1. In his latest presentation, Stephen Downes once more advocated personal learning. Previously I have often wondered if it is just a matter of style and taste whether one emphasizes an individual access to learning or not, i.e., whether one prefers learning in the same way a backpacker individual tourist learns about the world, or if one finds it easier and more effective to go with a guided group tour to get the proven optimized standard sight.
Photo CC-BY by Flickr user supermoving
As a tourist, I never liked the group style, but for learning, the lazy way of letting stuff just wash over me, did have its charme. And it sounds even more promising now since the optimization is ever more perfected by personalization. And since it seems like a ‘science-based’ way to success.
2. There may be even a third type of learners: the machine ‘students’ who do their machine learning. Previously, I often wondered why machines needed to ‘learn’, at all: they don’t need to gradually accumulate their knowledge because they just do a database lookup; they don’t have to learn their rules because they receive them through programming; and learning for any kind of later independence seemed unimaginable.
Now I know that it is not rules, but patterns, what they learn: they learn from data that comes in large amounts and from data that is randomly distributed — much like lifelong human experiences. So our automated fellow learners are already far away from the group path, and they are rapidly approaching our human styles.
3. In an Australian conference last week, @SBuckShum talked about the Cognitive Automation and recommended that we
“Cultivate those qualities that are distinctively human”
So what will prove as ‘distinctively human’ ?
I think it is exactly the personal approach which cannot be standardized, that cannot be automated. It is the individuality and subjectivity that guarantees sufficient diversity for further evolution and to avoid collapsing into a ‘black hole’ of power law distributions, and that guarantees sufficient embodiment for staying grounded in reality.
By contrast, approaches that only accept objectivity, and strive for algorithms that promise an optimized, rational, assessable, uniform solution — are eligible for cognitive automation, sooner or later.
4. This also applies to the optimization of learning. Only standardized, easily assessable learning can be optimized, and in particular, content knowledge is the ideal example. If we compare learning to carrying a load upstairs to the attic storage, there are two distinct goals. One is that the load should be stored up there, and the other is to work out one’s muscles. If it is only necessary that the stuff is accumulated up there in our brain, a ‘lift’ would be a welcome optimization. But jobs that only rely on such content storage, are probably the first to be eliminated.
In a recent blog post, Stephen calls it an ‘either or’ dichotomy:
“are you providing content knowledge or, are you providing literacies?”
and I think it it really a shame how ignorant our universities are in dealing with this question.