Multiple choice computational thinking assessment for introductory physics

Justin Gambrell

doi:10.17918/00010581

Computational thinking proficiency is an important educational goal in physics. Since computational integration in physics is relatively new, there is yet to be a validated and reliable assessment of computational thinking in physics. This thesis works towards that goal. We examine computational thinking in introductory physics. We first explore the thoughts and perceptions of physicists in academia and industry through interviews about computation in introductory physics. Then, using the results of these interviews, we developed and validated an assessment of computational thinking in introductory physics. In Chapter 2 we discuss our interview study. We present a qualitative analysis of 26 interviews asking academic ($N_{a} = 18$) and industrial ($N_{i} = 8$) physicists about the teaching and learning of computational thinking in introductory physics courses. We find that academic and industrial physicists value students' ability to read code and that Python (or VPython) and spreadsheets were the preferred computational language or environment used. Additionally, the interviewees mentioned that identifying the core physics concepts within a program, explaining code to others, and good program hygiene (i.e., commenting and using meaningful variable names) are important skills for introductory students to acquire. We also find that while a handful of interviewees note that the experience and skills gained from computation are quite useful for students' future careers, they also describe multiple limiting factors of teaching computation in introductory physics, such as curricular overhaul, not having "space" for computation', and student rejection. In Chapter 3 we discuss the development and validation of our computational thinking assessment. To engage with and effectively use computational environments, students need to invoke computational thinking (CT) skills. In physics, CT means thinking in such a way that computations describing physical phenomena are streamlined, efficient, and well documented, and this is typically executed via a programming environment. Physics education researchers have developed and implemented curricular materials to develop students' CT skills, presenting the need for a standardized assessment of CT that may evaluate these efforts. We present the development and validation of a multiple choice assessment of CT for introductory physics students. We draw on specific CT practices--identified through interviews with physics experts--to create the assessment items, which are set in the context of introductory mechanics concepts. We analyze 1,134 student responses to the assessment to measure its validity and reliability. While we did not get our computational thinking assessment in a form that is ready for dissemination, we did find the computational practices that were well suited to this multiple choice assessment. They were: translating physics into code, decomposing, algorithm building, highlighting and foregrounding, utilizing generalization, debugging, and conditional logic. We also found the practices that were not suited to this form of assessment such as: manipulating data, analyzing data, generating data, working in groups, and demonstrating affective dispositions towards computation. These findings inform us of how we need to modify our assessment to better capture computational thinking in introductory physics.

Multiple choice computational thinking assessment for introductory physics

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Multiple choice computational thinking assessment for introductory physics

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