Supporting Introductory Python Learning Through Graphical Output: Evaluating Thonny-py5mode Using Task-Technology Fit

Authors

• Tristan Bunn, Torrens University AustraliaFollow
• Aaron Bere, Torrens University AustraliaFollow
• Michelle Douglas, RMIT University AustraliaFollow

Abstract

Coding environments that integrate graphical output into programming instruction can enhance engagement, comprehension, and motivation among beginners. This study evaluates Thonny-py5mode: a plugin for the Thonny IDE that emulates the Java-based Processing creative coding IDE but instead supports Python via the py5 library. Guided by the Task-Technology Fit (TTF) framework, we extend the model with personalised learning, hedonic motivation, and effort expectancy to capture factors influencing Thonny-py5mode user adoption in educational contexts. The study analyses survey data from 143 students in a first-year university Python course that incorporated four weeks of Thonny-py5mode use, including an assessment requiring participants to recreate six graphical challenges. We employ Structural Equation Modelling (SEM) to test nine hypotheses mapped to our extended TTF framework. Our results show that Thonny-py5mode’s usability and task support, along with its ease of use, contributed to a strong perceived TTF, which in turn influenced students’ intention to continue using it. These results highlight the Thonny-py5mode design priorities that can encourage adoption and sustained use in related educational coding environments and tools.

DOI

https://doi.org/10.62273/RDCV4243

Recommended Citation

Bunn, Tristan; Bere, Aaron; and Douglas, Michelle (2026) "Supporting Introductory Python Learning Through Graphical Output: Evaluating Thonny-py5mode Using Task-Technology Fit," Journal of Information Systems Education: Vol. 37 : Iss. 4 , 546-565.
DOI: https://doi.org/10.62273/RDCV4243
Available at: https://aisel.aisnet.org/jise/vol37/iss4/7