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BPEC: Computational Thinking in STEM: A Whole-School Model for Broadening Participation and Education in Computing

BPEC: Computational Thinking in STEM: A Whole-School Model for Broadening Participation and Education in Computing
BPEC:STEM 中的计算思维:扩大计算参与和教育的全校模式
批准号:
1441041
负责人:
Kai Orton
金额:
$59.98万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-01 至 2017-08-31

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中文摘要
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英文摘要
Northwestern University proposes a strategy to introduce all students to computational thinking (CT) skills in a context that highlights the value and relevance of computational methods. The Computational Thinking for All (CT4ALL) approach embeds activities in existing high school science and mathematics classrooms as a way to (a) enhance students' CT skills, (b) infuse modern, computational practices of STEM professionals into existing STEM courses, and (c) broaden the appeal of computing by concretely demonstrating its applicability to diverse fields. The project staff have forged a strong partnership with Chicago Public Schools who are embarking on a district-wide effort to infuse computer science into all of their schools. CT4ALL complements this strategy. The project will work intensively with two schools, both serving predominantly low-income, minority populations. The intended outcome is to develop and share practical knowledge of these implementations working within the substantial constraints of large, resource-limited public high schools.Northwestern will analyze data gathered from student attitude questionnaires, student learning assessments, students' work on capstone projects and teacher attitude surveys to inform this study The project will use a Design Based Implementation Research study to develop and refine a whole-school model of this concept that is guided by the following research questions:(1) what are the CT learning gains for students in the schools;(2) how do students' attitudes toward CT change, particularly for girls and other traditionally underrepresented groups;(3) what are the CT learning gains for the STEM teachers; and(4) to what extent to teachers' confidence, interest, and attitudes towards CT change as a result of their participation?
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Collaborative Research: RAPID: Investigating Impacts of and Response to COVID-19 in the Technology Innovation Enterprise
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Computational Methods for Analyzing Toponome Data