Some principles of intelligent tutoring by Stellan Ohlsson. (Instructional Science May 1986, Volume 14, Issue 3-4, pp 293-326)
Research on intelligent tutoring systems is discussed from the point of view of providing moment-by-moment adaptation of both content and form of instruction to the changing cognitive needs of the individual learner. The implications of this goal for cognitive diagnosis, subject matter analysis, teaching tactics, and teaching strategies are analyzed. The results of the analyses are stated in the form of principles about intelligent tutoring. A major conclusion is that a computer tutor, in order to provide adaptive instruction, must have a strategy which translates its tutorial goals into teaching actions, and that, as a consequence, research on teaching strategies is central to the construction of intelligent tutoring systems.
Be sure to notice the date: 1986, when you could write:
The computer offers the potential for adapting instruction to the student at a finer grain-level than the one which concerned earlier generations of educational researchers. First, instead of adapting to global traits such as learning style, the computer tutor can, in principle, be programmed to adapt to the student dynamically, during on-going instruction, at each moment in time providing the kind of instruction that will be most beneficial to the student at that time. Said differently, the computer tutor takes a longitudinal, rather than cross-sectional, perspective, focussing on the fluctuating cognitive needs of a single learner over time, rather than on stable inter-individual differences. Second, and even more important, instead of adapting to content-free characteristics of the learner such as learning rate, the computer can, in principle, be programmed to adapt both the content and the form of instruction to the student’s understanding of the subject matter. The computer can be programmed, or so we hope, to generate exactly that question, explanation, example, counter-example, practice problem, illustration, activity, or demonstration which will be most helpful to the learner. It is the task of providing dynamic adaptation of content and form which is the challenge and the promise of computerized instruction*
That was written decades before we were habituated to users adapting to the interface, not the other way around.
More on point, the quote from Ohlsson, Principle of Non-Equifinality of Learning, was proceeded by:
But there are no canonical representations of knowledge. Any knowledge domain can be seen from several different points of view, each view showing a different structure, a different set of parts, differently related. This claim, however broad and blunt – almost impolite – it may appear when laid out in print, is I believe, incontrovertible. In fact, the evidence for it is so plentiful that we do not notice it, like the fish in the sea who never thinks about water. For instance, empirical studies of expertise regularly show that human experts differ in their problem solutions (e.g., Prietula and Marchak, 1985); at the other end of the scale, studies of young children tend to show that they invent a variety of strategies even for simple tasks, (e.g., Young, 1976; Svenson and Hedenborg, 1980). As a second instance, consider rational analyses of thoroughly codified knowledge domains such as the arithmetic of rational numbers. The traditional mathematical treatment by Thurstone (1956) is hard to relate to the didactic analysis by Steiner (1969), which, in turn, does not seem to have much in common with the informal, but probing, analyses by Kieren (1976, 1980) – and yet, they are all experts trying to express the meaning of, for instance, “two-thirds”. In short, the process of acquiring a particular subject matter does not converge on a particular representation of that subject matter. This fact has such important implications for instruction that it should be stated as a principle.
The first two sentences capture the essence of topic maps as well as any I have ever seen:
But there are no canonical representations of knowledge. Any knowledge domain can be seen from several different points of view, each view showing a different structure, a different set of parts, differently related.
Single knowledge representations, such as in bank accounting systems can be very useful. But when multiple banks with different accounting systems try to roll knowledge up to the Federal Reserve, different (not better) representations may be required.
Could even require representations that support robust mappings between different representations.
What do you think?