The previous chapter gave an overview of m‑learning and introduced some of the benefits and limitations of the genre. There is a body of evidence to support the
proposition that mobile devices offer unique advantages as educational tools. They are engaging, ubiquitous, typically cheaper to purchase than home computers and are able to extend the curriculum socially, geographically, across a wide age-spectrum and to users with special needs (Kagohara, et al., 2012). The hypermedia environment provided by such devices may be used to foster a cognitive-constructivist approach and the devices tend to engage the user in a way that traditional materials do not. McGuire (1996) and Tabuenca, et al. (2015, p.54) encapsulate these points in reminding us that lifelong learning is now considered the norm and that the lifelong learner must “constantly change their learning context, location, goals, environments, and also learning
technologies”. M‑learning is a tool that can overcome many of the barriers thrown up by these changing contexts.
However, Human Computer Interaction (HCI) brings its own barriers to both learning and user-engagement if the equipment and software design are not aligned with cognitive theory. Cognitive Load Theory (CLT) has particular implications for medical education identified by Young et al. (2014) as being related to the high complexity of the skills to be learnt. This complexity introduces what is known as a high element interactivity, which can hamper cognition and provide a barrier to learning. This concept is explained in section 2.2.1.
In deciding how to approach this study, to ensure originality and to determine whether there are any gaps in current knowledge concerning the use of touch-screen devices in m‑learning, a recognised model for the assessment of mobile devices was used. In 2006, Koole and Ally produced a model that they called a Framework for the Rational Analysis of Mobile Education (FRAME).
Figure 2-1: The FRAME model (Koole and Ally, 2006). The three circles of the diagram represent the device usability (A), the learner aspect (B) and the social aspect (C) of
m‑learning.
This model is very useful in that it provides an overview of the aspects of m‑learning that may offer opportunities for research. The authors state that:
“The FRAME model is the first comprehensive theoretical model to describe m‑learning as a process resulting from the convergence of mobile technologies, human learning capacities, and social interaction. It addresses contemporary pedagogical issues of information overload, knowledge navigation, and collaborative learning. It is hoped that this model will help to guide the
development of future mobile devices, the development of learning materials destined for m‑learning, and the specification of teaching and learning strategies for mobile education” (Koole and Ally 2006, p.1).
“synergies” between the user and the device. In other words, what the device affords the learner regarding functionality and network connectivity. The social technology
intersection does not relate specifically to the learner, as it describes device communication technology. This area would be of interest to a mobile equipment
designer, but device design is not a variable that can be manipulated by an educator. The
interaction-learning section represents the features of m‑learning that relate strongly to
social constructivism, and there has already been much written about this (Manuguerra and Petocz, 2011 Wang and Shen, 2012; Laurillard et al., 2013; Martin and Ertzberger 2013).
The FRAME model intersection relating to context learning seems to cover an area that has been less represented in the topic literature. This intersection relates to device usability by the learner, Koole and Ally (2006) state that this part of the model relates the characteristics of mobile devices to cognitive tasks associated with the manipulation (and storage) of information and that these processes affect cognitive load. A thorough search of the topic literature relating to cognitive load and m‑learning showed very few studies in this area, which is likely to be due to the fact that smartphones and particularly tablets are a relatively new phenomenon. The apparent lack of research in HCI as a source of cognitive load when using mobile devices as educational learning tools suggested that there might be a contribution to be made to knowledge in this area. It also provided a very good fit with the researcher’s own interests in cognitivism, material design and medical education.
Medical education often requires the learner to be in a learning environment other than the classroom. This situated learning leads to the concept of situated cognition, whereby thinking is embedded in the specifics of a particular encounter or context. Young et al. (2014), indicate that in a clinical setting there may be participants other than the learner, perhaps a patient, or other staff members. There may also be very different learning environments such as the accident and emergency department, the operating theatre or the ward. From the learner’s point of view any increase in the number of environmental elements involved in learning will also increase cognitive load and present a barrier to successful schemata formation and therefore to learning (Sweller, van Merriënboer and Paas, 1998; Piaget, 2001). Using a cognitive strategy in learning is somewhat in
contradiction to the constructivist approach favoured by modern educationalists. CLT relates strongly to instructional design, and the didactic method of teaching, whereas constructivism emphasises the conceptualisation of knowledge through social interaction. However, in medical-education and particularly computer-mediated learning, there is still a need for instructional design (Young et al., 2014). Learning anatomy often requires rote- learning. Learners are often required to memorise long lists of anatomical structures in the correct order, such as the names of the cranial nerves or the names of the bones of the wrist. There are also sets of skills that must be learnt that are firmly in the
psychomotor domain (Bloom, 1956; Dave, 1967; Anderson and Krathwohl, 2000) such as operating an endoscope or conducting an endotracheal intubation. These areas of learning may require instructional design to be considered, and although there may be room for constructivist approaches, cognition plays a significant role.
Mobile devices have been identified by various authors as having the facilities to aid situated cognition and provide a platform for multimedia learning that has been found to encourage engagement (Manuguera and Petocz, 2011; Clark and Luckin, 2012). However, as a piece of computer hardware, mobile devices are also capable of adding to the
cognitive load of the user (Hollender et al., 2010). This additional load can be due to many factors including software design, hardware limitations, network issues and distraction by non-task-related events. All of these are well-known in the field of HCI (Hurtienne 2009) and have been studied exhaustively in the field of traditional computing. Mobile devices, on the other hand, do not yet appear to have been fully assessed from educational
perspectives such as CLT. Mobile devices offer new physical features that are not found in traditional computers. These include capacitive touch-screens and an array of sensors including global positioning, heat and light sensors, gyroscopes and latterly physiological measurement tools such as heart-rate monitors and pulse oximeters (Deegan and
Rothwell, 2010; Deegan, 2015; Martin and Ertzberger, 2013). Many of these features can be used in education and research, and offer a new platform and new strategies for the presentation of learning materials. There is, therefore, a potential for assessment of these devices as learning tools and because they are human-computer interfaces, a cognitive approach is favoured. This is not merely because cognitivism is aligned with the tradition of HCI, but also because effective learning requires cognition (van Merriënboer and Ayres, 2005).
At the time that this study was first proposed (2012) there was little published research on CLT in m‑learning. In the four years since there have been a number of publications, some looking at software, a few looking at hardware, but no studies comparing the differences in cognitive load between m‑learning on a touch-screen device and traditional classroom or paper-based learning. This chapter is intended to provide a rationale for the research presented in this thesis by critically evaluating the recent literature that has been published in the field.
This review is not intended to be systematic in the formal sense of the word, but to ensure that the review was conducted in a rigorous way, a systematic approach was used to select the papers for review, and also to identify the key themes relating to m‑learning and CLT. A preliminary scoping search of the literature was conducted using key search terms to include multimedia, touch-screen devices, m‑learning, learning technology,
cognitive load theory, e-learning, m‑learning, cognitivism, distance learning and
computer-based learning. The resulting range of publications was then narrowed down
using Boolean search terms AND, OR and NOT to obtain titles that were more specific to the topic of m‑learning on portable computing devices such as smartphones and tablet computers. Advanced search functions were also employed to narrow the search to recent publications (unless relevant from a historical perspective) and to ensure that key authors and key sources were represented in the search. The literature extracted
included research papers, journal articles, policy documents, conference proceedings, press and media releases, webinar content and some grey literature. An initial reading was conducted from which a conceptual map (Figure 2-2) was created to help identify key themes that emerged from the literature.
This diagram helped in identifying contemporary issues, looking for distinctions and connections and identifying where there was scope for informing professional practice. The diagram also identified some key questions that were required to increase
understanding and knowledge of the subject area. These themes included: identifying the origins and definitions of the topic, querying the epistemological and ontological grounds behind the area of interest, looking at major issues relating to the topic and investigating
how knowledge on the topic is structured and organised. Many of the papers were not specifically related to m‑learning or cognitive load and were therefore not focused enough for inclusion in this review chapter.
Figure 2-2: A conceptual map providing a technical and pedagogical overview of
m‑learning
Having performed a broad search, the most relevant texts were then revisited in order to critically evaluate the contemporary issues in m‑learning related to cognition and
cognitive load. Further searches were carried out using terms that were more focussed on m‑learning and CLT. Emphasis was given to recent papers and conference proceedings (since 2010) were included as these were more likely to include the use of tablet devices, and would reflect the current state-of-the-art in terms of device functionality (such as screen resolution, choice of screen size and processor speed). This decision was later
reinforced by a finding in a paper by Sung, Chang and Yang (2015) who presented a histogram of devices used in m‑learning (in languages) that shows a sevenfold increase in the use of mobile devices in the year 2010.
All of the papers were skim-read, and the abstracts scrutinised for relevance to the research question and for quality assessment. The papers were then checked against the current SCImago Journal and Country Ranking system (SJR). This system of ranking employs the same algorithms used to determine journal impact factor and measures the scientific influence of an average paper from a particular journal based on citations per document over a two-year period. In view of the recency of m‑learning, it was noted that the number of publications on this topic is relatively limited in comparison with other types of education theory, particularly papers that also include CLT. To maintain a broad enough range of sources for this review and avoid publication-bias, all papers from ranked journals were considered for inclusion. Two papers were excluded as they were works-in-progress and did not offer any results. Papers from unranked sources were checked for quality and all were excluded. Limitations of these papers included misunderstandings about the definition of m‑learning, non-statistically significant findings, spelling errors and typographic errors, inappropriate sampling (such as the use of male-only participants in papers from Saudi Arabia) and out-of-date references, some of which suggested recency bias (whereby trends from a particular time period are predicted to continue into the future). This type of bias is particularly applicable to
m‑learning due to the rapidly-changing nature of the underlying technology. For example, a paper from 2006 noted network-speed as a barrier to m‑learning, but was quoted in a paper from 2015 by which time mobile networks had improved in speed with the
introduction of the fourth-generation (4G) mobile communication technology standards. Having determined the studies of high enough quality for inclusion, key points from each paper were entered into an Excel spreadsheet (Microsoft Inc.) to give a structured view of the concepts covered. Some papers did not specifically include CLT, but used methods such as pre and post-testing to determine the effectiveness of m‑learning in various contexts. These were included in the review, as pre/post-testing is an indirect measure of