Abstract

Computation is becoming an increasingly important part of physics education. However, there are currently few theories of learning that can be used to help explain and predict the unique challenges and affordances associated with computation in physics. In this study, we adapt the existing theory of computational literacy, which posits that computational learning can be divided into material, cognitive, and social aspects, to the context of undergraduate physics. Based on an exploratory study of undergraduate physics computational literacy, using a newly-developed teaching tool known as a computational essay, we have identified a variety of student practices, knowledge, and beliefs across these three aspects of computational literacy. We illustrate these categories with data collected from students who engaged in an initial implementation of computational essays in an introductory electricity and magnetism class. We conclude by arguing that this framework can be used to theoretically diagnose student difficulties with computation, distinguish educational approaches that focus on material vs. cognitive aspects of computational literacy, and highlight the benefits and limitations of open-ended projects like computational essays to student learning.

Highlights

  • NEED FOR A THEORY OF COMPUTATIONAL LEARNING IN PHYSICSThe field of physics is becoming increasingly computational

  • Based on an exploratory study of undergraduate physics computational literacy, using a newly developed teaching tool known as a computational essay, we have identified a variety of student practices, knowledge, and beliefs across these three aspects of computational literacy

  • In the course of this analysis, we have identified a variety of different elements that are associated with the material, cognitive, and social computational literacy of undergraduate physics majors

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Summary

Introduction

NEED FOR A THEORY OF COMPUTATIONAL LEARNING IN PHYSICSThe field of physics is becoming increasingly computational. NEED FOR A THEORY OF COMPUTATIONAL LEARNING IN PHYSICS. Within the last 30 years, computation has grown in status and sophistication, to the point that it is regarded by many as a third pillar of physics, on equal footing with theory and experiment [1,2]. A major goal in physics education research is to make physics education more authentic to the discipline, and based on these trends many physics programs will soon need to tackle the challenge of robustly integrating computation into their curricula [3]. The history of computational physics education bears this out: for example, in the late 1960s researchers at MIT developed the LOGO computing language in part to help children explore physics in a fully Newtonian “microworld” [4]. In the 1980s, Andrea diSessa developed the BOXER system, which was successfully used to teach certain physics and calculus concepts to students as young

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