Using Jupyter Notebooks to foster computational skills and professional practice in an introductory physics lab course

Abstract

In this paper, we detail the integration of Python data analysis into a first-year physics laboratory course, a task accomplished without significant alterations to the existing course structure. We introduced tailored laboratory computational learning goals and designed activities to address them. We emphasise the development and application of Jupyter Notebooks, tailored with exercises and physics application examples, to facilitate students' mastery of data analysis programming within the laboratory setting. These Notebooks serve as a crucial tool in guiding students through the core principles of data handling and analysis in Python, while working on simple experimental tasks. The results of the evaluation of this intervention offer insights into the advantages and challenges associated with early integration of computational skills in laboratory courses, providing valuable information for educators in the field of physics education. This study demonstrates a practical and effective way of embedding computational skills into the physics curriculum, and contributes to the ongoing efforts of the physics education research community.