I’m jotting down a few reflections on how you choose a topic for your doctorate. I think for many of us it isn’t as simple as ‘something I’m really interested in’, or ‘what I’m good at’, or ‘what I’d like to specialise in’. Care must be taken in your choice, yet focusing on what you’re most interested in or have the most skills or experience in may not be the best way to select a PhD project. Essentially a PhD needs to be useful.
For me, none of the above things were really true. My project was offered to me as one of two possible options, a ‘smart learning’ project, or a project about rolling out Mahara blogs at a university. I had initially tried to start a PhD in another topic entirely, at another university (in another country), with a proposal based on potential new systems of managing and identifying intellectual property for digital products using metadata and the semantic web. That topic is of real interest to me, I’ve followed argument and technological developments for years. But the PhD never got off the ground, it had too many problems: finding the right institution and supervisor, or even getting a response from some research offices at all. In truth that project was too novel, no one really understood it, probably especially me. And looking back with the insight of having now completed three-quarters of my thesis, it would have been much harder, more work and more prone to my own somewhat religious fervour in writing and researching it, therefore being opinionated, on a mission. My view now is that because I am not passionate about the topic I ended up researching I am far better placed to research and write with a more solid attitude to bracketing my own presuppositions and biases. Mainly because in overt simple terms, I don’t really have any. If I suspect I do I readily admit they might exist, and I don’t care passionately about the success or failure of the work in research terms. The findings are what they are, positive or negative.
The topic of smart learning is broad and open to interpretation, and can encompass a variety of associated aspects of interest such as data and artificial intelligence, as well as the core related focus of learning and technology enhanced learning. Theoretical discussion can include wide reaching societal attitudes to emerging technologies and digital literacies in contexts of drier epistemology, making it interesting, yet keeping it relevant. A big part of writing a thesis is knowing what to write and what not to write, and choosing how you connect things together. This is I think what is meant by ‘developing your argument’. You’re developing a position about your topic, without necessarily being concerned about the pros and cons of it, beyond knowing in depth what those might be.
I have several friends who regret their choice of topic. One says it just isn’t fashionable or useful, another thinks they made the wrong choice because they ‘cared too much’ about it, another thinks their work dated very quickly so became redundant. Others feel non committal, that it did what it was supposed to do to get them to the next level of work or opportunity. They seem far more ready to admit they didn’t much care one way or the other what the results told them, that the results were enough, whatever their outcome. I think this is key to being a good researcher, to put your feelings and interests in your back pocket, and just do the research, whatever it’s about.