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Fowler, S. R.; Zeidler, D. L. – Journal of Biological Education, 2016
The purpose of this study was to explore science content used during college students' negotiation of biology-based socioscientific issues (SSI) and examine how it related to students' conceptual understanding and acceptance of biological evolution. The Socioscientific Issues Questionnaire (SSI-Q) was developed to measure depth of evolutionary…
Descriptors: Evolution, Biology, Scientific Concepts, Science Instruction
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Wolfe, Michael B.; Tanner, Shawna M.; Taylor, Andrew R. – Discourse Processes: A Multidisciplinary Journal, 2013
We examine students' processing and representation of arguments and counterarguments in one-sided scientific texts. In Experiment 1, students read texts about evolution and TV violence. Sentence reading times indicated that subjects slowed down reading to the extent that arguments were both more consistent, and inconsistent, with the text…
Descriptors: Persuasive Discourse, Language Processing, Scientific Concepts, Student Attitudes
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Partin, Matthew L.; Underwood, Eileen M.; Worch, Eric A. – Journal of College Science Teaching, 2013
To develop a more scientifically literate society, students need to understand the nature of science, which may be affected by controversial topics such as evolution. There are conflicting views among researchers concerning the relationships between understanding evolution, acceptance of evolution, and understanding of the nature of science. Four…
Descriptors: Scientific Literacy, Science Instruction, Evolution, Undergraduate Students
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Deniz, Hasan; Donnelly, Lisa A.; Yilmaz, Irfan – Journal of Research in Science Teaching, 2008
In this study, using multiple regression analysis, we aimed to explore the factors related to acceptance of evolutionary theory among preservice Turkish biology teachers using conceptual ecology for biological evolution as a theoretical lens. We aimed to determine the extent to which we can account for the variance in acceptance of evolutionary…
Descriptors: Evolution, Ecology, Multiple Regression Analysis, Biology