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Stojšić et al. (2022). Students’ acceptance of mobile augmented reality applications in primary and secondary biology education,
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Introduction
A lot of biology learning materials are abstract and difcult to understand due to the complexity
of life concepts, especially if the learning content is microscopic or not available for direct observation
(Chang, Chung and Huang, 2016; Nurhasanah, Widodo and Riandi, 2019; Wang et al., 2022). Therefore,
digital visualization technologies have become essential for biology education since special equipment
(such as high-tech microscopes) is often not affordable for educational institutions (Erbas and Demirer,
2019; Jenkinson, 2018).
The current COVID-19 pandemic highlighted the need for digital resources that can be used in
blended, hybrid, and online K-12 teaching (Crompton et al., 2021). Still, in order to successfully organize
technology-enhanced learning solutions (e.g., using digital technologies for presenting knowledge
differently, creating active hands-on learning activities, and providing solutions for evaluation of acquired
knowledge), teachers need to have skills in using digital tools, as well as a certain level of smart pedagogical
competences (Daniela, 2021).
Despite much broader availability of immersive technologies (such as augmented reality [AR]
and virtual reality [VR]) to educational institutions in recent years, adoption is lagging. However, due to
the ongoing pandemic, the potential of using VR and AR content in blended/hybrid learning has been
emphasized (Garcia Estrada and Prasolova-Førland, 2022).
In the literature (Chang, Chung and Huang, 2016; Chien et al., 2019; Erbas and Demirer, 2019;
Students’ Acceptance of Mobile Augmented Reality Applications in
Primary and Secondary Biology Education
Ivan Stojšić1* , Natalija Ostojić2 , Jelena Stanisavljević3
1University of Belgrade, Faculty of Biology, Centre for Educational Technology,
Didactics’ Training and Career Guidance of Biology Teachers, Belgrade, Serbia, e-mail: ivan.stojsic@bio.bg.ac.rs
2Šabac Gymnasium, Šabac, Serbia, e-mail: natalija.ostojic190@gmail.com
3University of Belgrade, Faculty of Biology, Belgrade, Serbia, e-mail: jelena.stanisavljevic@bio.bg.ac.rs
Abstract: Augmented reality is often indicated as a usable educational technology that can be integrated into biology
classes to overcome the shortcomings of traditional teaching (such as lack of visualization of abstract teaching content, students’
low participation and interest in classes, and their insufcient understanding of complex topics). Mobile applications with augmented
reality experience mode have the potential to be used in online, blended/hybrid, and in-person teaching, which is particularly
important during emergencies. This study’s purpose was to determine primary and secondary school students’ acceptance of
augmented reality content in commercial mobile applications that can be used as a supplement in biology teaching. A total of
188 students (from schools included in this research) completed the online questionnaire. The results showed that the majority
of students perceived mobile augmented reality applications as useful and easy to use, had a positive attitude, and expressed
intention to use this educational technology if given the opportunity. The importance of prior evaluation regarding educational
usability and performance is highlighted since technical quality (of used mobile applications) had a strong positive effect on
perceived usefulness and perceived ease of use. There were no statistically signicant differences between female and male
and primary and secondary students, but students with prior experience with augmented reality rated perceived usefulness
higher. Despite positive results, we need to raise our concerns regarding the reliability of using mobile augmented reality in
biology education due to the lack of usable free content and the frequent cancellation of authoring tools and applications.
Keywords: augmented reality, biology teaching, mobile application, Technology Acceptance Model, technology-enhanced
learning.
Original scientic paper
Received: August, 13.2022.
Revised: November, 11.2022.
Accepted: November, 16.2022.
UDK:
378.091:004
378.147::57
10.23947/2334-8496-2022-10-3-129-138
© 2022 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*Corresponding author: ivan.stojsic@bio.bg.ac.rs
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Fuchsova and Korenova, 2019; Hung, Chen and Huang, 2017; Hwang et al., 2016; Jenkinson, 2018; Lu
and Liu, 2015; Safadel and White, 2019; Wang et al., 2022; Weng et al., 2020; Yapıcı and Karakoyun, 2021),
AR is often indicated as a relatively new and usable technology for biology (including ecology) teaching
and learning at all levels of education. Yavuz et al. (2021) pointed out mobile AR (MAR) applications (apps)
as affordable and sustainable for massive adoption in different areas (including education). According to
Laine (2018, p. 2), MAR can be dened as “a type of AR where a mobile device (smartphone or tablet)
is used to display and interact with virtual content, such as three-dimensional (3D) models, annotations,
and videos, that are overlaid on top of a real-time camera feed of the real world”. Using mobile devices to
integrate AR content in in-person or remote educational settings is more accessible and less expensive
than with other types of AR hardware (such as smart glasses, headsets, AR projection systems, etc.)
since most students already have appropriate smartphones (Nurhasanah, Widodo and Riandi, 2019).
Although still limited and content-specic (Erbas and Demirer, 2019), the body of literature
concerning the use of AR in biology (including ecology) education is constantly growing. On the one hand,
several studies have shown that AR can positively affect students’ achievement in biology and ecology
in formal and informal learning settings (Hwang et al., 2016; Lu and Liu, 2015; Nurhasanah, Widodo and
Riandi, 2019). On the other hand, a number of studies didn’t nd any signicant difference in students’
academic achievement (learning outcomes) between AR and traditional learning materials or other digital
aids (Chang, Chung and Huang, 2016; Chien et al., 2019; Erbas and Demirer, 2019; Hung, Chen and
Huang, 2017; Wang et al., 2022; Weng et al., 2020). Still, Chang, Chung and Huang (2016) reported
better knowledge retention, Wang et al. (2022) reported a reduction in students’ cognitive load, and Chien
et al. (2019) and Weng et al. (2020) reported statistically higher scores in the experimental group (that
used the AR technology) on questions related to higher levels of Bloom’s revised taxonomy (such as the
level of analyzing). Also, the majority of analyzed studies reported benets of AR regarding students’
motivation, self-efcacy, satisfaction, and/or participation in biology lessons.
According to Nurhasanah, Widodo and Riandi (2019, p. 482), the use of AR in biology classes “will
undoubtedly attract more students’ interest in school”. Similarly, Hung, Chen and Huang (2017) pointed
out that AR may not be superior compared to other aids and teaching materials, but it is at least equally
effective and can help students learn biology, spark their interest, and reduce classroom boredom. In
addition, Lu and Liu (2015) emphasized that learning activities with AR can be especially helpful for low
academic achievement students, and Chang, Chung and Huang (2016) indicated students’ opportunity to
experience constructivist learning as one of the most important advantages of using AR in schools.
Dengel et al. (2022) pointed out that teachers should be capable of designing their AR experiences
and indicated ve accessible AR authoring toolkits for educational purposes (Vuforia Studio, BlippAR,
AWE, AR Media Studio, and Areeka). However, teachers often lack specic knowledge and skills to develop
or customize their own digital materials, such as AR experiences (Daniela, 2021; Mota et al., 2018).
Also, Daniela (2021, p. 714) emphasized that it is not clear “how much effort the teacher should put into
developing the materials”. Fuchsova and Korenova (2019) suggested a few commercial biology-themed
MAR apps that are affordable and appropriate for teaching. Still, using so-called “off-the-shelf” apps in
learning environments is not a straightforward process since available immersive experiences need to be
evaluated rst and matched with teaching content and lesson goals (Stojšić et al., 2019b). For example,
Dreimane and Daniela (2021) analyzed 41 MAR apps (from the App Store) related to the anatomy of
the human body, but only seven met the selection criteria. The same authors believe that commercial
MAR apps can be successfully integrated into the learning process (but before all else teachers need to
understand the educational potential and limitations of those apps) and proposed an evaluation framework
with 19 criteria divided into three groups: (a) technological performance, (b) information architecture, and
(c) educational value (Dreimane and Daniela, 2021).
Besides the availability of MAR apps (both custom and off-the-shelf) to biology teachers and students
and their usability, graphics quality, and effectiveness, it is also important to assess the acceptance of
those apps. According to Yavuz et al. (2021, p. 1), acceptance of MAR is “one of the factors inuencing
its adoption”. Yapıcı and Karakoyun (2021) conducted a case study with prospective biology teachers and
the result showed that future biology teachers had mostly positive views about the use of AR in biology
teaching. Although limited in scope, previous studies (Fuchsova and Korenova, 2019; Hung, Chen and
Huang, 2017; Hwang et al., 2016; Safadel and White, 2019) also suggested that the majority of students
accept AR and have positive attitudes (perceptions) regarding the use of this technology for biology
learning. However, we did not nd studies that deal with determining primary and/or secondary students’
acceptance of commercially available MAR apps (for biology learning) during pandemic teaching.
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The purpose of the study and used research model
The purpose of this study was to determine primary and secondary students’ acceptance of free
commercial MAR apps that can be used as a supplement in biology teaching/learning, as well as to
identify potential variables that inuence acceptance.
In the literature (Balog and Pribeanu, 2010; Cabero-Almenara, Fernández-Batanero and Barroso-
Osuna, 2019; Huang and Liaw, 2018; Huang, Liaw and Lai, 2016; Mailizar and Johar, 2021; Wojciechowski
and Cellary, 2013), students’ acceptance of AR, VR, and other immersive technologies in educational
settings was often researched using the Technology Acceptance Model (TAM; Davis, 1989; Davis, Bagozzi
and Warshaw, 1989). According to Trivunović and Kosanović (2021), the TAM model provides insights into
the reasons for acceptance and use of technology in teaching and learning processes. In other words, the
TAM constructs (factors) explain the complexity of the process of technology acceptance by the user (in
our case the student).
In the present study, we used an adjusted and shortened version of the AR Acceptance Model
(based on the TAM model) proposed by Cabero Almenara, Barroso Osuna and Llorente Cejudo (2016).
On the basis of the used research model (Figure 1), the following hypotheses were formulated:
H1. Technical quality has a positive effect on perceived usefulness.
H2. Technical quality has a positive effect on perceived ease of use.
H3. Perceived ease of use has a positive effect on perceived usefulness.
H4. Perceived ease of use has a positive effect on attitude toward use.
H5. Perceived usefulness has a positive effect on attitude toward use.
H6. Perceived usefulness has a positive effect on intention to use.
H7. Attitude toward use has a positive effect on intention to use.
Figure 1. Research model (based on Cabero Almenara, Barroso Osuna and Llorente Cejudo,
2016).
In addition, but in line with the research purpose, we formulated the following research questions:
RQ1. Does gender inuence differences in students’ acceptance of MAR apps?
RQ2. Does prior achievement in biology (grade at the end of the rst semester) inuence differences
in students’ acceptance of MAR apps?
RQ3. Does the type of school (primary or secondary) inuence differences in students’ acceptance
of MAR apps?
RQ4. Does prior experience with AR inuence differences in students’ acceptance of MAR apps?
We decided to formulate research questions instead of hypotheses since there are no sufcient
and conclusive results from previous studies regarding the inuence of investigated variables (gender,
biology grade at the end of the rst semester, type of school, and prior experience with AR) on students’
acceptance of the educational use of AR technology.
Materials and Methods
In this research, mobile apps with AR experience mode were used to complement students’
knowledge acquisition of certain biological teaching content. At the time we started our research, there
was a need for digital solutions that could help students’ better understanding of biology learning materials
since the classes were shorted to 30 minutes due to pandemic measures (with only half of the students in
the classroom). The research was conducted during the second semester of the 2020-2021 school year
through four stages.
In the rst stage, we were looking for AR content in free commercial mobile apps that could be
used in biology classes. The search didn’t include MAR apps that are only for iOS devices (e.g., iPad and
iPhone) since in the Republic of Serbia the majority of schools’, teachers’, and students’ owned devices
(tablets and smartphones) are Android-based.
In the second stage, we checked the performance and graphics quality of the AR experience mode
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Stojšić et al. (2022). Students’ acceptance of mobile augmented reality applications in primary and secondary biology education,
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(of biology-related content) in found MAR apps, as well as used the heuristic questionnaire proposed by
Radu (2014) to evaluate those apps. After the evaluation process, we were left with only a few MAR apps
(EON-XR, Expeditions [the app is no longer available], Edmentum AR Biology, and WWF Free Rivers)
that we tried to match with the teaching content in different grades of the primary and secondary school
biology curriculum.
In the third stage, the integration process was planned and realized following steps from the AR/
VR integration model proposed by Stojšić et al. (2019a). For instance, the steps included evaluation of
school infrastructure and availability of necessary devices (teachers’ and students’ owned smartphones
and tablets), as well as taking security measures and preparing students to use MAR apps.
In the fourth stage, the selected biology-related AR content was included in some biology classes
(in both in-person and online groups) as an additional part of activities created by the second author
(biology teacher). For example, the EON-XR app was employed with the teaching content related to
nutrition and the human digestive system. At the end of this research (end of the second semester), the
students were offered to ll in an online questionnaire. Participation was voluntary and anonymous.
Participants
Three state primary schools (two rural and one urban) and one urban state secondary school
in the Republic of Serbia took part in this research. A total of 188 students (from schools included in
this research) completed the online questionnaire correctly and timely. Participants’ characteristics are
presented in Table 1.
Table 1
Participants’ descriptive characteristics (N = 188)
Instrument
An online questionnaire in the Serbian language was created (with Google Forms) as the instrument
for this research. The rst part of the questionnaire contained questions related to students’ demographics
and school information (e.g., gender, type of school, biology grade at the end of the rst semester, etc.).
The second part of the questionnaire included a ve-point Likert-type scale (from 1 – strongly disagree
to 5 – strongly agree) with 19 items grouped to measure ve TAM constructs (technical quality, perceived
usefulness, perceived ease of use, attitude toward use, and intention to use). The items were dened
as positive and negative statements and mostly adapted from Cabero Almenara, Barroso Osuna and
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Stojšić et al. (2022). Students’ acceptance of mobile augmented reality applications in primary and secondary biology education,
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Llorente Cejudo (2016). Table 2 shows Cronbach’s alpha values for TAM constructs and means and
standard deviations of items.
Table 2
Cronbach’s alpha values for TAM constructs and means and standard deviations of items
Note. The item “I had a hard time mastering the use of MAR apps.” was excluded from the perceived ease
of use construct due to low item-total correlation. For the negative items (marked with an asterisk [*]), a reverse
scoring method was used.
Data Analysis
Statistical analyses were performed using IBM SPSS Statistics software (version 24). Besides
descriptive statistics (arithmetic means, standard deviations, frequencies, and proportions), path analysis
(based on multiple regression analysis) was used for testing the research model (hypotheses 1-7).
Additionally, four MANOVA (multivariate analysis of variance) tests were run to examine the relationship
between independent variables (gender, biology grade at the end of the rst semester, type of school, and
prior experience with AR) and dependent variables (the TAM constructs).
Results
Analyzing the results from the TAM-based scale (Table 3), it can be concluded that students
included in this research accepted the use of MAR apps as a supplement in biology teaching/learning.
According to students, MAR apps had sufcient technical quality (M = 4.00, SD = 0.73), they were easy to
use (M = 4.34, SD = 0.60), and mostly useful for biology learning (M = 3.93, SD = 0.76). Also, the students
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Stojšić et al. (2022). Students’ acceptance of mobile augmented reality applications in primary and secondary biology education,
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had a positive attitude toward the use of MAR apps in biology teaching (M = 4.12, SD = 0.75) and to a
certain degree, expressed their intention to use (M = 3.83, SD = 0.80) this educational technology in the
future (if given the opportunity).
Table 3
Means and standard deviations of TAM constructs
To test hypotheses, the path analysis based on multiple regression analysis was used. Therefore,
path coefcients are standardized beta values. The results of the path analysis (Table 4) have revealed
statistical signicance of all paths in the tested research model.
Table 4
Results of the research model testing
The path between perceived ease of use and attitude toward use was found signicant at a .05 level,
whereas the paths between perceived ease of use and perceived usefulness and perceived usefulness
and intention to use were found signicant at a .01 level. The rest of the paths were found signicant at a
.001 level. Consequently, all hypotheses are supported. Figure 2 shows the nal model.
Figure 2. Results of the path analysis (Final model).
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Stojšić et al. (2022). Students’ acceptance of mobile augmented reality applications in primary and secondary biology education,
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To answer formulated research questions, four MANOVA tests were performed. Preliminary tests
were conducted to check violations for normality, linearity, univariate and multivariate outliers, homogeneity,
and multicollinearity assumptions (see Pallant, 2020).
The rst research question investigates potential differences in students’ acceptance of MAR apps
regarding gender (independent variable). The TAM constructs (technical quality, perceived usefulness,
perceived ease of use, attitude toward use, and intention to use) were used as dependent variables.
During preliminary testing, three cases were removed due to multivariate outliers. The results of the
MANOVA test showed no signicant difference between female and male students on the combined
dependent variables, F(5, 179) = 2.05, p = .073, Pillai’s Trace = .05, partial η2 = .05.
The second research question explores potential differences in students’ acceptance of MAR apps
regarding biology grades at the end of the rst semester (independent variable). The TAM constructs were
used as dependent variables. During preliminary testing, three cases were removed due to multivariate
outliers. Also, the “insufcient” group was excluded because there was only one case in it. Due to the
limited number of cases, groups “sufcient” and “good” were merged into one group. The results of the
MANOVA test showed a statistically signicant difference between students with different biology grades
(“sufcient/good”, “very good”, and “excellent”) on the combined dependent variables, F(10, 356) = 2.14,
p = .021, Pillai’s Trace = .11, partial η2 = .06. However, when the results for the dependent variables were
considered separately (using the sequential Holm-Bonferroni method for alpha level correction), none of
the differences reached statistical signicance.
The third research question examines potential differences in students’ acceptance of MAR apps
regarding school type (independent variable). Five dependent variables were used (the TAM constructs).
During preliminary testing, three cases were removed due to multivariate outliers. The results of the
MANOVA test showed no signicant difference between primary and secondary school students on the
combined dependent variables, F(5, 179) = 1.99, p = .083, Pillai’s Trace = .05, partial η2 = .05.
The fourth research question explores potential differences in students’ acceptance of MAR apps
regarding prior experience with AR (independent variable). Again, the TAM constructs were used as
dependent variables. During preliminary testing, three cases were removed due to multivariate outliers.
The results of the MANOVA test showed a statistically signicant difference between students with and
without prior experience with AR on the combined dependent variables, F(5, 179) = 2.51, p = .032, Pillai’s
Trace = .07, partial η2 = .07. When the results for the dependent variables were considered separately
(using the sequential Holm-Bonferroni method for alpha level correction), the only difference to reach
statistical signicance was perceived usefulness, F(1, 183) = 8.53, p = .004, partial η2 = .04. An inspection
of the mean scores indicated that students with prior experience with AR perceived higher usefulness of
MAR apps (M = 4.09, SD = 0.67) than students without prior experience with AR (M = 3.78, SD = 0.76).
Discussion
Using smartphone-supported apps is a way to provide everyone a chance to use AR experiences
in the learning process (Dreimane and Daniela, 2021). Therefore, this research deals with primary and
secondary school students’ acceptance of MAR apps (that can be used as a supplement in biology
teaching/learning).
Based on the results, it can be concluded that the majority of students accepted MAR apps which
is in line with previous studies related to biology (including ecology) content teaching (Fuchsova and
Korenova, 2019; Hung, Chen and Huang, 2017; Hwang et al., 2016; Safadel and White, 2019).
The seven hypotheses were tested using the path analysis. The results showed that technical
quality was a very strong predictor of students’ perceived usefulness and perceived ease of use. Perceived
ease of use had a signicant positive effect on students’ perceived usefulness of MAR apps. Perceived
usefulness was the most important predictor (β = 0.62) of students’ attitude toward use regarding MAR
apps. Also, perceived ease of use had a signicant positive impact (β = 0.12) on students’ attitude toward
use, which was theorized in the research model but not always the case in prior studies regarding MAR
apps (e.g., Koutromanos and Mikropoulos, 2021; Yavuz et al., 2021). Furthermore, attitude toward use
was the most inuential predictor (β = 0.62) of students’ intention to use MAR apps. Similar results were
reported in studies (regarding immersive technologies) by Cabero-Almenara, Fernández-Batanero and
Barroso-Osuna (2019), Koutromanos and Mikropoulos (2021), and Wojciechowski and Cellary (2013).
In addition, perceived usefulness had a signicant positive impact (β = 0.18) on students’ intention to
use MAR apps. Although Huang and Liaw (2018) pointed out that in many studies perceived usefulness
has been seen as the most signicant predictor of students’ intention to use various digital technologies
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Stojšić et al. (2022). Students’ acceptance of mobile augmented reality applications in primary and secondary biology education,
International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 10(3), 129-138.
(e-learning systems, virtual worlds, and VR), we should be mindful that the authors of those studies used
different TAM constructs (often without attitude as a construct). Additionally, Wojciechowski and Cellary
(2013) did not nd a signicant effect of perceived usefulness on intention to use in their research.
In addressing research questions 1 and 3, the results of MANOVA tests showed no statistically
signicant differences regarding gender and school type on students’ acceptance of MAR apps. Cabero-
Almenara, Fernández-Batanero and Barroso-Osuna (2019) also reported that no signicant differences
were found regarding the inuence of students’ gender on the degree of acceptance of AR. However,
Pombo and Marques (2020) reported that primary school students perceived higher educational value of
the EduPARK MAR game than secondary school students.
In relation to research questions 2 and 4, the MANOVA test results showed statistically signicant
differences regarding biology grades (at the end of the rst semester) and prior experience with AR on the
combined dependent variables (the TAM constructs). However, for biology grades, none of the differences
reached statistical signicance when the dependent variables were considered separately. The results
are encouraging since students found AR content useful for learning regardless of their prior achievement
in biology. According to Salmi, Thuneberg and Vainikainen (2017), AR is one of the few pedagogical
solutions that especially benets those students who are below average in school achievement. For
prior experience with AR, the only difference to reach statistical signicance was perceived usefulness
indicating that students with prior experience with AR perceived higher usefulness of MAR apps. These
results are similar to the ndings of Stojšić et al. (2020) and suggest that the positive students’ responses
were not just products of a novelty effect (see Akçayır and Akçayır, 2017).
Conclusions
The results of this research showed that both primary and secondary students accepted MAR
apps and perceived their usefulness, as well as they had a positive attitude toward the use of AR in
biology teaching and expressed their intention to use this educational technology more frequently if given
the opportunity. Still, we need to take into consideration that the BYOD (bring your own device) model
remains the only way for many schools and teachers in the Republic of Serbia to introduce certain digital
innovations in teaching practice (Atanasković et al., 2022). Furthermore, we should bear in mind that
“each AR application is unique, inuencing students in specic ways according to its design” (Radu, 2014,
p. 1534). Therefore, to ensure a meaningful, effective, and successful integration, app evaluation is a
necessary step, as well as using appropriate integration models (such as one proposed by Stojšić et al.,
2019a). Also, the importance of prior evaluation of apps was highlighted in the results of this research
since technical quality (of used MAR apps) had a very strong positive impact on perceived usefulness and
perceived ease of use.
Like in the study done by Dreimane and Daniela (2021), we also nished the evaluation process
with a limited number of usable MAR apps. Teachers’ access to suitable AR/VR content is a bottleneck
when it comes to the broader adoption of immersive technologies in education (Garcia Estrada and
Prasolova-Førland, 2022). We should emphasize that the Expeditions app was discontinued in June
2021. Additionally, one of the biology-themed MAR apps used in the study by Fuchsova and Korenova
(2019) is no longer available as well. Therefore, usable AR content (in currently available free mobile
apps that can be used for biology teaching) is limited and learners cannot always use it independently
(due to low information architecture and/or educational value, see Dreimane and Daniela, 2021), but it
can be integrated (as a supplement) into activities and teacher-created materials to engage students
with the teaching content in classrooms or online. However, the question regarding the reliability of using
free MAR apps (as the main option for integrating AR in educational settings) is still open. Utilizing AR
authoring tools is not more reliable either. For example, Dengel et al. (2022) also raised questions about
reliability since over half of AR authoring tools reported in the scientic articles (43 papers were included
in the meta-analysis) were not accessible or discontinued. The same authors pointed out that “having to
change to a different Authoring Toolkit after a year or two is tedious and could keep educators putting
in the effort of learning how to use such toolkits” (Dengel et al., 2022, p. 9). In addition, in their SWOT
analysis, Stojšić et al. (2019a) indicated the rapid obsolescence of mobile devices and the cancellation of
authoring tools and apps as threats that could jeopardize the wider adoption of immersive technologies
in learning environments.
This research has some limitations. The rst limitation relates to the selection process since
we only included free Android mobile apps (with AR content about biology). Moreover, selection and
evaluation processes can involve aspects of subjectivity, which cannot be fully eliminated using evaluation
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Stojšić et al. (2022). Students’ acceptance of mobile augmented reality applications in primary and secondary biology education,
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questionnaires and frameworks (Dreimane and Daniela, 2021). The second limitation is that we didn’t
have the means to monitor the actual use of MAR apps in online groups of students. The third limitation
is the voluntary response bias (possible differences between students who lled in the questionnaire and
those who did not).
Acknowledgements
The authors would like to thank dr. Nina Adanin (Northwest Missouri State University) for her help
during the data analysis.
Conict of interests
The authors declare no conict of interest.
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