Collaborative auto-ethnography – an antidote to big data in MOOCs?

Firstly, thanks to @helencrump for the title of this post. The alternative title was ‘#oldsmooc – the MOOC that keeps giving”. But I think Helen’s one sounds much more impressive 🙂

I’m not quite sure where I’m going with this post but there was a flurry of activity on my twitter stream last night/this morning and I just wanted to try and capture some of the ideas that have been floated.

Regular readers of this blog will know that a group of #oldsmooc-ers recently presented a paper at the EMOOCs Conference, called “Signals of success and self directed learningwhere we took a collaborative auto-ethnographical approach to create a range of narratives which described our different measures of success in participating in the mooc.  We felt that this personal reflection would help to address some of the gaps in understanding what actually motivates learners in MOOCs and probably more importantly for us as a group to explore the extent “connection, self-efficacy and self-directed strategies facilitate learning in a MOOC”.   As a self selecting group we were very conscious that we could not ensure our narratives were typical of other leaners on the MOOC, but we did try and collect some more data from other participants.  I have tried to document the story of our collaboration, which has been a key  signal of success for us all.

Getting the paper accepted was a great moment for us all, as was the conference presentation and tweets it generated.  We were still unsure if our approach and methodology really had any traction.  But last night Paige altered us all to this bit of activity on the current #rhizo14 MOOC.  Again through connections and network (aka some of our group taking part in another mooc) it seems our ideas and approach are being explored by other learners.

I think this is really important.  We know that existing accepted educational metrics don’t really apply in the MOOC context, particularly for retention. Despite the promise of MOOCs and big data being able to give us insights into how people learn, I, like others, am still not so sure about some of the methods being used and in turn the patterns that are emerging. As well as the quantitative data, we need to get much more qualitative data exploring as many different narratives as possible from learners. It’s only by doing that that can we really start to help develop our understanding of how people define success in MOOCs. And in turn, we can ask more challenging questions of/from the quantitative data.

Being a bear of very little brain, I like seeing the pretty patterns and swirly diagrams, but find it confusing when they don’t seem to relate to my own experiences.  Mind you,  if this article in the Sunday Observer is to be believed we won’t need to grapple with big data -v – little data -v- educational theory for much longer as soon the google robots will have worked it out all out for us and will have “fixed” education.

My experience of learning on MOOCs has been very different from my traditional educational experiences. I know I didn’t (and still don’t really) enjoy formal education, and I am much happier (and hopefully more creative) in connected, loosely structured learning experiences than read a bit, do the test, read a bit more ones.

Anyway below is a collation of the tweets from last night this morning, which range from us being all “check us out with starting an auto-ethnographic revolution” to more serious questions about the nature of open collaborative spaces, self disclosure and the importance of failure. On the last point, Pat Parslow referenced the “confessional” booth at the PELeCon Conference, which again got me thinking about the use of the language of guilt around what are perceived to be non traditional ways of doing things. But that’s probably a post for another day.

8 comments

  1. Most of the universe is made up of dark matter — the qualitative stuff that’s invisible to quantitative analysis but holds everything together. And it’s the tiny tales, not the grand narratives, that matter. Small stories, loosely joined.

  2. Hey Sheila, yes, we were extremely inspired by ur work. We were thinking of some sort of participatory/collaborative writing thing and did not know what it was really called, i think. And then it clicked with your paper (funny enough, that collab ethnography approach, which i had read of before i read your “auto” version, is influenced by Deleuze, who is also behind rhizomatic thinking, so in our case, the approach to research intersects with the theory behind what Dave calls rhizomatic learning)

  3. Hi Sheila. I’m still mulling all this over. I just read @francesbell’s Feb. 22 post (“Reflecting, recollecting, research – Auf wiedersehen #rhizo14”). Recording our experiences as we navigate our way through a wicked problem or place (including MOOCs) is useful and important, I think. It’s hard to follow the bread trail backwards from the end sometimes, especially when there are so many branching paths. We are all used to blogging our experiences in periodic summary reports, but it is hard for someone else to get much of a sense of how we navigated our way to the destination that we are describing.

    Here at Otago, we just finished the first week of a the first of two 13-week semesters. I decided to try using hashtags in two courses as a way of gathering and disseminating what (and who) we are finding and how we are finding, navigating, and connecting artefacts and individuals (@Bali_Maha is doing something similar, so we’ll compare notes). I started using Storify to build an ongoing narrative of how this journey unfolds. I’m not sure how this relates to collaborative auto-ethnography, but it arises from similar motivations, I think. The “#LibraryFutures Archive 2014” is here: http://goo.gl/DCbqTr and the “#Vote4KidsNZ Archive 2014” is here: http://goo.gl/5xdHGy.

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