MET:Mobile Learning in Higher Education

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This page originally created by Nadia Desai and Ya-Yin Ko

Mobile learning, or “m-learning” for short, commonly refers to any type of learning that takes place with the support of easily portable and wireless electronic devices.[1] In one sense, mobile communication devices can be transformed into “classrooms on the move” offering information and learning on demand via text, multimedia, and interactivities. For example, m-learning potentially supports:

  • Mobile educational gaming and simulations to motivate or engage learners reluctant to participate in traditional classroom-based environments
  • Socially-networked learning between individuals around the world
  • Learning that is contextualized in locations outside of the classroom or institution
  • Real-time sharing of observational learning from different locations
  • Educational outreach to co-learners/experts/practitioners/communities outside the classroom or institution
  • Remote testing

However, as this article will explain, there are varying interpretations of the term m-learning, which have important implications for the conceptualization and design of learning environments. In the context of higher education, the proliferation of mobile devices and the concurrent developments in m-learning are significant, as learners have become “nomads” who can simultaneously have conversations with instructors and classmates, complete assignments, do research, and make use of course management tools outside of home or campus.[2] In general, m-learning remains a “minor adjunct to learning activities” in higher education, but as mobile devices continue to shape learners’ daily lives, educators must consider how these new modes of connection affect learning practices and modern conceptions of teaching and learning environments.[3]

Example of mobile learning using an Augmented Reality (AR) app (Wikitude World Browser) with GPS, displaying information about a lake as a digital overlay at the actual location of the lake

Definition of m-learning

Scholars and educators have actively discussed and debated the meanings of m-learning; there is no single universal definition. Definitions have shifted depending on the educational/technological landscape or the scholarly/disciplinary context in which it is explored. Building on work presented by Mike Sharples in 2006, Pachler, Bachmair, & Cook (2010) delineate three broad phases that characterize how the definition of m-learning has evolved along with experiments conducted by educators around the world since the 1990s:

  1. A focus on devices, exemplified by m-learning experiments starting in the mid-1990s: Educators during this phase attempted to define which wireless devices could be used effectively for instruction and training.[4] Devices explored include mobile phones, e-books, classroom response systems, Personal Digital Assistants (PDAs), game devices, and other personal media players.
  2. A focus on learning outside the classroom, exemplified by experiments starting in the early 2000s: This phase saw educators trying to define what the devices could do to support learning situations outside of school, such as field trips, “bite-sized learning,” and “personal learning organisers”.[5]
  3. A focus on the mobility of the learner, exemplified by experiments starting in the mid-2000s: Carrying on into the present time, this phase is about defining m-learning as an increasingly ubiquitous reality that is re-configuring learning spaces and crossing over into informal learning and lifelong learning.[6]

(Note that this is not a comprehensive history of m-learning. The first examples of m-learning based on portable electronic devices pre-date the 1990s. See the M-Learning wiki entry.)

The shift from the device-centric definition of mobile learning to a learner-centric one “allows mobile technology to be viewed as a means of supporting learning mobility rather than defining it."[7] In this context, m-learning can be defined as encompassing “any type of learning that takes place in learning environments and spaces that take account of the mobility of technology, mobility of learners and mobility of learning."[8]

Definitions of m-learning often risk oversimplification or overlapping with e-learning and distance learning. For instance, framing m-learning in terms of learning outside the classroom at flexible times ignores the fact that people were always able to learn outside of the classroom—whether for formal education or other ends. Though larger than mobile phones, paper books are typically portable and are accessible at all times of day. Similarly, framing m-learning in terms of the mobility of the learner potentially ignores the fact that people have been free to learn from conversations in coffee shops, reading on a park bench, or visiting a museum in the absence of mobile devices.

Thus the challenge of carving out a unique definition of m-learning is that it needs to be sufficiently specific so as not to replicate the basic tenets of distance learning or e-learning. It would need to account for multiple characteristics including time-space flexibility; the mobility of the devices and the learners; technological affordances such as GPS navigation, Voice over Internet Protocol (VoIP), or mobile-technology-mediated interactions with other learners and physical environments; and how these characteristics cross-fertilize to produce unique learning experiences. It is a fine point, but Kress & Pachler (2007) aptly express what is new about m-learning as follows:

the answer to ‘what is mobile?’ is...‘all the world’. All the world has become the curriculum; the world itself has become curricularised...That which is ‘mobile’ is not knowledge or information, but is the individual’s habitus: whether I am out in the countryside, in my bed, or in a classroom is, relatively speaking, beside the point. What is not beside the point is the ability to bring things into conjunction which might previously have been relatively difficult to join. The habitus of the individual for whom all the world is always already seen as a curriculum, becomes shaped by that experience and expectation: always expecting and ready to be a ‘learner’.[9]

According to Pachler, Bachmair, & Cook (2010), what is “new” can also be described as

the convergence of services and functions into a single device, its ubiquity and abundance, portability and multi-functionality; abundance in particular in the sense of a shift away from educational institutions having to provide technological devices towards the learner doing so. What is also new, and very significant in our view, is the boundary- and context-crossing mobile technologies and devices enable in relation to learning.[10]

In an effort to define m-learning as a convergence of personal, technological, and social spheres, Koole (2009) has developed the Framework for the Rational Analysis of Mobile Education (FRAME) model. The model presents m-learning in terms of the convergence of three aspects: the learner aspect, the device aspect, and the social aspect.

The FRAME Model[11]

While the FRAME definition of m-learning covers much of the necessary territory, it is by Koole’s own description a hypothetical and “ideal mobile learning situation."[12] Several assumptions are made within the situation represented by Koole's Venn diagram: first, it is assumed that mobile technology is prevalent, equally accessible, and equally usable to all participants in the learning situation; second, it is assumed that those who possess the technology want to use it to learn; and third, it is assumed that the technology is stable throughout the learning situation. Technology changes, often and radically.

As Traxler (2007) argues, m-learning is “essentially personal, contextual, and situated; this means it is 'noisy' and this is problematic both for definition and for evaluation.”[13]

Pedagogical perspectives

Many scholars have attempted to conceptualize pedagogies that are unique to m-learning. The challenge is threefold: First, broad conceptualizations that centre around the flexibility of time and place, collaborative knowledge building, changing patterns of learning activity, or changing dynamics between teachers and learners, are often also true of other technologies, e-learning, and distance learning in general.[14] Second, more focused conceptualizations that stem from the properties of particular devices, systems, or platforms are “unstable” due to their transience and diversity in the rapidly evolving technological landscape.[15] And third, the concept of mobility itself is not new, and has long been explored by scholars and educators.[16] The critical question is therefore: What is actually new about m-learning pedagogy? Is it necessary, or should it simply be an extension of existing frameworks on e-learning or distance learning?

Consider for instance the following general concept of m-learning, from Koszalka & Ntloedibe-Kuswani (2010):

...m-learning by definition differs from e-learning and distance learning insofar as its instructions and support mechanisms are facilitated through m-technologies for learners who are mobile themselves. Instructional activities are not within a set place. Rather learners are engaged, often synchronously with others and learning resources, while outside the borders of a formal classroom. The focus of learning changes from learners as consumers of learners as idea generators, producers of artifacts, and sharers of new knowledge through these artifacts.[17]

The learning features that they’ve identified—instructional activities and learner engagement taking place outside of the classroom; learners generating ideas and sharing knowledge; producing artifacts—were technically all possible prior to the proliferation of mobile devices. Therefore, while there are attempts to define m-learning according to non-techno-centric terms, it is challenging not to draw distinguishing features of m-learning from the technology itself.

M-learning as personalized, authentic, collaborative experience

Despite the challenges noted above, scholars have proposed various m-learning pedagogies that distinguish it from traditional modes of classroom-based pedagogy, regardless of whether they are strictly unique to m-learning. Building on Traxler’s (2007) argument that the conceptualization of m-learning should focus on the “underlying learner experience,” Kearney et al. (2012) propose that the three distinctive characteristics of m-learning experiences from a pedagogical perspective are personalisation, authenticity, and collaboration.

Framework comprising three distinctive characteristics of m-learning experiences, with sub-scales[18]

In this framework, personalisation draws on motivational and socio-cultural theory to emphasize choice, agency, and customization in learning experiences. Authenticity refers to learning experiences that are relevant to real-world practices and contexts that are personally meaningful or valuable to the learner, and collaboration points to the high level of multi-modal, spontaneous social interactivity and resource-sharing that is made possible by mobile devices. These constructs, in the context of m-learning, transcend the typical time-space constraints of traditional, classroom-bound formal learning.

The authors argue that this framework helps practitioners evaluate applications of m-learning in terms of pedagogy, but acknowledge it cannot be all-encompassing as other important factors such as learners’ specific characteristics and needs, and the role, preferences, and characteristics of teachers would influence individual situations.

M-learning as digitally-facilitated site-specific learning

Others have also emphasized the collaboration aspect in conceptualizing a pedagogy of m-learning, but with a stronger simultaneous focus on learners’ interactions with the physical environment. Koszalka & Ntloedibe-Kuswani (2010) argue that mobile technology is “at its best” when used for communication rather than content delivery; activities should focus on collaboration, prompt learners to communicate frequently, and share data that is collected from the learners’ locations.[19]

Like Koszalka & Ntloedibe-Kuswani, Laurillard (2007) proposes that the physical locations/environments in which mobile learners are situated are key to the conceptualizing m-learning in distinctive pedagogical terms. For Laurillard (2007), the emphasis is “more on the nature of the physical environment in which the learner is placed, and hence the ‘digitally-facilitated site-specific’ learning experience that is now possible with mobile technologies, that was not possible with a desktop and landline."[20]

Scholars who describe m-learning in terms of collaborative, knowledge-building pedagogies are generally cautious about overstating their promise. Koszalka & Ntloedibe-Kuswani (2010) argue that the existing research tends to lack rigour and do not provide strong evidence of demonstrable differences in learner engagement and outcomes. However, they note that m-learning can be viewed in light of its capacity to enrich and support both classroom and distance education, and its potential to increase access for those who are typically at a educational disadvantage due to location or economic status.

M-learning as content and assessment delivery

Many current applications of mobile devices for learning focus on content and assessment delivery or course management—an “add-on” to standard classroom practices. This is not consistent with the m-learning pedagogies espoused by scholars such as the ones mentioned above, but its application is prevalent enough that it ought to be recognized as a distinctive approach to m-learning. In higher education, a common use of m-learning as a content and assessment delivery tool comes in the form of commoditized mobile apps that are meant to help “on-the-go” learners study textbook materials with flashcards, quizzes, and summarized chunks of information (see for instance McGraw-Hill’s “Study Econ” mobile app or Wiley’s CPA Review Apps). Furthermore, many higher education faculty have conceived of m-learning in terms of providing students with podcasts and downloadable lectures (see for example Evans, 2008; Nataatmadja & Dyson, 2008).

Implications for design of m-learning environments in higher education

Many other pedagogical and theoretical frameworks have been proposed in relation to m-learning. Kearney et al. (2012) summarize some of the features or theoretical models that other scholars have used to characterize m-learning:[21]

  • Portability, social interactivity, context sensitivity, connectivity, individuality[22]
  • Engagement, presence, flexibility[23]
  • Personal, contextual, situated[24]
  • Structures, agency, cultural practices[25]

A question that arises with regards to m-learning is what constitutes the “learning space”—and how this learning space is different from what it once was prior to the arrival of mobile devices, or even computers. As mobile devices and wi-fi networks become increasingly accessible to the general public, the learning space conceivably becomes more fluidly and seamlessly integrated between physical and virtual environments, across considerably wider geographies. According to Sølvberg and Rismark (2012), the flexibility of the learning space means “an increasing proportion of the learning activities may take place outside the confines of a controlled classroom, and the teacher role may be challenged accordingly."[26] Outside of the classroom, learners may move between topics in different ways and have different patterns of how, when, and where they access the mobile technology. Thus Sølvberg and Rismark suggest that an “environment- and time-independent pedagogy” may be need for m-learning[27]; teachers must understand how m-learning intersects with learning within the classroom, the campus, and off-campus spaces, and design learning experiences that accommodate how learners manoeuvre and study within these spaces.[28]

While it might be reasonable to expect that teachers seek to understand how students learn, what constitutes a learning space can be highly variable depending on who the learners are and a multitude of contextual factors. It is a fundamental challenge, then, for teachers to define and understand the learning space of their students, which may differ significantly between individual learners because of the high variability of access points to the technology and the multitude of ways that individuals utilize, synthesize, and repurpose learning in different environments. This is especially true of higher education learners, who have vastly greater latitude in setting schooling and learning schedules than their K-12 counterparts.

Given the multitude of evolving pedagogical and theoretical models, and constantly changing technology, designers of learning environments such as faculty, e-learning developers, and instructional designers face a complicated task in incorporating m-learning into learning environment designs. After studying a series of m-learning projects conducted within faculties of education in Australia, Herrington, Herrington, & Mantei (2009) propose the following design principles for m-learning:[29]

  1. Real world relevance: Use mobile learning in authentic contexts
  2. Mobile contexts: Use mobile learning in contexts where learners are mobile
  3. Explore: Provide time for exploration of mobile technologies
  4. Blended: Blend mobile and non mobile technologies
  5. Whenever: Use mobile learning spontaneously
  6. Wherever: Use mobile learning in non traditional learning spaces
  7. Whomsoever: Use mobile learning both individually and collaboratively
  8. Affordances: Exploit the affordances of mobile technologies
  9. Personalise: Employ the learners’ own mobile devices
  10. Mediation: Use mobile learning to mediate knowledge construction
  11. Produse: Use mobile learning to produce and consume knowledge

Those who are responsible for designing and evaluating m-learning experiences may not find it feasible to satisfy the full range of principles and criteria of quality m-learning discussed thus far. In addition, learners’ needs are highly variable across different contexts—therefore some may find, for instance, that simply incorporating podcasts sufficiently addresses the needs of a learning environment. Others who work in the field of distance education may find that m-learning is already implicit and supported through their existing practices.

Nevertheless, some common threads can be drawn from the body of scholarly literature. The most frequently cited features and potentials of m-learning can be summed up as mobility/ubiquity, personalization, and collaboration.[30] At a minimum, practitioners considering m-learning as part of a higher education learning environment design should inquire as to how the environment can or should exploit those potentials. These questions would need to account for the context of the specific learners. Relative to K-12 learners, higher education learners tend to be more “nomadic” and independent in how they move between locations, how they choose to learn, and how they communicate and socialize. They also have discipline-specific requirements and needs related to preparing for professional life.

Applications in higher education

Current applications

M-learning is an opportunity to break away from instruction that takes place in the classroom to another location while maintaining communication through a network. In the ideal case it integrates studies that take place in several venues to promote both experiential and communal learning. Kim, Mims, & Holmes (2006) mention increasing numbers of institutions of higher education offering courses using mobile wireless technologies as alternative teaching and learning tools. For proponents of m-learning, the paradigm has much more potential than just delivering courses or parts of courses. Such areas include:

  • Delivering education/learning
  • Fostering communication/collaboration
  • Conducting assessments/evaluations
  • Providing access to performance support/knowledge
  • Facilitating research and data collection

Examples of current m-learning applications in higher education include the following:

  • Abilene Christian University (ACU): According to ACU Chief Information Officer Kevin Roberts, freshmen use iPhones or iPod Touches to receive homework alerts, answer in-class surveys and quizzes, get directions to their instructors’ offices, and check their meal account balances, among more than 15 other web applications. The website Mobile Learning at ACU captures their vision for mobile technology, along with information about ACU’s other mobile learning efforts.
  • Athabasca University uses m-learning for the preparation of ESL adults for the workplace. This is a course of lessons and self-assessments on the English language. Each section covers an area on basic grammar and contains a number of short exercises. The course tests knowledge of English grammar and provides practice in using knowledge to make correct and appropriate sentences. More details about this can be found at
  • The 2013 NMC Horizon Report (Higher Education Edition) cites examples of tablet initiatives in higher education, which take advantage of the portability of tablets for fieldwork. According to the report, geology students at the College of Wooster in Ohio are "using iPads to take and annotate photos of Icelandic terrain (, and similarly, earth science students at Redlands College in Australia are using them to collect and share data on indigenous rocks ( In these scenarios, the immediate access to recording and analytical tools enables direct and active learning in the field...Professor Messner at Virginia Commonwealth University secured iPads for his students so they could create multimedia news stories from happenings on the campus and surrounding community. The students learned the importance of social media in journalism and found the iPad useful for gathering news and sources:"[31]
  • Interactive Learning for iPods - McGraw Hill EZ Test Online: Instructors in higher education have the capability to access test banks and deliver them in the form of quizzes via the iPod (the iQuiz application can be downloaded from iTunes for $0.99). Students can download the quiz to their iPods and use it interactively to practice and learn content specific to their course. Students can self-assess and receive scores instantly.
  • Collaborative Research (Reality Mining), MIT: Researchers at the MIT Media Lab conducted a social network analysis experiment using Nokia 6600 smartphones. By the end of the experiment researchers had over 450,000 hours or 60 years of continuous data on human behaviour. There is interest in using this data in a wide range of fields that include epidemiology, sociology, physics and organizational behaviour.
  • Faculty at Singapore Management University (SMU) adopted multiple uses of mobile devices to enrich students' experience in an entrepreneurship course. The faculty experimented with mini lectures, guided walking tours, and an "SMS-enabled treasure hunt": "The treasure hunt forces learners to leave the comfort of their seminar room...students were tasked to explore historical places and traditional Chinese business clusters in the vicinity of SMU and the Singapore River (once the very lifeblood of Singapore, its main trade artery and the heart of entrepot trade) to appreciate location-specific, historical and ethnic features of Chinese business.”[32]

Emerging applications

Through the affordances that mobile technology creates such as wirelessness and mobility, higher education environments can facilitate more opportunities for collaboration and interactivities that link students to each other through the spirit of intellectual curiosity and connecting the campus community.[33]

Augmented Reality (AR) is an emerging type of computer-generated environment that is seeing a convergence with the m-learning terrain. Currently experiments are taking place in higher education institutions, but there is minimal research-based evidence supporting or disputing its effectiveness as a learning support. An example is the Handheld Augmented Reality Project (HARP), a collaboration between the University of Wisconsin (Games Lab), Harvard Graduate School of Education and the Teachers Education Program at MIT. The project hopes to conduct experimental research on AR and publish results that will help schools and teachers make informed decisions when considering this technology. Specifically, the project will collect data that centres on the experience of using AR technology in classrooms. AR simulations such as this are played in a real-world environment using handheld devices. Students (1) adopt the role of professionals; (2) work in teams to conduct virtual investigations on complex problems, and (3) develop skills through game play.

Challenges of m-learning

Critiques of m-learning

Kearney et al. (2012) used their m-learning pedagogical framework based on authenticity, personalization, and collaboration, described above, to analyze 30 m-learning activities gathered from refereed literature. They found that very few rated highly on the scales for authenticity and personalization. However, they acknowledge that learning activities are designed for different purposes, and thus their ratings in the three categories do not necessarily reflect the quality of the activities. Their acknowledgment reveals the difficulty of evaluating or critiquing m-learning activities in isolation from learning environments as a whole. Nevertheless, their results indicate that many recent implementations of m-learning do not fully exploit the potentials of the medium; many reveal issues such as contrived contexts, lack of customization options, and lack of social interactivity.

Koszalka & Ntloedibe-Kuswani (2010) reviewed 10 case studies of m-learning across the world, in countries including South Africa, Pakistan, Britain, and the United States. They found that in the case of developing countries and rural locations, m-learning can provide some degree of educational enhancement or new access to educational opportunities that did not previously exist. But the authors note that it is not clear whether “the environment, participants, or activities themselves are responsible for enhanced learning or whether the addition of the m-technologies enhances the effects of the situation under study."[34] In summary, they contend that the evidence of learning enhancement through interactive and collaborative m-learning is available but largely anecdotal; case studies are often based on biased research without clear research questions.

From a technical perspective, Picek & Grčić (2013) list the following weaknesses of m-learning:[35]

  • Size, battery life, usability, and cost of mobile devices
  • Difficulty and impracticality of unified m-learning solutions that work well across a variety of hardware and software
  • Difficulty of designing effective m-learning solutions as it requires multiple skillsets including instructional design, media design, and interface and user experience design
  • Impersonal teaching may result if implementations lack multi-modal, affective interactions
  • Potential pitfalls around device and data security

From a more philosophical perspective, Kress & Pachler (2007) ask questions about what it means to live in a world where m-learning is taken to its extreme. If learning around the world becomes exceedingly mobile and personalized, how might that impact communities? If all the world is seen as “occasions and resources for learning,” “[w]here are the opportunities for (seeming) downtime?”; and if the world is characterized as a “pedagogic market,” “where is the time to opt out?”[36]

Obstacles to implementing m-learning

Although mobile technology has been pervasively adopted into personal lives, it has not made a universal breakthrough in education. The development of m-learning and its widespread use in education has yet to overcome several obstacles. These include:

  • Availability of high speed connections[37]
  • The small screen size with poor resolution, colour, and contrast. On hardware designed to fit in a pocket, small screen size continues to be a defining feature of handheld mobile devices[38]
  • Availability of widely used Learning Management System (LMS)[39]
  • Institution and student security and privacy[40]
  • The availability of compatible platforms for the development of learning materials[41]
  • Connection speeds (for downloading larger files)[42]
  • For institutions, start-up may need high capital investment in infrastructure and learning elements[43]
  • Rapid technological changes at this embryonic stage works against stability and sustainability of viable applications[44]
  • The industry is dominated by proprietary solutions, although open-sources solutions are emerging[45]
  • Connectivity has to be ubiquitous and not intermittent[46]
  • Resistance from faculty to adopt new technologies and teaching approaches[47]

The future of m-learning depends very much on standardization of infrastructure and software platforms. Mobile learning may be best approached if looked at not as the “next new thing” but as an alternate delivery mode that encompasses tools that are currently available and best practices that have been used and tested.[48]


  1. El-Hussein & Cronje, 2010, p. 12.
  2. Alexander, 2004.
  3. El-Hussein & Cronje, 2010, p. 12.
  4. Pachler, Bachmair, & Cook, 2010, p. 30.
  5. Pachler, Bachmair, & Cook, 2010, p. 34.
  6. Pachler, Bachmair, & Cook, 2010, p. 41.
  7. Low, 2007.
  8. El-Hussein & Cronje, 2010, p. 20.
  9. Kress & Pachler, 2007, pp. 27-28.
  10. Pachler, Bachmair, & Cook, 2010, p. 6.
  11. Koole, 2009, p. 27.
  12. Koole, 2009, p. 27.
  13. Traxler, 2007, n.p.
  14. Laurillard, 2007, pp. 154-155.
  15. Traxler, 2010, p. 129.
  16. Pachler, Bachmair, & Cook, 2010, p. 6.
  17. Koszalka & Ntloedibe-Kuswani, 2010, p. 142.
  18. Kearney et al., 2012, n.p.
  19. Koszalka & Ntloedibe-Kuswani, 2010, p. 152.
  20. Laurillard, 2007, p. 156.
  21. Kearney et al., 2012, n.p.
  22. Klopfer, Squire, & Jenkins, 2002.
  23. Danaher, Gururajan, & Hafeez-Baig, 2009.
  24. Traxler, 2009.
  25. Pachler, Cook, & Bachmair, 2010.
  26. Sølvberg & Rismark, 2012, p. 24.
  27. Sølvberg & Rismark, 2012, p. 24.
  28. Sølvberg & Rismark, 2012, pp. 30-31.
  29. Herrington, Herrington, & Mantei, 2009, p. 134.
  30. Giousmpasoglou & Marinakou, 2013, p. 418.
  31. Johnson et al., 2013, pp. 17-18.
  32. Menkhoff, & Bengtssonp, 2012, p. 232.
  33. Alexander, 2004.
  34. Koszalka & Ntloedibe-Kuswani, 2010, p. 149.
  35. Picek & Grčić, 2013, p. 65.
  36. Kress & Pachler, 2007, pp. 29-30.
  37. Elias, 2011.
  38. Elias, 2011.
  39. Sife, Lwoga & Sanga, 2007.
  40. Kambourakis, 2013.
  41. Pollara, 2011.
  42. Elias, 2011.
  43. Salmon, 2005.
  44. Pollara, 2011.
  45. Sife, Lwoga & Sanga, 2007.
  46. Elias, 2011.
  47. Giousmpasoglou & Marinakou, 2013.
  48. Elias, 2011.

Stop Motion Video

A stop motion video by Hoda Amer, Spring 2017 :


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