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Assessing Youth's Computational Thinking in the context of Modeling & Simulation

Abstract

"The objectives of this presentation are to describe how Computational Thinking (CT) is fostered in Project GUTS: Growing Up Thinking Scientifically, a middle school program that engage students in modeling and simulation of complex systems, to introduce the pedagogical approach used, to describe the various exploratory methods used to assess youths computational thinking, and present preliminary findings. Computational thinking (CT) is a term coined by Jeannette Wing (2006) to describe a set of thinking skills, habits and approaches that are integral to solving complex problems using a computer. CT rests upon three pillars: reasoning at multiple levels of abstraction, understanding and applying automation, and analyzing the appropriateness of the abstractions made [Cuny, Snyder, and Wing, 2010]. Dave Moursund (2009) suggests the underlying idea in computational thinking is developing models and simulations of problems that one is trying to study and solve. In Project GUTS the power of modeling and simulation is harnessed to engage students in computational thinking while they seek to understand, study and solve real-world problems. Project GUTS: Growing Up Thinking Scientifically is an NSF-AYS funded afterschool and summer program that engages middle school students in investigating and modeling community issues as complex systems. Over three years, it has served over 900 sixth through eighth grade students and 70 teachers in urban, suburban and rural settings in New Mexico. 70% of the participants are from under-represented groups in STEM. Project GUTS design is based by two premises: 1) place-based education, using the students neighborhood and school as the context for investigations, engages students and retains their interest in STEM; and 2) studying local phenomena both in life and through using agent-based models created in StarLogo TNG builds computational thinking skills while increasing students understanding of complex systems. Evidence of youths computational thinking was seen in their framing of problems, creating of artifacts, and approaching novel problems in terms of abstraction, automation, and analysis. Data sources used include assessments of student models, pre- and post- surveys, timed user tests (task specific), and student interviews. Results show that the participants learned to create their own models, gained facility in programming in StarLogo TNG, and adopted computational thinking as a problem solving technique. Importantly, participants showed they could transfer the use of computational thinking skills from one real-world scenario to another. The significance of this research is that it a) presents a model of what computational thinking looks like in practice for middle school students, b) proposes a pedagogical progression for developing of computational thinking, c) describes an exploratory set of tools used to assess youths computational thinking, and d) provides preliminary evidence of computational thinking in youth."

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