Exploring pre-service teachers’ intentions of adopting and using virtual reality classrooms in science education

Abstract

This study investigated how pre-service teachers perceive and plan to use a virtual reality classroom for science teaching during microteaching practices. The UTAUT 2 model was adopted as the conceptual framework for this study. Data were collected through an online survey from eighty-three pre-service science teachers from a large metropolitan university in Gauteng Province, South Africa. The collected data were analysed using descriptive and regression analysis. The results revealed that pre-service teachers demonstrated a high level of acceptance and intention to use Virtual reality classrooms in their microteaching practice and future classroom teaching. Thus, implying that they were receptive to the idea of using virtual reality classrooms in their microteaching practice and future classroom practice. Results further indicate that the preservice teachers are fascinated by the utilization of virtual reality classrooms for their microteaching practice based on two significant factors: social influence and technology self-assurance. However, results show that age and gender do not moderate the influence of performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, self-efficacy, anxiety and attitude on preservice teachers’ behavioural intention to accept and the virtual reality classroom for their microteaching practice and future classroom teaching. The implications of these findings for science teaching and learning are discussed as it delves into the motivations and considerations of pre-service teachers when incorporating virtual reality classrooms into their teaching practices for science education.

Full text

Education and Information Technologies (2024) 29:20299–20316
https://doi.org/10.1007/s10639-024-12664-5
Abstract
This study investigated how pre-service teachers perceive and plan to use a virtual
reality classroom for science teaching during microteaching practices. The UTAUT
2 model was adopted as the conceptual framework for this study. Data were col-
lected through an online survey from eighty-three pre-service science teachers from
a large metropolitan university in Gauteng Province, South Africa. The collected
data were analysed using descriptive and regression analysis. The results revealed
that pre-service teachers demonstrated a high level of acceptance and intention to
use Virtual reality classrooms in their microteaching practice and future classroom
teaching. Thus, implying that they were receptive to the idea of using virtual reality
classrooms in their microteaching practice and future classroom practice. Results
further indicate that the preservice teachers are fascinated by the utilization of vir-
tual reality classrooms for their microteaching practice based on two signicant
factors: social inuence and technology self-assurance. However, results show that
age and gender do not moderate the inuence of performance expectancy, eort ex-
pectancy, social inuence, facilitating condition, hedonic motivation, self-ecacy,
anxiety and attitude on preservice teachers’ behavioural intention to accept and
the virtual reality classroom for their microteaching practice and future classroom
teaching. The implications of these ndings for science teaching and learning are
discussed as it delves into the motivations and considerations of pre-service teach-
ers when incorporating virtual reality classrooms into their teaching practices for
science education.
Keywords Behavioural intentions · Microteaching · Pre-service teachers · Science
education · Virtual reality classrooms
Received: 7 December 2023 / Accepted: 21 March 2024 / Published online: 17 April 2024
© The Author(s) 2024
Exploring pre-service teachers’ intentions of adopting and
using virtual reality classrooms in science education
Ayodele AbosedeOgegbo1· MaforPenn1· UmeshRamnarain1·
OniccahPila1· ChristoVanDer Westhuizen1· NoluthandoMdlalose1·
IvanMoser2· MartinHlosta2· PerBergamin2
Extended author information available on the last page of the article
etal.[full author details at the end of the article]
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1 Introduction
Virtual Reality (VR) has emerged as an innovative technology in various elds,
including education. VR is an interface that immerses users in an articial three-
dimensional (3D) environment created by a computer or mobile device (Durukan et
al., 2020). It combines elements of the real and virtual worlds, allowing for the cre-
ation of new environments where physical and digital objects can coexist and inter-
act in real time (Cooper et al., 2019). Nonetheless, the simultaneous existence and
interplay of these worlds can be observed through the utilisation of head-mounted
eye goggles and wired attire, which enable the user to participate in authentic three-
dimensional environments (Al Breiki et al., 2023). Research has demonstrated that
VR technology oers the opportunity for individuals, irrespective of their position,
geographical location, or economic circumstances, to partake in the educational
process (Al-Amri et al., 2020; Shen et al., 2019). Hence, teachers are incorporat-
ing this innovative technology into classroom settings to instruct various academic
disciplines, including the natural sciences, medical education, and science education
(Broisin et al., 2017; Paxinou et al., 2020). In the context of science education, VR
classrooms oer students a simulated environment where they can actively engage
with scientic concepts and phenomena. These classrooms provide a platform for stu-
dents to visualise abstract concepts and explore diverse scientic scenarios (Shen et
al., 2019). By utilising VR classrooms in science education, students have the oppor-
tunity to delve into complex scientic theories, conduct experiments, and engage in
hands-on learning experiences that would appear dicult to replicate in traditional
classrooms. This in turn oers them a unique and immersive learning experience
(August et al., 2016; Al-Amri et al., 2020). This immersive learning experience has
the potential to enhance students’ understanding, learning outcomes, attitudes, moti-
vations, and interests in science (Arici et al., 2019; August et al., 2016; Al-Amri et al.,
2020), making it an attractive option for pre-service teachers. Hence, incorporating
VR classrooms into teaching practices can help teachers create dynamic and engag-
ing learning experiences that foster students’ interest and motivation to learn science.
The use of VR has been well-received by both students and teachers, as studies
have shown a positive perception towards its adoption in the classroom (Al Breiki et
al., 2023). As a result, teachers worldwide have begun embracing this technology to
teach science subjects (Yang & Huang, 2021). Within the South African context, the
utilisation of VR in teaching and learning is a relatively new concept primarily used
for gaming and is still not as widespread as other educational technologies such as 3D
simulations, videos and interactive smart boards (Homan, 2018). More importantly is
the exposure of pre-service teachers (PSTs) to the use of VR classrooms during their
microteaching practice.
The research reported in this article constitutes a part of a larger study on the use of
a VR classroom to enable collaborative and contextualised microteaching practised
by pre-service science teachers. Microteaching is a training strategy used to facilitate
the acquisition of pedagogical skills by student teachers through engaging in short
lesson presentations on a single, tightly dened topic (Banga, 2014). These short
lesson presentations oer preservice teachers the chance to practice real teaching
situations, helping them gain condence and prociency. Additionally, these focused
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Education and Information Technologies (2024) 29:20299–20316
teaching sessions are valuable tools for developing skills and preparing future teach-
ers for their own classrooms. In the microteaching practice, preservice teachers teach
microlessons in small group settings for a controlled duration of 5 to 20 min (Asare
&Amo, 2023). This microlesson can be used in both online and face-to-face teach-
ing settings. It allows preservice teachers to apply theoretical concepts from their
training programs to real-world teaching scenarios. However, the emergence of sim-
ulated learning environments has prompted some universities to begin combining
traditional micro-teaching methods with virtual or mixed-reality learning environ-
ments (Ledger & Fischetti, 2019). This innovative approach can assist preservice
teachers in acquiring crucial technology integration skills and the mindset needed
for the technologically advanced and dynamic future classrooms they will encounter
in their teaching careers (Ledger & Fischetti, 2019). In light of this, it is essential to
understand pre-service teachers’ intentions in adopting and utilising virtual reality
classrooms in science education. Particularly, this study aims to inform PSTs’ use of
VR for future science teaching by exploring the motivations and considerations of
pre-service teachers when incorporating virtual reality classrooms into their microte-
aching practices. Several theories and models have been developed regarding the
identication of the factors that impact users’ acceptance of technology. The most
signicant among them is the Unied Theory of Acceptance and Use of Technology
(UTUAT), which provides a comprehensive understanding of the determinants of
technology acceptance. However, this study employs the UTUAT2 model, which is
known for its strong predictive capability as demonstrated in the original study of the
model (Venkatesh et al., 2012). This study is guided by the following two research
questions:
What is the level of acceptance and intention among pre-service science teach-
ers towards utilising a virtual reality classroom for science teaching during their
microteaching experience and in future classrooms?
How do the UTUAT2 constructs impact the acceptance and behaviour intention
to use Virtual Reality (VR) classrooms for science teaching during microteach-
ing and in future classrooms among pre-service science teachers?
2 Literature review
In recent years, there has been a rise in the availability and usage of virtual reality
(VR), augmented reality (AR), and mixed reality (MR) technologies across various
elds (Cipresso et al., 2018; Yildirim et al., 2020). Augmented reality refers to a vir-
tual environment that combines real surroundings with virtual objects, allowing users
to interact with digital images in real time while observing the actual scene (Azuma,
1997). On the other hand, virtual reality is a computer-generated simulation of a three-
dimensional environment that immerses users in a simulated learning environment,
replicating real-life experiences using computer technologies (Martín-Gutiérrez et
al., 2017; Yildirim et al., 2020). Mixed reality, on the other hand, encompasses a
spectrum between a real scene and a fully immersed virtual environment (Milgram &
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Education and Information Technologies (2024) 29:20299–20316
Kishino, 1994). Studies have indicated that incorporating modern technologies like
virtual reality (VR) into science education has the potential to enhance the teaching
and learning of physical concepts and phenomena that cannot be directly observed
in daily experiences (Al-Amri et al., 2020; Al Breiki et al., 2023). The eectiveness
of virtual reality (VR) classrooms in promoting scientic learning among pre-service
teachers, as compared to other interactive technologies such as augmented reality
(AR) or mixed reality (MR), lies in the complete immersion experience that VR
oers. This enables pre-service teachers to actively participate in realistic and com-
plex virtual environments, where they can simulate scientic phenomena, engage in
practical activities, and observe scientic concepts within a controlled and secure
environment (Yildirim et al., 2020). However, the acceptance and willingness of
teachers to use VR technology, as well as their perception of its benets for teaching
and learning, play a crucial role in motivating and inuencing their behaviour towards
adopting this innovative technology in science teaching (Khukalenko et al., 2022).
For instance, some factors that have been argued to inuence how users perceive
and accept e-learning and VR technologies include their condence in using new
technologies, their willingness to try new things, their anxiety about using new tech-
nologies, how much they enjoy using the technology, societal norms, the quality of
the content and system, their previous experience with similar technologies, and the
conditions that support their use (Jimenez et al., 2021). A recent study found that sci-
ence teachers are more likely to have a positive attitude towards using virtual reality
if they believe that it oers advantages over traditional teaching methods (Al Breiki
et al., 2023). This positive attitude, however, depends on facilitating conditions such
as the teachers’ perceived readiness and condence in using VR technology, which
ultimately impacts their adoption of the technology in their teaching. Shen et al.
(2019) conducted a study on how university students’ intentions to use virtual reality
for learning are inuenced by the four constructs of the unied theory of acceptance
and use of technology (UTAUT) model and the four modes of Kolb’s learning styles.
The authors discovered that the sampled students believed that using virtual reality
head-mounted displays (VR HMDs) would enhance their learning eectiveness and
academic performance, thus increasing their intention to use them. This intention was
found to increase when students perceived VR HMDs as easy to use and when they
had access to facilitating conditions like sucient resources, convenient facilities,
and infrastructure (Shen et al., 2019).
Research has indicated that there are certain factors that impact pre-service teach-
ers’ willingness to use technology. These factors include how useful teachers believe
the technology is, how easy they perceive it to use, and their own condence in using
it eectively (Joo et al., 2018). These factors align with dierent layers of the virtual
reality-enabled scientic experiment framework, which includes the visceral (emo-
tional), behavioural, and reective aspects of using technology in education (Xie
et al., 2022). Bower et al. (2020) categorised factors that inuence the intentions
of pre-service teachers to utilise virtual reality in their classrooms into internal and
design-related issues. Monteiro et al. (2022) argued that cultural factors play a role
in the adoption of virtual reality for practical or experiential learning. For instance,
the authors discovered that developed countries and regions tend to prioritise per-
formance expectancy while developing countries focus more on eort expectancy
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Education and Information Technologies (2024) 29:20299–20316
when forming their attitudes towards new technologies like virtual reality. This dif-
ference may stem from variations in technological self-ecacy and availability of
resources. Additionally, social inuence and facilitating conditions were identied
as signicant contributors to positive attitudes towards virtual reality for practical
learning. However, if users experience a high level of anxiety, including the fear of
making mistakes and feeling apprehensive and intimidated about using virtual real-
ity for practical learning, these positive attitudes or behavioural intentions may not
be activated. Based on the result of the study, Monteiro et al. (2022) emphasise the
importance of understanding these cultural factors to design and utilise virtual real-
ity technology that can overcome cultural barriers or be tailored to specic cultural
contexts.
3 Conceptual framework
The conceptual framework of this study is based on the Unied Theory of Acceptance
and Use of Technology (UTAUT 2) model developed by Venkatesh et al. (2012).
The UTAUT-2 model is an enhanced version of the UTAUT framework which is a
comprehensive technology acceptance model (TAM) and combines various concepts
from dierent models to assess technology use and acceptance. It integrates ideas
from the TAM, the Diusion of Innovations Model, the Theory of Reasoned Action,
and other technology use models. The integration of these models allows researchers
to study user behaviours and dene outcomes based on previous research in the eld.
The UTAUT framework advocates that an individual’s intention to use technology is
inuenced by factors such as performance expectancy (perceived usefulness of the
technology), eort expectancy (perceived ease of use), social inuence (apprecia-
tion of technology within the individual’s social network) and facilitating conditions
(availability of resources to use the technology). On the other hand, the UTAUT2
model proposes that in addition to these factors, intention to use technology is also
inuenced by hedonic motivation (perceived enjoyment of the technology), price/
value (trade-o between perceived benets and monetary costs), and habit (passage
of time since initial technology usage), along with age, gender and experience as
moderators (Venkatesh et al., 2012). Nevertheless, studies have indicated that the
ability of the UTAUT model to predict the acceptance of technology can be improved
by increasing the number of external variables (Wong et al., 2013). Consequently,
several variables like self-ecacy, anxiety, satisfaction, perceived risk, and trust,
have been recommended to complement the UTAUT2 model (Khalilzadeh et al.,
2017).
According to the UTAUT2 model (Venkatesh et al., 2012), this study suggests
that the intention of pre-service teachers to adopt virtual reality classrooms (VRCs)
is inuenced by several factors. These factors include performance expectancy, eort
expectancy, facilitating conditions, social inuence, and hedonic motivation. In this
particular study, the virtual reality technologies used are owned by the institution, and
the virtual reality classroom used is a free application designed by the institution and
access to the application is freely provided to pre-service teachers. As a result, the
“price value” construct is not applicable in this study. The students in this study are
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Education and Information Technologies (2024) 29:20299–20316
newly introduced to the use of VR technology and platforms. Hence, the “habit” and
“experience” construct are not applicable either. Studies have highlighted the role of
technological self-ecacy, anxiety and attitude on the acceptance and actual usage of
systems (Pan, 2020; Schlebusch, 2018). Considering that the use of VR technology in
teacher education programs, particularly in South Africa is still relatively new, under-
standing pre-service teachers’ self-ecacy, anxiety, and attitude towards the use of
such technology is considered very important for its adoption in microteaching prac-
tice and classroom teaching. Hence, the current study aimed to predict pre-service
teachers’ adoption of virtual reality classrooms (VRCs) by modifying the UTAUT2
model to include variables such as self-ecacy, attitude, and anxiety. Figure 1 shows
the modied UTAUT2 model for the context of this study.
Based on the conceptual (modied UTAUT 2 model) applied in this study, there
are 8 hypotheses and 16 sub-hypotheses. Table I shows the overall hypotheses that
are tested using a two-tailed test with a 95% condence level.
4 Research method
This study is based on quantitative research involving using the UTAUT2 survey
developed by Venkatesh et al. (2003). The survey was administered using Google
Forms to third-year pre-service science teachers at a large metropolitan university in
Gauteng province, South Africa where advanced learning technologies are strongly
embraced. The survey involved a specic selection of Eighty-three students who
were enrolled in the teaching methodology and practicum module during their third
Fig. 1 Conceptual model of the study
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year. In addition, 42.2% were male and 57.8% were female. Of the participants,
95.1% were between the ages of 18–25, while 4.9% were between the ages of 26–30.
Biographical information gathered shows that the sampled science students were
from dierent areas of subject specialisation, which include Natural sciences and life
sciences (9.6%), life sciences and physical sciences (34.9%), physical sciences and
mathematics (12.0%), life sciences and mathematics (6.0%), life sciences and ICT
support (2.4%), life sciences and Geography (20.5%), others (14.5%). Two lecturers
who are also leading researchers in the eld of science and technology education
reviewed the adapted UTAUT2 instrument to make sure that every item was suit-
able for use in the actual study. The survey was employed as a baseline assessment
in this study in order to collect data on how to guide and prepare students for the use
of virtual reality before exposing them to the VR classroom experience. The survey
administered consists of 34 statements arranged based on nine constructs (perfor-
mance expectancy, eort expectancy, social inuence, facilitating condition, attitude,
hedonic motivation, anxiety, self-ecacy, and behavioural intention). The statements
were answered by respondents on a ve-point Likert scale ranging from ‘strongly
Hypothesis
H1 Performance Expectancy has a positive and signi-
cant inuence on the intention to use VRC
H1a, b The inuence of Performance Expectancy towards in-
tention to use VRC is moderated by Gender and Age
H2 Eort Expectancy has a positive and signicant inu-
ence on the intention to use VRC
H2a, b The inuence of Eort Expectancy towards intention
to use VRC is moderated by Gender and Age
H3 Social Inuence has a positive and signicant inu-
ence on the intention to use VRC
H3a, b The inuence of Social Inuence towards intention to
use VRC is moderated by Gender and Age
H4 Facilitating conditions have a positive and signicant
inuence on the intention to use VRC
H4a, b The inuence of Facilitating Conditions on intention
to use VRC is moderated by Gender and Age
H5 Hedonic motivation has a positive and signicant
inuence on the intention to use VRC
H5a, b The inuence of Hedonic motivation towards inten-
tion to use VRC is moderated by Gender and Age
H6 Self-ecacy has a positive and signicant inuence
on the intention to use VRC
H6a, b The inuence of self-ecacy towards intention to use
VRC is moderated by Gender and Age
H7 Anxiety has a positive and signicant inuence on
the intention to use VRC
H7a, b The inuence of anxiety towards the intention to use
VRC is moderated by Gender and Age
H8 Attitude has a positive and signicant inuence on
intention to use VRC
H8a, b The inuence of attitude towards intention to use
VRC is moderated by Gender and Age
Table 1 Hypotheses
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disagree’ (1) to ‘strongly agree’ (5). All respondents’ inputs were recorded in an MS
Excel table. Data was analysed using descriptive statistics, correlation analysis and
multiple linear regression analysis using SPSS software.
5 Results and discussions
A Kaiser-Meyer-Olkin (KMO) test of sampling adequacy was performed to measure
whether or not the sampling size was sucient for factor analysis. Analysis shows
that the KMO value was achieved at a value of 0.676 which is between 0 and 1,
indicating that the sample is sucient for factor analysis (Tabanick & Fidell, 2013).
Similarly, Bartlett’s test of sphericity was also conducted to measure the relation-
ship between items. Findings reveal that p < .001 which is below 0.05, indicating
that the sample has enough correlations between variables for factor analysis. Pre-
liminary analysis to test the assumption of multicollinearity was also performed. The
assumption for multicollinearity states that the variance ination factor (VIF) values
above 10 and tolerance value less than 0.10 indicate multicollinearity. However, the
results showed that there were no violations of any of these assumptions because the
VIF value is between 1.0 and 2.2, which is < 10, and the tolerance value is between
0.45 and 0.94, which is > 0.10. To investigate the presence of missing data across
all variables, the Little’s MCAR (missing completely at random) test was used. The
test resulted in a chi-square value of 11.362 with 15 degrees of freedom and a sig-
nicance level of 0.727, which is higher than the P-value of 0.05. This implies that
the pattern of missing data is not completely random (MNAR). However, the value
of missing data across the whole variable was less than 5%; as a result, excluded
pairwise deletion approach was employed to handle the missing values in this study.
Furthermore, the UTAUT2 survey was examined for face validity by a group of
professionals composed of university teacher educators with science and technol-
ogy education backgrounds and construct validity using factor analysis, as shown in
Table 1. The Principal Component Analysis Extraction Method was used to analyse
the factor on 34 items. The objective of the factor analysis was to determine whether
the related items were grouped together under the same construct. The factor load-
ings for each item can be found in the Appendix. Results of the factor analysis show
that only 9 factors were eective enough in representing all the 34 statements that
were extracted from the analysis. According to Hair et al. (2012), the acceptable total
variance explained by all components in factor analysis should be between 70 and
80% variance with a required minimum factor loading of 0.300. The contribution of
each component (initial Eigenvalues percentage of variance) to the total amount of
variance (70.83%) explained by the given principal component analysis is shown
in Table 2. In addition, a reliability analysis was conducted for the constructs using
Cronbach’s Alpha. As summarised in Table 2, each of the dimensions appears to have
a moderate to high degree of reliability since each computed statistic is above 0.50
(Hinton et al., 2014). Thus, indicating that all variables used in the measurement are
reliable.
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5.1 Level of acceptance and intention among pre-service science teachers
towards utilising a virtual reality classroom for science teaching during
microteaching practice
The acceptance and intention to use virtual reality classrooms for science teaching
were categorised into three levels: low, moderate, and high, as proposed by Deris
and Shukor (2019). According to their classication, a mean value ranging from 1.00
to 2.33 indicates a low level, 2.34 to 3.66 indicates a moderate level, and 3.67 to
5.00 signies a high level of acceptance and usage (Deris & Shukor, 2019). Table 2
above also presents the specic levels of acceptance and intention for each construct
related to virtual reality classrooms for science teaching, as well as the overall level
of acceptance and intention. According to the data presented in Table 2, the average
values for various factors related to the acceptance and intention to use virtual reality
classrooms for teaching science were between 3.47 and 4.59. These values indicate
a high level of acceptance and intention among pre-service science teachers. The
only exception was anxiety and facilitating conditions, which had an average value
of 3.47 and 3.62 respectively, suggesting a moderate level of acceptance and inten-
tion. Overall, the average value for all the factors combined was 4.08, indicating a
high level of acceptance and intention to adopt virtual reality classrooms for teaching
science in the future. Findings from this study showed that pre-service teachers rated
hedonic motivation towards the use of VR classrooms highest and anxiety towards
the use of VR classrooms lowest, which is similar to other technology acceptance
studies (Bower et al., 2020). Nevertheless, results indicate that the sampled pre-ser-
vice science teachers showed a high acceptance and intention to use virtual reality
classrooms for science teaching. This is evident from the high average score of 4.35.
The high willingness and intention demonstrated by sampled pre-service teachers
might be attributed to their awareness and understanding of the signicant empha-
sis placed by the South African government on prioritising technologies that can
enhance teaching and learning in the fourth industrial revolution (4IR). Similarly,
Table 2 Mean, standard deviations, validity, and reliability
Dimension Num-
ber of
items
Factor Range Initial
Eigenvalue
Percentage
of variance
Cron-
bach
Alpha
Mean Std
Deviation
Level
Performance
Expectancy
40.455 − 0.747 3.532 0.576 4.22 0.586 High
Eort Expectancy 40.534 − 0.707 4.158 0.761 3.89 0.609 High
Social Inuence 40.627 − 0.700 5.856 0.805 3.98 0.677 High
Facilitating Condition 4 0.494 − 0.754 7.370 0.643 3.62 0.687 Moderate
Hedonic Motivation 30.764 − 0.871 9.835 0.882 4.54 0.517 High
Self-Ecacy 30.556 − 0.815 3.173 0.826 4.17 0.691 High
Anxiety 30.552 − 0.878 5.115 0.664 3.47 0.803 Moderate
Attitude towards using
VR
40.527 − 0.800 3.121 0.794 4.36 0.684 High
Behavioural Intention 5 0.608 − 0.790 28.668 0.740 4.35 0.539 High
Overall 4.08 0.643 High
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Education and Information Technologies (2024) 29:20299–20316
higher education institutions across the country are continually incorporating tech-
nology into teacher training, helping teachers stay up-to-date with the advances in
technology that are changing teaching and learning practices and the world of work.
This encourages teachers to make the most of these technologies for eective learn-
ing. The ndings regarding the positive and high willingness of pre-service teachers
to integrate VR classrooms in their future educational practice align with the ndings
of similar research studies (Cooper et al., 2019).
5.2 Inuence of UTAUT2 constructs on the acceptance and behavioural
intentions of pre-service science teachers to use virtual reality (VR) classroom for
science teaching
Firstly, a Pearson correlation coecient was calculated to ascertain signicant rela-
tionships among the examined variables. According to Pallant (2016), the Pearson
correlation coecient value can indicate a small/weak relationship (r = .10 to 0.29), a
medium/moderate relationship (r = .30 to 0.49) or a large/strong relationship (r = .50 to
1.0). Findings show that pre-service teachers’ behavioural intention towards adopting
and using virtual reality classrooms for science teaching was directly related to their
performance expectancy, eort expectancy, social inuence, facilitating condition,
hedonic motivation, self-ecacy, anxiety and attitude with values between (0.30–
1.00), all of which have statistical signicance as shown on Table 3. Nevertheless,
the results showed that the factor related to the participant’s perceptions of the social
inuence has the strongest relationship (r = .611, p < .01) with teachers’ behavioural
intention toward adopting and using virtual reality classrooms for science education.
In addition, the results showed that the factor related to the participants’ anxiety has
no relationship (r = .136, p = .225) with their behavioural intention towards using the
virtual reality classroom for science. However, no signicant relationships can be
found between gender and age with respect to their hypothesised relationships with
Performance Expectancy, Eort Expectancy, Attitude, Social Inuence, Facilitating
Condition, Hedonic Motivation, Self-Ecacy, and Anxiety.
A multiple linear regression test was used to determine the variable eect of
UTAUT2 constructs: performance expectancy, eort expectancy, social inuence,
facilitating condition, hedonic motivation, self-ecacy, anxiety, attitude, gender and
age on pre-service teachers’ acceptance and behavioural intention to use virtual real-
ity classroom for their microteaching practice and future classroom teaching. The
signicance of the model was examined using the analysis of variance (ANOVA).
Results of the ANOVA show that the total F value (8.027) is statistically signi-
cant at p-value < .001b. Thus, indicating that there is a statistically signicant linear
relationship in the regression model. Further analysis reveals that the coecient of
determination in the model summary is obtained at 0.538. This implies that 53.8%
of the variance in pre-service teachers’ intentions will be explained by the variation
of the UTAUT2 constructs, while the remaining 46.2% will be explained by factors
other than the independent variables not contained in the regression model as shown
in Table 4.
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Education and Information Technologies (2024) 29:20299–20316
6 SI, ANX, EE, SE, HM, PE, FC, ATT
Findings from Table 4 also show the results of the calculated F value of 8.027 with
a signicant F less than 0.001 which is less than a p-value of 0.05 (5%), thus stat-
ing that all independent variables simultaneously aect pre-service teachers’ behav-
ioural intention. Further analysis show that social inuence explains about 37.3%
of the variance in behavioural intention, attitude accounts for 32.4%, self-ecacy
Table 3 Correlation analysis for the various constructs
Con-
structs
1 2 3 4 5 6 7 8 9 10 11
1.
Behav-
ioural
Intention
1 1
2.
Perfor-
mance
Expec-
tancy
0.502**
3. Eort
Expec-
tancy
0.451** 0.463** 1
4. Social
Inuence
0.611** 0.469** 0.436** 1
5. Facili-
tating
Condi-
tion
0.396** 0.235*0.476** 0.491** 1
6.
Hedonic
Motiva-
tion
0.411** 0.508** 0.357** 0.363** 0.264*1
7. Self-
Ecacy
0.520** 0.524** 0.387** 0.451** 0.445** 0.348** 1
8.
Anxiety
0.136 0.159 0.063 0.384** 0.388** 0.067 0.304** 1
9.
Attitude
0.569** 0.597** 0.412** 0.548** 0.318** 0.634** 0.432** 0.127 1
10.
Gender
0.157 0.159 − 0.048 0.036 − 0.032 0.024 − 0.043 − 0.006 0.086 1
11. Age − 0.120 − 0.079 − 0.138 − 0.107 − 0.099 − 0.042 − 0.229*0.090 − 0.045 0.081 1
**. Correlation is signicant at the 0.01 level (2-tailed).; *. Correlation is signicant at the 0.05 level
(2-tailed)
Table 4 Model summary
Model R R2Adjusted
R2
Std. Error
of the
Estimate
Change Statistics
R2
Change
F Change df1 df2 Sig. F
Change
1 .733a0.538 0.471 0.392 0.538 8.027 10 69 < 0.001
a Dependent Variable: BI; b Predictors: (Constant), How old are you? What is your Gender?
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Education and Information Technologies (2024) 29:20299–20316
explains around 27.0%, performance expectancy accounts for 25.2%, eort expec-
tancy explains around 20.3%, hedonic motivation accounts for 16.9%, and facili-
tating conditions explains about 15.7% of the variance in behavioural intention as
shown in Fig. 2.
In addition, the ndings of the multiple linear regression analysis indicate that the
social inuence variable (SI) generated a t-value of 2.884, with a signicance value
of 0.005. Similarly, the self-ecacy variable (SE) produced a t-value of 2.058, with a
signicance value of 0.043. Since both variables have signicance values of p < .05,
this suggests that both variables positively and signicantly inuence the intention
of pre-service teachers to use virtual reality classrooms for science teaching, as pre-
sented in Table 5.
Furthermore, Table 5 shows the signicant factors that inuence pre-service
teachers’ behavioural intention to use VRC for their microteaching and future class-
room practice. The table also provides information on the feasibility of estimating the
model, as well as an explanation of the independent variables used.
A separate hierarchical linear regression was used to determine if age and gender
were moderating relations between preservice teachers’ behavioural intention to use
VRC and the various UTUAT2 constructs. Based on the stated hypotheses in Table 1,
the null hypothesis is rejected if the calculated p-value in Table 5 exceeds 0.05, and
the null hypothesis is not rejected if the p-value in Table 5 is within the 0.05 range.
Based on the correlation and regression analysis, the result of the hypotheses test-
ing shows that only social inuence (β = 0.341; p < .05) and self-ecacy (β = 0.217;
p < .05) had a positive and signicant inuence on preservice teachers behavioural
intention to accept and use Virtual reality classroom for their microteaching practice
and future classroom teaching, supporting H3 and H6. The result of the hypothesis
testing also demonstrate that performance expectancy (β = 0.069; p = .540), eort
Fig. 2 Percentage of variance explained in behavioural intention by each UTUAT2 variable
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Education and Information Technologies (2024) 29:20299–20316
expectancy (β = 0.106; p = .325), facilitating conditions (β = 0.067; p = .547), hedonic
motivation (β = 0.057; p = .605), and attitude (β = 0.137; p = .266) had a positive but
insignicant inuence on preservice teachers behavioural intention to accept and
use VR classroom for their microteaching practice and future classroom teaching,
hence H1, H2, H4, H5, and H7 were not supported. In terms of the moderating eect,
results show that age and gender did not exhibit signicant (p > .05) interactions with
any of the constructs when considering all possible higher-order interactions. Hence,
hypotheses H1a, H1b, H2a, H2b, H3a, H3b, H4a, H4b, H5a, H5b, H6a, H6b, H7a,
and H7b in Table 1 were not supported. The equation of the multiple linear regression
model that was generated is as follows:
Behavioural Intention = 1.121 + 0.271 Social Inuence + 0.169 Self Ecacy + e.
The regression equation model shows that the variables: social inuence (X1)
and self-ecacy (X2) are positive. A summary of the output analysis is illustrated in
Fig. 3. However, it should be noted that non-signicant variables are not shown in
the gure.
The multiple linear regression equation suggests that pre-service teachers are con-
dent in their ability to use virtual reality classrooms and that there is a positive
relationship between their perception of social inuence and their intention to use VR
technology in their future educational practices. Hence, we can conclude that if their
perception of the social inuence variable decreases (not maintained) and the self-
ecacy variable is also low, then pre-service teachers’ acceptance and intentions in
using VR classrooms will tend to be lower. The results suggest that pre-service teach-
ers’ intentions to adopt the VR classroom is not only dependent on the level of con-
dence they possess in their ability to eectively utilise technology but also on their
personal perception of the opinions held by individuals within their environment or
social pressure exerted by various sources such as leadership gures, students, teach-
Model Unstan-
dardized
Coecients
Standardised
Coecients
T value P
value
B Std.
Error
Beta
Constant 1.121 0.496 2.261 0.027
Performance
Expectancy
0.063 0.102 0.069 0.616 0.540
Eort
Expectancy
0.093 0.094 0.106 0.990 0.325
Attitude 0.108 0.096 0.137 1.121 0.266
Social
Inuence
0.271 0.094 0.341 2.884 0.005
Facilitating
Condition
0.052 0.087 0.067 0.605 0.547
Hedonic
Motivation
0.060 0.115 0.057 0.519 0.605
Self – Ecacy 0.169 0.082 0.217 2.058 0.043
Anxiety − 0.089 0.067 − 0.132 -1.320 0.191
Gender 0.114 0.091 0.105 1.248 0.216
Age 0.002 0.023 0.009 0.106 0.916
Table 5 Multiple Linear Regres-
sion Analysis
a Dependent Variable: BI
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Education and Information Technologies (2024) 29:20299–20316
ing sta and other factors that motivates them to use the virtual reality classroom
(Wang & Wang, 2009). Therefore, in order to ensure the practicality and long-term
viability of incorporating virtual reality classrooms into the teaching methods of pre-
service teachers, it may be necessary for institutions to develop clear and compre-
hensive policies and guidelines that outline the purpose, scope, and acceptable use of
VR. Communicating these policies to all stakeholders, including pre-service teach-
ers, faculty, and administrators, can make the use of VR classrooms a feasible option
for pre-service teachers and have a signicant impact on their decision to integrate
VR into their teaching practices. If expectations regarding the use of VR technologies
in institutions are clearly dened, it can create a sense of normalcy around the use of
VR classrooms and can encourage continued implementation.
6.1 Limitations
The limitations of this study include the fact that the VR classroom used was devel-
oped within a specic model-based learning approach at a particular public South
African university. However, the researchers believe that the ndings can still be
valuable for other private and public South African universities looking to incorpo-
rate VR into science teacher preparation. Furthermore, it is important to note that
the UTAUT2 model does not take into consideration the potential impact of cul-
tural dierences on the adoption of virtual reality in education. Therefore, future
research could explore the adaptation of the UTAUT2 model to qualitatively explore
how cultural factors such as access to technology and diverse linguistic, cultural, and
socioeconomic factors inuence the acceptance, relevance and applicability of VR in
education in South Africa. Another limitation of this study is the absence of assess-
ment on pre-service teachers’ previous experience with virtual reality (VR) since the
purpose of the survey was to establish a baseline on their intention to incorporate VR
into their microteaching experience. As a result, it is assumed that the pre-service
teachers have not experienced the VR application, even though it is possible that they
are aware of its use in education. Since this study is part of a larger research project
exploring the use of VR classrooms, further investigation is needed to determine if
pre-service teachers’ intentions to integrate VR into their future classrooms actually
changed after the opportunity to experience the use of VR during their microteaching
Fig. 3 Results of the output analysis
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Education and Information Technologies (2024) 29:20299–20316
practice. Nevertheless, there is potential for teacher education programs and school
systems to take advantage of the interactivity and immersive experience provided by
VR technology, as it can help address any anxiety or concerns pre-service teachers
may have about using virtual reality technology, allowing them to feel comfortable
and condent in its use, as well as develop a positive attitude towards using virtual
reality technology in their future classrooms, ultimately improving their behavioural
intention towards its use and adoption.
7 Conclusion
This study contributes to the existing body of knowledge on how pre-service teachers’
willingness to utilise virtual reality (VR) classrooms for science instruction is inuenced
by various essential factors related to the acceptance and the use of technology. Accord-
ing to the ndings of this study, pre-service teachers demonstrated a high level of inten-
tion towards utilising virtual reality classrooms for their microteaching practice and in
their future careers. However, their intentions were found to be mostly inuenced by
their perceived social pressure and self-ecacy towards the use of VR technology. This
implies that the opinions and suggestions of important and prominent people can serve as
a driving force for pre-service teachers’ adoption and use of virtual reality classrooms for
their micro-teaching practice. This result is in line with previous studies that have shown
how technology users are greatly inuenced by the opinions of others within their social
circle (Venkatesh et al., 2012; Al Breiki et al., 2023). Additionally, the research ndings
showed a direct eect of technology self-ecacy on behavioural intention, indicating
that pre-service teachers’ acceptance to use and adopt VR classrooms for microteach-
ing and in their future classrooms is inuenced by their condence in their ability to use
technological tools.
Unlike the ndings of Venkatesh et al. (2012), the results of this study suggest that
pre-service teachers’ intentions to use virtual reality classrooms were not signicantly
related to their perceptions of expected outcomes, perceived eort, hedonic motivation,
attitude, anxiety, or facilitating conditions. This suggests that the UTUAT2 model may
not fully explain pre-service teachers’ willingness to adopt and use VR classrooms for
their microteaching and future classroom practice. However, the study still found a direct
association between pre-service teachers’ behavioural intention and the UTAUT2 vari-
ables, except for anxiety, gender and age. This implies that these factors are still relevant
and impactful in understanding pre-service teachers’ willingness to adopt and use virtual
reality classrooms, even if they are not direct predictors of behavioural intention. To eec-
tively prepare pre-service teachers for the adoption and use of virtual reality classrooms,
teacher education programs need to prioritize enhancing their perceptions of expected
outcomes, perceived eort, hedonic motivation, attitude, and facilitating conditions dur-
ing the planning stage of implementing the VR technology. The results of this study
indicate that in order to increase the use of virtual reality classrooms among pre-service
teachers, higher education institutions need to create training programs that prioritize
improving social inuence. This can be achieved by including activities such as peer
learning, collaboration, and mentorship programs, where pre-service teachers can learn
from their peers or experienced educators who have a positive impact on their percep-
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Education and Information Technologies (2024) 29:20299–20316
tion of using VR. In addition, it is important to design immersive and interactive experi-
ences for pre-service teachers to engage with VR technology, as this can greatly enhance
their self-condence and self-ecacy through hands-on engagement with the technology.
Alleviating pre-service teachers’ anxiety before exposing them to the VR classroom is
also crucial in optimizing their experience. According to McGarr (2021), virtual real-
ity environments provide pre-service teachers with unique opportunities to experience
examples of classroom life in a controlled manner, which thereby enhances their class-
room behaviours and management skills. Hence, promoting the benets and aordances
of using VR classrooms more than traditional teaching methods can help improve pre-
service teachers’ attitudes towards its adoption and use. Furthermore, providing organi-
zational and technical infrastructure that can make the use of VR tools visible in schools
can also help pre-service teachers develop a better attitude towards its adoption and use.
Supplementary Information The online version contains supplementary material available at https://doi.
org/10.1007/s10639-024-12664-5.
Acknowledgements The authors express their gratitude for the support provided by the Unity social
impact and Meta immersive learning joint fund in facilitating the broader study, of which this research
formed an integral part.
Funding Open access funding provided by University of Johannesburg. The Unity social impact and Meta
immersive learning joint fund provided support for a broader study, of which this research was a part.
While this particular study served as an initial evaluation and did not directly incorporate any materials
or resources from the larger project, the overall funding had a positive impact on the entire endeavour.
Open access funding provided by University of Johannesburg.
Data availability The authors collected the data that support the ndings of this study. Although the data
are not publicly available due to ethical restrictions, they can be obtained from any of the authors upon
reasonable request.
Declarations
Conict of interest No potential nancial or professional conicts of interest were reported by the
author(s).
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use
is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission
directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/
licenses/by/4.0/.
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Authors and Aliations
Ayodele AbosedeOgegbo1· MaforPenn1· UmeshRamnarain1·
OniccahPila1· ChristoVanDer Westhuizen1· NoluthandoMdlalose1·
IvanMoser2· MartinHlosta2· PerBergamin2
Ayodele Abosede Ogegbo
ayo3108@yahoo.com
Ivan Moser
ivan.moser@hs.ch
1 Department of Science and Technology Education, University of Johannesburg,
Johannesburg, (Gauteng), South Africa
2 Swiss Distance University of Applied Sciences (Fernfachhochschule Schweiz, FFHS), Brig
(Valais), Switzerland
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1.
2.
3.
4.
5.
6.
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