I just discovered Alan Kay. You can read his bio at Wikipedia, or watch him give a TED talk. But I'm posting about him because I was fascinated by his thoughts on science, why it's counterintuitive, and how we might teach it better. Sounds like some of what I'm trying to get a handle on about math and how to teach it.
His thoughts about what science is seem to run in the same direction as my thoughts on what math is. I hope to post more on this later.
Adults and children just love "being creative" and "expressing themselves". And, this is especially the case around the world when computers are introduced with applications that allow people to make things.
Let's look at this process over human presence on the planet. We find invention coupled with dogma. Many who have studied this have likened it to an erosion model of memory, both individually and culturally. (Once a little groove is randomly made by water it becomes very efficient in helping more water to erode it further.)
So "creative acts" are resisted, but once accepted for one reason or another will cling, and most often far beyond their merits. Most creativity is more "News" than "New", that is it is extremely incremental to the erosion gully.
Science attempts to be completely different than this. We are dancing with a universe of which we can only detect some of its shadows, and the universe leads. We are not free to be creative to make up stories we like or draw pictures we like unless they can be shown to fit to a high degree with the dance. For many important reasons these maps we try to make cannot be true, even if the maps themselves are internally perfect and beautiful.
So science goes *quite against* what anthropologists have determined are strong built in human characteristics about explaining the world.
And even trained scientists often have real problems with this. Our brains want to *believe* but science is not about belief.
This is one of the reasons that science almost requires a community of scientists, some of whom are less invested in particular theories and rationalizations of them than others. It is these more disinterested more skeptical scientists who help the invested behave -- and vice versa. So science is a kind of human system for deeply debugging human notions and rationalizations (about everything).
It is this epistemology that has to be learned (really one trains oneself in it) before one can deal with "written down science". There is nothing in any science writing that can help anyone with the goodness of the mapping. Why? Because once one gets to language, with or without the aid of mathematics, one is using the same representation systems that are also used for religion. One can say anything in language (for example all languages contain "not", which means any claim can be restated as a counter claim!). This extends quite simply to any representation on a computer.
So the basic process of is about doing direct stuff and imbibing its epistemological stances. However, so much successful science has been done -- and science not only builds on itself but requires its findings to be constantly intercorrelated -- that no scientist can recapitulate all this by direct experiment. So the learning process is (a) get down the epistemology by direct contact with the real processes (b) then you can deal with claims that you won't be able to directly substantiate.
This amount of rigor is difficult for we humans generally. But it is just this rigor that made the enormous differences in how well we can do the dance over the last few hundred years.
Trying to do less loses both the dance and the art. So we can think of science as the art form in which the greatest creativity ever must be used with the greatest constraints and possibility for failure. It goes far beyond mathematics and (say) composing something really beautiful in strict counterpoint, though both of these have strong tinges of this style.
I think it is possible to do the real deal with children, and we've managed to show this (for example, with the Galilean gravity investigation [Sue's note: this is shown in the TED talk]). The ability of the computer to do simple incremental addition very quickly gives us a differential mathematics that is completely understandable to the children that is also fast enough to carry out the integrations over time directly in real-time. For 10 year olds, this is really good science, and I would neither advocate them being less nor more rigorous.
For children, we mainly want to find really good ways to help them with (a) above.
Each age can match up to real science projects devised by us (it is *we* who have to be really creative!).
And as important as is creativity, *we* simply must understand the real and deep natures of the subjects we are trying to help children learn. Most importantly, we have to understand what simplifications retain the underlying epistemology of the content, and which simplifications completely undermine and confuse the subject matter. (The latter is seen almost invariably in most K-8 classrooms in the US with or without computers - the teachers simply don't understand the stuff, and the school district and state almost always water the stuff down to lose it in futile attempts to get better test scores, regardless of whether the testing is now just an empty gesture.)
So most of the small percentage of the children who do become fluent in real science do so outside the regular classroom, and very often via contact through some knowledgeable adult.