I’ve been corresponding with someone who reached out via the Contact form regarding what we hope will come out of the “Big Data” being generated from millions of students enrolled in and taking MOOC classes.
No doubt the “micro-behavior” of students as they progress through a class will be invaluable to those designing and teaching online courses, giving them insight as to how people navigate the various components of their lessons. But at this stage, I’d be more interested in looking at higher-level macro data that broke down general student activities (percentage of lectures viewed, number of assignments completed, test performance, course completion, etc.) by student type.
And by type, I’m not just talking about age, nationality and other demographics. For one of the most valuable things we can learn right now is what sort of students are enrolling in massive online classes and how each category of students succeeds or fails once enrolled.
Nearly everyone who has taken a MOOC course has been asked to fill out an online survey, either at the start of the course or at the end. And external research surveys (like the one I mentioned here) are also being used to generate the kind of data that would tell us who we’re dealing with in our massive classes. These surveys have generated important information (including the fact that a majority of students enrolled in most MOOCs already have a BA or equivalent – or more advanced – degree).
But a few more questions (like the following) might help us confirm whether these students (or younger students without a degree) fall into that category of autodidacts I mentioned previously.
(1) Have you independently studied this subject previously? (Y/N)
- If so, how? (classroom course/online course/recorded lectures/reading)
(2) Have you studied other subjects independently in the last 12 months? (Y/N)
- If so, how many? (1/2/3/4/5/more than 5)
(3) Do you consider yourself an independently-motivated learner?
…or something to that effect.
Questions like these generate data that can be combined with demographic information (notably education level) to create an “Autodidact Score” which can then be compared to the numbers used to measure student success within a MOOC (auditing/lecture completion, performance on tests and assignments, course completion, final grade, etc.).
The result of such an analysis could tell us if MOOCs are primarily benefiting those who haven’t had access to such educational resources in the past (such as poor students or students in remote or third world locations) vs. those who have already mastered the art of independently locating the resources needed to learn any subject of interest.
The goal of this kind of study would not be to simply demonstrate who is gaining the most advantage from these new online learning tools. For in addition to identifying distinct audiences that might need to be targeted differently to increase enrollment or success rates, it may turn out that a category of autodidacts can become a resource for increasing success of MOOC students across the board.
For instance, if it turns out that autodidacticism is a key ingredient to high performance, perhaps the characteristics that correspond to this type of learner can be used to identify those most likely to succeed in a MOOC class. Or perhaps such skills can even be taught, either as a standalone class or integrated into existing curricula.
Going further (and taking onus off the MOOC providers and putting it onto we students) perhaps it can become our obligation to either attach ourselves to an autodidact as part of the process of taking part in a MOOC class. Or if we feel we fall into that autodidact category ourselves, perhaps it’s our obligation to pair ourselves with a less experienced learner in order to help them through the course while also teaching them whatever skills go into making someone a resourceful, lifelong learner.