Having saddled myself with the agile learning term, one of the hazards I can't complain about is having to explain it: What does it really mean? What's different about it? What's agile about it. There's a working definition of the key elements on the agile learning wiki, which I continue to develop slowly and sporadically. Recently I've been reflecting on some more nuanced, but still half-formed, ideas, which feel more like blog-conversations than wiki-definitions. These are partly prompted by reading Douglas Rushkoff's excellent Program or Be Programmed (which deserves a blog post of its own), and also by the Learning Analytics course, devised by George Siemens and colleagues, which I'm currently participating in (and blogging about in detail over here).
What I'm toying with at the moment is a distinction between "weak" agile learning and "strong" agile learning. This is after John Searle's distinction between "weak" and "strong" artificial intelligence, but I suspect this kinship may be tenuous and, certainly, vainglorious. They might equally well be called, say, pragmatic agile learning and principled agile learning — or something else.
The weak version allows for things like intelligent curriculums, gamification and personalisation by the provider. The strong version wants to trust in learners' intelligence and give them the information and the data to personalise their own experiences.
Pragmatically, I'm drawn to the weak version. I distrust purism, believing every oyster needs some grit (for most of my three decades as a vegetarian, I've eaten meat a few times a year). But ethically and aesthetically I feel the strong version needs shouting about, because gung-ho enthusiasm for the Big Data/Scientific Management seems to be leading down a dangerous path. Let me explain.Continue reading "Deskilling Learning? On "strong" and "weak" agile learning"