Knowledge-Intensive Problem Solving.
In physics problem solving, it has been proposed that experts build more complete representations of the problem than novices because of the extra knowledge they have available (Heller & Reif, 1984 : Larkins, 1983, 1985). This is supported by Chi, Feltevich, and Glaser’s (1981 ; Chi, Glaser & Rees, 1983) finding that if experts and novices are asked to sort problem into related group, the resultant behaviour is quite different. Novices tended to group, the problem together that have the same surface features. Thai is, novices grouped two problem to gether if they used pulleys or ramps ; they were led by the keywords and the objects in the problem. However, experts classified problem in terms of their deep strcture. That is, they grouped problem together that coud be solved by the same principles, even though these problems “looked” diiferent and involved different objects.
Furthermore, Chi et al. discovered that even though experts solved the problems fours times faster than vovices, they spent more time than novices analysing and understanding the problems. Unlike the novices, who waded into the problem immediately applying equations, the esperts eleborated the representation of the problem by selecting the appropriate pronciples that applied in the situation. In essence, this ammounts to the complex categorisation of the problem situation with respect to their available knowledge.
Strategic differences have also been found between expert and novices. expert tend to work forwards to a solution whereas novices tend to work backwards (Larkin, McDermott, Simon & Simon, 1980). After spending some time analysing the problem, experts apply the principles they have selected to the given quantities in the problems, in order to generate the unknown quantities they need to solve the problem. This is, thus, a planned working-forward strategy. It is bith efficient and powerful and relies heavily on domain-specific knowledge about the problem. Novices, incontrast, have an impoverished repertoire of available principles. Thypically, they take the goal (e.g what is the maximum distance the spring will be compressed?) and find a principle that contains the desired quantity and usually no more than one other unknown quantity. They theb try to find this new unknown quantity and hence work backwards to the govens of the problem statement.