Revised Data Collection Method (Used in this Study)

As stated earlier, the response rate for the online activity was poor; it was therefore decided to discontinue this approach to data collection. This was regrettable given the fact that the online tool had taken six weeks of development time. Devising a new tactic for data collection offered the opportunity to minimise some of the possible causes of bias or risks to validity that were evident in the online method (covered in the previous section, p. 133). Furthermore, consecutive sampling was employed instead of

convenience sampling which decreases the systematic bias associated with the former. The pool of participants was reasonably representative of the target population (Bowers, House and Owens, 2011).

Location

For the revised data collection model the participants were invited from a series of

international study days being organised by the researcher. These were scheduled to take place over a 12-month period, and there were a sufficient number of delegates registered for the courses to achieve the group sizes required by the power calculation (n=64). The study days were located in Sydney, Australia; Oslo, Norway and Cheltenham, UK. This recruitment strategy provided a geographically-varied pool of potential participants who were all fluent English-speakers and had an interest in medical education and anatomy. It was thought that having the opportunity to explain the research project to the course delegates would result in a higher participation rate, and this proved to be the case with almost a 100% recruitment rate.

The participants were informed about the research project before attending the study days and were asked to bring along a touch-screen tablet and smartphone if they owned one, and wished to take part.

The data collection took place during the course programme by asking those who were interested to remain behind at the end of the afternoon session or to attend 40 minutes early at the beginning of the morning session.

The participant information sheet was provided to the participants, and the researcher gave a short presentation to explain the nature of the data collection task. Those who did

not wish to participate were given the opportunity to leave at the end of this introductory session if required.

Randomisation

Participants were then randomised into either the control group or the experimental group using a random number generator in Microsoft Excel (Microsoft Inc.). It was noted early in the process that, frustratingly, many of the participants who were assigned to the experimental group were the participants who had not brought a mobile device to use, or had brought a device but failed to bring headphones. It was thought to be a risk to

personal data protection to ask the other members of the group to exchange or share devices due to the potentially sensitive nature of the data and photographs contained on such devices. This issue was addressed by the purchase of six tablet computers, two smartphone devices and 20 pairs of headphones that could be provided to participants at further data collection sessions. This strategy ensured that consecutive sampling could be achieved, as nobody needed to be excluded for not bringing a device, and the required number of participants could be recruited in a 12-month period. It also facilitated random allocation into groups as participants who failed to bring a device could be allocated to the experimental group and participants who were required to use a tablet rather than a smartphone could be provided with such. The process used for data collection was as follows:

Learning Materials

The learning materials for the session were either a labelled textbook-style photograph of a replica human skull base (control group) or the interactive mobile app (experimental group). As the aim of the experiment was to assess the differences between the mode of delivery in each case, it was necessary to use careful instructional design to reduce any causes of extraneous cognitive load that may be due to the learning materials rather than the delivery method.

The learning task that was used for the study required the participants to memorise the structures found in the human skull-base. The options available to distance learners for learning human anatomy are somewhat limited compared to campus-based students

who typically have access to dissection laboratories, histological specimens and realistic anatomical models. Mobile-learners are typically confined to using learning materials that they can easily carry, and would therefore traditionally rely on textbooks for learning anatomy. This type of learning falls into the cognitive domain of learning taxonomy (Bloom, 1956) and is, therefore, relevant to the research question, and the data collection methods used. The choice of topic also conformed to good ethical practice as it was relevant to the participants’ own educational needs in that it offered some benefit to the participants that would not have been evident had they been required to learn a topic that was of no direct usefulness to their clinical practice.

The Human Tissue Act (2004) requires establishments that use real human tissue for teaching purposes to be licenced to use human remains. For this reason, many anatomy classrooms now use replicas. For the learning task, a standard plastic replica skull was purchased to be photographed. On delivery, it was thought that the model lacked the detail required for the learning task, as some of the structures were not replicated well enough to identify. A second model was sourced and purchased. As shown in Figure 3-1, the second replica was a resin-cast model and was indistinguishable from an authentic cadaver specimen.

Labelled photograph

The labelled photograph was a high-resolution image featuring the base of the resin-cast replica human skull. The structures were labelled in accordance with instructional design theory to reduce any sources of extraneous cognitive load (Sweller, 1989; Sweller, van Merriënboer, and Paas, 1998; Mayer, 2009). The text was placed in close association with the corresponding structures to reduce split-attention effects. The names of the nerves and vessels relating to each structure were listed underneath each label. The use of a labelled photograph provided a higher degree of ecological validity than would have been realisable in the discontinued online data collection tool where the photograph would have been presented on a computer monitor and would not have provided an authentic comparator to a textbook diagram. Presenting the diagram in the same format to all learners also avoided the possibility of a confounding variable that may have been intrinsic to the discontinued online version of the data collection. Learners having different screen resolutions and monitor sizes would have experienced the photograph with varying degrees of spatial resolution and size; this is a factor that is known to affect cognition (Raptis et al., 2013; Lin, Wang and Kang, 2015).

Interactive Mobile Application

The mobile application used in the learning activity was developed by the researcher and featured a log-in screen and a second screen showing the learning activity. The login screen featured a “keypad” lock that prevented access to the learning activity until a code had been provided and entered. This strategy allowed the participants to download the

app in advance without being able to open it until they were provided with the code after the pre-test. Pre-downloading was found to be necessary because some of the venues used for the data collection did not provide wireless networking and the mobile network connectivity was often slow. By downloading the app in advance, it was possible to ensure that all of the participants were able to commence the learning activity at the same time without any delays due to download errors. The login screen also featured an audio test button that allowed the participants to set up their headphones in advance to ensure that they could hear the audio component of the app clearly.

The second screen showed the learning activity. To reduce confounding variables and to ensure that intrinsic cognitive load was the same for both the control group and

experimental group, the learning activity was required to resemble the non-interactive activity as closely as possible, but also feature the interactive functionality that is typically offered by a touch-screen device.

Figure 3-2: Screenshot of the application used in the mobile-device learning-task application (Samsung Galaxy S7 smartphone)

Figure 3-2 (above), shows the mobile app screen after the learner has tapped on the Greater Palatine Foramen (outlined in orange) to reveal the labels. The app plays an audio description of the foramen first and then displays the labelling. The orange outline is emphasised (opacified) by the software to highlight that it has been selected. The task timer is shown at the bottom right of the screen.

Figure 3-3:Control group learning materials, a labelled photograph

To ensure comparability with the control-group activity, the same high-resolution photograph was used, and the same labels were employed. To activate the labels, the participant was required to tap on the various foramina (bone-windows) on the image. These were outlined in colour and when tapped an audio cue was spoken over the

headphones to identify the name of the foramen in question. Immediately after the audio description, a text label appeared next to the corresponding structure, including the names of the associated nerves and vessels. As for the non-interactive materials, this configuration follows the principles of good instructional design by reducing extraneous cognitive load due to the split attention effect (by placing the label close to the

corresponding anatomical structure) and the redundancy effect (by ensuring that the audio and text descriptions were not presented simultaneously (Mayer, 2009; Sweller, 1989).

The rationale behind reducing any potential causes of extraneous cognitive load in the learning activities was to eliminate any variables that did not directly relate to the mode of delivery. Task-completion time, for example, can be an indirect measure of cognitive load (longer task completion times being caused by a decrease in germane resources in working memory (Baddeley, 1974; 2000; Chen, Epps and Chen, 2011). Task-time in m‑learning can be affected by other processes such as the necessity for content scrolling on smaller screen sizes (Raptis et al., 2013). Excessive content-scrolling would have presented a confounding variable between the two experimental groups, as the non- interactive learners would only be required to look at a static photograph and not be required to scroll content throughout the task. To reduce the need for scrolling, the interactive activity was designed to be responsive to screen size, namely that the diagram would automatically shrink or expand to fit the available area. Users could use pinching and swiping to zoom into the content as required, the need for excessive scrolling was reduced, but not completely eliminated.

There was a timer on this screen which closed the activity at the end of ten minutes. The rationale for timing the activity was to ensure that both groups had an equal amount of time for the activity. This ensured that any differences in temporal demand could be attributed to the learning task.