Gave a lecture on the Computer Science Education Research course here at Uppsala University this week. What I tried to convey were different aspects on measuring in the context of doing computing/engineering education research. I did that in the context of my efforts of trying to shed more light on the development of professional competencies in degree programs, an area that is ripe with complexity and aspects where ways to measure is far from obvious.
While preparing for the lecture, or rather discussion seminar, I became somewhat cynic and wondered if pursuing research in complex environments where measurements typically have a subjective touch is just stupid. Wouldn’t it be better to look into things where one can identify clearly measurable aspects that reviewers will recognise and feel comfortable with and thus have a better chance of getting work published? Nah, not if this meant that what I could research would be limited to simple and uninteresting things.
Don’t get me wrong – I don’t think all measurable things are simple and uninteresting, just that there is a danger that it could be so. I think that there is a risk that what is looked at has to be reduced, or confined, to a level where what is measured more or less loses meaning. My advice, – easy to say, much harder to do -, was to strive for balance between the complexity of issues to research and possibilities to “measure”. I think such a balance could be achieved by searching for tools and theories suitable for addressing aspects of a complex issue, aspects that are of interest and provides interesting insights into the issue. I didn’t use this “map” (made by Roger McDermott, Robert Gordon University, Aberdeen, UK, in his presentation of our paper “Investigation into the Personal Epistemology of Computer Science Students” at ITiCSE in Canterbury 2013), but it would have been a good illustration to inspire discussions about this.