This week’s post is in an interview with Rhodri Thomas, Senior Project Manager in the Learning Innovation Office. Rhodri, who is something of a mobile learning guru at the OU, keeps his own blog on Mobile Learning support, which we urge you to check out. (At the moment you can see him give a presentation on mobile connections across the OU.) What he doesn’t know about mobile learning activity at the OU, isn’t worth the mobile it’s texted on. Here are some key points that emerged from our discussion:
A whole range of stuff can be covered by the description Mobile Learning:
· There’s the technology itself.
· Then there’s the user end of things, which addresses the needs of the student who’s out and about, so that they can multi-task as they travel (e.g. on public transport) and have access to content online (such as OU course material).
· There are devices that know where you are and allow you to exploit that information (e.g. geolocation apps).
· And there are devices that are able to handle multi-media.
Some background to ML development:
· User testing (such as surveying students and taking feedback, which IEY carry out) is helping to identify what websites and technical devices people are using.
· Currently, the most common activities continue to be quite traditional—i.e. reading material, catching up with assignments, participating in forums.
· But times are changing: more people are using tablets (such as ipads) and broadband speeds are getting quicker.
· In the US Higher Education sector, the focus has been for some time on developing apps. UK universities are looking to expand into this market, but most apps simply present a campus user experience and services (e.g. maps).
· For the OU, the Virtual Learning Environment (VLE) is important for building a continuous narrative from the production of course materials to their delivery for distance learners in an online environment.
7 key points:
· We shouldn’t be thinking about just ML apps, but more holistically in terms of the mobile web and services more generally.
· The question at all times should be: What tools are needed to do the job?
· Taking that one stage back, careful thought needs to be given to what it is you actually want to do. Apps work better with specific tasks in mind.
· In fact, ML should be seen as part of a bigger picture. For example, ML often works best when mediated through face-to-face engagement, such as when tying ML activities to tasks undertaken back at ‘base’.
· Flexibility is the thing. That is, the ability to switch from a ML web service (which you can use to work on a set of tasks wherever you are) to a desktop service (once you’re back in the office) to ML apps (if you have a specific task that needs being done).
· In short, mobile technology needs to be integrated into the learning outcomes from the beginning of the process.
· And for this, academics need training in and exposure to the range of ML possibilities that are available to them in their subject.
So, it seems that, as usual, some kind of training is needed. But, perhaps even more importantly, we ourselves need opportunities to play with the tools on offer. For, how can we come up with interesting uses for ML, if we haven't any experience of it