Many folks are seeking inspiration for their approaches to learning (new and old) from results that are emerging about how the brain works. Inspiration can come from anywhere, of course – but when you're building “real stuff” that's supposed to work in the world at scale (“learning engineering” we might say), the best sources for evidence-based principles to guide design success are likely to come from cognitive science, not (yet) lower level research from neuroscience and brain science.
However, it's important to distinguish sources of inspiration, which can be anything we imagine or have experienced, from sources of evidence that lead to principles that predict how things will work in the world. Kekule might have been inspired by his dream, but it was the hard work of much experimentation and data-gathering that showed his particular reverie corresponded to how the real world worked, while other ideas failed.
The creative educator or instructional designer can and should draw inspiration for tough challenges from everywhere and anywhere, if there isn't evidence already available to guide him or her. Unlike many challenges faced by an artist or author, however, instructional designers and educators also need to be grounded in how the real world actually works. (Even artists have to battle with the chemistry and material properties of the media they choose, it should be noted – you might want glass to be strong enough to support something in a certain way, but you may have to alter your artistic vision to match the reality.) Simply imagining how learning might work is not enough to build solutions that are effective for learners at scale – whether we like it or not, whether we get it right or not, how learning works in the world is going to affect the outcomes at scale.
So while “learning engineers” can draw inspiration from anywhere, where do we draw evidence for what is known about what will happen in the real world?
There is a lot of science available about brain and mind out there, gradually uncovering more and more about how our thinking machine works. Neuroscience, brain science, and cognitive science are big categories that cover a lot of it, but (as far as I know – I welcome postings showing me new things!) only cognitive science currently provides clear guidance and evidence guidelines for what works (and what is unlikely to work) at scale for learning design.
Neuroscience ranges from biochemical and molecular biology interactions up to how collections of neurons behave and signal. The research is uncovering increasing complexity around how the signaling system of the brain works – not just electrical impulses, but chemical interactions, among a myriad of cell types in the brain.
However, the experimental work so far is very low level compared with what's mostly needed as evidence for what works (and doesn't) for designing learning at scale. There's real value for learning in some ways: the pharmaceutical work to help with attention deficit issues by fixing dopamine deficiencies (controversial to say the least) has established benefits when applied to the correct student populations. This isn't so useful for designing learning experiences, however, even if it is helpful for specific student sub-populations.
As far as I know (I welcome pointers to good evidence to the contrary!), there are no new principles to guide the building of learning experiences that have emerged solely from neuroscience research itself. There's just not a tight enough link from this low level to the higher level behaviors we need – nothing (yet) like in health care, where the understanding of molecular foundations of many diseases has led to new, valuable treatments at scale that were not available before.
Brain science looks at structures in the brain and how those structures signal each other, with quite a lot of overlap with neuroscience . There's been a fascinating acceleration in understanding how parts of our brain are activated and move information around while we think, and some of it is beginning to mesh with learning tasks.
For example, there is very interesting work showing that functional magnetic resonance imaging (fMRI) scans of dyslexic brains fire very differently during a reading task than brains that are more expert readers. Even more tantalizing, as dyslexic brains do training to become better readers, their brain scans begin to converge to the expert pattern – you can literally track progress using the fMRI scans! This is remarkable – a window into the connection between mind (functional behavior at a large scale) and brain (the biology of what's happening in the brain that leads to behaviors on the outside).
While exciting and intriguing (and impinging on thousand-year-old questions of identity and philosophy – how do we think about a split-brain patient's identity?), just as with neuroscience, as far as I know (and again, I welcome being pointed to good evidence to the contrary!) there are no large-scale interventions in learning with evidence for effectiveness that have emerged solely from brain science research – no revolutionary, new, instructional practices have (yet) emerged from this line of research. This research is beginning to confirm some of the research from cognitive science, beginning to reveal structural counterparts to the psychological models of how minds work, but the evidence base in brain science has not (yet!) directly pointed to untried solutions for designing learning. (Will be very exciting if something emerges!)
Cognitive science is quite different. Rather than starting from the biology and trying to work up, cognitive science works from behaviors down to models, with empirical evidence used to adjust or abandon models of how information is processed by a mind. Experiments are done with people doing things, or learning things, in controlled conditions - they are simplified conditions, as all science relies on, but they are often recognizably directly related to messier learning tasks in the real world.
As a result, there is a lot of work in cognitive science that applies directly to what a “learning engineer” is trying to do. Various syntheses like E-Learning and the Science of Instruction by Clark and Mayer, or Why Don't Students Like School by Daniel Willingham summarize some of those key findings in ways that are enormously useful for those designing learning environments, or trying to improve their own students' performance.
Cognitive science is not just a source of inspiration. It really is a source of evidence-based principles that should be paid attention to for learning success at scale: things like how limited our working memory (the conscious part of our mind) actually is, and how important what we embed in long-term memory is to learners finding the next thing “easy” to master or “hard” to master, or how our dual audio and visual channels can really improve learning – or distract from learning – depending on what information flows through each at the same time.
Inspiration can come from anywhere – and, for our toughest problems with no other information to go on, needs to. However, when we're looking for principles describing how the world of learning actually will work at scale, not just how we wish it would work, cognitive science, more than neuroscience or brain science (as of today!), is the place to look for good principles with evidence to guide us.
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