# Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework

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## Abstract

Computational thinking (CT) draws on concepts and practices that are fundamental to computing and computer science. It includes epistemic and representational practices, such as problem representation, abstraction, decomposition, simulation, verification, and prediction. However, these practices are also central to the development of expertise in scientific and mathematical disciplines. Recently, arguments have been made in favour of integrating CT and programming into the K-12 STEM curricula. In this paper, we first present a theoretical investigation of key issues that need to be considered for integrating CT into K-12 science topics by identifying the synergies between CT and scientific expertise using a particular genre of computation: agent-based computation. We then present a critical review of the literature in educational computing, and propose a set of guidelines for designing learning environments on science topics that can jointly foster the development of computational thinking with scientific expertise. This is followed by the description of a learning environment that supports CT through modeling and simulation to help middle school students learn physics and biology. We demonstrate the effectiveness of our system by discussing the results of a small study conducted in a middle school science classroom. Finally, we discuss the implications of our work for future research on developing CT-based science learning environments.

## Keywords

Computational thinking Agent-based modeling and simulation Visual programming Multi-agent systems Learning by design Computational modeling Science education Physics education Biology education## Notes

### Acknowledgments

Thanks to Amanda Dickes, Amy Voss Farris, Gokul Krishnan, Brian Sulcer, Jaymes Winger, and Mason Wright (in no particular order), who helped in developing the system and running the study. Earlier versions of the paper were presented at CSEDU 2012, and ICCE 2012. This work is partially supported by NSF IIS # 1124175 and NSF Early CAREER # 1150230.

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