Off to a cracking start to Day 2 of ALASI 2015 this morning with a fabulous keynote from Peter Reimann. Learning Analytics meets Learning Research mediated through Design-based Research (DBR). It was a neat argument and the most lucid description of DBR that I’ve heard or read. Learning Analytics allows us to continuously record data at multiple levels, in different places and over time. DBR connects learning analytics to learning research and together they can provide an evidence-base for theory building and achieving generalisable results. We need to advance the way learning as a process is studied and align our methods with learning theory. Most important, we need to explain how learning is achieved. Inspiring stuff.
But, perhaps the most inspiring moment was when Peter calmly explained that quantitative disciplines like Physics and Chemistry rely on qualitative descriptions: the nature of matter, energy, materials and so on. Of course he is right, but heavens above, is this the moment to finally pop the qualitative-quantitative clash to bed with a mug of Horlicks?
I learned two new terms: retroduction and retrodiction, marvelled at his process causation diagram and left the keynote altogether more positive about the World and my place in it than when I set off this morning: a sure sign of an excellent keynote.
Not to be outdone, Clover snorted at my new terms and explained that goats had developed these ideas long before people. Not only does every goat carefully observe the properties of fences in general and electric fences in particular in order to develop theories about them but they also evaluate their theories on the basis of how well they explain past events. Like the time her boy, Pedro, miscalculated the height of the top wire and fetched up trussed and suspended upside down by his two front hoofs. (Pedro was unhurt apart from lost dignity and the most plaintive wailing I have ever heard from man or beast.)
After morning tea, I joined the session where Abelardo Pardo and Jurgen Schulte led us through Scaling instructor-driven personal support actions, an exercise in reverse engineering our teaching. We had to invent a scenario and then describe the data we would need to identify student actions and the rules we would use to determine the actions to take in response. The take-home message was, if you can describe it, we can build it and if we can build it, it will scale. It was a good session, an interesting exercise and some fascinating examples from participants.
ALASI concluded today with a final opportunity for discussion over lunch. A special highlight was meeting colleagues from Auckland and Christchurch who I had never met before. I guess in the end, making connections is really what ALASI is all about.