Evaluating pre-service teachers' acceptance of mobile devices with regards to their age and gender: A case study in Greece

Abstract

The unique characteristics of smart mobile devices have the great potential to enrich the teaching and learning experience. A number of studies have indicated that the successful pedagogical use of technology depends on teachers' attitudes and acceptance towards technology. The purpose of the present paper attempts to examine teachers' background variables, such as age and gender, with respect to whether and to what extent they influence the use of mobile devices in class. The Technology Acceptance Model (TAM) was used as the core framework for analysis while additional constructs were added in order to find and understand teacher acceptance of smart mobile devices better. Data were collected from 125 pre-service teachers who were studying in a one-year programme of pedagogical training in Greece. Results revealed that pre-service teachers have positive opinions about mobile devices. There is no gender and age difference regarding the purpose for using smart mobile devices. Implications of the findings and limitations along with future research directions are also discussed.

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I
nt. J. Mobile Learning and Organisation, Vol. X, No. Y, XXXX
Copyright © 200X Inderscience Enterprises Ltd.
Evaluating pre-service teachers’ acceptance
of mobile devices with regards to their age and
gender: a case study in Greece
Stamatios Papadakis
Department of Preschool Education,
Faculty of Education,
University of Crete,
Crete, Greece
Email: stpapadakis@gmail.com
Abstract: The unique characteristics of smart mobile devices have the great
potential to enrich the teaching and learning experience. A number of studies
have indicated that the successful pedagogical use of technology depends on
teachers’ attitudes and acceptance towards technology. The purpose of the
present paper attempts to examine teachers’ background variables, such as age
and gender, with respect to whether and to what extent they influence the use
of mobile devices in class. The Technology Acceptance Model (TAM) was
used as the core framework for analysis while additional constructs were added
in order to find and understand teacher acceptance of smart mobile devices
better. Data were collected from 125 pre-service teachers who were studying in
a one-year programme of pedagogical training in Greece. Results revealed that
pre-service teachers have positive opinions about mobile devices. There is no
gender and age difference regarding the purpose for using smart mobile
devices. Implications of the findings and limitations along with future research
directions are also discussed.
Keywords: TAM; technology acceptance model; mobile devices acceptance;
gender; age; pre-service teachers.
Reference to this paper should be made as follows: Papadakis, S. (20XX)
‘Evaluating pre-service teachers’ acceptance of mobile devices with regards to
their age and gender: a case study in Greece’, Int. J. Mobile Learning and
Organisation, Vol. X, No. Y, pp.xxx–xxx.
Biographical notes: Stamatios Papadakis is a graduate of the Economics and
Business (AUEB) University, Athens, Greece, Department of Information. He
received his MSc in Education from the University of the Aegean, Greece, and
PhD from the University of Crete, School of Education. He has been working
for a series of years as an ICT teacher in public sector secondary education. He
has published many articles in journals and has presented several papers in
conferences. His research interests include ICT in education, mobile learning,
novice programming environments and teaching of programming in primary
and secondary education.

S. Papadakis
1 Introduction
The recent increase in the use of Information and Communication Technologies (ICT)
and the increased reliance of today’s society on information and knowledge have
produced new challenges for educational institutions (Papadakis, 2016; Drossel et al.,
2017). Technologies are always seemed by most education providers as catalysts which
can revamp the process of teaching and learning (Ismail et al., 2016; Papadakis et al.,
2016).
The past five years have witnessed the unprecedented growth of the smart mobile
devices and an ensuing transformation in the educational landscape (Hwang and Wu,
2014). A growing body of evidence suggests that smart mobile devices – especially
tablets – are being used by learners and educators around the world to access
information, streamline administration and facilitate learning in new and innovative ways
(Papadakis and Kalogiannakis, 2017a, 2017b, 2017c). While formal education has
historically been confined to the four walls of classrooms, mobile devices can move
learning to settings that maximise understanding (Kraut, 2013). UNESCO believes that
mobile technologies can expand and enrich educational opportunities for learners in
diverse settings. Increasingly, K-12 schools are recognising mobile devices as important
learning tools with a vast range of classroom applications (O’Bannon and Thomas,
2014).
As other educational technologies, the success or failure of mobile learning
implementation will also depend on human factors (Ismail et al., 2016). Teachers as
individual innovation adopters are believed to play a crucial role in this innovation
change process for the adoption of mobile technologies (O’Bannon and Thomas, 2015;
Wang, 2016). Teachers’ background variables, such as age and gender, have been
discussed in research literature with respect to whether and to what extent they influence
the use of ICT in class.
Based on the Technology Acceptance Model (TAM), this study was to investigate the
determinants of mobile devices acceptance by pre-service teachers and to discover if
there exist either age or gender differences in the acceptance of mobile devices, or both.
The article is structured as follows. First, we provide a review of mobile learning and
the TAM. Then, we propose the hypotheses and the methodology of the current research.
Thereafter, the results of the collected data and the evaluation of the hypotheses are
reported. Finally, the results and implications are discussed, and limitations along with
future lines of investigation are identified.
2 Theoretical background
2.1 Mobile learning acceptance and individual personal factors
The use of digital media in schools is, among other things, associated with the goal of
supporting learning processes and improving the quality of education (Drossel et al.,
2017). Mobile devices form the foundation of ICT that is currently reshaping and
revolutionising global communications (Al-Hunaiyyan et al., 2017; Chang et al., 2018;
Papadakis et al., 2017, 2018). Mobile technology is one of the technology advances that

Evaluating pre-
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are considered to be one of a new paradigm of quality education nowadays (Ismail et al.,
2016). As mobile technology is widely disseminating, many researchers and educators
have addressed the possibility of using mobile technology as a learning tool
(Kalogiannakis and Papadakis, 2017a, 2017b; Yoo and Kim, 2017).
Mobile learning has been one of the reflections of the widespread adoption of
wireless technology and Internet access as required from people and/or society (Ozan
et al., 2015). It is a rising art of using mobile or wireless devices to enhance the learning
experience while on the move (Ismail et al., 2016). According to Webopedia, mobile
learning or m-learning is education via the Internet or network using personal mobile
devices, such as tablets and smartphones to obtain learning materials through mobile
apps, social interactions and online educational hubs. The phrase mobile learning is most
often used to describe the technology – the mobile devices and apps used in the
classroom, however, it may also be used to describe the support of always-on learning
with mobile technology (Webopedia, 2018). Figure 1 shows the six categories of mobile
learning.
Figure 1 Categories of mobile learning (adapted from Traxler and Kukulska-Hulme, 2005)
Mobile devices facilitate learning by blurring boundaries between formal and informal
education (Kraut, 2013). A mobile device is ‘any device that is small, autonomous, and
unobtrusive enough to accompany us in every moment of our everyday life, and that can
be used for some form of learning such as, iPad, tablets, smart phone,…’. Consequently,
mobile learning refers to the use of mobile or wireless devices for the purpose of learning
while on the move (Mai, 2014), either alone or in combination with other ICT, to enable

S. Papadakis
learning anytime and anywhere. Learning can unfold in a variety of ways: people can use
mobile devices to access educational resources, connect with others, or create content,
both inside and outside classrooms (Wang et al., 2009; Kraut, 2013).
The advantages in using mobile devices in student learning are quite enormous
(Kalogiannakis and Papadakis, 2017c, 2017d). The biggest advantages of using mobile
devices are to provide student-oriented teaching and learning contexts where the learning
of the students generally depends on their active involvement, and where teachers are
generally seen as a facilitator (O’Bannon and Thomas, 2015; Khan et al., 2016). One of
the push effects that make this technology a potential for teaching–learning purposes is
the increasing usage of mobile devices among the younger generation (Zaranis et al.,
2013; Ismail et al., 2016). Many projects have demonstrated that mobile technologies can
streamline assessments and provide learners and teachers more immediate indicators
of progress. Mobile technologies can also make educators more efficient by automating
the distribution, collection, evaluation and documentation of assessments (Kraut, 2013)
(see Figure 2).
Figure 2 Aspects of mobile learning
Teachers play a key role in the implementation of new technologies in the classroom
(Drossel et al., 2017). Teachers’ perceptions about the impact of mobile technology in
learning reflect their beliefs about how this technology influences learning processes
(Domingo and Gargante, 2016; Drossel et al., 2017). Traditional barriers to technology
integration (such as educational level and experience, school teacher’s gender and age,
their experience with technology in educational settings, and their views and attitudes
toward computing technology and its use, can influence the integration of technology
into the classroom environment) hinder the integration of mobile devices into the
classroom (Teo et al., 2015). These barriers can also prevent teachers from developing
the knowledge, pedagogy and self-efficacy necessary to move past ‘low levels’
of technology integration and enable teachers to take full advantage of the instructional
benefits that technologies provide (Ertmer and Orrenbreit-Leftwich, 2010; O’Bannon and
Thomas, 2015).

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Teachers’ demographic factors, such as gender and age, have been discussed in
research literature with respect to whether and to what extent they influence the use of
ICT in class (Teo et al., 2015; Hsu et al., 2017). Drossel, Eickelmann and Gerick (2017)
report that while age does not seem to play a significant role in the early days of ICT
implementation, recent studies have shown that older teachers have a less distinct
tendency to use ICT in class. Additionally, researchers proposed that acceptance and use
of technology is different for teachers according to their age (O’Bannon and Thomas,
2014).
The issue of gender differences in the use of technology in the classroom has
emerged in a number of research investigations. Research about teacher gender and
technology use has alluded to the fact that female teachers tend to have less use of
computers and/or technology in their pedagogy than their male counterparts, because of
their more limited access to technology, their interest level and their skill (Teo et al.,
2015). On the other hand, other studies were not able to detect gender differences in the
teachers’ use of ICT in class (Drossel et al., 2017).
2.2 The technology acceptance model
From the decade of 80, the focus is on creating models that could help to predict the use
of system and the acceptance of technology (Revythi and Tselios, 2017). Thus, there are
various theoretical models used in technology acceptance research [TAM, the Unified
Theory of Acceptance and Use of Technology (UTAUT), modified UTAUT and the
Theory of Planned Behaviour (TPB)]. A popular candidate among these is the TAM
(Davis, 1985). TAM was originally developed to predict IT acceptance and usage on the
job, and has been extensively applied to various types of technologies and users (Wang et
al., 2009). TAM aims to explain the acceptance of technology by users by grounding
itself on the perception of users (Davis, 1989). This model tries to predict behaviours
within specific situations and uses Fishbein and Ajzen’s ‘Theory of Reasoned Action’
(1975) as a baseline. According to Fishbein and Ajzen, the attitude of the individual
affects his/her intention to carry out a specific action and the determinants of how he/she
acts (Gokcearslan, 2017).
TAM postulates that the Behavioural Intention (BI) to use a technology depends on
the potential user’s attitude towards the technology, which in turns depends on the
perceived usefulness and perceived ease of use. Attitude (ATT) refers to an individual’s
personal affection towards the technology. According to the TAM model, Perceived Ease
of Use (PEU) has a causal effect on Perceived Usefulness (PU). PU and PEU affect the
intention to use a given tool, and this intention affects usage behaviour. PU is defined as
‘the degree to which an individual believes that using a particular system would enhance
his or her job performance.’ PEU is defined as ‘the degree to which an individual
believes that using a particular system would be free of physical and mental effort’
(Davis, 1985, p.26). Figure 3 shows a diagram of the TAM.
External variables have also been cited as indirect factors affecting behavioural
intention. Common ones include Computer Self-efficacy (EFF), Subjective Norms (SN)
and Facilitating Conditions (FC). Computer self-efficacy is the person’s perception of
how well he/she can handle the difficulties in using and learning about the technology.
This is not the same as perceived ease of use: A person may find an aspect of technology
difficult to use, but may still have the confidence to tackle the difficulty. Subjective norm
is measured by how strongly a person thinks that others want him/her to use educational

S. Papadakis
technology. Facilitating conditions refer to the perception of availability of resources,
knowledge and technical support that could assist or facilitate the use of the technology
(Gokcearslan, 2017) (see Figure 4).
Figure 3 Technology acceptance model (adapted from Davis et al., 1989)
Figure 4 Technology acceptance model with external variables (Davis, 1989)
The TAM model has been adapted for studies on many different forms of technology
and on the acceptance of learning systems using technology (Gokcearslan, 2017). The
implementation of the TAM is implemented by using a questionnaire, whose answers are
in Likert scale and starting from ‘strongly disagree’ to ‘strongly agree’.
2.3 Research model and hypotheses
Considering that teachers are the gatekeepers to technology integration in the classroom,
pre-service teachers will play an important role in the success or failure in the integration
of mobile technology in education. This motivated the researcher of this article to
conduct this study to investigate pre-service teachers’ perceptions of the use of mobile
devices in the classroom. Additionally, the researcher examined pre-service teachers’

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gender and age differences, as two factors that affect the implementation of m-learning in
Greek education system. The study tries to answer the following questions:
 What are pre-service teachers’ perceptions of the use of mobile devices in the
classroom?
 Is there a correlation between pre-service teachers’ gender and acceptance of mobile
learning?
 Is there a correlation between teachers’ age and acceptance of mobile learning?
3 Methodology
This study was conducted as a survey research study. Quantitative data collection tools
were used for data collection. The students were informed that they would be asked to
complete an optional questionnaire about their thoughts of the use of smart mobile
devices for learning.
4 Instrument
To measure the technology acceptance level towards smart mobile devices, TAM was
modified and applied for this current study. A questionnaire containing items for each of
the four TAM constructs was used. Prior TAM research has demonstrated that perceived
ease of use and perceived usefulness are critical factors in predicting the acceptance
and use of new technologies (Yeou, 2016). In addition to TAM questions, additional
items were used to gather demographic data as well as pre-service teachers’ gender and
age. The researcher adopted measurements validated by prior research studies, with
wordings revised for the targeted respondents and technology context. Specifically, the
questionnaire was designed to include two items for intention to use (ITU1-2), five items
for perceived usefulness (PU1-5), five items for perceived ease of use (PEOU1-5) and
three items for attitudes (ATT1-2). The items used were based on the scales from Davis
(1993). Additionally, the questionnaire included six items for computer (CSE1-6) and
smart mobile devices self-efficacy (SSE1-6). Computer self-efficacy was measured using
items from Compeau and Higgins (1995) and adapted for smart mobile devices self-
efficacy as well.
All items relating to the six constructs were measured on a 5-point Likert scale
ranging from 0 strongly disagree to 4 strongly agree. Cronbach’s alpha, the measure of
reliability, was calculated for the scales and subscales for items measured. The overall
scale had an alpha of 0.849. All scales and subscales were greater than 0.7, which is
considered ‘acceptable’ for exploratory research.
Content validity was established for the survey by using two experts in the field of
educational technology who reviewed the survey individually and marked information
they felt was unclear or inappropriate. A pilot study was conducted two weeks prior to
the study to ensure that the questions are clear and straightforward, and to validate the
initial results. Some improvements were made to the questionnaire such as rephrasing
as suggested by the experts and students (pre-service teachers) to communicate the
questions better.

S. Papadakis
All multiple-choice responses were coded, and % agree was calculated from
combining the ‘strongly agree’ and ‘agree’ responses. The data were analysed using
IBM SPSS Statistics for Windows, Version 21.0 (IBM Corp. Released 2013. IBM SPSS
Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.). In all cases, a significance
level of 0.05 was adopted.
4.1 Sample characteristics
Although all the 160 questionnaires were returned, 35 of those were partially completed.
The data analysis was therefore based on 125 completed questionnaires. The sample were
students (pre-service teachers) from the School of Pedagogical and Technological
Education or ASPETE. In response to the increasing demand for expanding its activities,
ASPETE has operated a number of geographically dispersed branches offering a one-
year duration programme of pedagogical training and further training or specialisation,
leading to the award of a ‘Certificate of Pedagogical and Teaching Competence’. The
sample was in the branch of Heraklion, Crete.
The anonymous questionnaires were handed out in all the classes, two months and a
half into the program, and were completed voluntarily. Subjects were asked to return the
completed questionnaires to their class representatives within two weeks. Data were
collected for two weeks from February 2017 to March 2017. Class representatives
collected the questionnaires and returned them to the researcher. Prior to the study, all
participants were given assurances on the confidentiality and anonymity of the data and
its representations. The author confirms that the relevant ethics, approvals have been
obtained.
4.2 Data analysis
The male students (n = 43) form ~34% of the sample while the female students (n = 82)
form ~66% of the sample. In this study, 100% of pre-service teachers owned
smartphones. The majority of the pre-service teachers also reported that they were
experienced users of technology. Using a 5-point scale (1 = novice; 5 = expert), the pre-
service teachers were asked to rate their expertise with technology (M = 4.32,
SD = 0.485). Pre-service teachers are categorised by gender, by age (three age groups):
‘20–30’, ‘31–40’ and ‘more than 40 years’ and by study levels (see Table 1). These
descriptive statistics and demographic characteristics demonstrate an un-biased data
collection process, which adds to the sanctity of the findings of this study. Table 1 gives
descriptive statistics for each of the constructs in the TAM model.
Table 1 Descriptive statistics of the constructs
Construct Mean Standard deviation Skewness Kurtosis
Behavioural intention of use (BI) 4.08 0.797 –1.069 1.506
Perceived usefulness (PU) 3.93 0.601 –0.504 1.188
Perceived ease of use (PEU) 3.85 0.799 –0.314 –0.531
Attitude (ATT) 4.09 0.641 –1.10 4.039
Computer self-efficacy (CSE) 4.14 0.689 –0.363 –0.802
Smart mobile devices self-efficacy
(SSE) 4.02 0.739 –0.302 –0.562

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The means ranged from 3.85 to 4.09, indicating that teachers experienced all four
categorical concerns of the TAM model over harnessing the mobile technology in their
teaching practices. Additionally, the means of the constructs regarding the two external
factors (4.14 and 4.02) reveal that pre-service teachers have a good self-efficacy in both
computer and smart mobile devices usage. The descriptive statistics of the measurement
items were presented with respect to mean values and standard deviations. As shown in
Table 2, all the items showed generally positive perceptions towards acceptance of the
mobile devices, with all mean scores over 50% of the total score.
Table 2 Descriptive statistics and scales’ reliability of the TAM constructs
Construct Minimum Maximum Mean Standard deviation Alpha
Behavioural intention of use (BI) 0.8548
BI-1 1 5 4.19 0.904
BI-2 1 5 3.97 0.822
Perceived usefulness (PU) 0.8670
PU1 1 5 3.91 0.773
PU2 1 5 3.81 0.849
PU3 1 5 4.25 0.715
PU4 1 5 3.80 0.751
PU5 1 5 3.93 0.774
Perceived ease of use (PEU) 0.8454
PEU1 1 5 4.00 0.907
PEU2 1 5 3.91 0.933
PEU3 1 5 3.83 0.849
PEU4 1 5 3.64 1.027
PEU5 1 5 3.88 0.885
Attitude (ATT) 0.8253
ATT1 1 5 4.18 0.723
ATT2 1 5 4.13 0.729
ATT3 1 5 3.96 0.856
Computer Self-efficacy (CSE) 0.8195
CSE1 1 5 4.22 0.717
CSE2 1 5 4.41 0.752
CSE3 1 5 4.30 0.825
CSE4 1 5 3.64 1.081
Smart mobile devices
Self-efficacy (SSE) 0.8214
SSE1 1 5 3.94 0.918
SSE2 1 5 4.27 0.797
SSE3 1 5 4.18 0.856
SSE4 1 5 3.68 1.075

S. Papadakis
In an attempt to reveal possible differences in student–teachers’ perceptions across
their gender, the researcher used t-test for independent samples in the TAM Model.
The results show that female student–teachers’ perceptions toward mobile devices
(M = 58.89, SD = 6.773) are lower than males’ perceptions (M = 60.35, SD = 6.773), but
this difference was not significant, t(123) = 1.016, p = 0.312. The same results were for
the sub-scales (behavioural intention to use, perceived usefulness, perceived ease of use
and attitude). Results are presented in Table 3.
Table 3 Results of independent sample t-test between males and females
Mean Std. Dev.
t-value df p
Male Female Male Female
Behavioural intention
to use 4.00 4.12 0.906 0.735 –0.812 123 0.640
Perceived usefulness 3.9953 3.9098 0.64806 0.57705 0.755 123 0.785
Perceived ease of use 4.0279 3.7610 0.74525 0.81616 1.789 123 0.562
Attitude 4.0775 4.0976 0.78961 0.55305 –0.165 123 0.869
To determine whether there was a relationship between the mobile devices acceptance of
the pre-service teachers and their demographic factors, we ran Pearson Correlations.
There was a positive relationship between technology expertise and mobile devices
acceptance ( 0.396, 0.05)rp

. As computer expertise increases, the pre-service
teachers are more positive about the benefits of using mobile devices in the classroom.
Correlations were also administered to determine if there was a relationship between
smart mobile expertise and mobile devices acceptance. Similarly, significant
relationships were found ( 0.358, 0.05)rp (see Table 4).
Table 4 Correlations between mobile devices acceptance and demographic factors
Components TAM Gender Age Studies
Computers
experience
Mobile
devices
experience
TAM 1
Gender –0.091 1
Age 0.088 –0.105 1
Studies –0.080 –0.026 0.123 1
Computers experience 0.396** –0.197* –0.048 0.007 1
Mobile devices experience 0.358** –0.163 –0.039 –0.050 0.841** 1
Note: ** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
A series of Analyses of Variance (ANOVA) were conducted to examine any statistically
significant difference in mobile learning acceptance of the participants with different age
groups. The participants’ ages were separately categorised into three levels. The former
included the levels 21–30, 31–40 and over 40. The results for the TAM model show that
no statistically significant difference was found in the participants’ mobile devices
acceptance


2, 122 0.54, 0.05Fp. Additionally, the results of the constructs of the
TAM model show no statistically significant difference of the participants with different

Evaluating pre-
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age groups. Only in the perceived usefulness was found a statistically significant
difference. Tukey’s post hoc comparisons revealed that two of the groups: those who
were between 21–30 years old and those 31–40 years do not differ; however, both
significantly differed from the teachers who were 40 years and older. Those 40 years and
older were significantly less supportive on all items associated with support for using
mobile devices in the classroom. Results from individual ANOVAs and post hoc
comparisons are presented in Table 6. This implies that in comparison with older
teachers, the younger teachers tended to have higher usefulness for using smart mobile
devices to their teaching practices. Additionally, no statistically significant differences
found in the acceptance of ICT as a teaching framework


2,122 2.117, 0.05Fp.
Although the teachers in the age group of 21 to 30 years 18.95, 2().953MSD


outperformed the other two groups 17.92, 3.761,(MSD

 for group 31–40 years;
16.87, 4.359MSD for group over 40 years) those differences were not statistical
significant. This finding suggests that when it comes to using ICT to daily teaching
practice, younger teachers were not more confident than those who were over 40 years or
between 31 and 40 years (see Table 5).
Table 5 Age level differences
Age level (Mean, SD)
(1) 21–30 (2) 31–40 N = 60 (3) over 40 F Post hoc
TAM N = 46 59,37 (8.168) N = 19 0.538
Behavioural
intention to use 60.95 (5.826) 4.08 (0.758) 58,78 (7.604) 1.414
Perceived
usefulness 4.34 (0.554) 4.1167 (0.63277) 3.98 (0.913) 3.643* (1) > (3)
(2) > (3)
Perceived ease
of use 4.2632 (0.51230) 3.8600 (0.76207) 3,8348 (0.55666) 0.704
Attitude 3.6632 (0.79457) 4.1111 (0.69615) 3.9217 (0.85321) 0.684
Note: * p < 0.05.
Table 6 Pre-service teachers’ perceptions of ICT as a teaching context
Age Mean SD Sig.
21–30 16.87 4.359
0.125
31–40 17.92 3.761
Over 40 18.95 2.953
Regarding pre-service teachers’ perceptions of ICT as a teaching context, the survey
items were designed to elicit a response based on a 6-point Likert scale, between
0 = Strongly Disagree and 5 = Strongly Agree. The results in Table 2 show that student–
teachers have positive perceptions about ICT (M = 17.69, SD = 3.926). There were no
difference between male and female teachers t(123) = 0.506, p = 0.614. The male were
(M = 17.44, 3.756) the female were (M = 17.82, 4.028). Additionally teachers’ perception
with regard to their age shows no statistical difference F(2122) = 2.117, p = 0.125
(see Table 6).

S. Papadakis
5 Discussion
The participants of this research common understanding to m-learning and its
applications is that there are two basic factors defining mobile learning technologies:
mobile devices and mobile applications. They believe that mobile information and
communication technologies are important enablers of the new social structure and
especially mobile learning is a new way of learning in contemporary education and it is
one of the latest stages information society has reached. Thus, they perceive that mobile
learning will be one of the main bodies for the brand new educational system and they
believe that mobile technologies have great potential for facilitating more innovative
educational methods.
Pre-service teachers find themselves in an interesting dichotomy with respect to
mobile devices integration (O’Bannon and Thomas, 2015). Furthermore, pre-service
teachers’ background variables, such as age and gender, have been discussed in research
literature with respect to whether and to what extent they influence the use of ICT in
class. The primary null hypothesis proposed this research indicates zero correlation,
which means there is no correlation between teachers’ gender and mobile devices
acceptance. The conclusion of this research was consistent with the research objectives
and hypothesis. The men teachers’ scores were higher than the women teachers’ scores,
but the independent sample test presented that the p value is not less than 0.05; therefore,
the findings from the research fail to reject the hypothesis. This result is in line with other
studies that were not able to detect gender differences in the teachers’ use of mobile
devices in class (O’Bannon and Thomas, 2015; Pullen et al., 2015; Drossel et al., 2017;
Hsu et al., 2017).
The second null hypothesis of this research suggests a zero correlation, which means
there is no correlation between teachers’ age and mobile learning acceptance. The results
were established with the research objectives and hypothesis. The evidence demonstrated
that an equal variance t-test failed to reveal a statistically consistent difference between
the mean number of the teacher’s age and mobile devices acceptance. The results
specified that the younger teachers’ results were higher than the older teachers result, but
not at a 95% confidence level. Similar to other studies, this implies that pre-service
teachers’ age has no significant influence on acceptance of mobile devices (Pullen et al.,
2015; Hsu et al., 2017).
A positive view regarding the teachers’ use of ICT for instructional purposes is a
predictor for teachers’ ICT use in class (Drossel et al., 2017) and it may be considered an
intrinsic motivational factor (Yeow, 2016). The study results mean that pre-service
teachers think that it is easy to use the ICT in their daily teaching practice. Similar to the
other studies, pre-service computer teachers’ perspectives about current use, instructional
use and future use of the mobile technologies were generally positive (Çakıroğlu et al.,
2017). This is very important as teachers who consider themselves competent users of
ICT are more likely to use the various forms of ICT in class than teachers who do not
consider themselves to be competent in the field of ICT. This study revealed that 100%
of the participants owned smartphones. This 100% ownership of smart mobile devices is
considered very important, as it has several potential implications for classroom
integration and instruction. Frias-Martinez, Virseda and Gomero (2012) address that pre-
service teachers’ high ownership rates of mobile technologies can facilitate formal
education as traditional barriers – teacher access and time for training – are greatly
reduced (O’Bannon and Thomas, 2015).

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Furthermore, the present study revealed that pre-service teachers have positive
perceptions toward using mobile devices and consequently mobile learning in their daily
teaching practices. These results are similar to the other studies that have posed pre-
service positive attitude towards the use of mobile technologies in their future teaching
practice (Prieto et al., 2015). This is very important as several studies have been able to
show that teachers’ perceptions of whether the use of ICT in class improves student
learning outcomes and motivation predict the teachers’ use of ICT in school (Drossel
et al., 2017). As a result, a positive view regarding the pre-service teachers’ use of mobile
devices for instructional purposes is a predictor for teachers’ mobile devices use in class.
6 Limitations and future research
The author is aware of certain limitations of this study. Although rigorous research
procedures were employed, this study has some limitations that could be addressed in
future studies. First, the sample size was rather small so that reported results should be
interpreted with care. Second, the findings and their implications discussed in this paper
were based on one study that targeted a specific user group in Greece. The sampling
method has potential bias, as a sample of willing respondents (i.e. convenience sample)
may not be generalisable. If future researchers wish to make generalisations from the
data, they should randomise their sample to include other geographical areas. Moreover,
this study only adopted questionnaire to conduct survey. It is also suggested that both
qualitative and quantitative assessments should be parallelly performed to collect data
and used as complement for each other.
While the survey development was aligned with the literature on mobile devices
acceptance and content validity and internal consistency was established, additional
testing for reliability of the instrument could be performed. In addition, the survey relied
on self-report data; thus, participants may not have answered honestly or accurately, and
there is no method for verifying their answers. There were considerably more female
participants than male; however, this statistic is characteristic of a population of pre-
service teachers.
7 Conclusion
The introduction of mobile technologies into the classroom requires a process of change
in learning and teaching (Wang, 2016). Mobile devices have various distinctive features
such as individualised interfaces, real-time access to information, context sensitivity,
instant communication and feedback. These features may be able enhance the effects
of certain pedagogies, such as self-directed learning, inquiry learning or formative
assessment. But as many scholars and researchers have mentioned, these features of
mobile devices are not sufficient conditions for positive learning effects (Sung et al.,
2016).
In this context, the role of teachers is one of a key agent in the introduction of ICT
into relevant teaching and learning scenarios (Drossel et al., 2017). Understanding
teachers’ perceptions of mobile technology provides a means for promoting a more
meaningful use of this technology in the classroom setting (Domingo and Gargante,
2016).

S. Papadakis
The purpose of this research was to investigate gender and age differences, and to
identify the degree to which these factors influence the acceptance and use of mobile
devices in education. While it was found in this study that there was no correlation
between teachers’ gender and age and mobile devices acceptance, to capitalise on the
advantages of mobile technologies, teachers need to be trained to successfully
incorporate them into pedagogical practice. Although mobile devices can enhance
educational effects, the actual impact of mobile learning programs needs to be enhanced
by longer intervention durations, closer integration of technology and the curriculum,
and further assessment of higher level skills. For instance, Sung, Chang and
Liu (2016) propose two directions for research and practices to achieve orchestration in
mobile integrated education. The first is strengthening the functions and expanding
the applicability and breadth of learning-oriented software. The second direction is
strengthening professional teacher-development programs for mobile-enhanced
instruction. In many instances, a government’s investment in teacher training is more
important than its investment in technology itself. UNESCO’s research has shown that
without guidance and instruction, teachers will often use technology to ‘do old things in
new ways’ rather than transform and improve approaches to teaching and learning
(Kraut, 2013).
Accessibility issues could hinder teachers’ decision to integrate mobile technology
(Wang, 2016). Furthermore, mobile learning providers must improve the user
friendliness and ease of use of mobile learning systems to attract more users to use
mobile learning. For example, designers should provide easier-to-use user interfaces
hiding the complexity and details of the hardware and software involved, including touch
screen menus, handwriting recognition, natural language processing, etc. (Wang et al.,
2009). Also, poor support networks can result in negative perceptions and ultimately
resistance to mobile technology use (Wang, 2016).
The results of this study will be used to assist educators and policy makers in
understanding concerns involved in the implementation and integration of the mobile
technology in their schools and in teaching practices for better adoption through
appropriate efforts and interventions.
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