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The Use of Mobile Learning Technologies for Corporate Training and Development: A Contextual Framework


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This chapter presents a thorough review on the mobile learning concept. It also explores how businesses are using mobile learning (m-learning) technologies for the training and development of their human resources. The research involved semi-structured interviews and an online survey. The research participants were expected to share their opinions about the costs and benefits of using m-learning applications (apps). The findings reported that the younger course participants were more likely to embrace the m-learning technologies than their older counterparts. They were using different mobile devices, including laptops, hybrids as well as smartphones and tablets to engage with m-learning applications at work, at home and when they are out and about. This contribution has identified the contextual factors like the usefulness and the ease of use of m-learning applications (apps), individual learning styles and their motivations, time, spatial issues, integration with other learning approaches, as well as the cost and accessibility of the m learning technology. In conclusion, this contribution identifies future research avenues relating to the use of m-learning technologies among businesses and training organisations.
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The use of mobile learning technologies for corporate training and
development: A contextual framework
Ashley Butler
Moonee Valley City Council, Australia
Mark Anthony Camilleri
University of Malta, Malta
Andrew Creed
Deakin University, Australia
Ambika Zutshi
Deakin University, Australia
This is a prepublication version.
How to Cite: Butler, A., Camilleri, M.A., Creed, A. & Zutshi, A. (2020). The use of mobile learning technologies
for corporate training and development: A contextual framework. In Camilleri, M.A. (Ed.) Strategic Corporate
Communication in the Digital Age, Emerald, Bingley, UK.
This chapter presents a thorough review on the mobile learning concept. It also explores how businesses
are using mobile learning (m-learning) technologies for the training and development of their human
resources. The research involved semi-structured interviews and an online survey. The research
participants were expected to share their opinions about the costs and benefits of using m-learning
applications (apps). The findings reported that the younger course participants were more likely to
embrace the m-learning technologies than their older counterparts. They were using different mobile
devices, including laptops, hybrids as well as smartphones and tablets to engage with m-learning
applications at work, at home and when they are out and about. This contribution has identified the
contextual factors like the usefulness and the ease of use of m-learning applications (apps), individual
learning styles and their motivations, time, spatial issues, integration with other learning approaches, as
well as the cost and accessibility of the m-learning technology. In conclusion, this contribution identifies
future research avenues relating to the use of m-learning technologies among businesses and training
Keywords: mobile learning; m-learning; m-learning framework; m-learning contextual factors;
corporate training; training and development.
Department of Corporate Communication, Faculty of Media and Knowledge Sciences, University of Malta, Malta. Email:
1. Introduction
Managers are pressurised to find new and better ways to communicate with their employees.
They need to understand how to harness emerging communication technologies to improve the
performance of their organizations (Lee, Mazmanian & Perlow 2020; Camilleri, 2019).
Therefore, this chapter clarifies how businesses and training organisations can use m-learning
technologies to improve their engagement with their human resources. Following a critical
review of the relevant literature, an exploratory research identified the use of m-learning for
the continuous training and development of employees. The data was gathered through a short
online survey questionnaire and via semi-structured interviews. In conclusion, the researchers
identify the contextual factors that can influence the successful implementation of m-learning.
They also put forward a m-learning framework for business practitioners and training
2. The m-learning concept
The corporate sector is increasingly using mobile technologies to train employees and develop
their skills and competences. This learning methodology is known as mobile learning (m-
learning). Whilst there are various definitions for this term (see Wu, Jim Wu, Chen, Kao, Lin
& Huang, 2012; Sung, Chang & Liu 2016; Grant 2019; Janson, Söllner & Leimeister 2019;
Petrucco, 2020), this study addresses a gap in the extant academic knowledge as it investigates
how, where and in what context m-learning is being utilised during the training and
development of managers and employees.
Several organisations are striving to differentiate themselves by using m-learning practices in
their learning and development strategies (Noe, Clarke & Klein 2014). The corporate
investments in the education and training of human capital has often yielded increased
productivities and an improved organisational performance (Camilleri, 2020; Joseph & Gaba
2020). Examples of work-based m-learning include the provision of podcasts or short videos
that are increasingly being accessed through mobile phones, tablets or mp3 players to augment
classroom training. M-learning can also involve online simulations, or more informal scenarios
where employees engage in collaboration through social networks on their mobile devices
(Collins & Halverson 2010; Park & Gu 2018). Additionally, they are used during work-based
learning, including work placements, formal on-the-job training, or in informal coaching and
mentoring (Laouris & Eteokleous 2005; Pimmer, Pachler & Attwell 2010). Sometimes m-
learning courses are delivered through a kaleidoscope of different media. This multi-
dimensionality is analogous to m-learning, where actors, including employees or students, can
immerse themselves in the educational technologies (Naumovska, Lee & Zajac 2012; Tischer
& Leaver 2017; Camilleri & Camilleri, 2017a; Ungureanu & Bertolotti 2018). The burgeoning
nature of this promising field of study indicates a need to expand on the bubble concept and to
investigate innovative approaches that address the businesses’ training needs in management,
especially those relating to the provision of corporate education or on-the-job training.
The ubiquity of mobile technologies is one of the reasons why it may prove difficult to define
the m-learning concept (Grant 2019; Ferreira et al. 2015). Despite this ambiguity, its definition
can be associated with electronic learning (e-learning). E-learning occurs through various
electronic media including personal computers and laptops, among others, as these devices can
access online and offline programmes. However, the mobile users can access m-learning
content from any place, while they are out and about. Some studies consider m-learning as an
evolution or as a subset of e-learning instead of being another discipline. Garcia-Cabot, de-
Marcos and Garcia-Lopez (2015), and Crescente and Lee (2011) among others believe that
mobile learning is an offshoot of e-learning as it has emerged as a more advanced technology
than the latter one. Yet, some recent studies suggest that m-learning is a stand-alone discipline
(Grant, 2019). Some commentators argue that the ‘mobile’ component of m-learning refers to
the mobility of their users. However, the word ‘mobile’ can also refer to the portability of the
mobile device (O’Malley et al. 2005).
Recently, various researchers are considering other aspects of m-learning as they conceive that
these technologies enable their users to learn in different contexts (Grant, 2019). The mobile
users can easily assess their educational content from different locations. They do not have to
physically attend courses that are provided by educational institutions or by corporate training
providers. In this sense, the m-learning devices are not driving the activity of learning, but they
are merely the enabling medium (Brown & Mbati, 2015). M-learning can include academic or
work-related content that is readily accessed through multiple locations at various times. This
argumentation is associated with the notion ‘anytime anywhere learning’, which is increasingly
becoming a common theme, particularly in the academic literature (Ferreira et al. 2015; Chen
& Yan, 2016; Kukulska-Hulme, 2012).
Another perspective on m-learning relates to the context or setting where learning takes place.
This view also seeks to break away from more traditional, formal styles of learning. In this
case, m-learning has often been described as informal (Crescente & Lee 2011). A few authors
have linked m-learning with microlearning (Skalka & Drlik, 2018), situated learning (O’Malley
et al. 2003) and/or personalised learning (Chen, 2008). Others made reference to the physical
location or to the spatial environment where the learning occurs (Crompton, 2013). Various
researchers have often relied on the traditional learning theories to clarify that the context is
very important for m-learning. This is clearly explained in Kolb’s (1984) theory of learning,
whereby the learners’ knowledge is drawn from a combination of direct experiences and from
socially acquired understandings that will ultimately impact on the individuals’ attitudes,
intentions and behaviours. Considering that learning is a highly individual experience, it is
reasonable to conclude that a person’s prior knowledge, motivations and values will also affect
how they engage and consume new knowledge. This idea is supported by Jha-Thakur, Gazzola,
Peel, Fischer and Kidd’s (2009) review of learning theories. The authors conclude that various
individuals learn in different ways, as they are influenced by their surrounding contexts. Other
researchers like Pachler, Bachmair and Cook (2013) have also grounded their definition on
mobile learning from similar learning theories, as they assert that learning is affected by the
students’ context.
While, for the time being, there is no clear definition about m-learning (Grant, 2019; Ferreira
et al. 2015), it may appear that its modality is emerging, as more users are becoming acquainted
with its unique and distinctive characteristics. For instance, Crompton (2013, p.4) suggested
that m-learning is delivered through multiple contexts. It involves the transmission of content
through interactive, [wireless] electronic devices. Note that we have included wireless in this
definition as the wireless capabilities enable the technology to be utilised in multiple contexts.
However, it is important to distinguish between different wireless, mobile technologies.
According to Wu et al.’s (2012) meta-analysis on m-learning research, the most commonly
used technologies were mobile phones and personal digital assistants (PDAs). Other common
mobile technologies include the tablets, mp3 players, e-book readers (e.g. Kindle), and laptops,
among others. Crescente and Lee (2011) maintained that although laptops may be transported
with relative ease, they should not be counted as mobile learning devices. They argued that
they are not as portable as other mobile technologies like the smart phones. Traxler (2007)
contended that whilst individuals will usually premeditate to use their laptops (for work and
educational purposes), he noted that they habitually (and regularly) carry their phones with
them. Arguably, in the light of the latest technological advancements in terms of wireless
networks, software, and hardware, the laptops ought to be included in the category of mobile
learning as they provide additional features that can improve the delivery of the learning
outcomes of education or of professional training and development (Camilleri & Camilleri,
2017a; 2017b; Chang, Chen, Yu, Chu & Chien, 2017).
3 The use of m-learning
The notion of ‘anytime anywhere learning’ is receiving considerable attention within the extant
literature (Grant, 2019; Du, Yang, Shelton & Hung, 2019; UNESCO, 2019). However, there is
less focus on how, where and when students or course participants are using m-learning
technologies (Kukulska-Hulme, 2012). Very often, the educators and/or practitioners are
engaging with m-learning technologies to fill down-time gaps with bite-sized learning (So,
2016). The use of these technologies may appear to improve the students’ learning experience
as they can use them whenever they please, in the most appropriate places for professional
development, in the comfort of their home, or while they are out and about (Kukulska-Hulme
2012; Camilleri & Camilleri 2019).
Squire (2009) uses two terms, namely, ‘cocooning’ and ‘camping’ to explain how mobile
learning involves the creation of one’s own personal learning contexts. He held that cocooning
is the act of creating a personalised learning space, whilst camping is a constructed personal
workspace that is created in public areas. An example of the latter may include an individual
going to her/his favourite café and using headphones and a tablet to watch a vodcast as part of
her/his m-learning journey. Crescente and Lee (2011) note that people who are comfortable
with the use of their mobile device would enjoy learning through this technology. Hence,
individuals can improve their learning journey in different contexts, at their own pace. This
argumentation is synonymous with the situated learning theory and with the blended learning
approaches that are aimed at maximising the students’ motivation through education
technologies (Cajiao & Burke, 2016).
Prensky (2001) suggested that there are individuals who feel more comfortable with the use of
technologies as they have used them since a very tender age. He labelled these individuals as
‘digital natives’. These ‘natives’ include those individuals who have been raised in the
(wireless) internet age. Moreover, he explained that other individuals may belong to the ‘digital
immigrants’ segment in society. Those individuals were born before the proliferation of digital
technology. This latter generation is more accustomed to paper-based communications. The
digital natives have a higher ability to access content quickly and possess multi-tasking abilities
(Prensky 2001; Chen & Yan 2016). This is consonant with the motivation theory. Herzberg
(1968) argued that if learners can perform their tasks well, then they will be motivated to
continue learning about them. The premise is that the digital natives are well acquainted with
the use of technologies. Therefore, it is very likely that they will perceive the ease of use as
well as the usefulness of m-learning (Davis, 1989). The learners will be willing to use the
mobile technologies as they have the competences and the technical abilities to engage with
An alternate view is that the learning context can disadvantage m-learning as there are
distractions in different spatial environments. Moreover, individuals may have distinct
cognitive abilities, skills and memory capacities. O’Malley et al. (2005, p. 41) asserted that m-
learning is a ‘highly fragmented experience’. The levels of concentration and reflection that is
required during formal learning cannot be maintained because the mobile users may find
themselves in situations that are intermittent and unpredictable, whilst simultaneously
demanding their attention. Various studies have investigated how multi-tasking or distractions
in the environment can hamper learning and understanding. Hembrooke and Gay (2003) found
that students who were allowed to use their laptops during a lecture for web-browsing
performed relatively worse than other students who were prohibited to use them. Their study
suggested that the individuals’ ability to engage in simultaneous tasks is very limited.
Other authors have highlighted the notion of divided attention which can be expressed as
receiving input from two simultaneous stimuli, or through rapidly switching between stimuli
(Chen & Yan, 2016). Hence, whilst it can be said that mobile devices are particularly suited
to multi-tasking (Pettit & Kukulska-Hulme, 2007), this may be to the detriment of acquiring
and processing new information. For instance, Doolittle and Mariano (2008) found that the
students’ mobile multi-media learning was negatively affected when they were distracted. They
reported that their divided attention can disrupt their m-learning. Therefore, the setting where
m-learning is being delivered can have an effect on the quality and effectiveness of learning.
One may use a metaphor relating to the ‘doorway effect’ to describe this matter. For instance,
individuals walk through a doorway and forget what their original intent was. This issue can
affect their ability to access information about objects, including the ones they had recently
interacted with (Radvansky & Copeland 2006).
Arguably, if the individuals’ memory can be easily erased by moving to a new location, they
will not be in a position to learn and retain new knowledge when moving through contexts, or
when entering different virtual places. Radvansky, Tamplin and Krawietz (2010) investigated
this phenomenon. They tested multiple scenarios where they observed that the individuals
experienced a short-term memory loss because of a physical change in the external
environment (see also Radvansky Krawietz & Tamplin 2011; Pettijohn & Radvansky 2018).
Radvansky et al. (2010) implied that this effect may be due to a range of different
circumstances, including neurological processes. Hence, future research ought to investigate
the effectiveness of utilising the mobile technologies in different locations, where the learners’
context is changing, like for example, switching from laptop to a smartphone, reading on screen
to listening to a podcast, or simply changing from one app to another with altered functionality.
4. Methodology
This research builds on the extant literature that is focused on m-learning and its related
concepts within the context of corporate training and development. The continuous
professional training may be delivered inhouse, or it can be outsourced to specialised, external
training organisations or it can be provided by academic individuals. Such training is usually
given within the organisational premises in workplace environments in order to nurture the
skills and competences of the human resources (Pimmer, Pachler & Atwell 2010). In this case,
the course participants may also benefit from collaborative and social interactions with their
colleagues. Very often, the managers and employees are learning through informal, incidental
channels (Matthews & Candy 1999; Naumovska, Lee & Zajac 2012; Tischer & Leaver 2017;
Ungureanu & Bertolotti 2018).
In this light, this study used a mixed method approach to explore the respondents’ attitudes and
perceptions towards m-learning technologies. The research was carried out in the Australian
city of Melbourne. The descriptive data was collected among course participants via a short,
electronic survey questionnaire and though semi-structured interviews. This mixed-
methodological approach allowed for a larger, heterogeneous sample (Merriam & Tisdell 2015;
Birnbaum 2004). At the epistemological level, the survey provided a positivist view about the
respondents’ attitudes, while the interviews presented interpretive insights about their
preferences and predispositions (Saunders et al. 2018). The latter resulted in a rich, inductive
data on their beliefs, intentions and behaviours.
Ontologically, the quantitative survey has validated previous studies on this topic and can be
replicated in future research. Moreover, the interpretative data that was gathered during the
interview sessions was rigorously compared with previous findings from the relevant literature
(Kukulska-Hulme 2012; Vavoula & Sharples 2009; Wu et al., 2012). In sum, the mixed method
approach has triangulated the findings of this research.
The researchers disseminated by email their online survey questionnaire among practitioners
having used mobile learning as part of their corporate learning and training. The practitioners
received explanation about the rationale of this study and appropriate instructions on how to
complete the questionnaire. Firstly, the respondents identified which mobile technologies they
had, or were using. If they chose the option ‘None of the above’, the survey automatically
excluded them from completing the survey and redirected them to the survey termination page.
The other questions featured 5-point Likert scales. Therefore, the respondents were expected
to indicate their level of agreement with the survey items, where 1 represented strongly disagree
and 5 represented strongly agree.
In addition, the researchers gathered interpretative, qualitative data through six semi-structured
interview sessions. Four of the six interviews were face-to-face and the other two were carried
out through Skype. On average, the interviews took about 35 minutes to complete. The semi-
structured interviews involved open-ended questions to better understand participants’
opinions on the themes of this study. The researchers have used interview guiding questions
during their conversations with the research participants (see Appendix A) and annotated their
detailed responses. After the data gathering process, the interpretative findings were
categorised and coded by using NVivo software, according to the relevant themes of this study.
The key words included learning delivery, learning outcomes, learning constraints,
accessibility, learning style, individual differences, technology, type of content, learning
environment, and time, among others.
5. The results
There were eighty course respondents who completed the survey questionnaire (n = 80). They
represented a response rate of almost ninety percent (87.9%) of all the targeted respondents.
Thirty-one respondents were males, and forty-nine were females. The mean age of the
respondents was 34 years.
The majority of respondents who were in the 18-24 age category, indicated that they preferred
to learn via m-learning, whilst a few of them indicated that they wanted to learn in a classroom
environment. They showed a slight preference toward learning via a mobile device rather than
reading a textbook. However, there were mixed attitudes among the respondents who were in
the 25-34 age group. The 35-44 age group indicated that they clearly preferred the classroom
than the mobile learning. Moreover, the older respondents (those were more than 45) reported
that they were more likely to learn from a textbook. This descriptive research suggests that the
digital natives had different perceptions toward m-learning when compared to the digital
immigrants. This finding suggests that the training organisations and/or the individual trainers
ought to consider the ages of their course participants before implementing m-learning.
Perhaps, it would be wiser for them to use blended approaches where traditional teaching
resources are supplemented with m-learning technologies.
The qualitative study relied on a convenience sample of four females and two males who
followed courses that included m-learning. Their age ranged between 25-66 years. These
interviewees had varying levels of m-learning experiences. All interviewees shared their
opinions about the advantages and disadvantages of using m-learning technologies as they
communicated their user perspectives. Generally, these interviewees agreed that their mobile
technologies enabled them to instantly access their course content from wherever they were.
They maintained that they could follow their courses and learn at their own pace. These
findings mirror previous findings in the academic literature, particularly those that describe m-
learning as “anytime anywhere” (see Ferreira et al. 2015; Chen & Yan 2016; Kukulska-Hulme,
2012). The interviews communicated about the benefits of these ubiquitous technologies. For
example, “I like the convenience and control…. I can do it anywhere in my spare time … I can
learn at my own pace, do any activity I want….. I can stop it and start it.“. “You couldn’t do
that in a classroom environment” (interviewees R2 and R5). These were some of the most
popular opinions that emerged during this study.
Interestingly, the findings of this qualitative research are consistent with what was reported in
previous studies. The interviewees argued that the m-learning technologies enabled them to
access their course content where and when they wanted. Collectively, these findings indicated
that the mobile users were inclined to maximise their productivity throughout the day as they
were willing to use their devices whenever they can. This issue is noteworthy to those training
organisations that are planning to utilise m-learning technologies as it enables them to engage
with their course participants outside of their conventional workplace environments.
The interviewees responses revealed that m-learning ought to be used in conjunction with
traditional learning methodologies. They hinted that it could be used to revise course material.
Another factor of this preference was dependent on the simplicity or complexity of the taught
content. The interviewees agreed that the more complex the content, the more likely the users
would prefer traditional learning methods. Interviewee R1 held that she would use the textbook
to learn about highly complex technical issues. Generally, the interviewees agreed that they
would recommend using the m-learning technologies during downtime to increase their
productivity. For instance, interviewee R5 maintained that their course participants are
encouraged to use their m-learning applications (apps) when they are commuting to work or at
leisure. Similarly, interviewee R1 echoed that she uses her time in a more productive manner
if she is carrying her laptop in times of transit.
The results of this study are in stark contrast with Wu et al.’s (2012) contribution. This study
reported that the research participants (in both the quantitative as well as in the qualitative
studies) liked to use their laptops, netbooks and/or hybrids (i.e. tablets with keyboards)
technologies, as opposed Wu et al.’s (2012) findings . The reason for this is that today’s laptops
have decreased in size and are much lighter. This technology has improved throughout the past
twenty years or so. In fact, all interviewees maintained that they were using a laptop at work
rather than a desktop computer. Of course, this is not a representative sample of the whole
population. Further research is required in this regard to better understand how m-learning in
organisations may (not) be adapted for laptops and/or hybrids.
This study reported that the respondents are increasingly engaging in education technologies
in different ways and for different reasons. The relevant literature as well as the findings of this
research suggest that formal m-learning may also involve accessing course content from higher
education institutions. This may include accessing educational videos like TED Talks for the
professional development of soft skills, among others. This is a good example that is associated
with microlearning, an action orientated approach that offers bite-sized education to online and
mobile users (Skalka & Drlik 2018). Microlearning epitomises Miller’s (1956) seminal work
on the individuals’ capacity to process information. It may be easier for individuals to process
smaller bits of information through short videos and podcasts (that can be readily available on-
demand) in a gradual manner than to absorb larger chunks of information (Bodie, Powers &
Fitch-Hauser 2006).
6. A contextual framework for m-Learning
There are a number of contextual factors, including the course content, its learning outcomes,
the users’ perceived ease of use, usefulness and enjoyment with m-learning technologies, et
cetera, that could determine whether the individuals would use them for training and
development purposes (Ferreira et al., 2015; Crescente & Lee, 2011). Sometimes, there may
be other issues like the individuals’ accessibility to these technologies or their spatial
environment that can also have an effect on their engagement with m-learning (Doolittle &
Mariano, 2008). There may be certain distractions in the environment that can disrupt m-
learning and/or decrease their effectiveness.
Today, mobile users can avail themselves of noise cancelling headphones and can easily
engage in m-learning when they are out and about in public places and/or commuting. This is
synonymous with Squire’s (2009) ‘cocooning’, as individuals can create their own personalised
learning spaces in different contexts. In a similar vein, Csikszentmihalyi’s (1975) flow theory
suggests that individuals can be completely focused on specific tasks (Csikszentmihalyi,
Aduhamdeh & Nakamura 2014). They may immerse themselves in their training and
development through m-learning. Of course, they have to be in the right environment where
there are no distractions. Hence, the contextual setting of m-learning can influence its
effectiveness. For example, experiential learning theory suggests that the individuals learn
through their ongoing interactions with their surrounding environment as they find meanings
to problems and develop their understanding (Illeris, 2007). Similarly, Kolb’s (1984) learning
theory posits that knowledge may result from a combination of direct experiences and socially
acquired understandings (Matthews & Candy 1999). Laouris and Eteokleous (2005) discuss
about the critical factors that could influence the outcomes of m-learning. Hence, this
contribution builds on these theoretical insights and on the findings from this study. The authors
of this chapter put forward a contextual framework for m-learning. They identify the specific
factors, including; accessibility and cost; the usefulness of the learning content; the ease of use
of the technology; time; extrinsic and intrinsic motivations (e.g. rewards and perceived
enjoyment, among others); integration with other learning approaches; individual learning
styles and predispositions; and spatial issues and the surrounding environment, as featured in
Figure 1.
Figure 1: A contextual framework for m-learning
(source: Butler, Camilleri, Creed & Zutshi, 2020)
The authors argue that these eight contextual factors can have an effect on the successful
implementation of m-learning.
i. Time: This relates to the time that the users dedicate to learn to use and to engage in m-
Spatial issues
and the
Usefulness of
the learning
Ease of use of
learning styles
Extrinsic and
and cost
with other
ii. Spatial issues and the environment: These relate to the physical location of the user when
they access m-learning content.
iii. The usefulness of the learning content: The learning content (video, audio, written, or a
combination of these) has to be useful to improve the mobile users’ knowledge, skills and
iv. Ease of use of the technology: The m-learning technology has to be easy to use. It may
(not) be connected to wireless networks (if it is, there should not be connectivity problems
when accessing the content). The m-learning technology may require passive or active
learning (for example, reading and/or interacting through games).
v. Individual learning styles and predispositions: The m-learning technology should consider
the individuals’ age, cognitive knowledge (e.g. memory); skills; visual, auditory and/or
kinaesthetic abilities, as well as their preferences toward certain technologies. The
technology may require interaction with peers or facilitators in synchronous, or
asynchronous modes (these issues will depend on the learning outcomes of the mentioned
vi. Extrinsic and intrinsic motivations: Organisations and professionals should also consider
extrinsic and intrinsic motivations to entice the mobile users to use the m-learning
vii. Accessibility and cost: These relate to the accessibility and cost of the m-learning
technology. It can be available through different mobile platforms. It may be used by wide
range of users (who have different learning needs) for different purposes. The software
and/or hardware ought to be reasonable priced.
viii. Integration with other learning approaches: The m-learning technology ought to be
complemented and blended with offline teaching approaches.
This proposed framework represents different contextual factors that can have an effect on the
successful implementation of learner-centred corporate education (see Grant, 2019; Janson,
Söllner & Leimeister, 2019). These eight factors are influencing the effectiveness of m-learning
during the training and development of human resources. Hence the arrows are pointing
inwards. However, the factors in the outer circle are related to each other and they can lead to
further considerations. M-leaners may choose a short video over a longer podcast to learning
or revise depending on the content or their situation. There are innumerable other examples of
contextual learning due to the diversity of people, organisations and learning resources, objects
and opportunities. For example, how does time, is related to the spatial issues and the
environment. The mobile users will use their downtimes wisely at the office, at home, or whilst
commuting to and from work if they engage with the m-learning applications. Their down time
may provide them with an opportunity to improve their learning journey.
7. Conclusions and implications
The contextual factors for mobile learning encompass a variety of dimensions including time,
spatial issues and the environment, the usefulness of the learning content and the ease of use
of the technology, individual learning styles and predispositions, extrinsic and intrinsic
motivations, accessibility and cost, as well as integration with other learning approaches. The
authors posit that this comprehensive framework can support businesses in their human
resources training and development. It enables them to identify all the contextual factors that
can have an effect on the successful roll out of m-learning designs.
This chapter has featured a critical review of the relevant literature and has presented the
findings from an empirical research. The data for this study was gathered through quantitative
and qualitative methodologies. The researchers have disseminated a survey questionnaire
among course participants and have organised semi-structured interview sessions with
corporate training participants. In sum, this study reported that the younger course participants
were more likely to embrace the m-learning technologies than their older counterparts. They
suggested that they were using laptops, hybrids as well as smartphones and tablets to engage
with m-learning applications at home and when they are out and about. These recent
developments have led many businesses to utilise the mobile technologies to engage with their
employees or to use them for their training and development purposes. Therefore, this
contribution has identified the contextual factors that should be taken into account by
businesses and/or by training organisations. Thus, the authors have presented their proposed
framework for mobile learning. This framework is substantiated by their empirical research
and by relevant theoretical underpinnings that are focused on m-learning.
The authors are well aware that every study has its inherent limitations. In this case, this sample
was small, but it was sufficient for the purposes of this exploratory study. Future studies may
include larger sampling frames and/or may use different research designs. The researchers
believe that there is still a knowledge gap in academia on this topic. For the time being, just a
few studies have explored the use of mobile learning among businesses. The mobile learning
technologies can be rolled out for the training and development of corporate employees. The
training organisations can encourage their course participants to engage in self-directed
learning and development through formal, informal or micro learning contexts. Corporate
educators and services providers of continuous professional training and development can use
the mobile learning applications to improve the employees’ skills and competences. This may
in turn lead to increased organisational productivities and competitiveness.
Birnbaum, M. H. (2004). Human research and data collection via the Internet. Annual Review
of Psychology, 55(1), 803–832.
Bodie, G. D., Powers, W. G. & Fitch-Hauser, M. (2006). Chunking, priming and active
learning: Toward an innovative and blended approach to teaching communication-related
skills. Interactive Learning Environments, 14(2), 119–135.
Brown, T. & Mbati, L. (2015). Mobile learning: Moving past the myths and embracing the
opportunities. The International Review of Research in Open and Distributed Learning, 16(2),
Cajiao, J. & Burke, M. (2016). How instructional methods influence skill development in
management education. Academy of Management Learning and Education, 15(3), 508–524.
Camilleri, M. A. & Camilleri, A.C. (2017a). The technology acceptance of mobile applications
in education. In 13th International Conference on Mobile Learning (Budapest, April 10th).
International Association for Development of the Information Society (UK).
Camilleri, M.A. & Camilleri, A.C. (2017b). Digital learning resources and ubiquitous
technologies in education. Technology, Knowledge and Learning, 22(1), 65-82.
Camilleri, M.A. (2019). The Use of Data Driven Technologies in Tourism Marketing. In
Ratten, V., Alvarez-Garcia, J. and De l Cruz Del Rio-Rama, M., Entrepreneurship, Innovation
and Inequality: Exploring Territorial Dynamics and Development, 1st Edition, Routledge,
Oxford, UK.
Camilleri, M.A. & Camilleri, A.C. (2019). The students’ readiness to engage with mobile
learning apps. Interactive Technology and Smart Education, 17(1), 28-38.
Camilleri, M.A. (2020). Using the balanced scorecard as a performance management tool in
higher education. Management in Education. 10.1177/0892020620921412
Chang, Y. S., Chen, S. Y., Yu, K. C., Chu, Y. H. & Chien, Y. H. (2017). Effects of cloudbased
mlearning on student creative performance in engineering design. British Journal of
Educational Technology, 48(1), 101-112.
Chen, C. M. (2008). Intelligent web-based learning system with personalized learning path
guidance. Computers and Education, 51(2), 787-814.
Chen, Q. & Yan, Z. (2016). Does multi-tasking with mobile phones affect learning? A review.
Computers in Human Behavior, 54(C), 34-42.
Collins, A. & Halverson, R. (2010). The second educational revolution: Rethinking education
in the age of technology: The second educational revolution. Journal of Computer Assisted
Learning, 26(1), 18–27.
Crescente, M. & Lee, D. (2011). Critical issues of m-learning: design models, adoption
processes, and future trends. Journal of the Chinese Institute of Industrial Engineers, 28(2).
Crompton, H. (2013). ‘A historical overview of m-learning: toward learner-centred education’.
In Berge, Z. and Muilenburg, L. (eds). Handbook of mobile education. New York, USA,
Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco, USA, Jossey-
Csikszentmihalyi, M., Abuhamdeh, S. & Nakamura, J. (2014). ‘Flow’. In M.
Csikszentmihalyi, Flow and the Foundations of Positive Psychology (pp. 227–238).
Dordrecht, Netherlands, Springer.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS quarterly, 319-340.
Doolittle, P. E. & Mariano, G. J. (2008). Working memory capacity and mobile multimedia
learning environments: Individual differences in learning while mobile. Journal of
Educational Multimedia and Hypermedia, 17(4), 511-530.
Du, X., Yang, J., Shelton, B. & Hung, J. L. (2019). Is learning anytime, anywhere a good
strategy for success? Identifying successful spatial-temporal patterns of on-the-job and full-
time students. Information Discovery and Delivery, 47(4), 173-181.
Ferreira, J., Klein, A., Freitas, A. & Schlemmer, E. (2015). Mobile learning: Definition, uses
and challenges in increasing student engagement and retention using mobile applications:
Smartphones, Skype and texting technologies. Cutting-edge Technologies in Higher
Education, 6D, 47-82.
Grant, M. (2019). Difficulties in defining mobile learning: Analysis, design characteristics, and
implications. Educational Technology Research and Development, 67(2), 361–388.
Hembrooke, H. & Gay, G. (2003). The laptop and the lecture: The effects of multitasking in
learning environments. Journal of Computing in Higher Education, 15(1), 46–64.
Herzberg, F. (1968). One more time: How do you motivate employees? Cambridge,
Massachusetts, Harvard University. Graduate School of Business Administration.
Illeris, K. (2007). What do we actually mean by experiential learning? Human Resource
Development Review, 6(1), 84-95.
Janson, A., Söllner, M. & Leimeister, J. (2019). Ladders for learning: Is scaffolding the key to
teaching problem solving in technology-mediated learning contexts? Academy of Management
Learning and Education (In-Press). Published Online 9 September.
Jha-Thakur, U., Gazzola, P., Peel, D., Fischer, T. & Kidd, S. (2009). Effectiveness of strategic
environmental assessment: The significance of learning. Impact Assessment and Project
Appraisal, 7(2), 133-144.
Joseph, J. & Gaba, V. (2020). Organizational structure, information processing, and decision-
making: A retrospective and road map for research. Academy of Management Annals, 14(1),
Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and
development (Vol. 1). Englewood Cliffs, USA, Prentice-Hall.
Kukulska-Hulme, A. (2012). ‘Language learning defined by time and place: A framework for
next generation designs’. In Diaz-Vera, J. (ed.). Left to my own devices: Learner autonomy and
mobile assisted language learning. Innovation and leadership in English language teaching.
Bingley, UK, Emerald Group Publishing Ltd, pp. 1–13.
Laouris, Y. & Eteokleous, N. (2005). ‘We need an educationally relevant definition of mobile
learning’. Proceedings of 4
World Conference on mLearning, Cape Town, South Africa.
Lee, M., Mazmanian, M. & Perlow, L. (2020). Fostering positive relational dynamics: The
power of spaces and interaction scripts. Academy of Management Journal, 63(1), 96–123.
Matthews, J. & Candy, P. (1999). ‘New dimensions in the dynamic of learning and knowledge’.
In Boud, D. and Garrick, J. (eds). Understanding learning at work. New York, USA,
Merriam, S. B. & Tisdell, E. J. (2015). Qualitative research: A guide to design and
implementation. Hoboken, USA, John Wiley and Sons.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our
capacity for processing information. Psychological Review, 63(2), 81.
Naumovska, I., Lee, P. & Zajac, E. (2012). When practices diffuse in a bubble: Reverse
mergers and the internet wave. Academy of Management Annual Meeting Proceedings,
Noe, R., Clarke, A. & Klein, H. (2014). Learning in the twenty-first-century workplace. Annual
Review of Organizational Psychology and Organizational Behavior, 1(1), 245–275.
O’Malley, C., Vavoula, G., Glew, J., Taylor, J., Sharples, M., Lefrere, P., Lonsdale, P.,
Naismith, L. & Waycott, J. (2005). Guidelines for learning/teaching/tutoring in a mobile
environment. Public deliverable from the MOBILearn project (D.4.1), retrieved 3 March 2020,
Pachler, N., Bachmair, B. & Cook, J. (2013). ‘A sociocultural ecological frame for mobile
learning’. In Berge, Z. and Muilenburg, Y. (eds). Handbook of mobile education, New York,
USA, Routledge.
Park, D. & Gu, J. (2018). The effects of learning transfer on perceived usefulness and perceived
ease of use in enterprise e-learning-focused on mediating effects of self-efficacy and work
environment. Management and Information Systems Review, 37 (3), 1–25.
Petrucco, C. (2020). ‘Meaningful learning by creating technology-mediated knowledge
boundary objects between school and the workplace’. In: Rehm, M., Saldien, J. and Manca, S.
(eds). Project and design literacy as cornerstones of smart education. Smart innovation,
systems and technologies volume 158. Singapore, Springer.
Pettijohn, K. A. & Radvansky, G. A. (2018). Walking through doorways causes forgetting:
recall. Memory, 26(10), 1430–1435.
Pettit, J. & Kukulska-Hulme, A. (2007). Going with the grain: Mobile devices in practice.
Australasian Journal of Educational Technology, 23(1), 17–33.
Pimmer, C., Pachler, N. & Attwell, G. (2010). Towards work-based mobile learning: What we
can learn from the fields of work-based learning and mobile learning. International Journal of
Mobile and Blended Learning, 2(4), 1–18.
Prensky, M. (2001). Digital natives, digital immigrants part 1. On the Horizon, 9(5), 1–6.
Radvansky, G. A. & Copeland, D. E. (2006). Walking through doorways causes forgetting:
Situation models and experienced space. Memory and Cognition, 34(5), 1150–1156.
Radvansky, G. A., Krawietz, S. A. & Tamplin, A. K. (2011). Walking through Doorways
Causes Forgetting: Further Explorations. Quarterly Journal of Experimental Psychology,
64(8), 1632–1645.
Radvansky, G. A., Tamplin, A. K. & Krawietz, S. A. (2010). Walking through doorways causes
forgetting: Environmental integration. Psychonomic Bulletin and Review, 17(6), 900-904.
Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H. &
Jinks, C. (2018). Saturation in qualitative research: Exploring its conceptualization and
operationalization. Quality and Quantity 52(4), 1893–1907.
Skalka, J. & Drlik, M. (2018). ‘Conceptual Framework of Microlearning-based Training
Mobile Applications for Improving Programming Skills’. In Auer, M., and Tsiatsos, T. (eds).
Interactive Mobile Communication Technologies and Learning. Cham, Switzerland, Springer
So, S. (2016). Mobile instant messaging support for teaching and learning in higher
education. The Internet and Higher Education, 31, 32-42.
Squire, K. (2009). Mobile media learning: multiplicities of place. On the Horizon, 17(1), 70–
Sung, Y., Chang, K. & Liu, T. (2016). The effects of integrating mobile devices with teaching
and learning on students’ learning performance: A meta-analysis and research
synthesis. Computers and Education, 94(2016), 252–275.
Tischer, D. & Leaver, A. (2017). Through a glass darkly: Tracing the mundane organisation of
a bubble network. Academy of Management Annual Meeting Proceedings, 2017(1),
Traxler, J. (2007). Defining, discussing and evaluating mobile learning. International Review
of Research in Open and Distance Learning, 8(2), 1-12.
UNESCO (2019). Anytime, anywhere learning for improved education results in Russia. Paris,
France, United Nations Educational, Scientific and Cultural Organization.
Ungureanu, P. & Bertolotti, F. (2018). Building and breaching boundaries at once: An
exploration of how management academics and practitioners perform boundary work in
executive classrooms. Academy of Management Learning and Education, 17(4), 425–452.
Vavoula, G. & Sharples, M. (2009). Meeting the challenges in evaluating mobile learning: A
3-level evaluation framework. International Journal of Mobile and Blended Learning, 1(2),
Wu, W., Jim Wu, Y., Chen, C., Kao, H., Lin, C. & Huang, S. (2012). Review of trends from
mobile learning studies: A meta-analysis. Computers and Education, 59(2), 817–827.
Appendix A: Interview Guiding Questions
The qualitative study involved a semi-structured interview sessions. The research participants
were expected to answer the following questions.
Interview Guiding Questions
1 What is your preferred learning method?
2 What do you (or don’t) like about mobile learning?
3 Is mobile learning an effective way for you to learn?
4 How comfortable are you with using mobile learning?
5 Would you like to learn through the mobile learning or via a textbook? Why?
6 Where and when do you access m-learning content?
7 How long do you usually spend accessing m-learning content?
Do you like to learn through small bite-size learning (this notion was explained to
the interviewees)? Why (or Why not)?
9 Has your employer ever used m-learning? Discuss. How was it?
10 What can organizations do to improve their mobile learning technologies?
11 Do you use m-learning whilst doing other tasks such as housework and/or
commuting to work?
12 Do you find it distracting if you use m-learning whilst doing other tasks?
13 What is your preferred device to learn with?
14 How easy is it for you to recall the info that you learned whilst multi-tasking?
15 What does mobile learning mean to you?
16 Would you like to use mobile learning in future? Why?
... Digital media usage in the 21st-century-learning process is likely to be enhanced for students' advantages. According to Butler, Camilleri, Creed, and Zutshi [2], younger participants are more inclined to embrace mobile learning (m-learning) technology compared to older ones, and the use of laptops, hybrids, smartphones, and tablets in m-learning applications will increase on the home front and on the go. ...
... It is clear that in uncertain times, the teaching and learning techniques used should match the changing societal demands [7]. In the transformational process of education, active teaching methods and technology incorporation into learning surroundings become inevitable, if not necessary [1][2][3]. Thus, amid the COVID-19 pandemic, the use of online learning and m-learning technologies for teaching and training processes has become essential [8,9]. In particular, m-learning technologies and tools such as smartphones and tablets have various features that work toward enhancing mobile student learning (e.g., timely information access, customized interfaces, context sensitivity, rapid and real-time communication, feedback opportunities, etc.) [10]. ...
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The most current highly infectious disease, which has become a global health challenge permeating entire sectors of society, is COVID-19. In the education sector, the transmission of COVID-19 has been curbed through the closure of institutions and the facilitation of online learning. The main objective of this study was to propose an integrated model of the unified theory of acceptance and use of technology combined with the DeLone and McLean model, to examine the influence of quality features, namely, performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and social influence (SI), on the intentions and satisfaction of users toward mobile learning (m�learning) use in the context of Saudi learning institutions. The study obtained m-learning user data using an online questionnaire, after which the data were exposed to partial least squares structural equation modeling to test the proposed research model. The findings supported the influence of PE, EE, and FC on intention toward m-learning use but did not support the significant influence of SI. Moreover, system, intention, and user satisfaction were found to positively and significantly influence m-learning-system usage, with system, information, and service quality being top drivers of such user intention and satisfaction.
... Notwithstanding, the majority of students are using their own mobile devices including tablets and smart phones to access educational apps to continue their learning journey at university [5,8,9,10]. Individuals may use mobile devices to access content (instrumentality) when they are out and about (mobility) [11]. ...
... During COVID-19, educational apps have supported many students in their learning journeys [1,9,13]. They enabled them to access learning management systems and also to engage in synchronous conversations with other individuals [3,4]. ...
Conference Paper
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Students are increasingly utilizing mobile learning applications (m-learning apps) in various contexts. They can access their content from anywhere, anytime. This research explores the students' perceptions about learning technologies in a higher educational context. It integrates the Technology Acceptance Model's (TAM) constructs with "perceived enjoyment" to better understand their dispositions to engage with educational apps. The data was gathered through an online survey questionnaire among 317 research participants who were following full time university courses in a Southern European country. The findings suggest that the students were motivated to use learning apps. Their perceived usefulness, ease-of-use and enjoyment were having a significant effect on their intentions to continue using them in the future. This contribution implies that "perceived enjoyment" construct can be combined with TAM to shed more light on the users' intrinsic motivations to use mobile apps for educational purposes.
... Course participants can use remote technologies, including their personal computers, smart phones and tablets to access their instructors' asynchronous, online resources including course notes, power point presentations, videos clips, case studies, et cetera (Butler, Camilleri, Creed & Zutshi, 2021;Hung, 2016;Ifenthaler & Schweinbenz, 2013). Moreover, in this day and age, they are utilizing video conferencing technologies to attend virtual meetings, and to engage in one-toone conversations, or in group discussions and debates with their course instructor and with other students. ...
... A critical review of the relevant literature reported that university students were already using asynchronous technologies, in different contexts, before the outbreak of COVID-19 (Butler et al., 2021;Sánchez-Prieto et al., 2017;Hung, 2016;Liu et al., 2010;Sánchez & Hueros, 2010). ...
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During the outbreak of the Coronavirus (COVID-19) pandemic, higher education institutions (HEIs) have shifted from traditional and blended learning approaches to a fully virtual course delivery. This research investigates the students’ perceptions on remote learning through asynchronous learning management systems (LMS) and via synchronous video conferencing technologies like Google Meet, Microsoft Teams or Zoom, among others. The data was gathered from a sample of 501 higher education students in a Southern European context. A survey questionnaire included measures that investigated the participants’ acceptance of interactive technology to better understand their utilitarian motivations to use them. The findings suggest that the research participants accessed asynchronous content and interacted with online users, including with their course instructor, in real time. While there are a number of theoretical or opinion papers on the impact of COVID-19 on higher education services, currently, there are still a few empirical papers that shed light on the factors that are having an effect on the students’ attitudes and intentions to utilize remote learning technologies. This contribution underlines the importance of maintaining ongoing, interactive engagement with students, and of providing them with appropriate facilitating conditions, to continue improving their learning journey.
... Technology has redefined various aspects of life, as it has advanced its role in work knowledge, human resources trainings and academic learning in early decades of twenty first century. ( Barbara White, 2014;Butler, Camilleri, Creed, & Zutshi, 2021). ...
Covid-19 has increased the pace of inclusion of digital technologies for the learning of students as opposed to learning in a traditional classroom setting. It has altogether transformed the learning and teaching environment and has quickly created the space to adapt to the online mode of learning. This investigation aims to analyze the impact of online methods and processes used by students focusing on the acceptance of technologies that universities adopt due to the global pandemic. Technology Acceptance Model is used as a basis for this study. An online survey was conducted through a questionnaire, and 426 responses were collected from students. The Structural Equation Model was used for processing the data. The findings have suggested that Technology Acceptance Model is helpful in the understanding of students' acceptance of online mode of education in the current scenario of a global pandemic.
... Technology has redefined various aspects of life, as it has advanced its role in work knowledge, human resources trainings and academic learning in early decades of twenty first century. ( Barbara White, 2014;Butler, Camilleri, Creed, & Zutshi, 2021). One of the important Technology that plays vital role is internet of things (IoT) as internet has drastically changed the way people do interaction virtually; in their social and work relationships (Shammar & Zahary, 2019). ...
Full-text available
Covid-19 has increased the pace of inclusion of digital technologies for learning of students as opposed to learning in traditional classrooms setting. It has altogether transformed learning and teaching environment and have quickly created the space for adaptation of online mode of learning. This method involving technologies mainly through internet has become necessity for many universities and educational institutes around the world. The resultant increase in online educational activities has created need for efforts to explore the factors that impact acceptance of technologies by students and their attitudes toward use of the technology. This investigation aims for analyzing the impact of online methods and processes used by students focusing on the acceptance of technologies that are adopted by universities due to the global pandemic. Technology Acceptance Model is used as basis for this study, however other factors such as expected benefits and perceived costs have also been considered in this article. Online survey was conducted through a questionnaire and 426 responses were collected from students. Structural Equation Model was used for processing the data. The findings have suggested that Technology Acceptance Model is helpful in understanding of students' acceptance of online mode of education in current scenario of global pandemic.
... The use of digital media in the learning process in the 21st-century education era is likely to be developed especially for students, according to Butler's research that younger course participants are more likely to embrace ML technology than their older counterparts. They suggest that they use laptops, hybrids as well as smartphones and tablets to engage with m-learning applications at home and when they travel' [2]. ...
... In addition, by managing and providing the study materials and educational contents, mobile learning also provides sufficient visualization and adaptation on the small display of mobile phones. The modern characteristic traits in smart phones, namely the browsing feature, color display screen and video streaming, make mobile learning both practical and promising [2]. In addition to this, mobile learning possesses the following advantages: mobility, sharing of information, independent self-education and facilitating communication among the students and teachers. ...
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The utilization of mobile learning continues to rise and has attracted many organizations, university environments and institutions of higher education all over the world. The cloud storage system consists of several defense issues since data security and privacy have become known as the foremost apprehension for the users. Uploading and storing specific data in the cloud is familiar and widespread, but securing the data is a complicated task. This paper proposes a cloud-based mobile learning system using a hybrid optimal elliptic curve cryptography (HOECC) algorithm comprising public and private keys for data encryption. The proposed approach utilizes an adaptive tunicate slime-mold (ATS) algorithm to generate optimal key value. Thus, the data uploaded in the cloud system are secured with high authentication, data integrity and confidentiality. The study investigation employed a survey consisting of 50 students and the questionnaire was sent to all fifty students. In addition to this, for obtaining secure data transmission in the cloud, various performance measures, namely the encryption time, decryption time and uploading/downloading time were evaluated. The results reveal that the time of both encryption and decryption is less in ATF approach when compared with other techniques.
Full-text available
This contribution investigates higher education students' perceptions about mobile learning (m-learning) applications, as well as the effects of social influences and of appropriate facilitating conditions, on their intentions to continue using them. A structured survey questionnaire integrated valid measures from the Technology Acceptance Model (TAM) and from the Unified Theory of Acceptance and Use of Technology (UTAUT) to better explain their acceptance and use of m-learning software. The findings reported that facilitating conditions including the provision of resources, ongoing training opportunities and technical support, were affecting the respondents' engagement with m-learning programs. The respondents indicated that they were not influenced by others, to use mobile technologies for educational purposes. The results also suggest that they were well acquainted (and habituated) with the use of mobile devices and their applications. Evidently, they helped them improve their learning journeys.
The success of the training can be seen from how the activation process takes place. One of them is by designing an innovative training model. This research aims to analyze results validation of the mobile training management model. This research uses descriptive quantitative analysis in determining the level of validation of research instruments. This research method uses descriptive quantitative research to assess the level of validation of the research instrument. The sample of this research is the participants of basic level seafaring training at the Surabaya Shipping Polytechnic, the Director of the Surabaya Shipping Polytechnic, and local government employees related to mobile training activities. The research was analyzed based on results validation of the training and education management experts. The results reserach show that the mobile training model instrument is declared valid. Results of expert assessment from training management and managing education are categorized as good. This shows that the mobile training development model in improving the quality of education and training is worthy of further research.
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This paper presents a critical review of the relevant literature on managerialism and performance management in higher education. Afterwards, it features an inductive research that involved semi-structured interview sessions with academic members of staff. The interpretative study relied on the balanced scorecard's (BSC) approach as it appraised the participants' opinions and perceptions on their higher education institution's (HEI) customer, internal, organizational capacity and financial perspectives. The findings have revealed the strengths and weaknesses of using the BSC's financial and non-financial measures to assess the institutional performance and the productivity of individual employees. In sum, this research reported that ongoing performance conversations with academic employees will help HEI leaders to identify their institutions' value creating activities. This contribution implies that HEI leaders can utilize the BSC's comprehensive framework as a plausible, performance management tool to regularly evaluate whether their institution is: (i) delivering inclusive, student-centered, quality education; (ii) publishing high impact research; (iii) engaging with internal and external stakeholders; and (iv) improving its financial results, among other positive outcomes.
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The success of innovative teaching/learning approaches aiming to foster problem solving in management education depends on useful and easy-to-use IT components in the learning process. However, the complexity of problem solving in self-regulated learning approaches may overwhelm the learner and can lead to unsatisfying learning outcomes. Research suggests the implementation of technology-enhanced scaffolds as a mechanism to guide the learners in their individual problem-solving process to enhance their learning outcomes. We present a theoretical model based on adaptive structuration theory and cognitive load theory that explains how technology-enhanced scaffolding contributes to learning outcomes. We test the model with a fully randomized between-subject experiment in a flipped classroom for management education focusing on individual problem solving. Our results show that technology-enhanced scaffolding contributes significantly to the management of cognitive load as well as to learning process satisfaction and problem-solving learning outcomes. Thereby, our paper provides new conceptual and empirically tested insights for a better understanding of technology-enhanced scaffolds and their design to assist problem solving and its respective effects in flipped classrooms for management education.
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Purpose: A relevant literature review suggests that today’s children are increasingly immersing themselves in ubiquitous technologies, including interactive media and digital games. Therefore, this research investigates the primary school students’ intrinsic and extrinsic motivations toward learning via gameplay through their mobile devices, at home, and at school. Design: This study was carried out among primary school students in a small European state. It used valid and reliable measures, that comprised the technology acceptance model’s key constructs. However, the empirical investigation also explored the students’ perceived enjoyment and social influences, as plausible antecedents for their behavioral intention to engage with the educational applications (apps). Findings: The findings reported that there were strong correlations between the students’ perceived usefulness of the mobile technologies and their behavioral intention to use them for their learning. The results also indicated that there was no significant relationship between the perceived ease of use and the children’s enjoyment in engaging with the educational apps that were used at school. Originality: To the best of our knowledge, there is no other study in academia that has explored the children’s technology acceptance, normative pressures and their intrinsic motivations to use mobile learning technologies in the context of primary education. Therefore, this contribution opens future research directions, as this study can be replicated in other contexts.
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The ongoing advances in technology have brought significant improvements in the processing speed and storage of large volumes of data. Tech-savvy organisations have already started using big data with a goal to improve their decision making, agility and customer-centric approaches. Today, many tourism marketers are hyper-targeting consumers with real-time mobile ad campaigns to drive conversions. They use analytics to identify how exogenous variables, including the broader economy, competitive offerings and even the weather can affect their organisational performance. Similarly, the smaller enterprises are economically gathering and storing data from each and every customer transaction. They use analytics to customise their offerings and improve their customer engagement. Therefore, this chapter builds on the previous theoretical underpinnings on smart tourism. It clarifies how smart, disruptive technologies have led to endless opportunities for tourism and hospitality marketers to gain a competitive advantage. It explains how they are leveraging themselves by utilising contemporary marketing strategies and tactics that are customer-focused. The researcher examines the use of big data, analytics, programmatic advertising and blockchain technologies in the realms of tourism and hospitality.
Purpose Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown. Design/methodology/approach This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance. Findings Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences. Research limitations/implications A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns. Practical implications The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs. Originality/value This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.
Beginning with Simon (1947)—and motivated by an interest in the effect of formal organizational structure on decision-making—a large body of research has examined how organizations process information. Yet, research in this area is extremely diverse and fragmented. We offer a retrospective of past research to summarize our collective knowledge, as well as identify and advance new concerns and questions. In doing so, we identify three critical issues: a division between an aggregation perspective and a constraint perspective of structure, little focus on informational sources of conflict, and uneven treatment of various stages of decision-making. We then offer a road map for future research that elaborates the role of organizational structure in decision-making. In this endeavor, we offer an ecological perspective of information processing that addresses the issues and provides opportunities to expand research in new directions.
Abstract Technological innovation has changed the relationship between formal, non-formal, and informal learning leading to rethink the definition of learning context: Activity Theory can help create a model integrating formal and formal learning through collaborative knowledge building involving schools and professionals/employers. Interaction between these two “activity systems” generates “boundary objects” that can be useful in both the educational setting and the workplace. This process can provide students with meaningful situated learning experiences that boost their motivation and interest, just because they are based on real-world problems that students will face in their future jobs.
Mobile learning, or m-learning, has become an umbrella term for the integration of mobile computing devices within teaching and learning. In the literature, however, use of the terms has been unsystematic. The purpose of this article is to critically examine the principles of mobile learning. First, I examine the extant literature with regard to defining mobile learning. Four definitions of mobile learning categories are described: (1) relationship to distance education and elearning, (2) exploitation of devices and technologies, (3) mediation with technology, and (4) nomadic nature of learner and learning. Second, in an effort to provide a basis on which to ground future mobile learning research, I propose a framework of design characteristics for mobile learning environments. Seven design characteristics are identified and discussed. Finally, I present implications for future research and instructional design. This paper contributes to the field of mobile learning by providing researchers more precise ways to identify and describe the characteristics of mobile learning environments, as well as describe the attributes of successful mobile learners.