Augmented Reality Trends in Education: A Systematic Review of Research and Applications

Abstract

In recent years, there has been an increasing interest in applying Augmented Reality (AR) to create unique educational settings. So far, however, there is a lack of review studies with focus on investigating factors such as: the uses, advantages, limitations, effectiveness, challenges and features of augmented reality in educational settings. Personalization for promoting an inclusive learning using AR is also a growing area of interest. This paper reports a systematic review of literature on augmented reality in educational settings considering the factors mentioned before. In total, 32 studies published between 2003 and 2013 in 6 indexed journals were analyzed. The main findings from this review provide the current state of the art on research in AR in education. Furthermore, the paper discusses trends and the vision towards the future and opportunities for further research in augmented reality for educational settings.

Full text

Bacca, J., Baldiris, S., Fabregat, R., Graf, S., & Kinshuk. (2014). Augmented Reality Trends in Education: A Systematic Review
of Research and Applications. Educational Technology & Society, 17 (4), 133–149.
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Augmented Reality Trends in Education: A Systematic Review of Research
and Applications
Jorge Bacca1*, Silvia Baldiris1, Ramon Fabregat1, Sabine Graf2 and Kinshuk2
1University of Girona, Institute of Informatics and Applications, Av. Lluis Santalo s/n, 17071 Girona, Spain // 2
Athabasca University, School of Computing and Information Systems, 1200, 10011-109 Street, Edmonton, AB T5J-
3S8, Canada // jorge.bacca@udg.edu // ramon.fabregat@udg.edu // baldiris@eia.udg.edu // sabineg@athabascau.ca
// kinshuk@athabascau.ca
*Corresponding author
ABSTRACT
In recent years, there has been an increasing interest in applying Augmented Reality (AR) to create unique
educational settings. So far, however, there is a lack of review studies with focus on investigating factors such as:
the uses, advantages, limitations, effectiveness, challenges and features of augmented reality in educational
settings. Personalization for promoting an inclusive learning using AR is also a growing area of interest. This
paper reports a systematic review of literature on augmented reality in educational settings considering the
factors mentioned before. In total, 32 studies published between 2003 and 2013 in 6 indexed journals were
analyzed. The main findings from this review provide the current state of the art on research in AR in education.
Furthermore, the paper discusses trends and the vision towards the future and opportunities for further research
in augmented reality for educational settings.
Keywords
Augmented reality, Systematic review, Trends of AR, Personalization, Inclusive learning in augmented reality
Introduction and definitions
In recent years, technology-enhanced learning (TEL) research has increasingly focused on emergent technologies
such as augmented reality, ubiquitous learning (u-learning), mobile learning (m-learning), serious games and
learning analytics for improving the satisfaction and experiences of the users in enriched multimodal learning
environments (Johnson, Adams Becker, Estrada, & Freeman, 2014). These researches take advantage of
technological innovations in hardware and software for mobile devices and their increasing popularity among people
as well as the significant development of user modeling and personalization processes which place the student at the
center of the learning process. In particular, augmented reality (AR) research has matured to a level that its
applications can now be found in both mobile and non-mobile devices. Research on AR has also demonstrated its
extreme usefulness for increasing the student motivation in the learning process (Liu & Chu, 2010; Di Serio et al.,
2013; Jara et al., 2011; Bujak et al., 2013; Chang et al., 2014).
An AR system allows for combining or “supplementing” real world objects with virtual objects or superimposed
information. As a result virtual objects seem to coexist in the same space with the real world (Azuma et al., 2001).
However, AR is not restricted only to the sense of sight; it can be applied to all senses such as hearing, touch and
smell (Azuma et al., 2001). AR allows for combining virtual content with the real world seamlessly (Azuma,
Billinghurst, & Klinker, 2011). This differs from the notion of a Virtual Environment (VE) where the user is
completely immersed inside a synthetic environment. In this sense, “AR supplements reality, rather than completely
replacing it” (Azuma, 1997). The Reality-Virtuality continuum (Milgram, Takemura, Utsumi, & Kishino, 1995)
clearly shows the relation between a real environment, AR and a virtual environment.
As an example of the current AR applications in education, Ibáñez, Di Serio, Villarán, & Delgado Kloos (2014)
created an AR application for teaching the basic concepts of electromagnetism. In this application students can
explore the effects of a magnetic field. For that purpose, the components used in the experiment (cable, magnets,
battery, etc.) can be recognized using the camera of a mobile device like a tablet. As a result students can see
superimposed information such as the electromagnetic forces or the circuit behavior using the tablet. The results of
this research show that AR improved academic achievement and provided instant feedback.
Some researchers have proposed different definitions of AR. For example, El Sayed, Zayed, & Sharawy (2011)
assert that AR enables the addition of missing information in real life by adding virtual objects to real scenes.
Supporting this definition, Chen & Tsai (2012) point out that AR allows for interaction with 2D or 3D virtual objects

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integrated in a real-world environment. Cuendet, Bonnard, Do-Lenh, & Dillenbourg, (2013) argue that “AR refers to
technologies that project digital materials onto real world objects.” These definitions are based on one of the features
of AR that is the possibility of superimposing virtual information to real objects. On the other hand, a broader
perspective has been adopted in the study of Wojciechowski & Cellary (2013). They define AR as an extension of
virtual reality with some advantages over virtual reality.
Current state of AR applications in education
A considerable amount of literature has been published in AR’s application in educational contexts for a wide variety
of learning domains. However, the state of current research in AR for education is still in its infancy (Wu, Lee,
Chang, & Liang, 2013; Cheng & Tsai, 2012). According to Wu et al., (2013a) and Cheng & Tsai (2012) the research
in this field should continue and should be addressed to discover the affordances and characteristics of AR in
education that differentiate this technology from others. Deepening this analysis will allow for discovering the
unique value of the learning environments based on AR. According to Chen & Tsai (2012) the potential of AR in
educational applications is just now being explored. Dunleavy, Dede, & Mitchell (2009) point out that “we are only
beginning to understand effective instructional designs for this emerging technology.”
Table 1 summarizes some review studies available in the literature on the topics related to AR in education.
Table 1. Recent review studies on topics related to AR in education
Study
Analysis dimension
Studies
reviewed
Summary of findings
(Martin et al., 2011)
The review considered the
evolution of technology trends in
education from 2004 to 2014
through a bibliometric analysis of
the Horizon Reports on the topic
of AR as well as on other topics
of technology enhanced learning.
10
The number of articles about AR is
increasing but according to the
analysis this technology is in their
initial stage in education. In the study
the evolution of AR to mobile
augmented reality is considered a
successful metatrend.
(Radu, 2012; Radu, 2014)
Review of studies that compare
student learning in AR versus
non-AR applications.
32, 26
The findings on the positive impact
are: Increased content understanding,
Learning spatial structures, language
associations, long-term memory
retention, Improved collaboration and
motivation. The findings on the
negative impact are: attention
tunneling, Usability difficulties,
ineffective classroom integration,
learner differences.
(Santos et al., 2014)
The review considered papers
published in IEEE Xplore.
Authors applied a meta-analysis
and a qualitative analysis in the
dimensions of display
methaphors, content creation and
evaluation techniques.
87
Authors conclude that there are three
main affordances of AR: real world
annotation, contextual visualization
and vision-haptic visualization. Also
authors state that the three
affordances are supported by existing
theories like: multimedia learning
theory, experiential learning and
animate vision theory.
A large and growing body of literature has reported factors such as: uses, purposes, advantages, limitations,
effectiveness and affordances of AR when they are applied in different learning domains. However, there is gap in
the literature with respect to systematic literature reviews looking at these factors of AR in educational settings.
Taking into account this, the aim of this systematic literature review is to present the current status of research in AR
in education. The study considers categories for analyzing the current state and tendencies of AR such as the uses of
AR in educational settings as well as its advantages, limitations, effectiveness; the availability of adaptation and

135
personalization processes in AR educational applications as well as the use of AR for addressing the special needs of
students in diverse contexts. The analysis of the different categories allows suggesting trends, challenges,
affordances, the opportunities for further research and a general vision towards the future.
The rest of the paper is organized in five sections. First section describes the research questions addressed in this
systematic review. Second section describes the methodological design of the study. Third section presents the
results jointly with the discussion of the findings. Fourth section follows with a discussion on the trends and the
vision toward the future. Finally, fifth section remarks some conclusions.
Research questions
There is a large volume of published studies that report advantages, limitations, effectiveness challenges, etc. of AR
in education. However, since AR is an emergent technology, it is important to get an overview of the advances and
real impact of its use in educational settings, describing how AR has been used for generate more student-center
learning scenarios. Within this context the research questions addressed by this study are:
 What are the uses, purposes, advantages, limitations, effectiveness and affordances of augmented reality in
educational settings?
 Have the inclusion of user modeling and adaptive processes been considered in augmented reality applications?
 How has augmented reality addressed the special needs of access and people preferences in educational
settings?
 What are the evaluation methods considered for augmented reality applications in educational scenarios?
Method
For this review, we considered the guidelines proposed by (Kitchenham, 2004) and adapted to this literature review:
Planning:
 Selection of Journals
 Definition of inclusion and exclusion criteria of studies
 Definition categories for the analysis
Conduct the review:
 Study selection
 Data extraction (Content analysis method was applied)
 Data synthesis
 Data coding
Reporting the review: This step includes the analysis of results, discussion of findings, trends and conclusions of the
review.
Regarding step 3 (Reporting the review), we followed the recommendations of the PRISMA (Preferred Reporting
Items for Systematic Reviews and Meta-Analyses) statement (Moher, Liberati, Tetzlaff, & Altman, 2009). The
PRISMA statement is the international and accepted updated version of the QUORUM (Quality of Reporting of
Meta-analysis) statement. In the following sub-sections we depict the most important steps followed according to the
methodology.
Step 1a: Selection of journals
The aim of this step has been to choose the most relevant journals for the systematic review in a consistent way. To
keep the process methodologically strong and scientifically consistent, a method has been defined in this research for
selecting journals.

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The Google Scholar h5-index for the category “Educational technology” was used as the starting point. This starting
point was selected since this category is more specific than “Education and educational research” category from the
Journal Citation Report Social Science Citation Index (JCR SSCI). In the later, most of the journals about
educational technology are indexed jointly with journals about educational research in general.
We chose the top 5 journals from “Educational Technology” category from Google Scholar h5-index and we named
this list “GS list.” In order to validate our initial “GS list,” we performed an iterative double check process using the
JCR SSCI tool in order to consider the impact factor of each journal and its “relatedness” with others. The
“relatedness” or most related journals is defined in the JCR taking into account the cited and citing relationship of the
journals and is based on the number of citations from one journal to the other and the total number of articles. The
iterative double check process was performed as follows: For each journal in the GS list, we searched the most
related journals to that one using the option “Related Journals” in the JCR SSCI web application (Journal Citation
Reports - ISI Web of Knowledge, 2012). As a result, we obtained one list of related journals for each journal in the
GS list. In this way, we obtained five lists of related journals which were named as RJ-GS1, RJ-GS2, RJ-GS3, RJ-
GS4 and RJ-GS5, where RJ stands for “related journal” and GS# stands for the corresponding journal from the GS
list.
We then independently sorted each of the lists RJ-GS1 to RJ-GS5 taking into account the impact factor. This process
is somewhat similar (by analogy) to a precipitation process (Gooch, 2007) were the journals with major impact factor
will “float” in each list. As a result, we obtained 5 independent lists of journals ordered by impact factor. Despite the
fact that lists were organized by impact factor, we had some similar journals in each list but at different positions.
For example, the British Journal of Educational Technology was at position 7 in the list RJ-GS1 but at position 4 in
RJ-GS2. In the remaining lists (RJ-GS3, RJ-GS4 and RJ-GS5) the journal was also in different positions. In order to
overcome this situation we combined all the elements of the lists (from RJ-GS1 to RJ-GS5) by pondering the position
occupied by each journal through the five lists. As a result, the definite list of journals ordered according to its
position was obtained. This list was named FL-JCR-SSCI list.
We then analyzed each journal from the FL-JCR-SSCI list and discarded journals that did not cover topics about
educational technology. This analysis was based on the “subject categories” reported for each journal in the JCR
SSCI web application. If necessary we analyzed the aim and scope of each journal to see if the journal could be
considered. As a result of this process, we had a new list of journals named ET-FL-JCR-SSCI. Where “ET” stands for
Educational Technology. This list contains only journals that cover the topic of Educational Technology ordered by
impact factor. Table 2 shows the first 5 journals of ET-FL-JCR-SSCI list that corresponds to the journals selected for
this review. We have to point out that this method allowed us to find the most important journals in educational
technology through a double check process considering impact factor and “relatedness” in the JCR SSCI.
Table 2. List of the first 5 journals of “ET-FL-JCR-SSCI list”
Journal title
Impact factor (JCR SSCI 2012)
Computers and Education
2,775
Internet and Higher Education
2,013
British Journal Of Educational Technology
1,313
Australasian Journal of Educational Technology
1,363
International Journal of Computer-Supported
Collaborative Learning
1,717
In order to also consider the Journal Citation Reports Science Citation Index (JCR SCI), we repeated the iterative
double check process with the journals indexed in the JCR SCI and obtained another list of journals, namely ET-FL-
JCR-SCI list. Table 3 shows the first four journals of this list that corresponds to the journals from the JCR-SCI
selected for this review. At this point we decided to include in the review, studies published in the first 4 journals of
each list (ET-FL-JCR-SCI and ET-FL-JCR-SSCI). However, the “Internet and Higher Education” journal was not
considered in the review since does not have studies published about AR in education. As a result, we included one
additional journal from the ET-FL-JCR-SSCI list so that the number of journals considered can be equal. Those
journals are the most relevant journals in Educational Technology according to our analysis. Those results were
validated by comparing them with the SJR and SNIP indexes obtaining similar results.

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Table 3. List of the first 4 journals of “ET-FL-JCR-SCI list”
Journal title
Impact factor (JCR SCI 2012)
Knowledge-based systems
4,104
Expert systems with applications
1,854
IEEE Transactions on education
0,95
IEEE Intelligent Systems
1,93
Step 1b: Inclusion and exclusion criteria
Taking into account the research questions, we considered general criteria that define the time frame for the study
and the type of studies that are relevant. Accordingly, we defined the following criteria:
General Criteria:
 Studies published between 2003 and 2013.
 Studies that describe applications or frameworks for augmented reality in education.
Specific Criteria:
 Studies that report advantages, disadvantages, affordances, limitations, features, uses, challenges and
effectiveness of augmented reality in educational settings.
 Studies that describe applications considering a user model and/or adaptive processes combined with
augmented reality.
 Studies that describe applications of augmented reality in education for people in contexts of diversity.
 Studies describing the evaluation methods for augmented reality applications in educational scenarios.
The following exclusion criteria were defined and accordingly, studies meeting these criteria were excluded:
 Studies not identified as “Articles” in the journals selected (e.g., book reviews, books, editorial publication
information, book chapters, etc.).
 Studies that mention the term “augmented reality” but are actually about virtual reality or other topics (and the
term appears only in the references section).
Step 1c: Categories for the analysis and data coding
In this step, we defined a group of categories of analysis with their corresponding sub-categories according to each
research question. Categories help us in grouping studies according to their shared characteristics.
During the systematic review process, some sub-categories emerged and others were refined in order to cover all
emerging information. The list of categories for the analysis classified by research questions (RQ) is as follows:
RQ1 - What are the uses, purposes, advantages, limitations, effectiveness and affordances of augmented reality in
educational settings?
 Field of Education: Based on International Standard Classification of Education (UNESCO, 2012).
 Target Group: Based on the International Standard Classification of Education (UNESCO, 2012).
 Reported purposes of using AR.
 Reported advantages of AR.
 Reported limitations of AR.
 Reported effectiveness of AR.
 Type of AR.
RQ 2 - Have the inclusion of combined adaptive or personalized processes been considered in augmented reality
applications?
 Type of adaptation process.
 Type of user modeling.
RQ 3 - How has augmented reality addressed the special needs of access and people preferences in educational
settings?
 Special Need addressed.
 Intervention method.

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RQ 4 - What are the evaluation methods considered for augmented reality applications in educational scenarios?
 Research sample.
 Research method
 Time dimension.
 Data collection method.
Content analysis allows to find the research trends of a topic by analyzing the articles’ content and grouping them
according to the shared characteristics (Hsu, Hung, & Ching, 2013). This method was applied in order to extract the
information of each paper. Two of the authors of the paper manually coded the studies separately according to their
characteristics and classified them according to the categories and sub-categories defined. In case of discrepancy, the
coders resolved it through discussion.
Results (Steps 2 and 3)
In this section the results of conducting the review are described and discussed. In step 2a we searched manually in
the selected journals and applied the inclusion and exclusion criteria in order to select the studies for the review. As a
result of this process we selected 32 studies from journals. Steps 2b and 2c were carried out by reading the papers
completely and the data coding process was performed taking into account the categories defined in step 1c. In order
to present the results this section was organized taking into account each research question addressed.
In total 30 studies were analyzed from the 5 journals selected from the JCR-SSCI and 2 studies were analyzed from
the 4 journals selected from the JCR-SCI. Table 4 shows the number of studies analyzed by journal. It is important to
note that in the table, the year 2013* includes the papers published until February 2014.
By analyzing the year of publication of the studies considered we found that the number of published studies about
AR in education has progressively increased year by year specially during the last 4 years. This means that many
researchers are interested in exploring the features, advantages, limitations of AR in educational settings. According
to these results, AR in education is an emerging topic and this finding corroborates the ideas of Wu, Lee, Chang, &
Liang (2013) and Cheng & Tsai (2012), who point out that the research on AR in education is in the initial phase. As
Bujak et al. (2013) suggest: “Augmented reality (AR) is just starting to scratch the surface in educational
applications.” One of the issues that emerge from these findings is that more research needs to be undertaken in the
topic of AR in education.
Table 4. Number of studies analyzed in this review by journal
Journal
Studies analyzed (2003-2013*)
JCR-SSCI Journals Total: 30
Computers & Education
23
Internet and Higher Education
0
British Journal of Educational Technology
4
Australasian Journal of Educational Technology
1
International Journal of Computer-Supported Collaborative Learning
2
JCR-SCI Journals Total: 2
Knowledge-based systems
0
Expert systems with applications
1
IEEE Transactions on education
1
IEEE Intelligent Systems
0
In the following subsections, our findings with respect to each research question are presented.
What are the uses, purposes, advantages, limitations, effectiveness and affordances of augmented reality in
educational settings?
With respect to the uses of AR in education, Table 5 presents the results obtained from the data coding process in the
category of “Field of education.” This table clearly shows the use of augmented reality by each field of education.

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The most striking result to emerge from the data is that most of the studies (40.6%) were applied in the field of
“Science.” This result indicates that most of the research done in AR applied to education has been concentrated on
identifying the benefits of AR in science education. A possible explanation of this is that AR has demonstrated to be
effective when applied to lab experiments (Ibáñez, Di Serio, Villarán, & Delgado Kloos, 2014; Lin, Duh, Li, Wang,
& Tsai, 2013; Enyedy, Danish, Delacruz, & Kumar, 2012), ecology (Wrzesien & Alcañiz Raya, 2010), field trips
(Kamarainen et al., 2013), mathematics and geometry (Blake & Butcher-Green, 2009), scientific issues (Chang, Wu,
& Hsu, 2013) and in general, activities where students can see things that could not be seen in the real world or
without a specialized device. Besides that, students “do not have to use their imagination to envision what is
happening. They can see it” (Furió, González-Gancedo, Juan, Seguí, & Rando, 2013) which also means that AR is
effective for teaching abstract or complex concepts. A prior study has noted the importance of AR in science
education. (Cheng & Tsai, 2012).
Following “Science” learning, “Humanities & Arts” was the second field of education in which AR was applied the
most (21.9%). Studies in this field of education focused on language learning (Liu & Tsai, 2013; Chang, Lee, Wang,
& Chen, 2010; Ho, Nelson, & Müeller-Wittig, 2011; Liu & Chu, 2010), visual art and painting appreciation (Di
Serio, Ibáñez, & Kloos, 2013; Chang et al., 2014), and culture and multiculturalism (Furió et al., 2013). Interestingly,
AR has been widely used in language learning due to the possibility of augment information and combining it with
contextual information to provide new experiences in language learning. On the other hand, thanks to the possibility
of adding virtual information to the real world AR has been applied in painting appreciation in order to provide an
enhanced experience.
In “Social Sciences, Business and Law” and “Engineering, manufacturing and construction,” AR is being explored.
Only 12.5% of the studies reviewed applied AR in “Social Sciences” and 15.6% applied AR in Engineering,
manufacturing and construction.
Finally the results of our review show that the less explored fields of education are “Health and welfare” (3.1%) and
Services and Others (travelling, transport, security services and hotel) with 6.3% of the studies reviewed. According
to our review, no investigations have delved in the field “Educational” (teacher training in all levels of education) as
well as the field of agriculture. The present results are significant in order to encourage researchers to explore the use
of AR in teacher training and agriculture, forestry, fishery, veterinary, etc.
Table 5. Augmented reality uses by “Field of education”
Sub-category
Number of studies
Percentage (%)
Educational
0
0.0
Humanities & Arts
7
21.9
Social Sciences Business and Law
4
12.5
Science
13
40.6
Engineering, manufacturing and construction
5
15.6
Agriculture
0
0.0
Health and welfare
1
3.1
Services and Others
2
6.3
Regarding the “Target group,” this category refers to the level of education of participants in the experiments in
which the study of AR in education was carried out. Table 6 summarizes the results. This table is quite revealing in
several ways. First, it is worth noticing that AR has been mostly applied in higher education settings (Bachelor’s or
equivalent level) and compulsory education (primary, lower and upper secondary education). Most of the studies
reviewed in these target groups applied AR for motivating the students, explaining topics, adding information and
other purposes that are discussed later. It seems possible that AR has been applied in settings with this target group in
order to improve the educational experience of the students and motivate and engage them by taking advantage of
the features of this technology. In the studies reviewed there were no evidence of AR applications in the field “Early
childhood education” (0%). A possible explanation of this result is that the technology could not be ready for being
used by children since many aspects of interaction, such as the tracking and use of markers, need to be solved. We
encourage researchers to explore the use of AR in this field.
On the other hand, “Post-secondary non-tertiary education” (0%) and “Short-cycle tertiary education” (3.1%) are
target groups that need further research on the impact of AR in educational settings. This target groups are part of the

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Vocational Educational Training (VET) in which AR could provide benefits in the learning process for facilitating
the access to the labor market. So far, not many studies have been reported in this area. Finally, there were no
evidence of using AR in “Masters or equivalent level” (0%) and “Doctoral” (0%) educational settings. This result
may be explained by the fact that Master’s and PhD students typically are involved in creating new AR applications
for the other levels.
Table 6. Target group in which AR studies were carried out
Sub-category
Number of studies
Percentage (%)
Early childhood education
0
0.00
Primary education
6
18.75
Lower secondary education
6
18.75
Upper secondary education
4
12.50
Post-secondary non-tertiary education
0
0.00
Short-cycle tertiary education
1
3.13
Bachelor’s or equivalent level
11
34.38
Master’s or equivalent level
0
0.00
Doctoral
0
0.00
Informal Learning
2
6.25
Not mentioned in the study
2
6.25
With respect to category “Purposes of using AR” in education, table 7 summarizes the results. Since one study can
report more than one purpose, each study can meet more than one sub-category. It can be seen from this data that
most of the studies used AR with the purpose of explaining a topic (43.7%) and augment information (40.6%).
Explaining the topic refers to the use of an AR application in order to support the learning of a specific topic
(Wrzesien & Alcañiz Raya, 2010; Chang et al., 2013). On the other hand, “augment information” refers to the use of
AR for providing supplemental material by means of markers placed on printed material that students used to access
digital resources (Huang, Wu, & Chen, 2012; Chen, Teng, & Lee, 2011)..
Table 7 also shows that the purposes of using AR combined with “Educational Game” (18.7%) and for “Lab
experiments” (12.5%) are being explored. In this sense, we encourage researchers to explore in detail the uses of AR
in educational games in order to identify its features, advantages and drawbacks. Furió et al., (2013) claim that “there
are few mobile learning games that use this technology.” Further research regarding to the role of AR for supporting
lab experiments needs to be done, for example, the analysis of the impact of AR for reducing the cost of lab
experiments or its strengths for offering a most inclusive experience for people with disabilities. Furthermore,
according to the results, very little was found in the literature on using AR for activities for “Exploration” and
discovering the world through AR (3.1%) and no studies were found with focus on using AR for evaluating a topic
(0%) and the use of AR for other educational purposes (0%) different from the ones mentioned before.
Table 7. Purposes of using AR in educational settings
Sub-category
Number of studies
Percentage (%)
Explaining the topic
14
43.75
Evaluation of a topic
0
0.00
Lab experiments
4
12.50
Educational Game
6
18.75
Augment information
13
40.63
Exploration
1
3.13
Other purposes
0
0.00
Another category analyzed in this systematic literature review deals with the “Reported Advantages” of AR in
educational settings. Table 8 shows the results of the reported advantages identified in the studies analyzed. Since
one study can report more than one advantage, each study can meet more than one sub-category. From the results, it
can be seen that the major advantages reported in the studies are: “Learning gains” (43.7%) and “Motivation”
(31.2%). These results corroborate the benefits of AR for improving the learning performance and motivating
students (Liu & Chu, 2010; Di Serio et al., 2013; Jara et al., 2011; Chang et al., 2014). Some studies have reported
other advantages of AR that are listed in table 8. However, these advantages need to be further explored in order to
understand the real benefits of AR-based learning experiences. On the other hand, very little was found in the

141
literature on advantages of AR in educational settings such as: “Increase capacity of innovation” (6.2%), “creating
positive attitudes” (6.2%), “Awareness” (3.1%), “Anticipation” (3.1%), “Authenticity” (3.1%), and “Novelty of the
technology” (0%). In this sense, there is a need of more research in order to validate if those factors are advantages
of AR in education.
Although interaction and collaboration have emerged as main advantages of AR, surprisingly data collection
methods (discussed later in research question 4) such as focus groups or conversational analysis did not appear
during the review. There are many evaluation mechanisms that have not been explored because the technology is not
enough mature, so there is a gap between the affordances of AR, its advantages, uses, research methodologies and
the evaluation mechanisms applied.
Table 8. Reported advantages of AR in educational settings
Sub-category
Number of studies
Percentage (%)
Learning gains
14
43.75
Motivation
10
31.25
Facilitate Interaction
5
15.63
Collaboration
6
18.75
Low cost
4
12.50
Increase the experience
4
12.50
Just-In-time Information
4
12.50
Situated Learning
3
9.38
Student-centred
3
9.38
Students' attention
3
9.38
Enjoyment
3
9.38
Exploration
4
12.50
Increase capacity of innovation
2
6.25
Create positive attitudes
2
6.25
Awareness
1
3.13
Anticipation
1
3.13
Authenticity
1
3.13
Novelty of the technology
0
0.00
Turning now to the category “Limitations of AR”, this category aims to identify the limitations of AR in educational
settings. Results are shown in Table 9. From this data it can be seen that the most reported limitation in the studies
reviewed are “Difficulties maintaining superimposed information” (9.3%). Students may feel frustrated if the
application does not work properly or if it is difficult for them to use the markers or the device in order to see the
augmented information. In order to overcome this limitation there is a need of improving the algorithms for tracking
and image processing. In addition to this, it is recommended that further research be undertaken in usability studies
for AR applications in education as well as guidelines for designing AR-based educational settings.
Another limitation reported was “Paying too much attention to virtual information” (6.2%). This limitation is related
to the novelty of this technology when it is used for the first time in the classroom. So, students may be distracted by
the virtual information showed or the technology itself. “Intrusive Technology” (6.2%) was also a limitation reported
which is connected with the use of HDM (Head-mounted displays) (Zarraonandia, Aedo, Díaz, & Montero, 2013)
because the device can interrupt the natural interaction with others.
Other limitations reported in the studies are: “Designed for a specific knowledge field” (3.3%) and “Teachers cannot
create new learning content” (3.1%). In this sense, it is recommended that further research be undertaken in
authoring tools for creating AR activities so that teachers can create their own content with AR support.
Table 9. Limitations of AR in educational settings
Sub-category
Number of studies
Percentage (%)
Designed for a specific knowledge field
1
3.13
Teachers cannot create new learning content
1
3.13
Difficulties maintaining superimposed information
3
9.38
Paying too much attention to virtual information
2
6.25

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Short periods of validation
1
3.13
Intrusive Technology
2
6.25
Not specified in the study
22
68.75
With respect to the category “Effectiveness of AR,” table 10 shows the results. Since one study can report more than
one sub-category of effectiveness, each study can meet more than one sub-category. Most of the studies reported that
AR applications lead to “Better learning performance” (53.3%) in educational settings. “Learning motivation”
(28.1%) and “Student engagement” (15.6%) were also reported. The results show that AR is a promising technology
for improving the student’s learning performance and motivate the students to learn thanks to the interaction and
graphical content used. “Improved perceived enjoyment” (12.5%) and “Positive attitudes” (12.5%) were less
reported but are also important in educational settings.
Table 10. Effectiveness of using AR in educational settings
Sub-category
Number of studies
Percentage (%)
Better learning performance
17
53.13
Learning motivation
9
28.13
Improve perceived enjoyment
4
12.50
Decrease the education cost
0
0.00
Positive attitudes
4
12.50
Student engagement
5
15.63
Regarding the “Type of AR” considered in the studies reviewed, table 11 summarizes the results. We considered
three types of AR according to the classification of Wojciechowski & Cellary (2013): marker-based AR, marker-less
AR and location-based AR. Marker-based AR is based on the use of markers. Markers are labels that contain a
colored or black and white pattern that is recognized or registered by the AR application through the camera of the
device in order to fire an event that can be, for instance, to show a 3D image in the screen of the device located in the
same position where the marker is. Marker-less AR is based on the recognition of the object’s shapes. And location-
based AR superimposes information according to the geographical location of the user.
The results in table 11 reveal that most of the studies used “Marker-based AR” (59.3%) which means that most of the
applications developed for educational settings use markers. A possible explanation for this result is that currently
the tracking process of markers is better and more stable compared to the marker-less tracking techniques. The use of
static markers decrease the tracking work needed and reduce the number of objects to be detected (El Sayed et al.,
2011). Therefore for educational settings the use of markers could be recommended so that students can have a better
experience with the technology until better techniques for tracking can be developed for marker-less AR. “Marker-
less AR” has not been widely used in educational settings (12.5%). However, there is a trend of using Microsoft
Kinect sensors and similar technologies in order to create AR applications for educational settings (Fallavollita et al.,
2013; Pillat, Nagendran, & Lindgren, 2012). Microsoft Kinect provides some advantages in tracking and registering
objects in marker-less AR.
Table 11. Types of AR applied in education
Code
Sub-category
Number of studies
Percentage (%)
SC1
Marker-based AR
19
59.38%
SC2
Marker-less AR
4
12.50%
SC3
Location-based AR
7
21.88%
SC4
Not specified in the study
2
6.25%
Interestingly, the development of “Location-based AR” (21.8%) applications is major compared to marker-less AR
applications. This can be due to the availability of sensors in mobile devices like the accelerometer, gyroscope,
digital compass and the possibility of using GPS. These technological advancements open possibilities for
developing applications of AR that can be aware of the user’s location in order to show information according to the
geographical position and/or orientation.

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Have the inclusion of combined adaptive or personalized processes been considered in augmented reality
applications?
In the studies reviewed only 2 out of 32 studies report some kind of personalized process and 1 out of 32 considered
a user modeling process. Barak & Ziv (2013) created “Wandering” which is an application for creating location-
based interactive learning objects (LILOs) and considers personalization as an “important requirement of the 21st
century skills” (Barak & Ziv, 2013). Personalization is considered for meeting the needs and interests of the
individual learners. However, in the study where the authors describe the Wandering application is not clear if they
have a user model. On the other hand, Blake & Butcher-Green (2009) propose an application for customized training
based on a scaffolding instructional approach and an agent architecture in order to training individuals from diverse
backgrounds. The type of adaptation process considered by the application is personalization based on historical
training profiles. However in the paper is not clear if the information for the user model comes from the learner’s
profile. In addition to this, the authors states that the system was being integrated with the AR environment when the
paper was written. The results of the paper are based on a simulated AR environment (Blake & Butcher-Green,
2009).
How has augmented reality addressed the special needs of access and people preferences in educational settings?
In the studies reviewed from journals there was no evidence of AR applications in educational settings that address
the special needs of students. This finding corroborates the idea of Wu, Wen-Yu, Chang, & Liang (2013) who state
that few systems have been designed for students with special needs. According to Lindsay (2007) the opportunities
for children with special needs and disabilities can be improved by a major policy initiative called “inclusion”.
Inclusive education is more than integration because integration refers to the learner adapting to the educational
setting while inclusion means that the educational setting adapts to the learner in order to meet their needs (Lindsay,
2007). Within this sense AR may offer unique advantages and benefits in order to create inclusive AR-based
educational settings. Further research is needed in order to identify the effectiveness and advantages of AR
applications for addressing the special needs of students.
What are the evaluation methods considered for augmented reality applications in educational scenarios?
With respect to the evaluation methods for AR applications in educational settings we considered four sub-categories
for the analysis. The results show that, regarding to “Research Samples” (table 12), most of the studies used medium
research samples “between 30 and 200” (78.1%) and some studies considered small research samples “30 or less
than 30” (18.7%). In our review we did not find studies that used research samples greater than 200 participants
(“More than 200” (0%)). A possible explanation of this result is that greater research samples would need more
devices (handheld devices, PC, web cam, tablets, etc.) so that each participant can have one device.
Table 12. Research samples in the studies reviewed
Sub-category
Number of studies
Percentage (%)
30 or less than 30
6
18,75
Between 30 and 200
25
78,13
More than 200
0
0,00
Not Specified in the study
1
3,13
Regarding the “Research Methods,” table 13 shows that most of the studies applied “mixed methods” (46.8%),
“Qualitative-Exploratory-Case study” (21.8%), “Quantitative-Descriptive research” (15.6%) and “Qualitative-
Exploratory-Pilot Study” (12.5%) as research methods to conduct the study. Few studies have applied “Quantitative-
Explanatory and Causal research” (3.1%) and “Qualitative-Exploratory-Experience Survey” (0%).
Table 13. Research methods applied
Sub-category
Number of studies
Percentage (%)
Qualitative-Exploratory-Case Study
7
21,88
Qualitative-Exploratory-Pilot Study
4
12,50
Qualitative-Exploratory-Experience Survey
0
0,00

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Quantitative-Descriptive Research
5
15,63
Quantitative-Explanatory and Causal Research
1
3,13
Mixed Methods
15
46,88
Other
0
0,00
Turning now to the “Time dimension” of the studies reviewed, table 14 shows that almost all of the studies were
identified as “Cross-sectional” (93.7%) and only 6.2% of the studies were identified as “Longitudinal Study.” An
implication of this result could be that the novelty of the technology in cross-sectional studies may affect the results
since students can be engaged with the AR application because it is new for them. Future studies conducted as
longitudinal studies need to be undertaken in order to follow the students in the long term and identify the advantages,
benefits, limitations when students are exposed to this technology for a long period and also when students are used
to using AR in the classroom as well as analyze the student’s behavior in different learning scenarios.
Table 14. Time dimension of the studies reviewed
Sub-category
Number of studies
Percentage (%)
Cross-sectional Study
30
93,75
Longitudinal Study
2
6,25
Finally, for “Data Collection methods” as table 15 shows, most of the studies applied “Questionnaires” (75%),
“interviews” (28.3%), “surveys” (18.7%) and “cases observation” (9.3%) as data collection methods. “Focus group”
(0%) and “Writing Essay” (3.1%) have either not been used or used very little. Since one study can apply more than
one data collection method this study counts for more than one category.
Table 15. Data collection methods
Sub-category
Number of studies
Percentage (%)
Questionnaires
24
75,00
Interviews
9
28,13
Focus-groups
0
0,00
Survey
6
18,75
Cases observation
3
9,38
Writing Essay
1
3,13
Other
1
3,13
Trends and future vision
This study presents a detailed systematic review of the state of the art in Augment reality as a promising technology
for supporting technology-enhanced learning. In this section we discuss our main findings highlighting what we
consider as the strongest future directions of research in this field.
AR technology
Marker-based AR as mentioned in the results section is the most used approach for supporting the development of
AR learning experience, followed by the location-based AR. A possible explanation for this result is that currently
the tracking process of markers is better and more stable compared to the marker-less tracking techniques. Besides
that one of the advantages of marker-based AR is the facility of implementation due to the available libraries which
support the development process. There is a challenge around the improvement of recognition algorithms for human
forms as a promising feature in the process of achieving more immersive and not intrusive AR learning experiences
(Zarraonandia et al., 2013). Accessibility and usability of the AR learning experiences are two important issues to be
addressed in future research since few studies have reported research on this field. In fact, only 4 out of 32 studies
consider those factors (Di Serio et al., 2013; Ibáñez et al., 2014; Ho et al., 2011; Cuendet et al., 2013). Further
research need to be undertaken in usability studies for AR applications in education as well as guidelines for
designing AR-based educational settings.

145
Some recent studies have reported new research directions:
 There is a need for “new methods for creating interactive 3D content for AR learning environments”
(Wojciechowski & Cellary, 2013; K.-E. Chang et al., 2014) and creating authoring tools for teachers to create
content.
 Understand how to design AR learning experiences according to the topic taking into account the skills of
learners (Bujak et al., 2013).
 Creating multisensory experiences with AR (Ho et al., 2011) and explore their impact in the learning outcomes.
 Carry out more studies for understanding the user experience and knowledge construction processes in AR
applications (Lin et al., 2013).
Attention to diversity and special needs in the learning process
Although there are not many reported papers considering samples which include people with special need of access
to educational context, the multimodal possibilities of AR applications seems to be a good option for addressing the
special necessities of diverse population.
Some challenges are the definition of frameworks for user model representations as well as frameworks to support
personalization processes. In fact, a starting point could be the analysis of existing frameworks for AR (Bujak et al.,
2013; Chen et al., 2011; Price & Rogers, 2004) in order to analyse the feasibility of integration of these frameworks
to offer an augmented but also an adaptive learning experience to users. These frameworks should at least include the
definition of semantics to represent the user’s profiles and their context, semantics to represent the metadata of the
AR resources as well as semantic to represent different possibilities of adaptations.
On the other hand, the multimodal possibilities of AR applications seem to be a good option for supporting therapy
processes for people with sensorial and physics impairments. It demonstrates that AR increases the motivation of the
user developing specific tasks which is one of the challenges in therapy processes (Correa, Ficheman, Nascimento, &
Lopes, 2009). Finally, it can thus be suggested that frameworks for personalized AR should consider pedagogical
and didactical components in order to guide the development of AR-based educational settings.
The need for longitudinal studies
Based on the conducted analysis, we conclude that more studies need to be undertaken considering a large scale
evaluation and longitudinal evaluations in future researches. Many cross-sectional studies have been conducted as
shown in the results of this review, being an efficient research method in order to establish comparisons between AR
learning experiences with respect to other cases of learning experience. However, in the case of educational settings
is also important to study the evolution of knowledge and skills over time, as proposed by longitudinal studies. Long
term analysis of the AR learning experience could give important inputs about the suitability of this technology for
supporting significant learning (Mendoza Gómez, 2005).
Vocational educational training (VET) as target groups for future research
According to our research, only 3.1% of the studies were carried out considering a sample of students from
vocational educational training institutions. From our point of view, VET institutions are promising research partners
not only for validation but also for demonstrating the possibilities of AR learning scenarios for improving and
acquiring professional competences. Possibilities offered for AR could reduce the cost of carrying out some learning
experience where expensive learning material is necessary. For example, physical materials to learn how to create a
gem could be difficult to buy by institutions with limited economic resources. In this sense, combining virtual objects
as different kind of physical materials with real objects such as the students’ hands could be a good option to explore.

146
Conclusions
In this paper a systematic literature review was reported. In total 32 studies from journals were analyzed by using the
content analysis method. We analyzed the following factors of the studies selected: Field of education, target group,
type of AR, reported purposes, advantages, limitations, affordances and effectiveness of AR in educational settings.
Besides that, adaptation processes and user modeling in AR as well as the addressing of individual special needs with
AR applications was also considered for the analysis. Regarding the evaluation methods we analyzed the research
sample, research method, and time dimension of the study. Furthermore, we defined a validated method for selecting
journals through a methodologically strong and consistent process that can be applied for systematic reviews in other
topics.
A short summary of the main findings of this review are:
 The number of published studies about AR in education has progressively increased year by year specially
during the last 4 years.
 Science and Humanities & Arts are the fields of education where AR has been applied the most. Health &
welfare, Educational (teacher training) and Agriculture are the research fields that were the least explored
fields.
 AR has been mostly applied in higher education settings and compulsory levels of education for motivating
students. Target groups like early childhood education and Vocational educational Training (VET) are
potential groups for exploring the uses of AR in future.
 Marker-based AR is the most used type of AR. In addition location-based AR is being widely applied. This can
be due to the availability of sensors in mobile devices like the accelerometer, gyroscope, digital compass and
the possibility of using GPS. Marker-less AR needs some improvement in algorithms for tracking objects but
the use of Microsoft Kinect is becoming more and more popular.
 The main purpose of using AR has been for explaining a topic of interest as well as providing additional
information. AR educational games and AR for lab experiments are also growing fields.
 The main advantages for AR are: learning gains, motivation, interaction and collaboration.
 Limitations of AR are mainly: difficulties maintaining superimposed information, paying too much attention to
virtual information and the consideration of AR as an intrusive technology.
 AR has been effective for: a better learning performance, learning motivation, student engagement and positive
attitudes.
 Very few systems have considered the special needs of students in AR. Here there is a potential field for
further research.
 Most of the studies have considered medium research samples (between 30 and 200 participants), and most of
the studies have used mixed evaluation methods. The most popular data collection methods were
questionnaires, interviews and surveys and most of the studies were cross-sectional.
This work contributes to existing knowledge in AR in educational settings by providing the current state of research
in this topic. This research also has identified relevant aspects that need further research in order to identify the
benefits of this technology to improve the learning processes.
Acknowledgements
This work is supported in part by the Spanish Science and Education Ministry in the ARrELS project (TIN2011-
23930). Jorge Bacca, Silvia Baldiris and Ramon Fabregat belong to the BCDS group (ref. GRCT40) which is part of
the DURSI consolidated research group COMUNICACIONS I SISTEMES INTELLIGENTS (CSI) (ref. SGR-1202).

147
Jorge Bacca would like to thank the financial support for the Grant (FPI-MICCIN) provided by the Spanish Ministry
of Economy and Competitiveness (BES-2012-059846). The authors acknowledge the support of NSERC, iCORE,
Xerox, and the research related gift funding by Mr. A. Markin.
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