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Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools

Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools

Scott R. Garrigan
Copyright: © 2022 |Pages: 14
ISBN13: 9781668424117|ISBN10: 1668424118|EISBN13: 9781668424124
DOI: 10.4018/978-1-6684-2411-7.ch003
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MLA

Garrigan, Scott R. "Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools." Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom, edited by Information Resources Management Association, IGI Global, 2022, pp. 46-59. https://doi.org/10.4018/978-1-6684-2411-7.ch003

APA

Garrigan, S. R. (2022). Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools. In I. Management Association (Ed.), Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom (pp. 46-59). IGI Global. https://doi.org/10.4018/978-1-6684-2411-7.ch003

Chicago

Garrigan, Scott R. "Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools." In Research Anthology on Computational Thinking, Programming, and Robotics in the Classroom, edited by Information Resources Management Association, 46-59. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-2411-7.ch003

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Abstract

Computational thinking (CT) K-12 curricula and professional development should prepare students for their future, but historically, such curricula have limited success. This chapter offers historical analogies and ways that CT curricula may have a stronger and more lasting impact. Two frameworks are central to the chapter's arguments. The first recalls Seymour Papert's original description of CT as a pedagogy with computing playing a formative role in young children's thinking; the computer was a tool to think with (1980, 1996). This “thinking development” framework emphasized child-centered, creative problem solving to foster deep engagement and understanding. Current CT seems to include creativity only tangentially. The second framework encompasses emergent machine learning and data concepts that will become pervasive. This chapter, more prescriptive than empirical, suggests ways that CT and requisite professional development could be more future-focused and more successful. It could be titled “Seymour Papert meets Machine Learning.”

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