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Deciphering Inclusivity for the Design of K-12 Computing Pathways

Published: 05 March 2021 Publication History

Abstract

Three districts representing unique contexts and challenges from across the United States have been iteratively designing and implementing inclusive computing pathways for K-12 students. In this paper, we identify barriers to inclusivity within each districts? K-12 computing pathway. Through a research practice partnership (RPP), we seek to develop a shared understanding of inclusiveness and apply that understanding to the development of targeted supports in the design and implementation of K-12 computing pathways. We analyze existing structures and systems for gaps in order to create new opportunities and resources for students, teachers, schools and districts.

References

[1]
Quinn Burke, Jeremy Roschelle, Colin Angevine, Kerri Ann O'Donnell, Kimberly Smith, and Josh Weisgrau. 2019. Developing Inclusive K-12 Computing Pathways for the League of Innovative Schools. IEEE, 1--1.
[2]
David Clifford. 2007. Equity Centered Design Framework. Retrieved from https://dschool.stanford.edu/resources/equity-centered-design-framework

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  1. Deciphering Inclusivity for the Design of K-12 Computing Pathways

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    cover image ACM Conferences
    SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
    March 2021
    1454 pages
    ISBN:9781450380621
    DOI:10.1145/3408877
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 05 March 2021

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    Author Tags

    1. computational thinking
    2. educational infrastructure
    3. equity

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    Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

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