Talking to my beloved last night, I said something along the lines that I'd never been excited by my thesis topic, and she pressed me. If that was the case, how did I end up choosing it? During the explanation I found that I'd raised my voice and became quite animated, and I realised that I might have rewritten history. I've previously written about some of the practical history of my candidature, but not the topical history. So how did I get here?
In the early noughties, I was doing a course in my masters called intelligent web systems, and doing a pretty bad job of it actually, mostly because I wasn't spending much time on it. That, and the fact that we could write our assignments in any language we liked, so I decided to start the night before it was due and do it in python, which I'd never used before. I digress. The final assessment was to write an essay of a couple of thousand words about anything that broadly fit into intelligent web systems. I was teaching at the time, so I was thinking about educational things, and so I chose to write about edutella, a p2p network for searching semantic web data with the aim of facilitating the exchange of educational resources.
In my final semester of the masters, I enrolled in a "dissertation", a one-semester course writing a few thousand words on a topic of my choice. I did the dissertation because I wanted to avoid having to enrol in the research masters program, preferring to try to go straight into the PhD, and needed to demonstrate that I could write. Not that I actually wanted to do a PhD, but the powers that be had made clear I had to do it if I wanted to keep my job. Still teaching and thinking about education, and in an attempt to make things as easy as possible for myself, I decided to continue on from my essay, looking at retrieval of educational resources.
The state of play was that, mostly, educational resources were stored in repositories. To retrieve them, systems would search human‑assigned descriptive metadata. That's pretty much still the case. I talked to some wise people in the school, and came to the conclusion that this library-style approach to retrieval could be improved using techniques drawn from the information retrieval community. For a start, I could extract text and search the primary resource, rather than secondary data.
That's where I got excited about things, but it's where I should have started to realise that doing a PhD wasn't a great idea for me. The dissertation ended up being about 5000 words, and I struggled. But I went ahead and blindly enrolled in the PhD anyway.
On the strength of my dissertation, and knowing the right people, I was seconded to RMIT's Teaching and Learning Portfolio, to do a scoping project aimed at helping improve the management and reuse of educational resources. During this time, along with Henric Beiers, I conducted a bunch of interviews, focus groups, and a survey. How did other universities manage resources? What are the barriers to reuse? How did educators want to find resources? This work would become a chunk of my thesis, and by that time I felt my path was pretty much set.
After three years part time, I decided to leave my job, go full time, and spend the next 18 months finishing my PhD. Six months in, working on applying IR methods to retrieval of resources from respositories, I realised I had no faith in the area. Millions and millions of dollars were being spent around the world trying to set up these repositories, and the top down approach just didn't seem to be working. I'm sure there are many, many people working on these projects who would vehemently disagree with me, but that's how I felt at the time. How had those educators said they wanted to find educational resources? They wanted to search with Google.
So I threw away six months' work and tried to regroup. I changed focus to filtering educational resources from search results returned by a regular search engine (Yahoo! because their API was easier to work with), changed to thinking more about learners in general rather than academics. The question of how such filtering systems should be evaluated became the next chunk of my thesis. The final part was the implementation of a simple filtering system, throwing a bunch of resource features at some machine learning classifiers and seeing what worked.
I'm now approaching the end of the road, I'm due to submit my thesis to the school in six weeks (I'm quivering with stress at the thought of how much work that's still left to do).
And after all that, it seems that at times I have been excited. But I sure as hell hate it now.