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Structuring a Science-Mathematics Partnership to Support Preservice Teachers' Data Analysis and Interpretation Skills.

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Title: Structuring a Science-Mathematics Partnership to Support Preservice Teachers' Data Analysis and Interpretation Skills.
Authors: Cook, Kristin L.1, Bush, Sarah B.2
Source: Journal of College Science Teaching. May/Jun2015, Vol. 44 Issue 5, p31-37. 7p.
Subject Terms: *Science education, *Mathematics education, *Student teachers, *Course content (Education), Interpretation (Philosophy)
Abstract: As science teacher educators attempt to prepare teachers to underscore the aims of the Next Generation Science Standards (NGSS Lead States, 2013) alongside the Common Core State Standards for Mathematics (NGAC and CCSSO, 2010), they must pay close attention to the intersection of these two content areas. To address this challenge, the authors (a science educator and mathematics educator) created an experience for preservice teachers (PSTs) to learn and apply core concepts and processes of mathematics and science within our methods courses. Specifically, this was a concerted effort to support PSTs' data analysis and interpretation skills within the context of scientific inquiry. In the unit described, codeveloped and implemented in a mathematics and science methods course, elementary PSTs are provided with clear scaffolds to apply their developing knowledge of data analysis and interpretation to a scientific inquiry. This integrated unit showcases one way teacher preparation programs can support PSTs' understanding of how to analyze and interpret data and their ability to teach it in their future classrooms. As we make concerted efforts to address the new national standards, teacher educators must model strategic interdisciplinary planning and curriculum to make authentic connections across subject areas. [ABSTRACT FROM AUTHOR]
Copyright of Journal of College Science Teaching is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: As science teacher educators attempt to prepare teachers to underscore the aims of the Next Generation Science Standards (NGSS Lead States, 2013) alongside the Common Core State Standards for Mathematics (NGAC and CCSSO, 2010), they must pay close attention to the intersection of these two content areas. To address this challenge, the authors (a science educator and mathematics educator) created an experience for preservice teachers (PSTs) to learn and apply core concepts and processes of mathematics and science within our methods courses. Specifically, this was a concerted effort to support PSTs' data analysis and interpretation skills within the context of scientific inquiry. In the unit described, codeveloped and implemented in a mathematics and science methods course, elementary PSTs are provided with clear scaffolds to apply their developing knowledge of data analysis and interpretation to a scientific inquiry. This integrated unit showcases one way teacher preparation programs can support PSTs' understanding of how to analyze and interpret data and their ability to teach it in their future classrooms. As we make concerted efforts to address the new national standards, teacher educators must model strategic interdisciplinary planning and curriculum to make authentic connections across subject areas. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of College Science Teaching is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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