micro-ethnography – Colin's EDC blog

My Micro-Ethnography and Week 7 round-up.

Here is the link to my Micro-Ethnography.

I created it using an “artificially intelligent” web design package. This is part of its editing facility:

I spent too long on this exercise at the expense of other things (such as tidying up this blog) which was not what I intended. Nevertheless, I feel like I have an understanding of Kozinets (2010) and an appreciation for other netnographers that I did not start with. I’d need to suck up my guts and get on with sorting niggling bits of grammar, spelling and structure for an assessment. As it is, I think it stands well enough for the kind of “low-stakes” exercise we’ve been asked for. I’m both happy and annoyed with it at the same time. I like it for what it is, and what it represents, and I’m frustrated that I haven’t been able to do more in the time that I had.

Week 6 otherwise has been quieter on the life-streaming front. I followed in the footsteps of Fournier, Kop and Durand (2014) and tried out Nvivo. Which I share their reservations about (for another blog post, perhaps). In the end I just eyeballed my data and counted in my head….

I checked out the excellent micro-ethnography submissions from my EDC cohort, and managed to get comments through from their blogs on to mine.

I’m interested in pursuing something around Virtual Reality and community for my assessment, so I’m trying to pull in relevant articles in to my lifestream.

Categories are set up for most (if not all) my posts, which I’ll need to do something with, but for now they can be selected to filter down to some of the themes of my lifestream.

On to block three…

Fournier, H., Kop, R. & Durand, G., (2014). Challenges to Research in MOOCs. Journal of Online Learning and Teaching, 10(1), pp.1–15.

Data – The Machine Will Out

“With a high volume of data, there was no other choice than to utilize a computer program to aid in organizing the data and increase rigor by coding all data systematically” (Fournier et al, 2014 p6).

Thanks to MOOCs, which are made possible via computers and the Internet, the data sets generated can be so vast that there is “no other choice” (ibid) to use a computer to analyse the results. Fournier recognises the shortcomings of the “restrictive nature” (ibid) of such tools but carries on with them regardless.

The software used was NVivo (see QSR video below). Does the software claim to be more than human? It seems like it.

“Maximize your knowledge. Gain an Edge, and make better decisions ” (0:24). Not just “better” but this software actually “helps you make intelligent decisions”(0:40) so you can “make robust decisions faster” (2:40) and “uncover insights faster” (4:09). “It’s the perfect option to start your research journey” (1:20).

This one was interesting though: “discover emerging themes, patterns and sentiment in minutes” (2:27). Sentiment! Interpreting sentiment is surely the domain of the human. Should we leave software to “[count] particular words, rather than interpreting them as a human researcher might do?” (Fournier et al, 2014 p6).

Fournier et al argue that human and machine working together is preferable for research in and around MOOC contributions. So I’m now signed up to a 14-day trial of this tool and I can see whether or not I feel my knowledge is “maximised”, an “edge” is gained, and my quick decision making is “better”, “robust” and “intelligent”. This will form part of my micro-ethnography submission, I hope.

QSR International Video source:

Fournier, H., Kop, R. & Durand, G., (2014). Challenges to Research in MOOCs. Journal of Online Learning and Teaching, 10(1), pp.1–15.

Micro-Ethnography – formation of a community and/or groups

I’ve been participating in the University of Nottingham’s excellent reusable learning objects course, which is actually entitled “Designing E-Learning for Health”.

For my micro-ethnography, I am currently looking at two possible routes.

The first is to take a snapshot of comments from two different stages in the course (beginning and middle, most likely as the course is still underway) and see if it’s possible to attribute each comment in a single activity to a position on Kozinet’s matrix as I discussed with my blog tutor, James, in the Week 5 round-up.

The course has also now offered participants a practical challenge. I wonder if it’s possible to look at tracking somehow, the formation of the groups through the comments section of each week. I’m not sure if this will be practical to achieve in the time frame, but I possibly might find that information easy enough to pull together when I’m looking at the Kozinet’s frame work and pulling in some ideas about how the community is forming, its nature and so on.

I also have some ideas about presenting some quantitative information surrounding the discussion analysis in Minecraft, but I’m probably just making a rod for my back. We’ll see how time goes.