currently. Besides, immersive
VR/AR devices do not have enough resources and software systems in learning to satisfy comprehensive skills required by language learning or to support long-term learning activities. And,
due to some technical deficiencies, using immersive VR/AR devices for a long time will make
users suffer from uncomfortable feelings like dizziness. Therefore, it’s argued that using
headset devices is likely to affect learners’ health.
Opportunities
The research participants and learning targets which concern researchers are relatively simple.
Future research, instead of being limited to higher education, will shift to elementary, junior and
senior high schools and teachers, who usually have greater demands of using technology to
improve traditional learning environments, such as enhancing motivation, meeting emotional
needs and improving learning performance (Deutschmann et al., 2009; Hassani et al., 2016; Lan
et al., 2016). For the learners in elementary, junior and senior high schools and at the pre-school education stage, there are certain complexities for learning in the virtual environment. In the future,
researchers should improve system functions and learning methods according to the learning
needs and the research participants’ acceptance of technology. Besides, the learning methods
included in the study activities are relatively scattered, and so it is necessary to establish a complete
and systematic language learning model in the VR/AR environment, so as to meet the long-term
development needs of language learning. The main goals of language learning in virtual environments include grammar, vocabulary, pronunciation/speaking, reading, writing and listening.
Current researches focus on activities related to the improvement of Pronunciation/Speaking and
Vocabulary, for which the purpose of the activity mode is to usually create a learning scenario
close to the real-life situation in the virtual environment and to use the strategies of situation learning, task-based learning and game learning to organize diversified interactive activities for learners.
These activities are good examples of the interactive and immersive features of VR/AR; however,
future research should focus on how to ensure the comprehensive development of language learning objectives in the virtual environment should be the focus of future research. We suggest that the
text image, characteristic of words, syntactic structure and functional usage can be visualized using
VR/AR technology – generating corresponding images, models, environments, etc., thereby expanding the learning strategies and activities. For example, Garrido-Iñigo and Rodríguez-Moreno (2015)
visually presented learning content related to English words and grammar, test topics and other
materials in a virtual environment, made full use of cooperative and competitive learning
methods to design a level-challenge game as a learning activity and, as a result, achieved positive
results in language learning.
Non-verbal information can be directly visualized in a virtual environment. In addition to verbal
information, non-verbal information (such as gestures, facial expressions, etc.) accounts for a large
proportion of the actual social application of language. The transmission of information is achieved
through the interaction of verbal and non-verbal channels. Researchers believe that the proportion
of meaning conveyed by nonverbal information is as high as 65% (Macedonia & Knösche, 2011).
Vygotsky also saw the importance of non-verbal information, believing that non-verbal language
20
X.-Y. QIU ET AL.
can be used to develop individual thinking as verbal language (McCafferty, 2004). VR has many interaction methods such as voice, gesture, touch and multi-channel, which is beneficial to increasing
non-verbal cues such as body language and verbal language in language learning. This not only
avoids the negative effects of the single voice interaction technology used in the past, but enhances
the ability of language comprehension, improves the conversational experience, and also helps to
achieve the cognitive learning effect. Lan et al. (2018) study provides a good example. It required
learners to fully use body language for interaction, and the result showed that gestures can also
be used as an aid to improve learner memory, namely, they help learners remember the pronunciation and meaning of specific words. Therefore, non-verbal information is also important for language
learning. Researchers should use VR technology to integrate learners’ visual and auditory channels,
and build learning activities that emphasize both language and non-verbal behaviour.
The creation of virtual scenes is based not only on real-life scenarios, but also on more extensible
functions. Studies have shown that humans’ complex behaviour and activities can also be simulated
in a virtual environment, and it is convenient to record and analyse them in depth. However, few
researchers currently use immersive VR/AR technology to study learners’ learning behaviours,
especially in the field of language learning. By recording learners’ language learning behaviors,
researchers can analyse learners’ behaviours in virtual environments, such as learning time, interaction information, action, posture, etc. (Tan et al., 2016). Therefore, the function of the multimodal
environment created by VR technology needs to be better extended in language learning. Hence,
the research on headset VR/AR in language learning has just begun, and it is predicted that its development process will gradually mature in the same way as desktop VR has.
Threats
Taking both characteristics of VR/AR and the analysis results of this research into consideration, there
are three main threats to this language learning research. First of all, researchers have failed to test
how VR/AR influences language learning with some key issues such as factors being referred in scenarios designing (immersion and presence), higher-order cognition evaluation (higher order skills and
collaboration/communication), variables reflecting learners’ emotions (self-efficacy, confidence and
learning anxiety) and correctness and fluency of operation or demonstration. Limited researching
dimensions can hinder VR/AR’s technology from further improvement and having more creativity
and efficiency in language learning activities. Secondly, VR/AR without unified application standards
in language learning, have obstacles in being applied and promoted. Some specific problems are as
follows: there is a lack of targeted research on the development of learning materials and resources,
no clear teaching scheme has been formed yet, and no application norms and standards of education have been established. This also shows that the integrating VR/AR into education requires
a long-term effort. Only through the establishment of a multi-party cooperation mechanism will it
be possible to eliminate threats and maintain the effective development of the language learning
research. Thirdly, learners may easily get addicted into the virtual world and fail to form a serious
learning attitude due to VR/AR technology. VR/AR technology began in areas outside education
and initially adapted to the needs of technology in various fields, especially in the entertainment
industry. If too much content unrelated to language learning is kept in the virtual environment, learners may deviate from their learning targets and fail to achieve the expected learning performance.
Therefore, researchers need to pay attention to the creation of virtual environments like reducing
irrelevant distractions to make VR/AR technology adapt to all aspects of language education.
This study reviews the development trend of VR/AR technology in language learning research and
summarizes it in SWOT. Being limited by VR/AR technology itself, current studies still have some
shortcomings and fail to provide abundant in-depth learning activities. However, it’s clear that
such technologies still have significant and valuable roles in promoting language learning scenarios
and methods. Therefore, this paper predicts that VR/AR will be a significant development trend in
future language learning, since it can achieve transformation of technology positioning from tools
INTERACTIVE LEARNING ENVIRONMENTS
21
to the environment. Thanks to the immersion and presence of VR/AR technology, it can build a
reasonable language learning environment that helps learners to enhance their learning autonomy
and sociality. Furthermore, all types of current technologies can satisfy requirements for various
learning goals in language learning and realize the co-located distributed collaborative activities.
Therefore, in the future, this field is likely to receive researchers’ continuous attention mainly in
terms of AR’s application and immersive VR’s application. In conclusion, by focusing on more
research issues, conducting various investigation activities and developing different software
system, VR/AR will provide us with more possibilities to facilitate language learning.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by Zhejiang Provincial Natural Science Foundation of China: [Grant Number LY20F020031].
Notes on contributors
Xing-Yue Qiu is a doctoral student at Department of Education Information Technology, East China Normal University,
China. His research interests include VR-supported language learning, collaborative learning, new media/technology
and smart learning space.
Chuang-Kai Chiu received the PhD degree in the College of Engineering from the Chung Hua University, Taiwan, in
2013. He is currently an associate professor at College of Education, Wenzhou University, China. His research interests
include ICT in education, mobile/ubiquitous learning, and big data analysis/mining.
Lu-Lu Zhao is a graduate student at College of Education, Wenzhou University, China. Her research interests include
educational technology, data analysis/mining and new media/technology in education.
Cai-Feng Sun is a graduate student at College of Education, Wenzhou University, China. Her research interests include
educational technology, data mining and learning analysis, new media/technology in education and VR in education.
Shu-jie Chen is a senior experimental technician at Wenzhou University, China. Her research interests include educational technology, application of computer, knowledge sharing in virtual learning environment, and big data analysis/mining.
ORCID
Chuang-Kai Chiu
http://orcid.org/0000-0003-3087-7586
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