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Mobile Technologies and Learning: Expectations, Myths, and Reality

  • Lina PetrakievaEmail author
  • David McArthur
Living reference work entry

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Abstract

M-learning is often approached as an innovative method to teach, but quite often without the proper planning of the actual learning process and proper understanding of the implications on the pedagogy of the learning process in such a setting. Because of the multiple stakeholders in the process – the institution, the learners, the educators, the policy-makers, etc. – it is very difficult to encourage educators to engage with something so different that will require a rethink of their teaching practices. In addition, with so many different technical elements and challenges, it is often simply just too daunting a prospect.

It is also unfortunate that m-learning is often only limited to simply mobile access. A good m-pedagogy will not just transfer the learning process to a mobile device but incorporate the very nature of mobile, flexible, user-guided, bite-sized learning.

The recent rise of learning and learner analytics has also highlighted the issue of how students engage with university systems and the ethical consideration of such data being collected and used.

Real m-learning needs to have a real purpose, and the stakeholders need to see the value in it for it to have a chance to be a success. Having all the correct m-pedagogy in place and if both educators and learners see the value of engagement, m-learning can bring real benefits – flexibility of access and freedom of engagement therefore allow a real meaningful tailoring of the learning process. Only very recently have real attempts been made to motivate progression toward adaptive learning. Incorporation of pedagogy and AI (artificial intelligence) methods seems to be pointing to a future of real, adaptive, and effective m-learning.

Keywords

M-learning M-pedagogy Digital literacy Learning analytics Learner analytics 

Introduction

With the development of the humble mobiles from a cordless phone that can be carried around to a supercomputer that can do almost anything imaginable that a piece of technology is capable of, the excitement of the potential use for education has grown exponentially (see “Characteristics of Mobile Teaching and Learning”). As new generations of learners become seemingly more digitally literate, the drive to engage more with technology-enhanced learning is being driven mostly by learners (because they are used to it) and by the management (seeing it as a cost-saving exercise and promotion opportunity).

However, the full understanding of the pedagogy in relation to the use of technology, the understanding of the real level of digital literacy of the learners, the fast pace of technological development, as well as the sometimes resistant-to-change educators or CAVEs – colleagues against virtually everything – have to be taken into account when any technology-enhanced learning and especially mobile learning solution are being implemented (see “1:1 iPads in First Grade: Case Study of a Teacher’s Concerns and Implementation”).

The term educators will be used in this chapter for lecturers, teachers, and many others involved in teaching in one way or another, bearing in mind that not all involved in mobile learning will be lecturers; there will be tutors, learning technologists, teaching fellows, and others.

Myths and Expectations

There is an expectation from learners that when they come to university or college, they will be given Wi-Fi access and access to technology, as well as provided with training of how to use it in an education setting. The Digital Student project (JISC 2014) shows that although the expectations vary greatly, some are quite widespread. According to Beetham (2014), some of the common are (see also “Characteristics of Mobile Teaching and Learning”):
  • Robust and ubiquitous Wi-Fi across campus locations

  • Easy to connect their own devices to the university network and access personal/social web services

  • Continued access to institutional devices, especially desktop computers with relevant software for their use

And while the students have some clear expectations in terms of the technology and connectivity, when it comes to the role of technology in their education and especially in terms of their chosen course and future career, the students are unclear (Beetham 2014). This is where the role of an educator comes in, and it is important to understand that this role comes with big responsibility. The information and communications technology (ICT) confidence of the teaching staff has a strong impact on the students and their own use of technology.

From an institutional point of view, “digital natives”(Prensky 2001) are an increasing proportion of the new learners, so they are not supposed to need so much support and training, apart from access to technology. Although a number of subsequent studies (Bennett and Maton 2010; Margaryan et al. 2011) have shown that the “digital natives” are a myth and there is much more variety and nuances in the skills, abilities, and attitudes of the learners, the institutions seem to cling on to that notion that learners use technology all the time and are able to learn how to use it on their own (see “Student Feedback in Mobile Teaching and Learning”).

With the ubiquitous access to mobile devices now, most institutions are also keen to implement a BYOD (bring your own device) strategy, as this is usually seen as a very cost-effective way to reduce the money spent on technology. However, the Digital Student project’s (JISC 2014) most recent findings published clearly state that students “don’t want technology to be a substitute for ‘the real people, in the same place, learning together” (Beetham 2014). That means that the institutions are still expected to maintain access to computer labs, printers, etc. and with the increased diversity of devices brought in as a result of BYOD, the technical and support staff actually have an increased workload, so overall the idea of using BYOD for cost-saving reasons for the institution ends up costing more to the staff (Keyes 2013).

Most of the universities and colleges have included the notion of creating digitally literate graduates in their policies, mostly with emphasis on employability, but the strategy to achieve that is usually simply relying on the teaching staff to be able to do that as part of their subject teaching and not recognizing the need for specialist on digital literacy to teach both the educators and the learners. Most of the time, the institutions will be keen to promote and show that they are implementing technology-enhanced learning or blended learning and have the attitude that all learning is suitable to be carried out using technology, even if that means simply putting up your lecture slides in the institutional virtual learning environment (VLE). Using VLE for every module is one easy way of showing that the institution is using “blended learning.” Providing electronic feedback is another common use of technology that often justifies the use of the term “blended learning.” However, there is a big difference between providing access electronically to teaching material and true blended learning and m-learning (Littlejohn 2007). Universities, colleges, and schools have all tried to modify and adapt the teaching to incorporate mobile technology, and almost all support departments are scrambling to create apps, so they can get to the learners quicker and closer. A quick search in the App Store and Google Play with almost any higher education institution’s name will show at least one app created. A lot of institutions are also using generic apps to get access to their resources like library (LibAnywhere, BorrowBox Library, etc.) and VLE (Blackboard Learn, Moodle Mobile, etc.). However, a lot of them are still struggling to understand and utilize the potential of true mobile learning, not just mobile access or mobile services. What is usually lacking is a proper m-learning and m-teaching strategy, with support for both educators and learners to fully benefit from m-learning.

Reality

Technical Side

The use of mobile devices in a learning and teaching setting also has technical limitations that need to be taken into account. Some of the issues of using mobile devices to access information are discussed by Petrakieva (2012, p. 159), and although the mobile device features are constantly improving, the majority of issues are still present simply due to the nature of the devices (see “Characteristics of Mobile Teaching and Learning”). Some of the main ones are:
  • Access to technology – educators can design good m-learning only if they are familiar with the particular technology, and this means having access to it. It also means that in order to make sure that everything will work on the learners’ devices, the institutions will have to either issue the devices to every learners, thus ensuring parity, or make sure that anything created is rigorously tested on all popular systems (iOS, Android, etc. devices; see figure below) while also offering borrowing options for those without a smart mobile device. The mobile operating systems market is converging at the moment with the main considerations focused on only two systems – Android and iOS – however, this could vary depending on the country and access to technology, so an overview of the particular context is still advisable (Fig. 1).

  • Wi-Fi and mobile Internet access – if the m-learning is to be part of a class, access to the Internet due to buildings that were built before Wi-Fi was available and simply the lack of bandwidth to support large classes are common problems. The same problems exist with the mobile network coverage, and not all learners will have mobile phone contracts that will allow them Internet access, or they may simply not wish to use a personal device and contract in class nor should they be expected to.

  • Software access – most of the m-learning solutions will involve specialist software, either ready to buy or custom made. In both cases there is the issue that most free versions have severe limitation and thus limited application or a license needs to be purchased which usually involves preparing a business case by the educator so the institution can justify spending the resources. The required know-how in choosing the right software; the skill set involved in using the software, potentially having to write a business case; and the time required are great deterring factors for educators not to pioneer m-learning in their institutions.

Fig. 1

Mobile operating systems new sales shares, first quarter of 2016 and 2017 (Gartner 2017)

When m-learning is concerned, it also should not be forgotten that the situation in the developing countries is very different in terms of access to technology, access to the Internet, etc. Although the difference between the availability of mobile technologies in the different parts of the world is narrowing, it is going to have an impact on the way m-learning is being used for some time yet. In a recent report by UNESCO (West and Ei 2014), it is very clearly shown how simple basic access to mobile reading makes a big difference to the people in the developing countries; however, simply having access is not enough. “People who think that literacy can be achieved by mere proximity to reading material should be reminded that it took the most talented linguists on the planet over a thousand years to decipher Egyptian hieroglyphs. The challenge wasn’t access to hieroglyphs; it was figuring out what they communicated. Humans may have a language instinct, but there is nothing natural about reading; it is a skill that needs to be taught and practiced, again and again and again” (West and Ei 2014). Similarly, there is nothing natural in using technology for learning either. Simply providing access to it to educators and learners will have a very minimal and limited effect. That is why a development strategy, support structure, communication, and willingness to change and develop are some of the major other components of a successful implementation of m-learning that are often forgotten or ignored.

For now, the focus will be on the developed markets where the mobile technology penetration is much higher and the m-learning can be considered as a viable option to go into the mainstream education. And although developed countries are assumed to be uniformly well-off, there are big differences within them. There is a big drive in a lot of countries for widening access and participation. “Across the world, higher education has turned from a privilege available to an elite few into a mass expectation”(David et al. 2012). All learners are assumed to have access to technology (PC/laptop at home, smartphone with Internet access, tablet); however, as mentioned earlier, access doesn’t equate skills to use the technology for educational purposes. The digital divide is still very much alive, and although there seems to be a shift from knowledge gap to usage gap (van Deursen and van Dijk 2013), there should not be an assumption that all learners will have access to mobile technologies to an extent of using them for m-learning.

The M-Learning Paradigm

Before any m-learning is implemented, a clear investigation of the particular needs of the learners and educators and the underlying pedagogical reasons is necessary. Implementing m-learning for the sake of doing it is a common reason why it fails to deliver on the expectations. Taking into account the universal instructional design (UID) principles for mobile learning suggested by Elias (2011) with an emphasis on solid pedagogical approach can be the key to a successful m-learning application.

The major stakeholders in the process – the learners and the educators – have to see the need and the value in investing in a potentially difficult new approach. Without this full investment in the process, after the initial novelty effect has worn off, the learners will simply revert to the familiar study patterns.

Following Maslow’s idea (1943) of hierarchy of needs, going from basic (deficiency) needs to growth (self-actualization) needs, the m-learning could be viewed in a similar way, taking into account the slight differences of the two main stakeholders: learners and educators (see Fig. 2). Although a real m-learning can only be achieved when all layers are present, so it should be outside, encompassing all, it has been put in the center, as a pearl in a shell that can only develop fully when the shell is complete and strong and surrounding environment is fertile.
Fig. 2

M-learning requirements hierarchy

The Learner Perspective

The most basic requirement to achieve real m-learning is to make sure that the learner has access to a smart mobile device. This may be taken for granted in most developed countries; however, that cannot be simply assumed when the learning is taking place in a developing country, for example (see “SMS Enriched Student Support: Transforming the Culture of Learning at University of the South Pacific”). And it is not so much the mobile device itself but their ubiquitous access to the Internet that makes the difference to what people use their devices for. And although in most universities there is free Wi-Fi access, the m-learning idea is to make the learning accessible anytime anywhere and that means learners having ready Internet access. Very few institutions yet are providing the mobile devices to their learners, and that means that most mobile devices are for personal use as well as for study.

The influence of the ICT self-efficacy to the adoption of m-learning (Callum and Jeffrey 2013) should also be considered. When the learner is faced with using new technology, the real and perceived lack of ICT skills may be a barrier to the engagement. The appropriate support in place is crucial for overcoming such problems.

Nurturing a positive attitude to the process is key to the success of the process, as even with all the technology setup and with all the skills necessary, if learners don’t want to engage, the implementation and use of m-learning will not be a success.

The Educators’ Perspective

Access to technology is even more crucial for educators as they need to be able to set up the m-learning to work with any mobile device that the learners can use or make sure that one is issued to them. Most of the time, this requires either a lot of resources (in the cases when devices are issued to the learners) or specialist technical knowledge, usually from technical staff in the institutions (in the cases when the m-learning has to be compatible with all devices). Because of this, most of the time, the educators end up simply setting everything up just through web access, and because all mobile devices now ubiquitously have this capability, this approach ensures that all learners can access it. But this ends up no different than the e-learning that educators are used to, and it becomes too easy for them to fall into the pattern of producing just e-learning materials and not really adapting the approach for a real m-learning. Providing the educators with the technology and the necessary training to use this technology is important for allowing them to adopt fully m-learning and to concentrate on best using the technology to support the pedagogy.

There is also a need to re-evaluate the pedagogical approach when using m-learning (Clark 2014) and not just add on technology or “mobile access” and claim that that is m-learning. The fundamental difference of using mobile devices, with their limited screens, limited creativity tools, and limited Internet access, does also provide an opportunity to develop a more natural, bite-sized delivery that is not just linear, but interlinked, thus allowing good educators to deliver a better learning process and achieve better learning outcomes by providing flexibility and allowing the learner to incorporate the learning into their life.

Once the m-learning pedagogy is thought through and properly planned and with the technology in place, the educators’ ICT skills will play a crucial role in translating the pedagogy in a real m-learning experience for the learners. If the educators have low self-efficacy in ICT, that will have an impact on the perceived difficulty of engaging with m-learning by the learners (Mac Callum and Jeffrey 2014). The expectation that educators will have adequate ICT skills to implement m-learning can be one of the major reasons for the educators not to get involved in m-learning as they feel they don’t have the skills and they will not be offered support to develop those skills (Aubusson et al. 2009).

Another one of the main features of the m-learning approach is flexibility. Allowing the learner to dictate how, when, and what they access is paramount, and in order to create an m-learning process that can accommodate that, and even more, to use this as its main advantage, means that the educator has to have an approach and attitude flexibility. Change should become an intrinsic part of the process, and thus the expectation that things will happen according to plan is tenuous. So flexible attitude is needed – plans will change, technology may fail, and learners will not behave as expected – and that should be taken as an opportunity to develop a better, more flexible, and more robust approach to m-learning and not as a sign of an impossible task. That flexibility and preparedness for change should be a vital part of the attitude of the educator to make the m-learning a success story.

One of the outcomes of the Jisc Digital Capabilities project (JISC 2017a), which investigated the different strands of what it means to be digitally capable, has been the framework illustrated in Fig. 3. It shows the ICT skills at the center since they are fundamental for any digital endeavor; however, they are not the only required element to be a digitally capable educator, especially if a more advanced approach is being used such as m-learning.
Fig. 3

Jisc Digital Capabilities framework (JISC 2017a)

The recent pilot of a Jisc Discovery Tool for evaluating digital capabilities highlighted in its feedback how much academic staff valued the discussion created around the subject and the opportunity for targeted development (Beetham 2017). Combined with the fact that only 36% followed up with the resources suggested for developing further their digital capabilities clearly still indicates how difficult it is to engage staff in developmental work, even when they are aware of their need for that development.

The Environment

The m-learning process can only really succeed when there is a need to implement it and both sides of the process – the educators and the learners – see the value in doing so. If the m-learning is used just as a box-ticking exercise to show that the institution is engaging with new technology and approaches, the process will inherently start off with the incorrect premise, and neither the educator nor the learners will have the impetus to engage with it. Both main stakeholders will have to see the need and the value in using m-learning and thus will be invested in making it a success.

Valuable outcomes from effective m-learning are of a bifold nature:
  • Firstly, successful student learning and motivation to engage with their studies, and with the technology used, can both be improved. This outcome forms an integral part of any learning process and pedagogy.

  • Secondly, the use of m-learning allows for the collection of data regarding the engagement with the learning process itself. This type of data has recently been identified as a major area of focus for most Institutions, as it can be used to identify potential problems and issue in the future.

Learning and learner analytics are seen as effective ways to predict and better guide interventions. Depending on how the gathered data is viewed and utilized, different types of analytics exist. The data analysis perspective also differs when looking at either learning or learner analytics.

Learning and Learner Analytics

Learning Analytics

Higher education institutions have become fixated with the concept of data gathering and the possibility of using such data to measure the educational contributions of online learning resources and applications.

The use of virtual learning environment (VLE) such as Moodle, Blackboard, Canvas, etc. provides effective online platforms and interfaces through which learners access their course materials and interact with their programs of study. Each VLE provides a plethora of data streams regarding the accessing of study modules, and interaction with resources therein, highlighting student engagement with their respective modules. The inclusion of third-party and in-built tools for assessment is frequently used by institutions to generate coursework, online tests, and written assessments through which the student learning pattern is monitored. The data harvested through these systems is analyzed through dashboards to identify student engagement and activity to predict student’s potential for success and to identify those who are at risk of failing to achieve their imaginable outcomes within their modules of study. There is a widespread interest in this type of analytical approach, and some good examples of practice, including Manchester Metropolitan University, the University of Bedfordshire, and the London South Bank University’s partnership with IBM, have used such systems. The Open University have also utilized their AnywhereApp to the same effect (De Quincey et al. 2016).

Students do not, however, limit themselves to learning only via university-based systems, e.g., VLE, etc. Utilizing their own mobile technologies and home-based computing systems allows for a wider and varied repertoire of research and learning options to be accessed (see “Student Feedback in Mobile Teaching and Learning”). Social media feeds such as Twitter and Facebook groups, blogs, wikis, and other social and/or informal learning contribute to student learning and are completely outwith any scope of measurement by university-based monitoring systems.

These learning pathways, and the associated knowledge garnered, cannot be tracked nor analyzed, by university systems, leaving a considerable amount of learning data potentially missing, which could lead to misinterpretation and misguided interventions planned on incomplete data.

An analysis of student engagement through dashboards and VLE data feedback does form a relatively effective overview of student learning from the teacher/lecturer perspective; however, the student’s view of learning analytics as a motivator is not necessarily the same.

Learner Analytics

The main focus of research in this field has largely fallen upon learning analytics, where the information is used to guide improvements and interventions to the learning process. When this data is used to monitor progress and guide interventions regarding the learner, the focus falls upon the learner engagement, and the overall analysis perspective changes. However, this raises potential ethical questions regarding how data is collected and the purposes for which it is used. This is still very much an open debate within the educational community.

The educational community is very much concentrating on the learning analytics and only seeing the learner analytics as a side aspect of it and thus avoiding the focus on it. The recently agreed JISC Learning Analytics Services terms and conditions state: “If Learning Analytics Services are agreed, Personal Data will be analysed to provide learner level information, and combined to provide grouped information, for example at a course or module level”(JISC 2017b), which clearly indicates the educational sector’s approach of using the term learning analytics for all aspects of data collection and analysis even when the data is used at a learner level and for learner intervention.

Very few studies exist regarding student’s views and interpretation of learning analytics and how the use of such data can work as a motivator for improving their overall experience and learning outcomes. A study carried out by Keele University on a group of their own computer science students (De Quincey et al. 2016) revealed that their perceptions of the IT systems used for analysis were limited in their output with regard to student motivation. Suggestions formulated by students for effective “learner analytics” and motivators for increased engagement included VLE dashboards to present data elements such as attendance and assessment tracking, engagement, activity, and progress meters.

National student surveys (NSS) carried out annually within the UK regarding programs of study within higher education institution offer final year students the opportunity to give their opinion and feedback regarding their experiences of their course of study. The outcomes of this survey are looked upon with great interest and importance by all institutions. Each year, for many institutions, the returned feedback from the NSS highlights the student’s need for improved “feedback” from their module and programs of study. Maybe it is time to implement the student perspective, provide visual learner analytic data that motivates positive study activities, and reap what mutually beneficial rewards appear.

The access to the learner analytics data for learners themselves could prove to be even more beneficial than the institutional use of it, as it may trigger more intrinsic motivation for the learners themselves.

M-Learning Trade-Off Issues

Educational pedagogy would generally dictate that, when using an online resource for information delivery and incorporating interactivity, when input is required, it is necessary to retain the context of the input requirement visible to the user. That however becomes a major issue when designing for small screen m-learning. Some compromises therefore need to be incorporated into the m-learning pedagogy to allow for effective small screen m-learning development.

One example of a system, developed as a learning object with varied device uses in mind, is a pilot project within Glasgow Caledonian University. The Pre ICT Induction (PICTI) (Petrakieva and McArthur 2017) is based upon many years of experience delivering ICT Induction to new students. The information delivered via the resource was designed to be viewed on medium to larger screen sizes; however the information could also be read on smaller handset devices. Issues for designing for smaller screen delivery became apparent when interactivity with the resource required students to input information. The moment a screen keyboard appears, the screen real estate available on small handheld devices reduced significantly from an already limited screen space as illustrated in Fig. 4.
Fig. 4

Different views of the Pre ICT Induction (PICTI) on a mobile screen and the same screen with the keyboard visible for entering data

Development of PICTI version 2 will look to creating an adaptable format to compensate for the issues discovered with the pilot project. This may require rethinking how user input is presented on screen. Utilizing multiple-choice answer responses instead of typed answers in some cases will eliminate some issues; however pedagogically, this may be too much of a sacrifice to retain screen real estate (Fig. 5).
Fig. 5

Pros and cons of using multiple-choice questions in m-learning

Future Directions

The communication side of the mobile devices was probably the first one to go mainstream – texting in class, texting announcements, expectation that learners will receive their email on their phone, etc. However, deeper learning with mobile devices requires more developmental pedagogical approach from the educators’ perspective and more engagement and correct attitude from the learners.

Having a more flexible approach to m-learning and acknowledging that it is also an individual tool for note-taking and collating information, quick access to info may be the use of mobile technology that should be encouraged and supported more, as this will develop some of the skills that will be used in the real world of work. So instead of trying to adapt the teaching to be delivered to mobile devices at all cost, it should be acknowledged that sometimes having a mixed approach to teaching – traditional, location, and time set learning with the addition of using technology and specifically mobile devices – may be a more practical approach for the time being.

Until the m-learning becomes a more adaptive learning, the process of setting it up may be a bit too complicated for most, hence the limited adoption (see “Characteristics of Mobile Teaching and Learning”). Recent attempts to develop adaptive learning by the Edinburgh-based company CogBooks that uses AI (artificial intelligence) techniques to learn from the learner’s behavior and to guide them to the most pedagogically sound next step may be the most appropriate stage in the development of m-learning. Then the educators can concentrate on developing the m-pedagogy and using a unified system to deliver the m-learning itself.

Cross-References

References

  1. Aubusson, P., S. Schuck, and K. Burden. 2009. Mobile learning for teacher professional learning: Benefits, obstacles and issues. Alternatives Journal 17 (3): 233–247.  https://doi.org/10.1080/09687760903247641.CrossRefGoogle Scholar
  2. Beetham, H. 2014. Students’ experiences and expectations of the digital environment | Jisc, 23-06-2014. http://www.jisc.ac.uk/blog/students-experiences-and-expectations-of-the-digital-environment-23-jun-2014. Accessed 3 May 2018.
  3. Beetham, H. 2017. Discovery tool – what we learned and where we go next. https://digitalcapability.jiscinvolve.org/wp/2017/08/31/discovery-tool-what-we-learned-and-where-we-go-next/. Accessed 3 May 2018.
  4. Bennett, S., and K. Maton. 2010. Beyond the “digital natives” debate: Towards a more nuanced understanding of students’ technology experiences. Journal of Computer Assisted Learning 26 (5): 321–331.  https://doi.org/10.1111/j.1365-2729.2010.00360.x.CrossRefGoogle Scholar
  5. Callum, K. Mac, and L. Jeffrey. 2013. The influence of students’ ICT skills and their adoption of mobile learning. Australasian Journal of Educational Technology 29 (3): 303–314.Google Scholar
  6. Clark, D. 2014. ‘Keynote speech, iTech 2014’. http://youtu.be/bO0W-2Kl_zQ
  7. David, M. et al. 2012. Widening participation in higher education.  https://doi.org/10.1057/9781137283412.Google Scholar
  8. De Quincey, E. et al. 2016. Learner analytics; The need for user-centred design in learning analytics. 3(9): 6–9.  https://doi.org/10.4108/eai.23-8-2016.151643.CrossRefGoogle Scholar
  9. Elias, T. 2011. Principles for mobile learning. International Review of Research in Open and Distance Learning 12: 143–156.CrossRefGoogle Scholar
  10. Gartner. 2017. Gartner says worldwide sales of smartphones grew 9 percent in first quarter of 2017. http://www.gartner.com/newsroom/id/3725117%0A. Accessed 3 May 2018.
  11. JISC. 2014. Digital student project | Jisc, 2014. http://www.jisc.ac.uk/research/projects/digital-student. Accessed 3 May 2018.
  12. JISC. 2017a. Building digital capability. https://www.jisc.ac.uk/rd/projects/building-digital-capability. Accessed 3 May 2018.
  13. JISC. 2017b. JISC Learning_analytics_report.pdf, JISC Learning analytics services appendix terms and conditions. https://analytics.jiscinvolve.org/wp/files/2017/07/Jisc-Learning-Analytics-Service-TCs_20170725_Final.pdf. Accessed 3 May 2018.
  14. Keyes, J. 2013. Bring your own devices (BYOD) survival guide. Boca Raton: CRC Press, Taylor & Francis Group.CrossRefGoogle Scholar
  15. Littlejohn, A. 2007. Preparing for blended e-learning. London: Routledge.CrossRefGoogle Scholar
  16. Mac Callum, K., and L. Jeffrey. 2014. Comparing the role of ICT literacy and anxiety in the adoption of mobile learning. Computers in Human Behavior 39: 8–19.  https://doi.org/10.1016/j.chb.2014.05.024. Elsevier Ltd.CrossRefGoogle Scholar
  17. Margaryan, A., A. Littlejohn, and G. Vojt. 2011. Are digital natives a myth or reality? University students’ use of digital technologies. Computers & Education 56 (2): 429–440.  https://doi.org/10.1016/j.compedu.2010.09.004. Elsevier Ltd.CrossRefGoogle Scholar
  18. Maslow, A. 1943. A theory of human motivation. Psychological Review 50: 370–396.  https://doi.org/10.1037/h0054346.CrossRefGoogle Scholar
  19. Petrakieva, L. 2012. The shift to mobile devices. In User studies for digital library development, ed. M. Dobreva, A. O’Dwyer, and P. Feliciati, 159–165. London: Facet Publishing.CrossRefGoogle Scholar
  20. Petrakieva, L., and D. McArthur. 2017. Pre ICT induction – PICTI. https://goo.gl/HW8CTA. Accessed 3 May 2018.
  21. Prensky, M. 2001. Digital natives, digital immigrants part 1. On the horizon 9 (5): 1–6. http://www.marcprensky.com/writing/Prensky–DigitalNativesDigitalImmigrants–Part1.pdf. Accessed 3 May 2018.CrossRefGoogle Scholar
  22. van Deursen, A.J., and J.A. van Dijk. 2013. The digital divide shifts to differences in usage. New Media & Society 16 (3): 507–526.  https://doi.org/10.1177/1461444813487959.CrossRefGoogle Scholar
  23. West, M., and C. Ei. 2014. Reading in the mobile era: a study of mobile reading in developing countries. http://unesdoc.unesco.org/images/0022/002274/227436E.pdf. Accessed 3 May 2018.

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Learning Development Centre / School of Health and Life SciencesGlasgow Caledonian UniversityGlasgowUK
  2. 2.Learning Development Centre / School of Computing, Engineering, and the Built EnvironmentGlasgow Caledonian UniversityGlasgowUK

Section editors and affiliations

  • Kshama Pandey
    • 1
  1. 1.Faculty of EducationMahatma Jyotiba Phule Rohilkhand UniversityBareillyIndia

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