The Limitations of m ‑ learning

Equipment Limitations

Models of quality in m‑learning typically feature technical factors such as functionality, hardware performance, interface-usability and device connectivity (Sarrab, Elbasir and Alnaeli, 2016). Such studies tend to examine the causal relationships between

technological features and learner satisfaction. There are studies to show that mobile devices may be lacking in some areas, compared to other types of computing device. In a paper of 2014, Souleles et al. took a qualitative phenomenographic approach in exploring users’ perceptions of m‑learning with iPads. The study found that in some learning- situations laptop computers have been shown to offer user-perceived advantages over mobile touch-screen devices. The participants, forty design-students, recognised certain benefits in using the tablets, but there were some reservations. The lack of a physical keyboard was a concern, with some participants stating that they would much prefer a laptop for any writing activity. Limitations in software and processing power were also identified, one participant stating that any sketches made on the touch-screen would then need be transferred to a personal computer for post-processing in Photoshop software (Adobe Inc.).

Due to the physical size of mobile devices, one of the principal factors that can affect the learning experience is screen-size. This factor is less applicable to tablet devices, as the screen size may be larger than that found on some laptop computers (for example an iPad Pro (Apple Inc.) has a screen size of 12.9” compared to a MacBook laptop (Apple Inc.) having a 12” display). Typical smartphone screen sizes range between 3.5” (iPhone 4) up to 6” (Motorola Moto 6) all of which are physically much smaller than a normal PC

monitor (typically around 24”). There have been a number of studies looking at the effect of screen size on the user experience, particularly in the field of web-design. Here it is desirable to use responsive page layouts (interfaces) that adapt their configuration according to the device and screen size in use (Bohyun, 2013; Mohorovicic, 2013; Snell, 2013). Following the introduction of smartphones, Findlater and McGrenere (2008) performed an empirical study to compare adaptive interfaces for small screens. The study lacked ecological validity in that the participants were only required to interact with a PC

monitor rather than a smartphone. However, the experiment attempted to simulate two screen-sizes by presenting content at 800x600 pixels (typical screen resolution of a PC at the time) and in a smaller window of 240x320 pixels (simulating a mobile device). The task, which involved making menu selections, was automatically monitored by the software and the results showed that participants were significantly slower when using the smaller screen configuration.

The same principles apply to the range of screen-sizes on mobile devices. Raptis et al. (2013) conducted a quantitative study in which participants were required to interact with smartphones having screen sizes of 3.5”, 4.3” and 5.3”. The experiment was

controlled for brand, attractiveness and application by using Samsung devices that were equipped with the same operating system. The findings suggested that devices having a screen size of greater than 4.3 inches improved the efficiency of the device when used for activities such as web browsing. Screen-size factors were assessed using pairwise

comparisons of participant completion times for certain activities and seemed to be related to tasks that were not easy to complete on the device, for example where content scrolling was required. There were certain limitations to the study, which did not gather any data at screen sizes associated with tablet devices, and failed to collect information on a representative cross section of everyday smartphone tasks (such as map navigation) but the results suggested that task-efficiency increased with screen-size.

User Resistance

One of the first universities to trial the iPad as a tool to deliver educational content was Stanford University (California, USA). Stanford, the birthplace of Google and Yahoo, might be expected to be a technologically-astute campus, but when iPads were introduced (as an experiment intended to reduce the excessive use of printed materials) many students were reluctant to accept them. In some classes, 50% of the students stopped using the devices altogether (Keller, 2011). However, the reasons for poor adoption by the students may not have been a fault of device or software design. There were network speed issues (students on average carrying more than two internet connected devices on campus placed a strain on network bandwidth), and it was identified that staff were slow in exploring the educational potential of m‑learning. Such educator-resistance to learning

technology is a recognised phenomenon. Kukulska-Hulme (2012) identified a survey undertaken in the USA revealing that the range of devices and technologies used by students in their personal lives are seldom used by the educators responsible for their teaching provision. The reason, in this case, lay in the fact that those responsible for curriculum-design and teaching-delivery are not necessarily conversant with the new technology, and therefore may not be able to visualise how this technology can be used in a pedagogical context. Kopcha (2012) also highlighted issues whereby teachers perceived the integration of learning technology to be a burden on their time and identified a barrier caused by lack of training in troubleshooting equipment faults. These findings agree with a similar, earlier study by Lim and Khine (2006) who additionally found that there were both intrinsic and extrinsic factors that may form a barrier to the integration of technology. Extrinsic factors included lack of access to equipment and lack of support; intrinsic factors took into account an underlying lack of belief that technology could enhance learning. Even when educators are technologically adept and willing to engage with m‑learning, it does not automatically guarantee successful outcomes. Laurillard (2008a) points out that it is important that education is not led by technology and, as educators, we need to be thinking about how the technology can best work for us. Laurillard uses podcasting as an indicative example. Technology has given us the ability to compress audio into easily downloadable files, allowing students to listen to lectures at a time and place of their own choosing, but Laurillard makes the point that

“...no one ever suggested that the reason why education is failing, is that learners do not have enough access to people talking to them” (Laurillard 2008b p.139).

This view is echoed by Chandler (2004), who states that instructors frequently make the crucial mistake of allowing technology to dictate the way that they create the learning experience rather than the other way around. Materials must be created with the user in mind rather than to showcase the capabilities of the hardware.

Cost

efficient as m‑learning (Arrigo et al., 2013). Early examples of e-learning included the adoption of CD-ROM (Compact Disc Read-Only Memory) as an inexpensive medium to replace physical volumes of text. More modern examples include educational apps for tablets and freeware education platforms such as Moodle. From a hardware perspective, m‑learning also has cost advantages. Tablet computers tend to be considerably more affordable than most home computers or laptops (Hashemi et al., 2011). However, Littlejohn (2003) recognised at an early stage that e-learning materials can be expensive to produce. This is particularly the case if the materials are designed to be accessed as mobile applications (apps) as these have long production times and may require the services of third-party developers. Software programming can be particularly time- consuming, and although the exact timing can be difficult to ascertain, one survey

suggests that to create one hour of e-learning activity can require a development time of up to 220 hours, and may exceed 750 hours for complex hardware emulation (Chapman, 2006). This is largely in agreement with my own experience as an app developer. When compared to the typical development-time ratio of approximately 34:1 hours (average) for instructor-led training, e-learning could be considered to be an impracticably expensive option. Conversely, the expense may often be justified by the fact that e- learning materials are re-usable. The granularisation of e-learning materials into

shareable, re-useable learning objects was first identified by Downes (2001), who, at the time, made the negative observation that global-sharing was a technically difficult objective. In modern telecommunications, improvements in network speed, data

compression and server technology make this concept realisable. The ability to distribute learning objects quickly and easily over global networks to multiple users may provide the economy of scale required to justify production costs. However, this model assumes that the materials themselves are congruent with learning. If the learning-task elements are not presented in a way that is easily mentally integrated by the learner, perhaps due to hardware limitations or lack of embedded learning theory in the software design, learning will not occur (Sweller, 1994; van Merrienboer and Ayres, 2004). Learning must be

strategically reconceptualised for the mobile platform, and must take into account recognised educational theory in its design and implementation. There is, therefore, the need to ensure that digital delivery systems offer a significant advantage (or at least do

not present a disadvantage) over conventional paper-based or classroom-based learning models.