Development and validation of a high school STEM self‐assessment inventory.

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Title: Development and validation of a high school STEM self‐assessment inventory.
Authors: Peters Burton, Erin1 (AUTHOR) epeters1@gmu.edu, Behrend, Tara S.2 (AUTHOR), Matray, Shari3 (AUTHOR), Hudson, Clarissa3 (AUTHOR), Ford, Michael3 (AUTHOR)
Source: School Science & Mathematics. Dec2020, Vol. 120 Issue 8, p477-490. 14p.
Subject Terms: *Self-evaluation, *Teacher researchers, *High schools, *School employees, Inventories
Geographic Terms: United States
Abstract: The development of inclusive STEM high schools that have no academic admission requirements has been a national goal in the United States. However, there is no umbrella organization that gives guidance for structuring such schools. The purpose of this study was to develop and validate a self‐assessment using critical components of successful inclusive STEM high schools for school personnel and educational researchers who wish to better understand their STEM programs and identify areas of strength. A multi‐phase methodology was employed. In the first step, eight in‐depth case studies were generated, and common themes were identified. In the second step, inventory items were iteratively generated and tested for reliability and validity, using a sample of 78 teachers and researchers in five schools. In the third step, the inventory was field‐tested in a single school. The OSPRI inventory demonstrates strong content validity and reliability. Teachers and researchers generally responded similarly, although some differences emerged that are indicative of their respective experiences and perspectives. School stakeholders and educational researchers can use this inventory, in whole or in part, to better understand their STEM programs and establish a future agenda that best capitalizes on their strengths. [ABSTRACT FROM AUTHOR]
Copyright of School Science & Mathematics is the property of Wiley-Blackwell 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: The development of inclusive STEM high schools that have no academic admission requirements has been a national goal in the United States. However, there is no umbrella organization that gives guidance for structuring such schools. The purpose of this study was to develop and validate a self‐assessment using critical components of successful inclusive STEM high schools for school personnel and educational researchers who wish to better understand their STEM programs and identify areas of strength. A multi‐phase methodology was employed. In the first step, eight in‐depth case studies were generated, and common themes were identified. In the second step, inventory items were iteratively generated and tested for reliability and validity, using a sample of 78 teachers and researchers in five schools. In the third step, the inventory was field‐tested in a single school. The OSPRI inventory demonstrates strong content validity and reliability. Teachers and researchers generally responded similarly, although some differences emerged that are indicative of their respective experiences and perspectives. School stakeholders and educational researchers can use this inventory, in whole or in part, to better understand their STEM programs and establish a future agenda that best capitalizes on their strengths. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of School Science & Mathematics is the property of Wiley-Blackwell 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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              Text: Dec2020
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