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Studies have shown that students, after having studied physics concepts and being familiar with them for an appreciable time, may nevertheless lack the ancillary knowledge needed to use such concepts reliably; correspondingly, they exhibit major misconceptions and errors. Provided in this paper is an analysis of the ancillary knowledge required to make a scientific concept of principle effectively usable. Property concepts are addressed since these are centrally important to descriptions needed in science. This analysis includes, as a subset, the ancillary knowledge for a simple entity concept. Furthermore, the ancillary knowledge of a property concept is essentially the same as that for a principle. The most important ancillary knowledge required to make a concept effectively usable is that required to interpret the concept appropriately. This analysis of the ancillary knowledge needed for concept interpretation points out some practical implications for the learning and teaching of scientific concepts/principles. Students could be made aware of the ancillary knowledge (focusing on specification of concept, concept values, independent variables; instantiation; and error prevention) required to interpret a particular concept of interest or for use as a general skill in effectively learning any newly encountered concept or principle. (JN)

A theoretical model specifying the underlying knowledge and procedures whereby human subjects can generate effective initial descriptions of scientific problems was formulated. The model is prescriptive since it does not necessarily try to simulate the behavior of actual experts nor assume that their performance is optimal. The model, elaborated in the domain of mechanics, specifies explicit procedures for redescribing problems in terms of a relevant knowledge base. To test the model, carefully controlled experiments were devised where human subjects were induced to act in accordance with alternative models and where their resulting performance was observed in detail. Such experiments, carried out with undergraduate physics students, showed that the proposed model is sufficient to generate excellent problem descriptions, that these markedly improve subsequent problem solutions, and that most components of the model are indeed necessary for good performance. Detailed data analysis also showed how the model predictably prevents the occurence of many common errors. Such a validated model of effective problem description provides a useful basis for teaching students improved scientific problem-solving skills. (Author/JN)

Several central ideas emerging from a systematic approach to teaching problem-solving in the quantitative sciences (chemistry, physics, engineering) are discussed. Areas addressed include: differences between teaching and performance, between naturalistic and effective functioning, and between detailed observations and gross statistical data; insights derived from naturalistic studies, focusing on preexisting knowledge of students, tacit knowledge of experts, and significant differences between problem-solving behaviors of students and of experts; and kinds of procedures and knowledge essential for good human problem-solving performance, pointing out general issues addressed by any theoretical model of good problem-solving and discussing characteristics of the knowledge base containing knowledge about a specific domain. Problem-solving procedures considered include initial problem description, synthesis of the problem, and assessment/improvement of the solution. Current problem-solving activities in science teaching (focusing on student behaviors and instructional practices) are addressed, followed by a discussion of improved methods for teaching problem-solving. These methods include teaching explicitly and separately the various kinds of knowledge essential for good problem-solving performance (including knowledge of how to describe problem effectively), procedures useful for making judicious decisions in search for solutions, procedures for assessing solutions for correctness/optimality, and methods for organizing large amounts of knowledge so information can be easily recalled/remembered. (JN)

This final report summarizes work done to formulate and test some cognitively-based principles useful for designing instruction in scientific or engineering fields. These principles stress the coherence and interpretability of the knowledge acquired by students. The principles were specifically applied to: (1) analyze the underlying knowledge and thought processes needed to interpret scientific concepts effectively; (2) investigate how actual experts and novice students interpret such concepts; and (3) devise and test instruction designed to teach such concepts more effectively. Attempts were also made to devise computational environments to explore and implement these instructional ideas. (Author/RH)