ABSTRACT
Knowledge Modern specialists in knowledge capture have already developed an impressive array of tools for addressing precisely the problem of how to think more robustly about 'knowledge' rather than 'information'. We now know that it is possible to represent common sense rules that lead children to go through a briefphase of saying 'I gived' instead of 'I gave'. We can equally represent complex chains of reasoning such as the way astronomers discovered the formula describing the motion of the planets, or the way investment bankers hedge their bets in the currency markets. We know that there is a consistent logic present in children who think that 63 - 36 = 30. Even better, we know how this mistake arises, why it persists, how to detect it, and how to fix it. Forever. (Not, incidentally, by saying 'Wrong! Do it again!') What all these examples have in common is a rich symbolic representational language which allows us to describe the innermost thoughts of learners in fine-grained detail. Not that we know 'the truth', just that we know considerably more fine-grained detail than did others in the past.
