The representation of physics problems in relation to the organization of physics knowledge is investigated in experts and novices. Four experiments examine the existence of problem categories as a basis for representation; differences in the categories used by experts and novices; differences in the knowledge associated with the categories; and features in the problems that contribute to problem categorization and representation. Results from sorting tasks and protocols reveal that experts and novices begin their problem representations with specifiably different problem categories, (...) and completion of the representations depends on the knowledge associated with the categories. For, the experts initially abstract physics principles to approach and solve a problem representation, whereas novices base their representation and approaches on the problem's literal features. (shrink)

A misconception regarding the ultrastructural basis of myocardial failure has been observed in laboratory studies involving medical students and practicing physicians, in medical textbooks, and in clinical instruction of students. This misconception attributes heart failure to overextension of individual cardiac muscle fibres and their sarcomeres, resulting in a mechanically based decline in contractile force production. The basis of the misconception is a set of component misconceptions which interact in reciprocally supportive ways. The interlocking nature of the component misunderstandings strengthens the (...) overall misconception, making it difficult to undermine. A contributor to many aspects of the faulty account of heart failure is a tendency toward oversimplification of complex phenomena in learning, instruction, and scientific research. Implications for medical education are considered. (shrink)

An investigation is presented in which a computer simulation model (DIAGNOSER) is used to develop and test predictions for behavior of subjects in a task of medical diagnosis. The first experiment employed a process‐tracing methodology in order to compare hypothesis generation and evaluation behavior of DIAGNOSER with individuals at different levels of expertise (students, trainees, experts). A second experiment performed with only DIAGNOSER identified conditions under which errors in reasoning in the first experiment could be related to interpretation of specific (...) data items. Predictions derived from DIAGNOSER's performance were tested in a third experiment with a new sample of subjects. Data from the three experiments indicated that (1) form of diagnostic reasoning was similar for all subjects trained in medicine and for the simulation model, (2) substance of diagnostic reasoning employed by the simulation model was parable with that of the more expert subjects, and (3) errors in subjects' reasoning were attributable to deficiencies in disease knowledge and the interpretation of specific patient data cues predicted by the simulation model. (shrink)

The purpose of the work reported here was to investigate the role of problem comparison and, specifically, analogical comparison in the induction of problem categories. This work was motivated by two factors. First, it is well-documented that experts and novices represent problems in very different ways and that solution success often depends on producing expert-like problem representations (DeGroot, 1965; Duncker, 1945; Chi, Feltovich, & Glaser, 1981; Hardiman, Dufresne, & Mestre, 1989; Novick, 1988; Schoenfeld & Herrmann, 1982; Silver, 1979, 1981). (...) Second, the problem representations produced by experts and novices appear to reflect differences in the way the two groups organize their knowledge bases. Although both groups appear to represent their problem-solving knowledge in terms of problem classes, or categories, expert categories tend to be defined in terms of deep structural features, whereas novice categories tend to be defined in terms of surface features (Adelson, 1981; Chase & Simon, 1973; Chi et al., 1981; Schoenfeld &. Herrmann, 1982; Silver, 1979, 1981). Because of this differential organization, experts are more likely than novices to retrieve solution-relevant information from their categories when constructing problem representations. (shrink)