MOOC Platforms and the A/B Testing of Course Materials
[The following is my *personal* opinion only. I know as much about FutureLearn as Google does. Much of the substance of this post was circulated internally within the OU prior to posting here.]
In common with other MOOC platforms, one of the possible ways of positioning FutureLearn is as a marketing platform for universities. Another might see it as a tool for delivering informal versions of courses to learners who are not currently registered with a particular institution. [A third might position it in some way around the notion of “learning analytics”, eg as described in a post today by Simon Buckingham Shum: The emerging MOOC data/analytics ecosystem] If I understand it correctly, “quality of the learning experience” will be at the heart of the FutureLearn offering. But what of innovation? In the same way that there is often a “public benefit feelgood” effect for participants in medical trials, could FutureLearn provide a way of engaging, at least to a limited extent, in “learning trials”.
This need not be onerous, but could simply relate to trialling different exercises or wording or media use (video vs image vs interactive) in particular parts of a course. In the same way that Google may be running dozens of different experiments on its homepage in different combinations at any one time, could FutureLearn provide universities with a platform for trying out differing learning experiments whilst running their MOOCs?
The platform need not be too complex – at first. Google Analytics provides a mechanism for running A/B tests and “experiments” across users who have not disabled Google Analytics cookies, and as such may be appropriate for initial trialling of learning content A/B tests. The aim? Deciding on metrics is likely to prove a challenge, but we could start with simple things to try out – does the ordering or wording of resource lists affect click-through or download rates for linked resources, for example? (And what should we do about those links that never get clicked and those resources that are never downloaded?) Does offering a worked through exercise before an interactive quiz improve success rates on the quiz, and so on.
The OU has traditionally been cautious when running learning experiments, delivering fee-waived pilots rather than testing innovations as part of A/B testing on live courses with large populations. In part this may be through a desire to be ‘equitable’ and not jeopardise the learning experience for any particular student by providing them with a lesser quality offering than we could*. (At the same time, the OU celebrates the diversity and range of skills and abilities of OU students, which makes treating them all in exactly the same way seem rather incongruous?)
* Medical trials face similar challenges. But it must be remembered that we wouldn’t trial a resource we thought stood a good chance of being /less/ effective than one we were already running… For a brief overview of the broken worlds of medical trials and medical academic publishing, as well as how they could operate, see Ben Goldacre’s Bad Pharma for an intro.
FutureLearn could start to change that, and open up a pathway for experimentally testing innovations in online learning as well as at a more micro-level, tuning images and text in order to optimise content for its anticipated use. By providing course publishers with a means of trialling slightly different versions of their course materials, FutureLearn could provide an effective environment for trialling e-learning innovations. Branding FutureLearn not only as a platform for quality learning, but also as a platform for “doing” innovation in learning, gives it a unique point of difference. Organisations trialling on the platform do not face the threat of challenges made about them delivering different learning experiences to students on formally offered courses, but participants in courses are made aware that they may be presented with slightly different variants of the course materials to each other. (Or they aren’t told… if an experiment is based on success in reading a diagram where the labels are presented in different fonts or slightly different positions, or with or without arrows, and so on, does that really matter if the students aren’t told?)
Consultancy opportunities are also likely to arise in the design and analysis of trials and new interventions. The OU is also provided with both an opportunity to act according to it’s beacon status as far communicating innovative adult online learning/pedagogy goes, as well as gaining access to large trial populations.
Note that what I’m not proposing is not some sort of magical, shiny learning analytics dashboard, it’d be a procedural, could have been doing it for years, application of web analytics that makes use of online learning cohorts that are at least a magnitude or two larger than is typical in a traditional university course setting. Numbers that are maybe big enough to spot patterns of behaviour in (either positive, or avoidant).
There are ethical challenges and educational challenges in following such a course of action, of course. But in the same way that doctors might randomly prescribe between two equally good (as far as they know) treatments, or who systematically use one particular treatment over another that is equally good, I know that folk who create learning materials also pick particular pedagogical treatments “just because”. So why shouldn’t we start trialling on a platform that is branded as such?
Once again, note that I am not part of the FutureLearn project team and my knowledge of it is largely limited to what I have found on Google.
See also: Treating MOOC Platforms as Websites to be Optimised, Pure and Simple…. For some very old “course analytics” ideas about using Google Analytics, see Online Course Analytics, which resulted in OUseful blogarchive: “course analytics”. Note that these experiments never got as far as content optimisation, A/B testing, search log analysis etc. The approach I started to follow with the Library Analytics series had a little more success, but still never really got past the starting post and into a useful analyse/adapt cycle. Google Analytics has moved on since then of course… If I were to start over, I;d probably focus on creating custom dashboards to illustrate very particular use cases, as well as