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ZDM

, Volume 51, Issue 6, pp 915–927 | Cite as

STEM education in the primary years to support mathematical thinking: using coding to identify mathematical structures and patterns

  • Jodie MillerEmail author
Original Article

Abstract

Cross-curricula opportunities afforded by STEM education (Science, Technology, Engineering and Mathematics education), supports an environment where students can develop twenty-first century competencies. One approach to addressing cross-curricula opportunities in STEM education is the introduction of computer science (computer programming—coding) as a basic skill/literacy for all students. Coding (computer programming) is a language that draws on a set of syntax rules (or blocks for primary school students) that informs a computer program to execute a series of functions. While there is evidence that computational thinking (the thinking used for coding/computer programming) and conceptual development in mathematics are connected, there is limited research related to how such a confluence applies to primary school students. The aim of this article is to provide insight into how mathematical knowledge and thinking, specifically the identification of mathematical patterns and structures, can be promoted through engagement with coding activities. The data for this article is drawn from year 2 students (n = 135) in two Australian primary schools. A teaching experiment approach was adopted for the study with a small intervention group (n = 40) undertaking coding lessons for 6 weeks. Data collection comprised of pre-test and post-tests with a focus on patterning and coding in conjunction with video-recorded lessons. The study provides evidence that the learning that takes place through coding instruction can lead to higher levels of students’ mathematical thinking in relation to identifying mathematical patterns and structures that can lead to generalisations.

Keywords

Mathematical thinking Primary Patterning Coding 

Notes

Acknowledgements

I would like to thank Emeritus Professor Elizabeth Warren for her continued mentorship and feedback for this study. I would also like to acknowledge Angela Hennessey for her work as the research assistant on this project, as well as the schools and students who have participated in the study. This research first appears in a MERGA conference paper (Miller and Larkin 2017).

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Copyright information

© FIZ Karlsruhe 2019

Authors and Affiliations

  1. 1.University of QueenslandSt Lucia, BrisbaneAustralia

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