Liu, C.-C. et al. (Eds.) (2014). Proceedings of the 22nd International Conference on Computers in
Education. Japan: Asia-Pacific Society for Computers in Education

Evaluating Augmented Reality for
Situated Vocabulary Learning
Marc Ericson C. SANTOSa*, Arno in Wolde LUEBKEa, Takafumi TAKETOMIa, Goshiro
YAMAMOTOa, Ma. Mercedes T. RODRIGOb, Christian SANDORa, & Hirokazu KATOaaInteractive Media Design Laboratory, Nara Institute of Science and Technology, Japanb
Ateneo Laboratory for the Learning Sciences, Ateneo de Manila University, Philippines*chavez-s@is.naist.jp
Abstract: Augmented reality (AR) is an emerging technology for communicating learning
contents. Several AR systems are designed for learning. However, studies that have investigatedinstructional strategies for applying AR are few. This investigation requires the implementationof prototypes that use state-of-the-art technology and sound learning theory.
In this work, we implemented two prototypes for learning Filipino and German words
by first developing a handheld AR platform. These prototypes demonstrate situated vocabulary
learning. Using our AR system, students can learn words related to their current environment.
We assessed the quality of these prototypes by conducting usability evaluations. For thetheoretical grounding, we leveraged on multimedia learning theory to design the content.
Through our handheld AR platform, we evaluated situated vocabulary learning bycomparing our prototypes to a flash cards application. In the first evaluation, students scoredsignificantly lower when using AR in an immediate post-test. However, this difference
disappeared after taking into account the variability in usability scores via analysis ofcovariance. Taking account usability is fairer when comparing an emerging technology to
traditional technology. Test scores were also not significantly different in a delayed post-test. In
the second evaluation, although the post-test score and answering time of students did not differ,
our results showed that they feel more satisfied and can keep their attention better when using
AR. For the first time, we demonstrated situated vocabulary learning by using AR. Moreover,
our preliminary study confirms the intuition that students can achieve the same score using AR,
but with benefits such as ease in maintaining attention and increased satisfaction.
Keywords: augmented reality, mobile learning, situated cognition, vocabulary learning

1. Introduction
Augmented reality (AR) is an emerging technology in the field of computers in education (Wu et al.,2013). We provide a useful summary of AR prototypes applied to learning in our review (Santos et al.,2014). Researchers do not usually take advantage of the most important feature of augmented reality:
showing the explicit relationship of the virtual content to objects found in the real world. As such, we
implemented and evaluated this display interaction for situated vocabulary learning (Figure 1).

Figure 1. Situated Vocabulary Learning. Student (left) is learning the word “asinan,” the Filipino word
for adding salt, on a dish inside a kitchen-like environment. Our interface (middle) integrates spriteanimations (right) to explicitly illustrate the virtual action on a real object found in the environment.

In situated vocabulary learning, the physical environment is the context of the vocabulary. In this paper,
we did a preliminary comparison of our approach (Figure 2) against a flash cards application which
doesn’t contextualize the vocabulary to the environment. For the first time, we evaluated usability,
learning, and motivation in AR-based situated vocabulary learning.

Figure 2. Nouns are displayed as labels, whereas verbs are shown as animations on real objects.

2. Related Works
People are familiar with AR because of the many AR browsers used to locate interesting places in theworld (Grubert et al., 2011). People use AR browsers to see virtual labels and symbols integrated with alive video feed of the real environment. Thus, understanding location-related information, such as
names of buildings, distances of restaurants, arrows for navigation, and so on, becomes easier. In our
AR application, instead of displaying names and direction, our system displays the objects of nouns andillustrates the action of verbs.
Several AR systems have been developed for educational settings (Santos et al., 2014). One
important work is Construct3D (Kaufmann et al., 2000; Kaufmann, 2002), which uses AR to teachstudents mathematics and geometry concepts. AR is suitable because students can interact naturallywith three-dimensional shapes without the use of a mouse and keyboard. While wearing ahead-mounted display, students can move around virtual shapes and perform operations on them.
Moreover, the students see the same shape, thereby allowing them to work together on the target virtual
object. Although Construct3D and other works take advantage of embodied cognition and collaborative
learning, previous prototypes do not use the main feature of AR. AR, where “3-D virtual objects areintegrated into a 3-D real environment in real time” (Azuma, 1997), displays the relationship of thevirtual object to the real environment. In the present paper, we teach vocabulary by displaying therelationship between virtual objects and the real environment.
We can apply handheld AR to show the relationship of the educational content to the realenvironment. Handheld AR has gained attention in the field of educational technology because of its
benefits such as ubiquitous learning (Dede, 2011), situated cognition (Specht et al. 2011), andcollaboration (Li et al., 2011). Billinghurst and Duenser (2012) argue that handheld AR technology ismature for this application. AR software can already run on mobile phones equipped with fast
processors, big display screens, data connection, built-in camera, and other sensors. Billinghurst andDuenser (2012) call for more interdisciplinary research to ground AR applications in learning theories.
For our experiments, we designed the content of our AR prototype by applying the principles ofmultimedia learning theory (Mayer, 2009) and its related research.

2.1 Vocabulary Learning Systems Applying Various Contexts
People learn new words in meaningful contexts. Words are interpreted as a part of a passage of text. As
such, Chen et al. (2013) proposed hypertext annotations for supporting quick definitions when reading
electronic text. To support vocabulary learning, many other interfaces leverage on constructing various
other contexts such as word games (Lin et al., 2008), virtual environments (Pala et al., 2011),
collaboration (Joseph et al., 2005), and interaction with robots (Wu et al., 2008).
A natural context for learning vocabulary would be the physical environment. As such, Edge etal. (2011) and Dearman & Truong (2012) propose to present words that are related to the student’s
environment. Their systems involve the use of handheld devices and GPS positioning to detect thestudent’s current environment and then present words that are related to that environment. In both
systems, the students browse the words on the device screen. We extended these works by using AR todisplay content onto the physical environment. In our AR system, the vocabularies are either animations
or labels on real objects as shown in Figure 2.

2.2 Systems for Situated Vocabulary Learning
Liu’s (2009) HELLO system demonstrated how handheld devices can be designed to promote
significantly better learning outcomes. The HELLO system uses the campus network to deliver content
for an English language learning system. It can detect the location of the user through QR codes spreadaround the school. At each location, students practice conversations with a virtual tutor on the device. In
the user testing phase involving 64 students, Liu reported that students who used their system scoredhigher compared with those who used printed materials and audio recordings. This effect is attributed topracticing English in situations that could really happen in specific locations.
Beaudin et al. (2007) employed a different strategy to teach Spanish. For their research, theybuilt a smart home learning environment that can detect user movements and intentions by using
various sensors. Equipped with a mobile device, the smart home can identify who the user is and present
relevant information. For example, they implemented a feature wherein voice-overs of Spanish words
or phrases are triggered when users touch specific objects. This interaction makes an explicit
connection between the Spanish content and the objects found in the learner’s environment, thereby
promoting situated cognition. Both the works of Liu and Beaudin et al. take advantage of near-transfer,that is, applying knowledge learned in a particular situation to another situation that is almost similar incontext (Dunleavy & Dede, 2014). Combining ideas from these prototypes, we implemented a handheld
AR platform (Section 3) to present contextual texts, images, and sounds in the real-world environment.

2.3 Multimedia Learning Applied to Vocabulary Learning
In multimedia learning theory, multimedia refers to pictures and words (both written and spoken). This
theory assumes three things, namely, dual-channels, limited capacity, and active processing (Mayer,2009). First, multimedia learning takes advantage of the two separate channels for perceiving visual andauditory information. Second, it recognizes that individuals have a limited capacity of information thatthey can attend to. Lastly, learning only takes place if the learner actively processes incoming
information by connecting them to prior knowledge.
Given that individuals have a limited capacity of information that they can attend to, Lin andYu (2012) investigated the cognitive load induced by different types of media presentations on a mobile
phone. In their study with 32 eight graders, they investigated the use of four multimedia modes, namely,
text, text with audio, text with picture, and text with audio and picture. They discovered that themultimedia mode does not have a significant effect on vocabulary gain and retention. However, thelearners rated the combined text-audio-picture as the mode that induced the least cognitive load.
Lin and Wu (2013) investigated the use of these four multimedia modes in a succeeding study
with 423 junior high school students. They did not find any significant differences in vocabulary
recognition nor in any interaction between multimedia mode and learning style preferences of thestudent. However, the participants who used text with audio and picture performed best in listening tests
followed by the text with sound group. This result confirmed the intuition that audio annotations
contribute to the construction of phonological knowledge of words and then applying this knowledge inlistening to sentences. More importantly, they reported that the learning effects of the audio weremaintained for two weeks with minimal attrition. Based on these works, we implemented features in our
AR platform (Figure 3) to allow users to access text, audio, and pictures during the learning scenario.
In a separate study with 121 senior high school students, Lin and Hsiao (2011) studied theeffects of the use of still images against simple animations in vocabulary learning. Their results showedthat the animation group performed significantly better in learning Chinese and English vocabularies
compared with the image group. They recommended the use of animations to illustrate dynamic words
and processes. Thus, to facilitate better understanding of vocabulary in our handheld AR platform, we
included a feature where sprite sheet animations can be used. We found this feature to be a simple
solution to illustrate verbs in our learning scenario.

3. Implementation
We created two AR applications for learning Filipino and German words in a real environment. We
achieved this objective by first creating a handheld AR platform that can display any situatedmultimedia – images, animations, sound, and text displayed on a real environment (Figure 3). We thenfilled the platform with content for the situated vocabulary learning of Filipino and German words.

3.1 Handheld Augmented Reality Platform
Figure 3 shows the package diagram of our platform and the sample interface enabled by our platform.
The main part of the platform is the Controller, which has access to learning contents, sensor (camera),
and user inputs. The Controller receives the marker ID and camera view matrix from the Tracker anduses these information to specify the behavior of the on-screen display. The Tracker was built using
ARToolkit, and the Renderer was built on OpenGL ES 2.04.

Figure 3. Package Diagram of Our Handheld Augmented Reality Platform (left); Sample Interface for
Situated Vocabulary Learning (right)
We used the ARToolkit (Kato & Billinghurst, 1999) to measure the camera pose with respect to thetarget object. Markers in the video feed were located using the ARToolkit, which also outputs themarker’s ID and the matrix representing the current view of the camera. The image was transformed tothe correct view using the matrix, and then it was rendered accordingly using OpenGL ES 2.04.
The platform runs entirely on iPad tablets. For our experiments, we used the iPad 2 (dual-core
A5, 512MB DDR2 RAM, 32GB, 601 g, 9.7 in display, 1024-by-768 at 132 ppi), and the iPad mini
(64-bit A7, 512MB DDR2 RAM, 16GB, 331 g, 7.9 in display, 1024-by-768 at 163 ppi). The platformworks with fiducial markers (Figure 3) to determine the target object and the viewing angle of thetablet’s back camera. We used the back camera set to 640x480 pixels at 30 fps to sense the marker andto provide a video feed. After identifying the marker, the platform loads the corresponding audio, text,
and image. Audio and text can be accessed using buttons (LISTEN, TRANSLATE, DESCRIBE). The
images can either be still images or sprite sheet animations (Figure 3; Figure 1). The images aretransformed depending on the camera view and are inserted in the video feed to suggest 3-D
registration, that is, to give an impression that the graphics co-exist with the real objects.

3.2 Situated Vocabulary Learning Content
We used the platform to construct two situated vocabulary learning systems: one for 30 Filipino words
and the other for 10 German words. We based the design of the content from previous works (Lin &Hsiao, 2011; Lin & Yu, 2012; Lin & Wu, 2013) by using a combination of text, audio, images, andanimations as content. The text data are the vocabulary, its translation in English, and the description of
the scene (only for the Filipino version). The audio data is the proper pronounciation of the vocabulary
as spoken by a native speaker. The image data are text labels, images, or labels, as shown in Figure 2.

4. Experiments
We explored the strengths of our AR applications for situated vocabulary learning over its non-situatedcounterpart (Figure 4) in two preliminary experiments. Through these experiments, we aim to evaluate
the use of AR for viewing vocabulary content that is situated in the real environment. We compared theAR applications to a non-situated version which is a tablet application that mimics flash card
interaction. Our comparison does not employ any kind of special instructional design such as gamemechanics and collaborative learning. As summarized in Table 1, users simply point the tablet PC toobjects found in their environment when using our AR application. On the other hand, the flash cards
application allows the user to flip through contents by pressing either next or previous.

Figure 4. Non-situated version of the AR applications.
Table 1. Comparison of Two Interfaces for Vocabulary Learning

Interaction

Inherent Feature
Visual Display
Place and Time

Situated (AR app)Non-situated (Flash cards app)
Users find an object with a marker.
Users press “next” or “previous” toThey then point the tablet PC to theswitch between contents.
marker to reveal the content.
Users can see the markers in their Users can quickly go through all theenvironment even when they are not material because they are arranged instudying.
a series.
Images and animations are displayed Static illustrations are shown on aon the real environment.
white background.
Users can only use it inside their laboratory at any time.

We considered inherent features of the interaction as part of the treatment. Thus, interventions were notdone to control it. For example, one advantage of an AR learning system is that the students see theobjects in their surroundings even when they are not studying. We imagine this feature to trigger
unintended rehearsal of the vocabulary, thereby improving learning. This unintended rehearsal is part of
AR learning; thus, we did not control this aspect. We did not forbid the students in the situated treatment
from visiting the study place when they are not studying.
Another inherent feature is that students tend to cover all the vocabularies several times in one
study session when flash cards are used. The flash cards are serially arranged, and students try to go
through all the content two to three times in one sitting. Even if this is the case, interventions were notmade because it is an inherent feature of the use of flash cards. Moreover, advising the students who use
the AR application to view all the content several times will interrupt their natural learning style.
For our experiments, we controlled both location and time constraints. All of our students were
only allowed to use the applications inside their respective laboratories. However, the applications areavailable to them at any time they want to study on that day. Given these features, we had sevenhypotheses which we tested for significance in the 0.05 level via student’s t-test and analysis ofcovariance (ANCOVA). The hypotheses are as follows:
H1. Students will perform better in an immediate post-test with non-situated vocabulary learning.
H2. Students will perform better in a delayed post-test with situated vocabulary learning.
H3. Students will rate situated vocabulary learning as a more motivating instructional material.
H4. Students will maintain their attention better with situated vocabulary learning.

H5. Students will find the contents of situated vocabulary learning as more relevant to them.
H6. Students will feel more confident with non-situated vocabulary learning.
H7. Students will feel more satisfied with situated vocabulary learning.

4.1 Experiment 1: Learning Thirty Filipino Words in Five Days
We adapted a between-groups approach with 31 participants (26 male, 5 female, aged 23–42,
information science graduate students) to test our application for studying Filipino words. The first
languages of the participants are Japanese (13), Chinese (5), Portuguese (3), German, English, Turkish,
Bosnian, Indonesian, Finnish, Arabic, Spanish, Nepali, and Wolof. In our experiments, we divided thepeople into the treatment groups with consideration to the distribution balance of their first languages.
Eighteen participants were recruited from one laboratory. We set up our system inside theirlaboratory (Figure 5) so that they can learn words related to their refreshment area. All of them haveexperienced using an AR application before. As such, AR is not a novel technology for them.
Twelve participants from three laboratories were asked to use the non-situated version. Similar
to the situated group, the non-situated group have used AR before and they are familiar with other novel
interfaces. We distributed tablet computers to them with the flash cards application installed.

Figure 5. Refreshment area with markers (left), Learner using situated vocabulary learning (middle),
Learner using non- situated vocabulary learning (right)
The participants used the assigned application for a recommended duration of 10–15 min per
day for five days. The situated version was used inside a refreshment area with a maximum of four
people using the application at the same time (Figure 5). On the other hand, the learners used thenon-situated version wherever they went inside their laboratory office.
In this experiment, we evaluated the participants’ learning outcomes and the usability of theapplication. On the fifth day, the participants answered the System Usability Scale (SUS) to measure
the perceived usability of the applications (Lewis & Sauro, 2009). They then immediately took a
post-test. After 12–14 days, they took a delayed post-test. The immediate post-test (27 items) anddelayed post-test (24 items) consists of questions on recognizing the word in a multiple choice question,
recalling the translation of the word, and guessing which word fits in different contexts.

4.2 User Testing 2: Learning German Words
We adapted a within-subjects design with 14 participants (8 male, 6 female, aged 17–20, science
majors) to test the application for learning 20 German words (10 situated and 10 non-situated). Eachparticipant used the situated and non-situated versions for a maximum of 8 min. Seven used the situatedversion first, whereas the other seven used the non-situated version first to balance any effect of theordering of the treatment. For the situated version, the learners viewed the content on a small areaaround a laboratory technician’s desk. The markers are placed near each other in a small area tominimize the time spent transferring from one object to another. This is important because we wanted toobserve the study time of the students. For the non-situated version, they used the application while
sitting inside the same room.
The students are then asked to answer 10 multiple choice questions that test their skill torecognize a word. Aside from logging the answer, we also logged the time it took for the learner toanswer the question. After taking the quiz, the participants also answered a subset of the Instructional
Materials Motivation Questionnaire or IMMS. We picked 30 questions that are applicable to our systemout of the 36 questions listed in the work of Huang et al. (2006). IMMS models the extent of motivation
one gets from an instructional material by using the ARCS model (Attention, Relevance, Confidence,and Satisfaction). This model has been applied to AR instructional materials by Di Serio et al. (2013).

5. Results and Discussion
Our experiments involved a small sample size, thus the results should be interpreted with caution. Theseshould be replicated with a bigger sample size. Nevertheless, these results can guide future design ofAR applications and experiments in situated vocabulary learning with AR. In our experiments, nosignificant differences were observed in learning outcomes between situated and non-situatedvocabulary learning. However, students report better attention and satisfaction in using our system. We
found evidences that support hypotheses H4 and H7 but not H1–3 and H5–6.

5.1 Experiment 1: No significant difference in usability and learnability
We computed the SUS score and its factors from the participant responses in Experiment 1. The resultsin Table 2 show that the AR application has an SUS score of 74%, which is close to its flash cards
application counterpart with 80%. According to Sauro (2011), both interfaces are above average (68%);
thus, they are both good interfaces. Moreover, the results in Table 3 show that our participants did nothave difficulty in learning these new interfaces.
We did not find a significant difference between the two interfaces. As such, using theseinterfaces to compare situated and non-situated vocabulary learning is reasonable. We achieved a goodusability score because we applied previous research in multimedia learning. Furthermore, our current
interface features are minimal, and the study task is simple.
Table 2. System Usability Scale Scores for Situated and Non-Situated Vocabulary Learning

SUS Score

Application
AR
Flash cards

N
18
13

Mean
74%
80%

Standard Deviation
12%
6%

T valueStandard Deviation
14%
7%
13%
5%

T value

1.64

Table 3. Factors of the System Usability Scale Scores
FactorUsability
Learnability

Application
AR
Flash cards
AR
Flash cards

N
18
13
18
13

Mean
70%
76%
90%
96%

1.50
1.53

5.2 Experiment 1: Significantly higher score with non-situated for immediate post-test but notfor the delayed post-test
Table 4 is a summary of the results of the immediate and delayed post-tests in Experiment 1. In theimmediate post-test, the non-situated group scored significantly higher with a moderate effect (d = 0.75)
than the situated group. The breakdown in Table 5 shows that the situated group scored lower than the
non-situated group in all types of questions. This result is indicative of an overall inferior mastery ofcontent rather than a weakness in a particular question type.
In most practical cases, people do not apply their learning immediately after studying. Rather,
they would use their knowledge after a few days, either for a test or to apply it to a new lesson. As such,the delayed post-test is a more important point of comparison for learning than the immediate post-test.
After 12–14 days, the significant difference in learning disappeared (Table 4).

Table 4. Total Scores in Immediate and Delayed Post-tests
Post-test
Immediate
DelayedGroup
Situated
non-situatedSituated
non-situated

N
18
13
12
13

Mean
71%
86%
68%
70%

Standard Deviation
20%
20%
23%
18%

T valueStandard Deviation
12%
20%
15%
15%
30%
24%
31%
23%
19%
16%

T value

2.14*
0.31

*p < 0.05
Table 5. Immediate Post-test Scores for Each Question TypeTypeWith illustrations
Recognizing Filipino
with choices
Recognizing Filipino
without choices
Translating fromEnglish to Filipino
Transfer of word usage
with choices*p < 0.05, **p < 0.01

Groupsituated
non-situatedsituated
non-situatedsituated
non-situatedsituated
non-situatedsituated
non-situated

N
18
13
18
13
18
13
18
13
18
13

Mean
87%
92%
80%
94%
64%
83%
55%
81%
75%
91%

0.99
2.54**
1.95*
2.54**
2.40*

5.3 Experiment 1: No Significant Differences in Immediate Post-test Scores After Considering
Usability as Covariant in ANCOVA
If both AR and flash card applications have the same SUS score, then we could do the fairest
comparison possible. However, despite our best efforts, a small difference of six SUS points was stillobserved between the two groups. We conducted ANCOVA to take into account this difference inquality. We assume that the quality of the implementation of the interface affects the students’ scores.
ANCOVA was conducted because the difference in SUS score is not significant. We also
checked the homogeneity of variance using the Levene’s test. The results of the Levene’s test showedthat no significant differences (p > 0.05) were observed. Thus, our data have homogenous variances.
The ANCOVA results (Table 6) show that no significant differences were observed in the test scores ofsituated and non-situated group for both immediate and delayed post-tests. We guess that if we can
improve our AR application to the same level as the flash card application, then students can perform
equally well with a novel interface.
Table 6. Analysis of Covariance of Post-Test Scores with System Usability Scale Score as Covariant
Test
Immediate
Delayed

Group

N

Mean

situated
non-situatedsituated
non-situated

18
13
12
13

71%
86%
68%
70%

Standard
Deviation
20%
20%
20%
16%

Adjusted
Mean
72%
85%
69%
69%

F valuep value

3.02

0.09

0.00

1.00

5.4 Experiment 2: No significant difference in Post-test and Motivation, but significantly
better Attention and Satisfaction with Situated Vocabulary Learning
No significant differences were observed in the immediate post-test between situated (m = 94%, sd =8%) and non-situated (m = 95%, sd = 8%) vocabulary learning. On the average, the non-situated groupanswered our multiple questions faster (m = 2.28 s, sd = 0.92 s) than the situated group (m = 2.60 s, sd =1.03 s) for each question. However, this difference was not significant.

Experiment 2 focuses on evaluating motivation by using the ARCS model. Although twointerfaces can arrive at the same learning result, performance in tests should not be the only measure of
success in creating interfaces. User experience is another important consideration. As such, we also
evaluated the interfaces in terms of its ability to motivate students to learn.
Overall, no significant difference was observed in the IMMS rating of situated and non-situatedvocabulary learning (Table 7). However, looking at the factors of the IMMS (Table 8), significant
differences were observed in the attention and satisfaction factors. The students report that the AR
application catches and holds their attention more than the flash cards. Moreover, they report higher
satisfaction with their learning experience. The learners were slightly more confident to use flash cards
probably because it is a more familiar interface. The learners rated AR to be higher in relevance by five
points, which is attributed to the implicit connection between learning contents and real environment.
However, no statistical significance was observed for the relevance and confidence factors.
Table 7. Instructional Material Motivation Survey Scores for Situated and Non-situated Scenarios

Motivation Score

Treatment
situated
non-situated

N
14
14

Mean
76%
71%

Standard Deviation
12%
11%

T value
1.34

Table 8. Factors of the Instructional Material Motivation Survey Scores
FactorAttention
Relevance
Confidence
Satisfaction

Treatment
situated
non-situatedsituated
non-situatedsituated
non-situatedsituated
non-situated

N
14
14
14
14
14
14
14
14

Mean
75%
65%
74%
69%
80%
83%
77%
66%

Standard Deviation
14%
14%
14%
13%
12%
8%
16%
18%

T value
1.84*
0.97
0.74
1.71*

*p < 0.05

6. Conclusion
We are the first to use AR for explicitly displaying the relationship between vocabulary learning
contents and real world environment for situated vocabulary learning. This preliminary study supports
our intuition that AR can enable the same knowledge acquisition with added benefits of better attention
and satisfaction. We did not employ special instructional strategies in our experiments such as gamemechanics or collaboration between students. As such, the differences in the learning experience are
attributed to the inherent advantages or disadvantages of the interfaces: augmented reality and flash
cards representing situated and non-situated vocabulary learning, respectively.
Our system can be improved by applying other learning theories and instructional strategies
that are not possible for traditional interfaces. Currently, we applied multimedia learning theory
because AR is essentially a presentation medium. In the future, we can apply insights on location-based
games and collaborative learning to create better augmented reality learning experiences.

References
Azuma, R. T. (1997). A survey of augmented reality. Presence, 6(4), 355-385.
Beaudin, J. S., Intille, S. S., Tapia, E. M., Rockinson, R., & Morris, M. E. (2007). Context-sensitive microlearningof foreign language vocabulary on a mobile device. In Ambient Intelligence (pp. 55-72). Springer.
Billinghurst, M., & Duenser, A. (2012, July). Augmented Reality in the Classroom. Computer, 45(7), 56-63.
Chen, I., Yen, J. C., & others. (2013). Hypertext annotation: Effects of presentation formats and learnerproficiency on reading comprehension and vocabulary learning in foreign languages. Computers &Education, 63, 416-423.

Dearman, D., & Truong, K. (2012). Evaluating the implicit acquisition of second language vocabulary using a live
wallpaper. Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems,(pp. 1391-1400).
Dede, C. (2011). Emerging technologies, ubiquitous learning, and educational transformation. In Towards
Ubiquitous Learning (pp. 1-8). Springer.
Di Serio, A., Ibanez, M. B., & Kloos, C. D. (2013). Impact of an augmented reality system on students' motivation
for a visual art course. Computers & Education, 68, 586-596.
Dunleavy, M., & Dede, C. (2014). Augmented reality teaching and learning. In Handbook of research on
educational communications and technology (pp. 735-745). Springer.
Edge, D., Searle, E., Chiu, K., Zhao, J., & Landay, J. A. (2011). MicroMandarin: mobile language learning incontext. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, (pp.
3169-3178).
Grubert, J., Langlotz, T., & Grasset, R. (2011). Augmented reality browser survey. Tech. rep., Institute for
Computer Graphics and Vision, University of Technology Graz.
Huang, W., Huang, W., Diefes Dux, H., & Imbrie, P. K. (2006). A preliminary validation of Attention, Relevance,
Confidence and Satisfaction model-based Instructional Material Motivational Survey in acomputer-based tutorial setting. British Journal of Educational Technology, 37(2), 243-259.
Joseph, S., Binsted, K., & Suthers, D. (2005). PhotoStudy: Vocabulary learning and collaboration on fixed &mobile devices. Wireless and Mobile Technologies in Education, 2005. WMTE 2005. IEEE International
Workshop on, (pp. 5--pp).
Kato, H., & Billinghurst, M. (1999). Marker tracking and hmd calibration for a video-based augmented reality
conferencing system. Augmented Reality, 1999.(IWAR'99) Proceedings. 2nd IEEE and ACM
International Workshop on, (pp. 85-94).
Kaufmann, H., Schmalstieg, D., & Wagner, M. (2000). Construct3D: A Virtual Reality Application forMathematics and Geometry Education. Education and Information Technologies, 5(4), 263-276.
Retrieved from http://dx.doi.org/10.1023/A%3A1012049406877
Lewis, J. R., & Sauro, J. (2009). The factor structure of the system usability scale. In Human Centered Design (pp.
94-103). Springer.
Li, N., Chang, L., Gu, Y. X., & Duh, H. B. (2011, July). Influences of AR-Supported Simulation on Learning
Effectiveness in Face-to-face Collaborative Learning for Physics. Advanced Learning Technologies(ICALT), 2011 11th IEEE International Conference on, (pp. 320-322).
Lin, C. C., & Hsiao, H. S. (2011). The Effects of Multimedia Annotations via PDA on EFL Learners’ Vocabulary
Learning. Proceedings of the 19th International Conference on Computers in Education.
Lin, C. C., & Wu, Y. C. (2013). The Effects of Different Presentation Modes of Multimedia Annocations on
Sentential Listening Comprehension. Proceedings of the 21th International Conference on Computers in
Education.
Lin, C. C., & Yu, Y. C. (2012). EFL Learners’ Cognitive Load of Learning Vocabulary on Mobile Phones.
Proceedings of the 20th International Conference on Computers in Education.
Lin, C. P., Young, S. C., & Hung, H. C. (2008). The game-based constructive learning environment to increase
English vocabulary acquisition: Implementing a wireless crossword Fan-Tan game (WiCFG) as an
example. Wireless, Mobile, and Ubiquitous Technology in Education, 2008. WMUTE 2008. Fifth IEEE
International Conference on, (pp. 205-207).
Liu, T. Y. (2009). A context-aware ubiquitous learning environment for language listening and speaking. Journalof Computer Assisted Learning, 25(6), 515-527.
Mayer, R. E. (2009). Multimedia learning. Cambridge university press.
Pala, K., Singh, A. K., & Gangashetty, S. V. (2011). Games for Academic Vocabulary Learning through a Virtual
Environment. Asian Language Processing (IALP), 2011 International Conference on, (pp. 295-298).
Santos, M., Chen, A., Taketomi, T., Yamamoto, G., Miyazaki, J., & Kato, H. (2014, March). Augmented Reality
Learning Experiences:Survey of Prototype Design and Evaluation. Learning Technologies, IEEE
Transactions on, 7(1), 38-56.
Sauro, J. (2011). Measuring usability with the system usability scale (SUS). Measuring usability with the systemusability scale (SUS). Retrieved from http://www.measuringusability.com/sus.php
Specht, M., Ternier, S., & Greller, W. (2011). Mobile augmented reality for learning: A case study. Journal of the
Research Center for Educational Technology, 7(1), 117-127.
Wu, C. C., Chang, C. W., Liu, B. J., & Chen, G. D. (2008). Improving vocabulary acquisition by designing astorytelling robot. Advanced Learning Technologies, 2008. ICALT'08. Eighth IEEE International
Conference on, (pp. 498-500).
Wu, H. K., Lee, S. W., Chang, H. Y., & Liang, J. C. (2013). Current status, opportunities and challenges ofaugmented reality in education. Computers & Education, 62, 41-49.