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abstract

Sports Analytics as a Context for Computational Thinking in K-12 Education

Published:22 February 2019Publication History

ABSTRACT

As educational jurisdictions around the world begin to integrate computational thinking (CT) into their K-12 mandatory curriculum, there is a need to ensure that classroom activities are engaging, relate to student interests, and effectively promote the development of CT skills. The analysis of sports performance data fulfills this criteria for some students and teachers and presents an exciting context in which students from all K-12 grades can access complex CT concepts. This lightning talk investigates sports analytic tasks within the context of the CT in mathematics and science taxonomy introduced by Weintrop et al. (2015). Specific classroom activities are presented including analyzing the distribution of soccer goals, baseball salaries, the location of successful basketball shots, and the placement of volleyball serves. Examples of data analytics projects will be shared and the four main components of the taxonomy (data practices, modelling and simulation practices, computational problem-solving practices and systems thinking practices) will be identified within each project.

References

  1. Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., et al. (2015). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127--147.Google ScholarGoogle ScholarCross RefCross Ref

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  1. Sports Analytics as a Context for Computational Thinking in K-12 Education

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                          • Published in

                            cover image ACM Conferences
                            SIGCSE '19: Proceedings of the 50th ACM Technical Symposium on Computer Science Education
                            February 2019
                            1364 pages
                            ISBN:9781450358903
                            DOI:10.1145/3287324

                            Copyright © 2019 Owner/Author

                            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.

                            Publisher

                            Association for Computing Machinery

                            New York, NY, United States

                            Publication History

                            • Published: 22 February 2019

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                            Acceptance Rates

                            SIGCSE '19 Paper Acceptance Rate169of526submissions,32%Overall Acceptance Rate1,595of4,542submissions,35%

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