“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.