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The relationship between cognitive style and success in a computer-augmented learning environment was investigated. Fifty-nine students enrolled in a developmental education course in algebra were assigned to one of two instructors and one of two treatment conditions (computer-augmented instruction or traditional instruction). Student cognitive style (field-independence-dependence) was determined by performance on the Group Embedded Figures Test. Significant variables identified from a stepwise regression included main effects for prior achievement, cognitive style, and instructor. In addition, a significant treatment by cognitive style interaction was found. Field-dependent students exhibited greater math achievement in a computer-augmented environment, whereas students with indiscriminate cognitive style demonstrated greater achievement in a traditional learning environment. The results supported the hypothesis that learning environments differentially effect students with dissimilar cognitive style characteristics.

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This article introduces the use ofproblem-finding models to analyzepublicpolicy, an approach that permits the analysis of an aspect ofpolicymaking that is generally ignored by most policy analysis approaches, namely, how perceptions of policy problems influence thepolicyprocess. This study usesproblemfinding in analyzing 20 years (1964-1984) of state board of education desegregation policy in Illinois.