Improving Student Perception and Confidence Using Programming in Manufacturing Engineering

Abstract

Programming is increasingly integral to engineering education, yet students often struggle with its cognitive demands and unfamiliar logical frameworks. Python, with its intuitive syntax and practical utility, has shown promise in bridging this gap, particularly when applied to real engineering contexts where programming tasks are grounded in familiar disciplinary problems. This paper presents the design and implementation of Python-based Jupyter Notebooks integrated into a second-year manufacturing processes laboratory course, where students have prior but limited exposure to Python from earlier coursework. The Notebooks enable students to perform engineering calculations, visualize experimental results, and work through data processing tasks within the context of existing laboratory activities. They also provide instructors with a means to scaffold learning and guide laboratory activities. Instructional resources were created to take advantage of these opportunities. They were organized and hosted as an open educational resource using the Jupyter Book framework and GitHub Pages to support independent student access throughout the term.  Formative survey feedback indicated that students found the notebooks accessible and practical, and most expressed intent to apply Python in future engineering work. Students also identified areas for improvement, particularly around opportunities to write code themselves and the clarity of inline documentation. These findings highlight a core design tension between scaffolding accessibility and skill development and inform recommendations for educators seeking to embed computational tools within discipline-specific laboratory courses.