Tuesday, 23 September 2014

#PhD qualitative versus quantitative #research: trials and tribulations

Why did not I simply stick to quantitative data, the beauty of numbers in simple, straight forward formulas ?!! That scream of despair kept me awake at night for weeks. Weeks filled with hopes and doubts on getting enough data for my PhD study, eagerly looking at mails and learning logs.

Up close and personal
Qualitative research brings along much more discussions with all stakeholders, for everyone needs to be willing to share. Due to the fickle nature of language, everyone also needs to understand what is meant by the researcher when ideas are investigated. A difficult endeavour.
Quantitative research is like watching ants. You track the colony, and you get a fairly good idea of what they are doing. In a way this is what happens with learning analytics or Big Data derived from watching humans as well. You get the facts of the human colony when studying learning analytics or quantitative research, but you do not get it’s spirit, it’s drive or reasons why.
I want to know more. I want to get into the minds of people, into the minds of online learners, and understand why they are doing what they are doing, and how they do it. But this means I need to get into a conversation with them. And as we all know not everyone wants to start a conversation with just anyone.

Finding what no wo/man has found before
The setting of my research is simple enough: finding out how experience online learners learn. To see whether the assumption of us ‘grand experienced learners’ is indeed filled with online connections (personal learning network), finding what no wo/man has found before (surfing the Web), and sharing (blogging our learned reflections, ideas and thoughts). But my chosen approach could not be anything else but qualitative. It had to be laboriously gathering written data from good, willing research volunteers who’s lives are already cramped with time staking demands. Why? Because there are no quantitative holistic tools available that will capture all learning (formal and informal), and offer insights into why these data emerge.

No holistic research tools, due to no learning blueprint
The research tools of today do not (yet) permit me to simply trace or track what a learner does while studying an online course. The course related resources can be tracked of course, but there is so much more that some of us do: connect with others (partners, face-to-face colleagues) to find solutions related to the course, gather extra information connected to the content of the course as well as our own contexts for the topic. Granted, xAPI provides some self-reported informal learning diaries. Twitter and blogs offer idea and diary options so personal learning reflections can be shared. But there is no holistic learning tool available yet. This is due to the fact that at this point we can only assume how people learn in such online environments (especially regarding informal/formal learning actions). So as long as we do not know what learners actually do to get to a full understanding of an online course in relation to their personal learning needs, no tool will grasp all that is needed. This of course provides a solid rationale for my research: getting a blueprint of how people learn in an online teaching environment.

Time is of the essence
Which brings me back to the tension between qualitative versus quantitative research. Up until now, and for this type of exploratory research the qualitative research options seem to be the best option to get an idea of how experienced online learners learn.
However, I do get the impression that all of us are losing out on time. Our time is under pressure. We work, have hobbies, care for our families, … and now with MOOCs rising, we learn as part of our lifelong learning or leisure learning realities. And this is where my research comes in, with yet another demand on precious time of learners that simply want to get on with life. This lack of time, manifests itself in people willing to engage in research, but finding they simply cannot do it (if they want to stay sane and on top of their lives). Some volunteers get slightly cross due to questions that they find irrelevant, others interpret the words I write in a different way than they were intended… and all of them have a point, as language is fluid, as is any meaning making. So, as a researcher I listen and try to find adaptations that can make life easier for my willing research volunteers. This is not always an easy task, but I owe it to them to try and make it work, or to make participation in my research easier.

Big Data or Big Emotions?
So now, with questions and discussions rising (on questions and instruments used), inevitably emotions come into the research and into the hearts of all volunteers. Inevitably people discuss ideas, have an opinion on what is asked and they feel positively or negatively inclined towards what is asked from them.
Sometimes this gets to me. I really want people to get a positive feeling from sharing their experiences, and in turn use their experience to provide guidelines to new learners, as well as insights to course facilitators and teachers. That way we all grow, and – hopefully – make learning a more intuitive, natural act. The way it is supposed to be.

Luckily most volunteers know this, they put their efforts in, knowing it will help others. And I am deeply indebted to all of them. The data keep pouring in … I am grateful.
(Another great cartoon by Nick D Kim: http://www.lab-initio.com/)