Of course, the sceptical side of me says that this is something like saying that so-and-so can tell us best how to win an auto race because he's a mechanic. There is, indeed, a distinction between knowing how something works and knowing how best to use it.
Reading through the points in thsi summary, they seem sort of right, but not exactly right. Let me clarify them.
- The brain is a machine with limited resources for processing the enormous quantity of information received by the senses. As a result, attention is extremely selective and the brain must rely on all sorts of shortcuts if it is to cope effectively.
My response: no criticism of this; it seems to be about right.
- Teachers/designers can adopt two strategies to reduce the risk of learners experiencing cognitive overload: provide less information (quantitative approach) or take much more care about how this information is communicated (qualitative approach).
But the other way to look at it is to, as promised, look at it from the perspective of neuroscience. What does the brain do in cases of cognitive overload? This is important because, if we know how the brain will adapt, we know how to shape our information (if at all).
This is the subject of the next few points, so I'll continue.
- It is easier for a person to focus their intention on the desired point if there is minimal noise (other information) surrounding it. Reducing noise also reduces context, so a balance needs to be struck.
People are able to focus on things even in the most extreme of circumstances if they are sufficiently interested. That's how you can have kids playing video games even while the house is burning down around them (I guess that's the sort of 'context' that would be important). By contrast, if you aren't really interested in what you are doing, the least amount of noise distracts you.
We know this because (as was just stated above) we know that the brain is extremely selective and filters out stuff that isn't important.
Perspective matters. From the teacher's point of view, the content (lessons or curriculum) is constant, while the level of background noise is the variable. From the point of view of the learner, however, the content is also variable. That's why you get two very different interpretations of the same phenomena.
- Overload can be reduced by grouping items/steps (what Itiel calls 'chunking'). Grouping can be accomplished by placing people/objects/events into categories, or by compressing a number of procedural steps into one, automatic action. Visually you may separate items by space, size or colour. Learners will naturally employ grouping as a strategy, although they may do this inappropriately and the process requires effort. Better for the designer/teacher to present material ready grouped.
Yes, there are different types of groups. Groups that make sense conceptually, especially if linked to a larger framework, are better (I would add that colour is rarely, if ever, a part of such a framework).
But is it better for the teacher to present the material already grouped? How does that follow? If the intent is to have the student learn the information (ugh, bad terminology) then we must ask, is it the groups that aide remembering and understanding, or the process of grouping that does this? If it's the latter, then presenting the information already grouped may help the teacher remember, but will do nothing for the student.
Because, as I noted above, it is better if the groups align with a pre-existing conceptual framework, it is better then if the student does the grouping, because that way the process allows the student to connect, in an organized way, new knowledge with existing knowledge.
- A side effect of grouping is that once the action is completely familiar (that old 'unconscious incompetence' phase), the individual finds it hard to explain how they do it; they lose control over the process because it has become automatic (so old hands may not always be the best teachers?). Grouping is essential to our functioning, but there are obvious dangers, i.e. unhelpful stereotyping.
I think it would have been better to present them separately.
There are mental processes that can become automatic. Add 1+1 for example. One of these processes is 'categorization'. You look at a bunch of things and automatically associate some with the others, based on habitually formed patterns of association. In some cases, such as grouping people by colour, this sort of automatic association can be inappropriate.
There are also mental processes that constitute sequences of steps. The steps involved in a logical derivation, for example. So a process that actually involves multiple steps may be performed by an experienced logician as though it were only one step (I called this 'skipping steps' in logic class and complained bitterly about it. "It's obvious," said the professor. "Whaaaa?" I responded).
These are very different phenomena that are essentially the result of the same neural process but which instantiate very differently and need to be approached very differently. Kind of like the way the steering used to recover from a spinout may be exactly the same as the steering required to navigate a hairpin curve. Sure, it's the same motion. But you would describe the two events very differently.
- Individuals use top-down processing to reduce overload. This draws automatically on their past experience of the particular context, existing knowledge and intelligence and avoids them having to evaluate all new information from the bottom up. An example would be how people can easily read a sentence in which the letters in each word are jumbled up.
This is not 'top down' processing as traditionally understood.
There is a very large difference between inferring something on the basis of similarity to a prototype (that is, apttern recognition), and inferring something based on a general principle or rule. By 'top down' we typically mean the latter. But when describing character recognition, as in the example, we are describing the former.
I would also be wary of building the (Darwinian?) intent into the process. People use pattern recognition. It reduces information overload. But it is not necessarily true that people use pattern recognition in order to reduce information overload. People use pattern recognition because that's how neural networks work. Perhaps evolution directed us in this way, perhaps it did not. Either way, our use of pattern recognition in a particular circumstance is not caused by some such intent. It occurs naturally, as though by habit.
- Designers/teachers need to take account of the way in which the information is likely to be encoded and processed - it's not 'what you teach' but 'what is learned'.
- Different parts of the brain specialise in different tasks. Individuals can engage in more than one task at the same time, as long as each uses a different part of the brain.
That's why I can read and write while listening to loud music, as I'm doing now, while my father couldn't.
- It's a myth that we only use 5-10% of the brain - we use it all.
- The brain continues to change throughout our lives, even though we stop adding new brain cells in our early 20s. Some parts of the brain are relatively hard-wired (through nature or nurture), some very plastic. It makes sense to concentrate in recruitment on finding those people with hard wiring which suits the job, because no amount of training will sort the problem out later. (Itiel did not go into detail about those capabilities which tend to be hard-wired and those which are more plastic - this is clearly important.)
It is also true that some parts are pretty much hard-wired -- good thing, too, or our hearts wouldn't beat and our eyelids wouldn't blink.
But as to how much this carries over into learning or into life skills - this is very controversial. I can certainly agree that there are people with currently existing wiring that may be more or less suited to the job. That's no more controversial than saying people learn different things. But to say that these capabilities are hard-wired is much more questionable.
- As you grow older the hard-wired capabilities persist - the most learnable capabilities go first.
- Language is more than just a means for expressing thought - in many ways it is thought. If a person is not exposed to any language in early years, then by the age of seven they are incapable of learning it.
People who have not specialized in the nature of language typically take language as a given - some sort of folk-psychological representation of Chomskyian generative grammar. And then suppose that this then must be the nature of thought.
Even if language is thought - which i would not grant for a second - we still know nothing about the nature of thought if we do not agree on the nature of language. Which we probably most emphatically do not.
- The two sides of the brain really do have different functions (I thought this was just pop psychology). The left brain concentrates on language and analytical skills; the right has the spacial abilities. The left side of the brain controls the right side of the body and vice versa. The left and right sides of the brain do not interact physically.
- The size of a person's brain is not an indicator of intelligence.
- 20% of your blood is in the brain.
- You never lose anything from long-term memory, just the ability to retrieve it. Retrieval is a function of how you encode memories / the number of links you provide.
'Retrieval' (properly-so-called) is a case of pattern recognition - and the less salient a pattern becomes in the mind, the less likely it is to be associated with a current perception.
- Working memory consists of 7+/-2 items (again I thought this was pop psychology).
What we put into working memory first depends on pattern recognition.
- To reduce cognitive overload, take out every word or picture that is not necessary or relevant to your learning goals. Even then, don't deliver more than the learner can handle (presumably by modularising the learning).
I have seen studies, for example, showing that a slide show with bullet points is more easily remembered than a slide show with the same bullet points and animated graphics.
I expect, though, that the placement of the 'NRC' logo in the corner of the same slides would not have an impact either way.
I also expect that the removal of 'unnecessary' letters in the bullet points would actually hinder memory. Fr exmpl, rmving mst f th vwls. Thy r nt necsry in the snse that we cn stl rd the sntnce, bt they hlp wth the grping.
So - better advice would be something like - present material that accords (perhaps with some cognition) with patterns that will already be familiar to the learner.
- Provide the learning when it is needed, not before.
- Be consistent in the manner of your presentation, e.g. the interface.
- Be consistent in the level of your presentation, i.e. not too complex, not too simple. Try to work with homogeneous groups; better still personalise the learning.
- Engage the learner by grabbing their attention, allowing them to determine their progress, providing constructive feedback, introducing an element of excitement/surprise.
- Be careful of allowing the learner too much control over the learning process if they don't have the metacognitive skills, i.e. they don't know what they know and what they don't know, nor how best to bridge the gap. Ideally help learners to increase their metacognitive skills, i.e. learning how to learn.
If the content has more to do with attention than, say, distraction, then taking control away from learners, even in areas where they do not have skills, may cause more harm than good.
And what are the metacognitive skills. What is 'learning how to learn', for example?
- Providing the learner with control over pace and allowing them to go back and repeat any step is important.
- The learning benefits by being challenging. Performance targets, rewards and competition can increase the degree of challenge, perhaps through the use of games.
Anyhow, those are my thoughts based on this reading of Shepherd's article. I also read a bunch of Dror's publications online and certainly have no quibble with his neuroscience. I just think that the study of teaching and learning involves more than just neuroscience, and that there are areas of complexity and potential confusion Dror may not have considered in his work.