A couple of weeks ago, I had a little poke around some of the standard reports that we can get out of the OU VLE. OU course materials are generated from a structured document format – OU XML – that generates one or more HTML pages bound to a particular Moodle resource id. Additional Moodle resources are associated with forums, admin pages, library resource pages, and so on.
One of the standard reports provides a count of how many times each resource has been accessed within a given time period, such as a weekly block. Data can only be exported for so many weeks at a time, so to get stats for course materials over the presentation of a course (which may be up to 9 months long) requires multiple exports and the aggregation of the data.
We can then generate simple visual summaries over the data such as the following heatmap.
Usage is indicated by colour density, time in weeks are organised along horizontal x-axis. From the chart, we can clearly see waves of activity over the course of the module as students access resources associated with particular study weeks. We can also see when materials aren’t
being accessed, or are only being accessed by a low number of times (that is, necessarily by a low proportion of students. If we get data about unique user accesses or unique user first use activity, we can get a better idea about the proportion of students in a cohort as a whole accessing a resource).
This sort of reporting – about material usage rather than student attainment – was what originally attracted me to thinking about data in the context of OU courses (eg Course Analytics in Context). That is, I wasn’t that interested in how well students were doing, per se, or interested in trying to find ways of spying on individual students to build clever algorithms behind experimental personalisation and recommender systems that would never make it out of the research context.
That could come later.
What I originally just wanted to know was whether this resource was ever looked at, whether that resource was accessed when I expected (eg if an end of course assessment page was accessed when students were prompted to start thinking about it during an exercise two thirds of the way in to the course), whether students tended to study for half an hour or three hours (so I could design the materials accordingly), how (and when) students searched the course materials – and for what (keyphrase searches copied wholesale out of the continuous assessment materials) and so on.
Nothing very personal in there – everything aggregate. Nothing about students, particularly, everything about course materials. As a member of the course team, asking how are the course materials working rather than how is that student performing?
There’s nothing very clever about this – it’s just basic web stats run with an eye to looking for patterns of behaviour over the life of a course to check that the materials appear to be being worked in the way we expected. (At the OU, course team members are often a step removed from supporting students.)
But what it is, I think, is an important complement to the “student centred” learning analytics. It’s analytics about the usage and utilisation of the course materials, the things we actually spend a couple of years developing but don’t really seem to track the performance of?
It’s data that can be used to inform and check on “learning designs”. Stats that act as indicators about whether the design is being followed – that is, used as expected, or planned.
As a course material designer, I may want to know how well students perform based on how they engage with the materials, but I should really to know how the materials are being utilised, because they’re designed to be utilised in a particular way? And if they’re not being used in that way, maybe I need to have a rethink?