The purpose of this study was to investigate the differences and similarities in the performance of integrated science process skills among the learners divided by their cognitive - style preferences. In this study, two evaluative instruments were used. The first, the Learning-Style Inventory (LSI) was used to measure individual cognitire styles. Cognitive styles were measured as the two sets of dualities, which were perception types and processing types and their combinations resulting in four learning styles. The second, The Test of Integrated Process Skills (TIPS II) was used to assess five integrated process skills in science. They were: stating hypotheses, identifying variables, operationally defining, designing investigation, and graphing and interpreting data. These tests were administered to one hundred and seven college students, who were elementary school education majors. Significant relationships (differences and correlations) were found between the cognitive-style preferences and the performance of integrated science process skills. Among the students by their perception types, abstract conceptualization type students out-performed concrete experience type students, and the more students prefer abstract conceptualization, the better they perform on the overall tasks of integrated science process skills and a particular task of process skills, but not others, such as graphing and interpreting data. It could be concluded that overall performance of integrated science process skills and a particular type of science process skill such as graphing and interpreting data might be influenced by the cognitive-style preferences in the ways of perceiving information (concreteness or abstractness). Similarities in the performance of integrated science process skills were also found among students having different cognitive-style preferences. Cognitive-style preferences in the ways of processing information (reflection or action) were not significantly related to the performance of integrated science process skills. Neither learning styles were, which were combinations of perception type and processing type. Furthermore, similar patterns in the TIPS II subscale score distribution were found among the students having different cognitive-style preferences. The common tendencies, in that the score for identifying variables was the lowest and for designing investigations was the highest, were found in every group. The implications of these results were discussed. It is very necessary to consider the individual differences in cognition, such as cognitive styles, in science teaching and learning context.
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