A nice observation, pointed out by Kay Bromley in a department meeting earlier this week whilst reporting on the OU’s changing model for student support: if we introduce learning analytics that trigger particular interventions, (for example, email prompts), we should expect a certain percentage of those interventions to result in additional calls for support…
Which is to say, a consequence of analytics driven interventions may be a need to provide additional levels of support.
Another factor to be taken in to account is the extent to which Associate Lecturers (that is, personal module tutors) need to be informed when an intervention occurs, because there is a good chance that the AL will be the person a student contacts following an automated intervention or alert…
As a “shot to nothing”, it being that snooker time of year. A minimal netwrok visualisation of some of the #octel forum activity, which has a structure of: forum(-forum)-topic-reply. Replies can be threaded in a topic, in which case the replies have the same parent but also carry an incremental “menu_order” attribute that gives the accession order of the reply within that thread (I think?).
A post_type attribute identifies whether an entry is a forum, topic or reply.
I grabbed three columns of data defining an edge list – parentID, postID, post_type (forum, topic or reply), and threw the data into Gephi. Different layout types bring out different structural elements, but I just wanted something quick and dirty to look at the data in “unthreaded” form to see how we might make use of the thread/menu_order data.
Here’s an example – the whole structure (not very compellingly laid out, it has to be said):
We can also filter by edge type to get a hint of the actual structure, for example, the forum structure (i.e. how forums and subforums (child forums) relate to each other):
Or the topic structure (how topics relate to forums):
Or the reply structure (how replies fall into topics):
Note that this is the one we need to work on, in respect of finessing the data to allow us to see the hierarchical nature of the thread structure within a topic.
Anyway, as I said, a shot to nothing, and something to ponder representation wise as I walk the dog…;-)
Last year I sat on a couple of panels organised by I’m a Scientist’s Shane McCracken at various science communication conferences. A couple of days ago, I noticed Shane had popped up a post asking Who are you Twitter?, a quick review of a social media mapping exercise carried out on the followers of the @imascientist Twitter account.
Using the technique described in Estimated Follower Accession Charts for Twitter, we can estimate a follower acquisition growth curve for the @imascientist Twitter account:
I’ve already noted how we may be able to use “spikes” in follower acquisition rates to identify news events that involved the owner of a particular Twitter account and caused a surge in follower numbers as a result (What Happened Then? Using Approximated Twitter Follower Accession to Identify Political Events).
Thinking back to the context of evaluating the impact of events that include social media as part of the overall campaign, it struck me that whilst running a particular event may not lead to a huge surge in follower numbers on the day of the event or in the immediate aftermath, the followers who do sign up over that period might have signed up as a result of the event. And now we have the first inklings of a post hoc analysis tool that lets us try to identify these people, and perhaps look to see if their profiles are different to profiles of followers who signed up at different times (maybe reflecting the audience interest profile of folk who attended a particular event, or reflecting sign ups from a particular geographical area?)
In other words, through generating the follower acquisition curve, can we use it to filter down to folk who started following around a particular time in order to then see whether there is a possibility that they started following as a result of a particular event, and if so can count as some sort of “conversion”? (I appreciate that there are a lot of caveats in there!;-)
A similar approach may also be relevant in the context of analysing link building around historical online community events, such as MOOCs… If we know somebody took a particular MOOC at a particular time, might we be able to construct their follower acquisition curve and then analyse it around the time of the MOOC, looking to see if the connections built over that period are different to the users other followers, and as such may represent links developed as a result of taking the MOOC? Analysing the timelines of the respective parties may further reveal conversational dynamics between those parties, and as such allow is to see whether a fruitful social learning relationship developed out of contact made in the MOOC?