ISEDJ

Abstract: This study examines the use of connectivist learning principles to teach first-year students about coding with Python in a Fundamentals of Information Systems course. The instructional design integrates tools such as Microsoft Excel, Google Colab, and AI chatbots to support conceptual understanding, promote knowledge exchange among students, and develop problem solving skills. Grounded in a connectivist approach, the module considers the relationship between students, Excel, Python, and AI tools as inter-connected nodes that influence how students construct, transfer, and apply knowledge. Students engaged in collaborative coding activities, progressing from designing and sharing spreadsheet-based solutions to developing Python programs and iteratively refining AI-generated results through prompt engineering. The study addresses four research questions: (1) To what extent does prior experience with Excel support students’ understanding of Python programming concepts? (2) How do digital tools and peer networks support student engagement and learning in coding? (3) How do students perceive the value of learning Python for academic and career development? and (4) To what extent are students motivated to continue learning coding independently? Survey results indicate that students find benefit in using networked collaboration and learning tools, recognize the value in learning Python, and favor further informal study. These findings also support the use of connectivist learning techniques as an effective framework for presenting coding instruction to first-year information systems students engaged in a learning scenario shaped by personal networks, technology, and AI tools.

Download this article: ISEDJ - V24 N2 Page 27.pdf

Recommended Citation: Frydenberg, M., (2026). From Excel to Python to AI: A Connectivist Model for Introducing Coding and Prompt Engineering to First-Year IS Students. Information Systems Education Journal 24(2) pp 27-43. https://doi.org/10.62273/CACW2093