There are many terms used to describe learning delivered on mobile devices. They include: bitesize, handy learning, ubiquitous, portable, pocketable, learning on the go, my learning, untethered, opportunistic, learning in the moment, snack-learning, courselets, learnlets, "bus stop" learning, a learning nugget or even a learning pill. These terms emphasize how formal mobile learning content is designed to be consumed. However, there are also overlapping but somewhat independent descriptors for mobile learning which emphasize the idea of mobile learning as composed of experiences rather than content. These include: social, situational, collaborative, and unstructured learning. These terms reinforce the idea that mobile learning can support informal, non-traditional learning methods. ADL believes this fact must be accounted for in any definition of mobile learning.
While there is no agreed upon denotation of mobile learning, the following are a list of historical definitions:
"Mobile learning, or m-learning, can be any educational interaction delivered through mobile technology and accessed at a student's convenience from any location (Educause Learning Initiative (ELI), 2010)."
"Any activity that allows individuals to be more productive when consuming, interacting with, or creating information, mediated through a compact digital portable device that the individual carries on a regular basis, has reliable connectivity, and fits in a pocket or purse (Wexler, S. et al., eLearning Guild, 2007)."
"Mobile learning should be restricted to learning on devices which a lady can carry in her handbag or a gentleman can carry in his pocket (Keegan, 2005)."
"The exploitation of ubiquitous handheld technologies, together with wireless and mobile phone networks, to facilitate, support, enhance and extend the reach of teaching and learning (The Mobile Learning Network (MoLeNET), 2014)."
“The intersection of mobile computing (the application of small, portable, and wireless computing and communication devices) and e-learning (learning facilitated and supported through the use of information (Quinn, 2000).”
"A type of learning that occurs when the learner is not at a fixed or pre-determined location. It is also considered a type of learning that occurs on a mobile device. The objective of Mobile learning is to provide access to knowledge based content anywhere, at any time (TrainingIndustry.com, 2014).”
“Mobile learning is where a learner can be physically mobile while at the same time remaining connected to non-proximate sources of information, instruction, and data communications technology (Woodill, 2010)."
ADL believes that a fixed definition of “mobile learning” could be limiting to some organizations. Many of the existing definitions of mobile learning are usually too learner-focused or too device-focused. Just read through the above examples again to judge for yourself. A universally accepted definition seems improbable. Therefore, ADL believes that both the learner and the devices of today as well as the future should be considered to provide a more flexible view of mobile learning. ADL describes mobile learning as:
“Leveraging ubiquitous mobile technology for the adoption or augmentation of knowledge, behaviors, or skills through education, training, or performance support while the mobility of the learner may be independent of time, location, and space.”
This description is intentionally generalized to allow for a growing number of mobile learning scenarios as well as future capabilities of new technology and device types. Koole (2009) states that key defining features of mobile learning are that it “… provides enhanced collaboration among learners, access to information, and a deeper contextualization of learning”.
Mobile learning should be viewed as a way to augment the learner through the use of ubiquitous technology that provide access to learning content and information, anytime and anywhere. Unlike other learning technologies, mobile learning is unique in that it can accommodate both formal and informal learning in collaborative or individual learning modes, and within almost any relevant context.
An important component of the access to information and collaboration concept described above is user-generated content. It is so central to the feature set of most mobile devices (e.g., cameras) that it effectively needs to be considered as part of the definition.
Mobile devices are uniquely positioned to enable “distributed cognition” (i.e., cognitive functions shared with machines), because they are carried everywhere with us and are always on. They can provide optimal performance support for our ability to exercise our uniquely human cognitive functions.
One important way to rethink learning for mobile devices is to think of the unique opportunities that the mobile platform has for supporting metacognition. Metacognition in its simplest form is “learning how to learn”. There is already a considerable body of research showing that it can be taught explicitly, and effective ways to do it. The mobile platform is well positioned to support metacognition due to its availability at the time of need. For example, a timer in the mobile device could simply prompt the user at periodic intervals to think about how whatever they are learning at the moment could be applied to their daily work life, etc. More advanced implementations could rely on the ability of the device to gather situation awareness data and guide the user in thinking of the best way to solve the problem that he or she is currently facing. Using mobile devices in this way falls into the “learning augmentation” category of use cases described in the Mobile Learning Project Planning Categories section of this handbook.
Expected benefits of mobile learning include, but are not limited to the following:
Coffield (2008), in a UK Learning and Skills Network report, identified "increased creativity and innovation, greater ownership of learning by learners, real world problem solving and the development of complex ideas and knowledge transfer" using mobile delivery.
JISC (2014) defines the “Potential benefits of mobile and wireless learning” as follows:
Rheingold (2002) states, “The mobile internet... will not be just a way to do old things while moving. It will be a way to do things that couldn’t be done before.” We now have the mobile tools to make a difference.
Perhaps the most significant potential of mobile learning is the ability to achieve what many performance support advocates believe has long been the learning profession's Mount Everest. As MIT professor and artificial intelligence pioneer Seymour Papert said, "You can't teach people everything they need to know. The best you can do is position them where they can find what they need to know when they need to know it."
Sophisticated users know it is now possible to deliver media-rich, interactive learning content to almost any smartphone. However, at the moment of need, the user may only need a checklist or reminder.
Quinn (2011) (p. 98) presents the Four C’s of Mobile Capability, to stimulate thinking about learning content design and interaction paradigms:
Quinn emphasizes that these can and should be mixed for maximum effect.
This topic deserves a special section because its use is becoming more common, and the use cases can be compelling for instructors of synchronous learning experiences. This mobile device application involves presenting a question to students through their learning medium (e.g., PowerPoint slides in a classroom). Students are asked by the instructor to send a text message to a designated address, with key codes corresponding to poll answer choices. The results are displayed immediately on the learning medium. This can be used by the instructor during a class to assess whether students understand a concept, to characterize the demographics of his/her audience in order to fine tune the delivery, or just to gauge how prevalent certain (possibly erroneous) assumptions or motivations are about the content in advance. Poll Everywhere® is an example of a vendor that sells this functionality.
This technology trend started with the ability to control system functions by having the system recognize a preset list of system commands. Full voice recognition of any words spoken into a computing device is robustly available nowadays, with 98% accuracy delivered by such software as Dragon Speak®. Implementations on mobile devices have been limited however, due to the devices’ inherently limited processing power and memory.
This has changed dramatically with the advent of Siri on the iPhone. Using AI-based Natural Language Processing (NLP), you can now “talk” to your phone, have it “understand” you, and consequently have it perform a wide range of tasks, including high-level, complex ones. This includes asking for information (“Is Margaret Thatcher still alive?”), to performing computing tasks (“Set up a meeting with John, Bob, and Sue for tomorrow morning at 11:00 to discuss the Octagon account.”)
One implementation issue for this capability is the data load imposed by these systems. A network research firm (Arieso) has found that the iPhone 4S (the only phone currently that fully implements Siri) doubles the network load for data compared to the previous iPhone, due mainly to the implementation of Siri. Siri uploads each voice query to Apple servers, with consequent control data being sent back. This increase in network “chattiness” may outpace the ability of carriers to scale their networks enough to meet the demand.
The use cases of a capability like Siri for mobile learning are significant. In addition to being able to control and navigate through learning content by voice, without having to look at the device, ultimately voice-based personal assistants for learning (see PAL) and intelligent tutoring systems could be included on mobile devices. For more information on intelligent tutoring systems, see http://www.memphis.edu/mitsc/.
Integrating QR codes into your mobile learning experience is worth considering if it involves field exercises. QR codes can be read via a camera on a mobile device and reader software to automatically browse to a website without having to type in a URL, receive text information, receive additional details, make a phone call, or a number of other actions. Uses can include placing a code on equipment with a link back to the operating instructions or manual, designing a scavenger hunt where students find objects and then learn about them in context by linking to the appropriate associated content. Both the creation tools and the readers are usually free. QR codes are especially useful for informal learning environments such as museums, and they will undoubtedly be used to enable augmented reality experiences in the future.Here is an example of a QR code (from Wikipedia – this code directs the user to the Wikipedia QR Code article):
Augmented reality (AR) is the overlaying of digital data (usually text annotations) onto the real world (usually images). This technology and its use cases have not reached maturity, but it is growing fast, and a key enabler is mobile devices. With object recognition technologies, it can transform the real environment of the user into one that can be queried, interacted with, and manipulated in rich ways. It is a perfect example of the “distributed cognition” concept mentioned earlier. A comprehensive list of applications for AR can be found on Wikipedia at http://en.wikipedia.org/wiki/Augmented_reality. AR can involve the use of QR codes to activate data overlays, however, the term “AR” usually connotes location awareness or object recognition so that there is no need for users to find and use QR code tags in the environment.
Although special (currently expensive) goggles are often associated with AR, a more and more common alternative is to use the camera of one’s mobile phone. A simple example would be to point your phone’s camera at a building or scene, and have text annotations pop up overlaid on the screen image that provide detail on what you are viewing, for example, historical information. The downside of using your phone’s camera is that the user is constrained to having to hold the mobile device in front of them at all times. There are also distortion effects from wide-angle mobile phone cameras as opposed to what the human eye sees.
As AR technology matures, there will surely be an explosion of “killer apps” for mobile learning. Consider building them now if you want to stay cutting edge and leverage these powerful capabilities for learning.Here is an example of augmented reality glasses (Google Glass):
From Wikipedia article on Google Glass at http://en.wikipedia.org/wiki/Google_Glass
As mentioned in the Augmented Reality section above, there are possibilities within mobile learning for location awareness capability, for devices that include a GPS. In addition to augmented reality applications, these location awareness-enabled applications could find and physically connect learners with the same interests, or connect learners to SMEs. Location awareness can be a powerful tool for many social learning-based applications. Dodgeball® (now defunct) is an example of a service that broadcast your location to other users whose profile matched yours in some predefined way and allowed you to follow up with phone calls to arrange meeting, etc. Other applications may follow.
There are obvious privacy issues with such a capability; most mobile phones allow you to disable location awareness as a global setting on your mobile device.
Wearable mobile computing devices are well-established in the military, for decision and logistical support. For non-military users, wearable devices usually take the form of special jackets that have pockets for mobile devices, and Bluetooth headsets currently. This capability does not necessarily add use cases to mobile learning, buts its added convenience may help make mobile learning more convenient and therefore universally adopted. Expect to see wearable devices driving rapid growth of mobile applications, including learning.
Mobile learning design should not be limited to simply substituting a touchscreen for a mouse or tapping for clicking, especially with the added capability of more than one finger being used to initiate actions. Mobile learning apps can and should leverage this new interface interaction paradigm in new and clever ways.
There is now some movement (for example, TapSense announced in 2011) towards extending the touchscreen concept so that they can distinguish what part of the hand was used for input, such as the fingertip, knuckle and fingernail. This could be used in a variety of ways for learning applications, limited only by the imagination of the designer.
Video chat technologies such as FaceTime® on the iPhone (where users can see each other via real time video while speaking) have the potential for applications in mobile learning. For instance, quickly showing another user something they are seeing that has learning value while talking about it, rather than relying on a more delayed/interrupted means of transmission such as emailing pictures or video. Also, seeing the person you are learning with or from has obvious value in terms of enhancing the impact of learning.
Video chat currently has two significant limitations:
Research over the past one hundred years has proven that learning retention can be improved by spacing out the learning and providing repetition and an opportunity for reflection.This spacing is effective both on the level of the initial content presentation as well as refresher/reminder training (to prevent knowledge decay of information that one seldom uses). Thalheimer (2006) reports that:
“The spacing effect is one of the most reliable findings in the learning research, but it is, unfortunately, one of the least utilized learning methods in the workplace learning field.”
Value of Repetition
Mobile devices provide inexpensive capabilities to deploy reminders and refresher materials.
Spaced learning can provide information to the user at the time of need. One of the most interesting current projects is Text4Baby at text4baby.org, which delivers appropriate SMS messages to pregnant women depending upon where they are in their term, and continues the service for the first year of their baby’s life.
Consider how spaced or timed, relevant learning could be beneficial to your learners.
With text messaging, voice calls, and email capability on mobile devices, there are a number of options for pushing content to learners. Pulling is usually through the device’s web browser or networked app. Pulling is typically the default (especially for adults), for any user on any computing device, since it ensures that the user is motivated and intentional in wanting to access information. Your analysis of the learner audience should include determining that pushing to learners is acceptable to them. Although most learners can control the settings on their device that hide or block pushed content, some will not know how to do so, and you may not want to give learners the option of skipping portions of the content.
It is worth considering whether you will use a push solution, especially if you have content that:
There is another dimension to pull mode, and that relates to the “just enough” concept of content chunking. Content and navigation interfaces should be structured so that the user is not bombarded with content that may not be relevant, mixed with valuable content that is all the user really want or needs. In addition to keeping content objects small for mobile learning (which enables the “just enough” paradigm), describe the objects adequately and present them as a buffet of choices rather than prescribing them, as much as possible.
Probably the easiest way to deliver such training would be through podcasts (see Mobile Learning Tools for details). Podcasts enable you to take advantage not only of mobile phones as delivery devices, but also MP3 players. Because of the high bandwidth necessary for audio delivered as data, podcasts are a good solution because they are loaded when the device is synched (see next section) with the user’s main computer. There is also a built-in mechanism for updating them (users subscribe to them—they are delivered automatically).