Just finished making my way through The Future of Learning from Steve Wheeler on Slideshare (see below) and thought it an excellent overview of the trends in learning overall and some possible technological advancements that could contribute to those trends. Very good and worth a look. Wheeler is Associate Professor of Education at the University of Plymouth and worth a follow on Twitter, Slideshare, and his blog (at least if you are passionate about learning).
What drew me in a bit more was that this presentation is a sensemaking exercise , drawing seemingly disparate bits and activities into a discerning whole, a narrative pieced together from fragmentary evidence. Evidence of learning itself.
I was also making my way through a recent JISC Report on Data Mash-Ups and the Future of Mapping, which is an excellent read as well. It also “shows how data mash-ups in education and research are part of an emerging, richer information environment with greater integration of mobile applications, sensor platforms, e-science, mixed reality, and semantic, machine-computable data and speculates on how this is likely to develop in the future.” True. But what I am keying on is not the maps, the technology, the semantics, or even the networks themselves. I am intrigued by the issue of complexity.
My mea culpa here is that I am not an original thinker; I am a cobbler. I pull together segments from smarter people in my own way and call it an abridged idea. Regardless, I am intrigued by the complexity of mash-ups and networks as mash-ups and knowledge flow as one gigantic mash-up. A mash-up as evidence of complexity.
Language is like this as well (and why I am a big fan of learning languages. A fan, mind you; I’m not very good at it). It is believed that human language emerged as an effect (a consequence?) of the use of complex tools in our earlier ancestors. Not a stick to poke at a rock. A flint attached to a stick; a lever, a fulcrum. Complexity. This complexity triggered language, which by any account is one of the most significant events in all of humankind. My argument here is that we are faced with a complexity now that rivals this; a complexity that spurs a development not unlike the development of language.
In short, complexity requires a language (to make sense out of it). Language is an expression of learning. Learning and complexity are natural partners. So learning, as Wheeler points out in reference to Siemens and Connectivism, is an exercise in complexity (in regards to networks, knowledge transfers and storage and the seemingly novel idea that knowledge can exist outside the human; that networks amplify our learning/our own voice). We need to search for complexity, for novel appropriations of disparate bits towards knowledge construction and representation. To bleed the edges a bit, as Wheeler points out, between formal learning (20%) and informal learning (80%). Appropriate our worlds entire for the purpose of knowledge construction.
This complexity serves pragmatic ends. As is said in the presentation, we live in a world where we cannot predict what the next generation will need to thrive. That is indeed true in terms of outputs, figures, applied skills, etc. What I do know that a love, appreciation, and desire to interact with complexity will be a part of that mix. An ability to solve a problem or articulate an understanding (or even find a job) not because we know the answer, but rather that we know that with some time we can assemble the bits to make an answer.
So mash-ups make perfect sense to me. I am taking mounds of disparate data and mapping it onto a language, my own. I am layering complexity on complexity (language itself is complexity, letters represent something else which represents something else which speaks to the primordial), and constructing a sensemaking layer on top of that. The sequence of learning might be the same, but the stress is different. Those middle bits of construction and reconstruction will shine.
So get out there and start playing with complex tools. Stimulate a part of your brain now dormant. Build and build again. Assemble your networks and assemble them well; constantly cultivate them. Understand where knowledge is stored, contained, circulated and know that isn’t always in you. Embrace this complexity.