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

TL;DR

This paper presents a practical approach to integrating Python data analysis using Jupyter Notebooks into a first-year physics lab, enhancing students' computational skills without disrupting the existing curriculum.

Contribution

It introduces tailored Jupyter Notebook activities with physics applications to develop data analysis skills in introductory physics labs, demonstrating an effective integration method.

Findings

Students improved data analysis skills through Notebook exercises.

The approach facilitated learning without major curriculum changes.

Challenges included balancing computational and experimental tasks.

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.