This is a post-peer-review, pre-copyedit version of an article published in Education and Information
Technologies. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10639-017-
A review of the use of virtual reality head-mounted
displays in education and training
Lasse X Jensen1 & Flemming Konradsen2
1 Centre for Online and Blended Learning, Faculty of Health and Medical Sciences, University of
2 Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen,
In the light of substantial improvements to the quality and availability of virtual reality (VR) hardware seen
since 2013, this review seeks to update our knowledge about the use of head-mounted displays (HMDs) in
education and training. Following a comprehensive search 21 documents reporting on experimental studies
were identified, quality assessed, and analysed. The quality assessment shows that the study quality was
below average according to the Medical Education Research Study Quality Instrument, especially for the
studies that were designed as user evaluations of educational VR products. The review identified a number of
situations where HMDs are useful for skills acquisition. These include cognitive skills related to
remembering and understanding spatial and visual information and knowledge; psychomotor skills related to
head-movement, such as visual scanning or observational skills; and affective skills related to controlling
your emotional response to stressful or difficult situations. Outside of these situations the HMDs had no
advantage when compared to less immersive technologies or traditional instruction and in some cases even
proved counterproductive because of widespread cybersickness, technological challenges, or because the
immersive experience distracted from the learning task.
Virtual Reality (VR); Head-Mounted Display (HMD); Education; Training; Educational Technology;
Since the 1960s the term Virtual Reality (VR) has been used to describe a wealth of very different
technologies, both software and hardware, such as the Sensorama Simulator (Heilig, 1962), online virtual
worlds (e.g. Second Life), massive multiplayer online role playing games (MMORPGs, such as World of
Jensen & Konradsen - A review of the use of virtual reality head-mounted displays in education and training (AAM version)
Warcraft), surgery simulators, rooms where all walls are covered in displays (Cave Automatic Virtual
Environments, CAVE), as well as a wealth of different Head-Mounted Displays (HMDs).
For decades it has been discussed if VR has the potential to revolutionize education. The argument is that VR
can be used for simulation-based education, where students and learners can practice new skills in a
simulated environment that enables correction, repetition and non-dangerous failure and at the same time
offers access to interaction with expensive or far-away environments. Despite the high hopes, these ideas
have been based on speculation more than praxis, and outside of dedicated training simulators for surgeons,
pilots, and military personnel the VR technology has not been on a level where it could be applied in
education and training at large.
This, however, changed in 2013 when the first developer versions of a HMD from the company Oculus Rift
introduced a new generation of consumer-priced VR technology. During the next couple of years a myriad of
competitors launched their own HMDs, making this new technology accessible to the wider public and for
research and education purposes as well. Hodgson et al. (2015) give an example of comparable VR hardware
from 2006 and 2014 that cost USD 45,000 and USD 1,300 respectively. In a 2016 report on technology
trends in higher education the New Media Consortium predicted that VR technologies will be adopted in the
higher education sector within 2-3 years (Johnson, Adams Becker, Estrada, Freeman, & Hall, 2016).
Aside from a much lower price the new generation of HMDs also offered a better quality user experience.
An often cited quality difference between pre- and post-2013 HMDs is in the so-called Field of View (FOV).
When putting on a HMD, the natural human FOV of 180 degrees is limited both horizontally and vertically
and this influences the realism of the VR experience. Before 2013 the typical FOV of HMDs was between 25
and 60 degrees, while most of the new generation of HMDs have FOVs above 100 degrees (Riva,
Wiederhold, & Gaggioli, 2016).
1.1 Previous work
There is a substantial body of older research which is looking at VR and education. This research, however,
does not limit the scope to HMDs and the vast majority of the work was published before the new generation
of low-price high-quality HMDs became available.
Two key concepts of VR theory are immersion and presence. They are sometimes used interchangeably, but
formally immersion describes the experience of using so-called immersive technology. This technology
works by exchanging sensory input from reality with digitally generated sensory input, such as images and
sounds (Ott & Freina, 2015). If you, subjectively, react to being immersed in a virtual environment in a way
where your brain and nervous system behave in a way similar to being in the same situation in the real world,
then you are experiencing presence (Slater, 2003). Often researchers make a distinction between immersive
virtual reality, where the virtual environment surrounds you (as is the case with HMDs or CAVE systems),
and non-immersive virtual reality, where you look into the virtual environment from the outside, typically
accessed through a traditional display of a desktop computer (Ott & Freina, 2015).
In their systematic review, covering 1999-2009, Mikropoulos and Natsis (2011) identified a number of
features, so-called affordances, of VR that are conducive to learning. These include the first-person
experience and sense of presence, which are both related to the unique experience of being in the virtual
environment, as well as a number of affordances that are related to the possibility of giving the learner access
to phenomena that are otherwise not available to everyday experience. This is in agreement with Ott and
Freina (2015) who found that the main motivation to use VR in education is that it makes it possible to
experience situations that are either inaccessible (in time or space) or problematic (dangerous or unethical).
Jensen & Konradsen - A review of the use of virtual reality head-mounted displays in education and training (AAM version)
Mikropoulos and Natsis (2011) found that the immersive systems only had an advantage vis a vis the desktop
systems when the tasks involved “complex, 3D, and dynamic” content. Many of the studies in the same
review reported that their study participants felt a sense of presence while being in the virtual environment,
and in three of the reviewed studies this was correlated with better learning outcomes. The few studies
examining the use of haptic devices for interacting with the virtual environments did not show an increase in
learning, leading Mikropoulos and Natsis (2011) to conclude that “carefully designed learning activities are
more important than an exotic interface that contributes to intuitive interaction”. In fact, even though they
found that both educators and learners share a positive attitude towards using VR for education, the
reviewers did not find evidence to conclude anything regarding retention of knowledge gained this way.
Being motivated by the emergence of a new technology this review followed a hardware centred approach
that focussed narrowly on VR simulations that are accessed through HMDs which completely block out
visual access to the surroundings, and are offering diagonal FOV above 70 degrees. The review included all
relevant published research on education and training with these HMDs. The objective was to systematically
assess the quality of the studies and synthesise and discuss their findings with a particular focus on how the
VR learner experience affects the learning outcomes.
This review includes all peer-reviewed research documenting experimental or quasi-experimental studies
that are relevant to the objective.
2.1 Search strategy
To ensure a comprehensive search, eight research databases were identified. Of these three are
interdisciplinary (SCOPUS, Web of Science, EBSCOhost), and the other five cover the fields of biomedicine
and health (PubMed), computer science and engineering (IEEE Xplore), education (ERIC), psychology
(PsycINFO), and social sciences (International Bibliography of the Social Sciences IBSS).
Each database was searched in March 2017 with keywords based on this Boolean search string:
(virtual reality OR head-mounted display) AND (education OR training OR learning)
The search was limited to only include peer-reviewed publications. Since the focus is on the current
generation of HMDs, it was also limited to studies that were published since 1 January 2013. To validate the
search string a number of test searches with alternative keywords (virtual world, cyberspace, HMD) were
undertaken. They did not generate any relevant references that were not already located when searching for
virtual reality and head-mounted display.
Jensen & Konradsen - A review of the use of virtual reality head-mounted displays in education and training (AAM version)
Fig. 1: Selection process flowchart
The search generated a total of 8177 references to journal articles, conference papers, and book chapters. A
small number of further documents (n=8) were identified in reference lists or as ‘similar articles’ suggestions
on journal and database websites, and they were included in the selection process. As a consequence of the
very wide search strategy this raw list of references included many duplicates (n=2271). After they were
removed, 5914 unique documents for primary sorting based on title and abstract remained.
2.2 Selection process
The high number of references in the gross list reflects the fact that some keywords (virtual reality, learning,
education, and training) are very general terms, and can be found in a great number of publications that are
not dealing with HMDs in an educational context. However, the interdisciplinary nature of the field means
that there is no agreed-upon vocabulary which could have limited the search without risking missing relevant
studies. Documents focussing upon studies of non-HMD technologies, such as virtual worlds, surgery
simulators, and learning management systems (often called virtual learning environments) were excluded.
Also studies of VR use in rehabilitation and health care, as well as non-experimental technical descriptions
of hardware or software were excluded.
This primary sorting, based on titles and abstracts, left us with just 165 documents. Next step was a
systematic sorting, based on the full text versions of the remaining documents. This sorting was based on a
five step approach, where a document had to pass every step to be included in the analysis. The criteria were:
1. Full text version accessible and available
2. Full text version is in English
3. Describes the use of a HMD with high FOV
4. Describes an experimental or quasi-experimental study of the educational use of HMDs
5. Reports original data that is not analysed more thoroughly by the same authors in another of the
Of the 165 documents, 21 passed all five steps, and were thus deemed appropriate for inclusion in the
Of the 21 studies 14 examined the learner experience (Andreoli et al., 2016; Bharathi & Tucker, 2015;
Fernandes et al., 2016; Gutiérrez-Maldonado, Ferrer-García, Plasanjuanelo, Andrés-Pueyo, & Talarn-
Caparrós, 2015; Janssen, Tummel, Richert, & Isenhardt, 2016; Kleven et al., 2014; Loup, Serna, Iksal, &
George, 2016; Madrigal, Prajapati, & Hernandez-Prera, 2016; Moesgaard et al., 2015; Pan et al., 2016;
Polcar & Horejsi, 2015; Ray & Deb, 2016; Reiners, Wood, & Gregory, 2014; Stavroulia et al., 2016). The
main aspects of learner experience being examined in the included studies can be divided into three groups,
namely: (1) Perceived presence, immersion and realism; (2) Physical discomfort when using HMDs; and (3)
Learner attitudes towards VR in education.
Of the 21 studies 11 measured learning outcomes for study participants using HMDs (Alhalabi, 2016;
Gutiérrez-Maldonado et al., 2015; Huang, Churches, & Reilly, 2015; Kahlert, van de Camp, & Stiefelhagen,
2015; Moesgaard et al., 2015; Nomoto et al., 2016; Polcar & Horejsi, 2015; Ragan et al., 2015; Rasheed,
Onkar, & Narula, 2015; Ray & Deb, 2016; Sportillo, Avveduto, Tecchia, & Carrozzino, 2015). Learning
outcomes are often divided into three domains, namely the cognitive, affective and psychomotor domains
(Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956). Of the 21 studies in the review one study looked at
affective skills acquisition, six studies examined cognitive skills acquisition, and five studies examined
psychomotor skills acquisition.
Online Resource 1 includes a detailed description of the 21 studies, including study purpose, findings,
educational topic, study participants, and type of HMD used.
2.3 Quality assessment tool
Of the 21 included studies 19 use exclusively or primarily quantitative methods. The remaining two are
primarily qualitative (Fernandes et al., 2016; Reiners et al., 2014). To assess the quality of the 19 quantitative
studies the Medical Education Research Study Quality Instrument (MERSQI) was employed (Reed et al.,
2007). MERSQI itself has been rigorously assessed, to evaluate its correlation with other quality assessment
tools and different proxies for research quality (David A Cook & Reed, 2015). Although the instrument was
developed for medical education it is in praxis discipline neutral and can also be used to assess the quality of
non-medical education research.
MERSQI consists of ten items, covering six domains, namely (1) study design, (2) sampling, (3) type of
data, (4) validity evidence for evaluation instrument scores, (5) data analysis, and (6) outcome. A total score
is calculated by simply adding the scores of the individual items. Each domain has a maximum score of 3,
making the maximum total score for a study 18.
3.1 Quality of included studies
Out of the 21 identified studies 19 were quantitative. Of these, the most used study design was comparison of
two non-randomized groups (n=13). None of the studies lived up to the criteria for randomized controlled
trials. The studies all had a high (above 75%) response rate, and in the majority of the studies the study
participants came from just one institution (n=15). A strength in the studies is that most of them (n=15)
measured objective data (as opposed to self-reporting).
The greatest weakness across the pool of studies relates to the item that deals with validity of the evaluation
instrument. Here the plurality of studies (n=9) scored zero points, and only one got the maximum score of 3.
The validity of the evaluation instrument is especially important when measuring learning outcomes as such
data is completely dependent on the appropriateness and difficulty of the test. Most (n=16) of the studies
were rated as having an appropriate analysis, but just six of them were given points for going beyond
descriptive analysis. The last item, outcome, shows that the majority of the studies deal with acquisition of
skills and knowledge (n=13), while 6 have a primary focus on attitudes and perceptions of the learners. No
studies examined outcomes related to behaviour or organisational change. Online Resource 2 shows the
detailed MERSQI scores for each of the 19 quantitative studies.
The mean MERSQI score of the quantitative studies was 10.9, with a range of 6.0–14.5. In a study of 26
reviews that all used the MERSQI to assess the quality of educational research, researchers found that across
the reviews the average of their overall total scores was 11.3, with a range of 8.9–15.1 (David A Cook &
Reed, 2015). This lower score, and lower research quality, in the present review is influenced by the fact that
ten of the studies took the form of user evaluations, and had less focus on scientific rigorousness. The
MERSQI mean of these ten studies was 10.3, with a range of 6–13.8, compared to a mean of the other
studies of 11.7, with a range of 9.0–14.5. However, since the search and selection process only identified 21
studies for inclusion, even the ones that scored very low on the MERSQI were retained in the pool.
There were two primarily qualitative studies, which could not be included in the quantitative MERSQI
analysis. In Fernandes et al. (2016) the researchers described six experiments each of which included two
sources of data: observations of learners playing a VR based educational game and a questionnaire that the
learners filled out after the experience. Their methodology follows an iterative design where elements, such
as the questionnaire or the game itself, are adjusted and improved between the experiments. In Reiners et al.
(2014) the researchers conducted a series of seven short experiments to examine learner attitudes towards
using VR technologies in the classroom. Their study included just 13 study participants recruited from both
inside and outside the university. After each VR experience each study participant is interviewed. The
interviews were based on a guide, which included both open ended and Likert scale questions. Furthermore
the study included observations of study participants during the VR experiences. Both of the qualitative
studies focussed on learner experience and did not examine learning outcomes.
3.2 Factors influencing immersion and presence
Creating a sense of presence through immersion is a main motivation for using immersive VR both for
education and in other domains. In the included studies a number of different factors influencing immersion
and presence were identified. Pan et al. (2016) found that shortcomings in the visual presentation of virtual
patients, such as lagging graphics, was limiting the sense of presence felt by medical professionals during the
VR experience. When evaluating an educational video game, Fernandes et al. (2016) found that the
awareness of people watching you while you were wearing the HMD was limiting the sense of presence.
Another factor that was identified was that standing up, as opposed to sitting down, led to increased sense of
presence (Reiners et al., 2014). In a study looking at correlations between personality traits and learner
experience Janssen et al. (2016) found that people with more anxious or reserved personalities not only had a
less positive experience in VR, but also felt less immersed. This led the researcher to conclude that learners
with certain individual traits and characteristics will benefit less from learning in VR.
3.3 The influence of immersion and presence on learning
All studies in the review were based on the idea, that more immersion has a positive influence on learning
outcomes. Examples of this are Loup et al. (2016) who found that learners with HMD were more engaged, or
Reiners et al. (2014) who observed that their study participants took the more immersive VR simulations
more seriously. This meant avoiding bumping into things in the virtual environment and approaching
dangers with greater care. Furthermore, when comparing three VR systems, Alhalabi (2016) found that study
participants in the most immersive system voluntarily spent more time on the learning task. Findings like
these all point towards the affordances of immersive technologies such as HMDs. This however, is
somewhat contrasted by Fernandes et al. (2016), who found that increasing immersion by adding 3D sounds
and a graphical rendering of user’s own hands to the virtual environment confused some study participants
and distracted from the learning task.
3.4 Cognitive skills acquisition
Six studies looked specifically on the acquisition of cognitive skills. Of these, five compared the learning
outcomes when using HMD to that of less immersive technologies or traditional classroom instruction. Just
one study found clear evidence of better learning outcomes for HMD than for CAVE and desktop based
systems (Alhalabi, 2016). Rasheed et al. (2015) found that HMDs were better for spatial awareness but
classroom teaching better for remembering facts, and Ray and Deb (2016) found that only after two weeks of
biweekly use did students in a group where HMDs were integrated in the classroom instruction do slightly
better than students who received traditional classroom instruction. When Polcar and Horejsi (2015)
compared knowledge acquisition across HMD, CAVE and desktop systems the HMD actually led to the
lowest acquisition of knowledge. As the only study Moesgaard et al. (2015) compared two variations of a
HMD-based learning experience: One version had the information delivered by voice-over and one had it
presented in dialogue. Although their sample was too small for statistical inference their findings indicated
that voice-over was better for remembering factual knowledge, such as dates, whereas the dialogue was
better for understanding connections between phenomena.
3.5 Psychomotor skills acquisition
Psychomotor skills are often trained with a training simulator in which the learner repeatedly goes through
the actions being trained until a level of proficiency has been reached. For earlier non-HMD-based training
simulators it has long been accepted that improved realism leads to better learning outcomes. This is
sometimes referred to as simulator fidelity (Hays & Singer, 1989). Some, however, have argued that realism
should only be applied to select elements in training simulations because realistic representation of very
complex environments can confuse the learner (National Research Council, 1994, pp. 52-55).
For HMDs the quality of simulator fidelity depends on the peripheral devices that enable kinaesthetic input
and haptic output in relation to bodily movements. Of the five studies that dealt with psychomotor skills
acquisition, three used a form of hand tracking (Kahlert et al., 2015; Nomoto et al., 2016; Sportillo et al.,
2015), one used a pointing device (Ragan et al., 2015), and one used a traditional gaming joystick (Huang et
al., 2015). Of these the hand tracking is the most immersive, i.e. has the highest simulator fidelity.
In Kahlert et al. (2015) the researchers tested learning transfer for a HMD-based simulator designed to teach
people to juggle three balls. After an average of 27 mins using the very simplified simulator three of nine
study participants were able to juggle with real balls. This was a simple pre-post-test, with no control group,
but nonetheless clearly a case where psychomotor skills learned in virtual reality can transfer into real life
skills. Ragan et al. (2015) examined a system, designed for training a visual scanning technique where the
task was to identify targets (armed persons) in a virtual environment resembling an urban setting. Subsequent
testing in the most complex scenario showed that those who had trained in more realistic scenarios with more
visual complexity were better at adhering to the proscribed technique. Because they did not study skills
transfer to a real world visual scanning task, but rather measured performance in the most realistic virtual
simulation, it cannot be ruled out that the training simply made the study participants better at ‘playing’ the
simulation. Something similar was seen in Sportillo et al. (2015), where the researchers found that study
participants wearing HMDs indeed got better at a virtual assembly task, but that this improvement did not
translate into better performance on equivalent real world assembly task.
3.6 Affective skills acquisition
Although five of the studies described VR simulations where the learner interact with a virtual agent –
systems that can be used for training interpersonal, communicative and other affective skills – there was only
one study that actually tried to measure the learning outcome of such a system (Gutiérrez-Maldonado et al.,
2015). This study compared two different formats for teaching diagnostic interview skills, one with HMD
and one on a desktop computer with stereoscopic/3D glasses, but found no statistically significant difference
in skills acquisition between the formats.
3.7 Physical discomfort
Eight of the 21 studies examined the problem of physical discomfort and cybersickness. The frequencies of
cybersickness symptoms reported vary from very rare (Fernandes et al., 2016; Madrigal et al., 2016) to
almost every participant (Kleven et al., 2014). In some instances the cybersickness symptoms made study
participants drop out of the experiments (Reiners et al., 2014). When present, cybersickness influenced the
learner attitude towards the technology negatively, and was correlated with lower learning outcomes (Polcar
& Horejsi, 2015). Some studies found that participants with extensive 3D gaming experience reported less
symptoms of cybersickness (Andreoli et al., 2016; Reiners et al., 2014), and older study participants reported
more cybersickness symptoms that younger (Andreoli et al., 2016).
3.8 Learner attitudes towards HMD technology
The 10 studies that examined the learner attitudes towards HMDs were in most cases based on the self-
reported opinion of the study participants. Generally, these studies examined if the experience is perceived to
be useful for learning and if the experience is perceived to be exciting/interesting. Across all of the studies
the researchers found that study participants were very positive towards both of these aspects. When
comparing HMDs to desktops they found a moderate preference for HMDs (Bharathi & Tucker, 2015;
Kleven et al., 2014). Aside from the issues of physical discomfort described above just one study identified a
number of less positive attitudes, namely a feeling of unsafety, because the HMD blocks out access to your
actual surroundings, and a feeling of boredom and emptiness, because the user is alone in the VR simulation
(Reiners et al., 2014). One study went beyond the self-reported attitudes and opinions and included
observational methods to determine learner attitudes. This study found that the HMDs triggered emotions
such as “joy, satisfaction, delight, and enthusiasm” (Fernandes et al., 2016). None of the studies measured if
these attitudes change over time.
As is clear from the findings HMDs do not automatically cause learning to occur, but they can be used as a
medium to access simulations in which learning may take place. Because of this, the question is not so much
whether HMDs as such are useful for education and training, but rather if a certain simulation is useful. The
discussion below will examine what it is that makes some training simulations and virtual experiences useful
for learning and use this knowledge to answer questions of relevance to instructors, trainers and VR
4.1 Using HMDs for cognitive skills acquisition
Often the immersive experience of being in a simulated reality overshadows the cognitive skills acquisition.
This is seen in for instance Moesgaard et al. (2015) where the study participants reported they were “too
enthralled by the [virtual] environment to notice the information that was presented to them”. However,
when used specifically to help the learner remember and understand visual and spatial aspects of a place, the
low-interaction virtual experience seems to have an advantage over non-HMD instruction. This advantage,
however, is limited by a number of factors, most importantly the learners’ unfamiliarity with the technology
and their tendency to cybersickness. All the cognitive skills being evaluated in the reviewed studies related to
what Bloom’s taxonomy calls lower level cognitive skills, characterized by remembering or understanding
facts (Bloom et al., 1956). No research has examined the use of HMDs to teach higher level cognitive skills.
4.2 Using HMDs for psychomotor skills acquisition
The studies examining psychomotor skills acquisition all found that repeatedly using a training simulator
made the study participants better at doing well in the simulator. Only two studies examined training transfer
(Kahlert et al., 2015; Sportillo et al., 2015), and although one of these found no transfer, it was shown that
transfer is possible and that successful psychomotor skills transfer depends less on the HMD and more on the
quality and realism of a peripheral haptic/tactile device. In training simulators that do not include HMDs,
such as many surgery simulators, research has shown significant levels of skills transfer between the
simulator and the real world task (Dawe et al., 2014). These simulators have the ability to increase
complexity slowly and have achieved a level of simulator fidelity where the most difficult simulation is very
close to the real world experience, in part due to the fact that the reality being simulated is also mediated by
technology. The usefulness of HMDs in psychomotor skills acquisition is therefore very limited. In cases
where the psychomotor skill is related to the movement of the head, such as visual scanning or observational
skills, the current technology offers high simulator fidelity. For the plethora of other psychomotor skills that
require physical interaction with specific artefacts and your surroundings in general efficient psychomotor
skills acquisition with HMDs will not be possible until there are significantly improved peripheral
technologies for including the user’s body movements into the simulation.
4.3 Using HMDs for affective skills acquisition
Affective skills, like psychomotor skills, require repetition in order to achieve mastery, and a successful VR
training simulator for affective skills must be highly interactive. Many affective skills are related to
interpersonal skills, and here the ability of the technology to create a believable simulation of a virtual
human or social situation is crucial. Although the topic is almost unexamined in current research, affective
skills acquisition seems to be a good place to use HMDs. Training simulators for affective skills are less
dependent on immersive peripheral devices that include your bodily movements in the simulation, and more
dependent on the ability of the simulation to evoke an emotional response in the learner. If the emotional
response can be created by exposure to sound and image in a HMD, then this can be used in training. VR
technology is already being used to treat irrational fear and phobias (Anderson et al., 2013) and for practicing
stress management strategies (Pallavicini, Argenton, Toniazzi, Aceti, & Mantovani, 2016). As artificial
intelligence improves there will be more and more affective skills which can be trained virtually.
4.4 Current barriers to the use of HMDs in education and training
The optimistic predictions of recent years about the imminent introduction of HMDs into the classroom were
based on the fact that the hardware was now much better and much cheaper. However, two fundamental
barriers were overlooked.
The first barrier relates to lack of content. Production of VR simulations is prohibitively expensive, and for
instructors this means that they have to use the content that is provided by VR content producers. However,
most educational VR simulations on the market are directed at self-learners. They were not designed as a
tool to be used at different educational levels and with different pedagogical approaches, but as stand-alone
learning experiences. This makes them less suitable for classroom use. For HMDs to become a relevant tool
for instructors they must have the ability to produce and edit their own content. This is starting to happen
with content based on 360 degree video footage, and currently the most promising use of HMDs in education
may not be to use educational VR simulations, but to use the HMD as a viewer of 360 degree video content
which can form the basis of subsequent educational activities such as classroom discussions, written
analysis, group work, or assessments.
The second barrier relates to the hardware. Current HMDs are primarily entertainment devices. They were
not designed for classroom use, and require a level of technical skills that is a challenge to many instructors.
Furthermore, they require frequent software updates and issues with streaming or preloading materials, and
user profiles make it hard for instructors to manage more than just a couple of HMDs. The practical
alternative would be the bring-your-own-device (BYOD) philosophy, but that requires all learners to have
high quality VR ready smartphones that are compatible with a Google Cardboard headset. This would mean
a less immersive experience, and raise questions of equity.
4.5 Issues of equity and equal access to education
As described in the findings the learners reported very different experiences when using HMDs. This ranged
from mild feelings of unsafety to severe cases of cybersickness that prompted some to leave the experiment.
Furthermore Janssen et al. (2016) showed that personality traits had a strong influence on the way the VR
simulations are experienced, and this in turn will influence any learning outcomes. A VR simulation which is
designed to engage the learner emotionally can be a very intense experience. In light of this, it is important to
not only consider the diversity of the users, but also to make sure that the context of use is perceived to be
safe and that the simulations are designed to avoid or minimize physical discomfort.
4.6 Limitations of this review
The studies included in this review came from a wide spectrum of training and educational contexts, and
although some had very positive findings it was not possible to make general statements about the benefits of
HMDs in education. This limitation was caused partly by the low number of studies and partly by the low
quality of the research. In regards to research quality an important weakness in many studies was the
measurement of learning outcomes without giving arguments for the validity of the evaluation instrument.
Furthermore, the review showed that much of the research was in fact user evaluations of VR products that
did not seek to uncover more general knowledge about learning in VR, and at the same time had an
unavoidable bias towards a positive evaluation. Another weakness in the evidence base was the high number
of media-comparative studies where HMD-based learning is compared to either classroom or online
learning. Some researchers have argued that this type of research is “logically impossible because there are
no valid comparison groups” (David A. Cook, 2005). According to this view, new insights can be reached by
comparing different variations of learning within the same media. This type of research will not help answer
which media or technology is best for instruction, but rather will inform our practice when using a certain
media or technology. These limitations are general for much education research, but maybe especially
pronounced for research in the nexus of learning and technology.
4.7 Future research
To gain more knowledge about the use of HMDs in education and training the interesting question is not if
HMDs should be used, but rather how and for what should HMDs be used. Even though this review could
identify particular types of skills where the HMDs are useful there is still a great need for more research to
With the exception of a single study all the reviewed research focussed on very short term use in an
experimental setting. Future research should focus on prolonged and repeated usage and examine, with both
quantitative and qualitative methods, how the findings relating to motivation, enthusiasm and time-on-task
change when the learners become familiar with the technology.
To better understand the barriers and uncover ways of mitigating them, the future research should move
away from laboratory style experiments, and examine the use of educational VR in an authentic setting, as
part of a real educational or training programme. As presented in the findings above the actual context of use
has a great influence on the learner experience and so far no best practice can be formulated.
The motivation for using HMDs in education is that it can expose learners to challenging or educational
situations and allow them to repeatedly practice new skills in an environment that enables correction and
non-dangerous failure. At first sight these affordances seem ideal for teaching almost any skill, and the
increased immersion offered by new VR technology seems well suited for successful educational approaches
and theories such as constructivism, active learning, or simulation-based learning. However, this review
paints a more complex picture, with a much more limited usefulness. While the studies found that learners
are generally very positive about using HMDs there are still substantial barriers to the use, especially in
regards to cybersickness symptoms, lack of appropriate software, and technical limitations of peripheral
The review identified a number of situations where HMDs are useful for skills acquisition. These include
cognitive skills related to remembering and understanding spatial and visual information and knowledge;
psychomotor skills related to head-movement, such as visual scanning or observational skills; and affective
skills related to controlling your emotional response to stressful or difficult situations. Obviously HMDs can
be used as a medium for training any skill, but it will have no added value vis-à-vis cheaper and less
immersive formats, and in some cases be counterproductive, because the immersive experience actually
distracts from the learning task.
The low average quality and limited number of studies in this review point to a need for further and more
rigorous research that examines the most promising uses of HMDs in an authentic educational or training
6 Compliance with Ethical Standards
Data from this review will be made available by contacting the first author directly. No human participants
were used in this study. The authors do not have any conflicts of interest in relation to the present work.
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Publication Learningoutcomefocus Learnerexperiencefocus Educationaltopic Studyparticipants TypeofHMD Resourceformat Purpose Findings
Alhalabietal.(2016) Cognitiveskills Engineering 48(students) OculusRift Unspecified Comparelearningoutcomesfor
History 72(mostlystudents) OculusRiftDK2 Seriousgame Evaluatelearnerperceptionof
Engineering 54(students) OculusRift Virtuallab Comparetaskperformanceand
History 437(various) OculusRiftDK2 Virtualagent Evaluatetheuserexperienceof
Gutiérrez‐Maldonadoetal.(2015) Cognitiveandaffectiveskills Immersionandrealism; Healthcare 52(students) OculusRiftDK1 Virtualagent Compareefficacyandusabilityof
Huangetal.(2015) Psychomotorskills Americanfootball 17(footballplayers) OculusRiftDK1 Virtualagent EvaluatetheefficacyofVR
Janssenetal.(2016) Immersionandrealism; Problemsolving 10(students) OculusRiftDK2 Seriousgame Examinetheroleof'individual‐
Kahlertetal.(2015) Psychomotorskills Juggling 9(unspecified) OculusRift Virtualartefact Examineifpsychomotorskills
Healthcare 12(students) OculusRift Virtualagent Evaluatetheattitudesofnursing
Loupetal.(2016) Learnerattitudes Science 57(highschoolstudents) OculusRift Seriousgame Comparelearnerengagementand
Madrigaletal.(2016) Learnerattitudes Medicine 9(medicalprofessionals) PhonebasedHMD Visualization Comparelearnerattitudes
Moesgaardetal.(2015) Cognitiveskills Immersionandrealism; History 40(unspecified) OculusRift Virtualfieldtrip Comparedifferencesinlearning
Nomotoetal.(2016) Psychomotorskills Drawing 25(unspecified) OculusRiftDK2 Virtualartefact Evaluateavisio‐hapticHMDbased
Healthcare 21(medicalprofessionals) OculusRiftDK2 Virtualagent Exploreattitudesofmedical
Polcar&Horejsi(2015)Cognitiveskills Physicaldiscomfort; Nospecifictopic 45(students) OculusRiftDK2 Virtualenvironment Comparelearningoutcomeand
Raganetal.(2015) Psychomotorskills Military 45(mostlystudents) nVisorSX111 Virtualenvironment Explorehowfieldofview(FOV)
Rasheedeta.(2015) Cognitiveskills History 20(pupils,aged8‐10) GoogleCardboard Virtualenvironment Comparethelearningoutcomes
Ray&Deb(2016) Cognitiveskills Learnerattitudes Engineering 40(students) GoogleCardboard Visualization Comparinglearningoutcomesand
Nospecifictopic 13(various) OculusRiftDK1 Virtualenvironment Compareattitudestovarious
Sportilloetal.(2015) Psychomotorskills Complexmachines 8(unspecified) OculusRiftDK2 Virtualartefact EvaluateaHMDbasedsystemfor
Identifyingbullying 10(teachers) OculusRift Virtualenvironment Evaluatelearnerattitudes
Online Resource 2: Assessing the quality of the 19 quantitative studies with the Medical Education Research Study Quality Instrument (MERSQI)
Validity of evaluation instrument
Rel. to other
Alhalabi et al. (2016)
Andreoli et al. (2016)
Bharathi & Tucker (2015)
Gutiérrez-Maldonado et al. (2015)
Huang et al. (2015)
Janssen et al. (2016)
Kahlert et al. (2015)
Kleven et al. (2014)
Loup et al. (2016)
Madrigal et al. (2016)
Moesgaard et al. (2015)
Nomoto et al. (2016)
Pan et al. (2016)
Polcar & Horejsi (2015)
Ragan et al. (2015)
Rasheed et a. (2015)
Ray & Deb (2016)
Sportillo et al. (2015)
Stavroulia et al. (2016)
Possible values for each item
Study design: Single-group cross-sectional or single-group posttest only: 1 • Single-group pretest and posttest: 1.5 • Nonrandomized, two-group: 2 • Randomized controlled trial: 3;
Sampling, institutions: One institution: 0.5 • Two institutions: 1 • Three or more institutions: 1.5;
Sampling, response rate: Not applicable • < 50% or not reported: 0.5 • 50%–74%: 1 • ≥ 75%: 1.5;
Type of data: Assessment by study participant: 1 • Objective: 3;
Validity evidence for evaluation instrument scores: Not applicable • Internal structure: 0/1 • Content: 0/1 • Relationships to other variables: 0/1;
Data analysis, sophistication: Descriptive analysis only: 1 • Beyond descriptive analysis: 2;
Data analysis, appropriate: Data analysis appropriate for study design and type of data: 0/1;
Outcome • Satisfaction, attitudes, perceptions, opinions, general facts: 1 • Knowledge, skills: 1.5 • Behaviours: 2 • Patient/health care outcome: 3;