Uncovering the Novice Experience: A Think-Aloud Study of Student Interactions with an Automated Coding Style Tool

Automated coding style assessment tools are increasingly used in programming education; however, there is still limited understanding of how novice learners interact with and process the feedback they provide. To address this gap, this study presents a qualitative think-aloud investigation involving nine students from an introductory Python course. Using a purpose-built web-based tool, StyleLine, as a research instrument, we captured students’ real-time cognitive, behavioural, and affective responses to automated style feedback. The findings indicate a divergence between students’ positive self-reported perceptions and their observed behaviours during interaction with the tool. While questionnaire data suggested high satisfaction and usability, the qualitative analysis revealed several learning challenges, including a tendency toward non-iterative (“one-and-done”) revision, instances of task abandonment when encountering unclear feedback, and inconsistent transfer of learned concepts across tasks. This study contributes empirical insights into how novice learners engage with automated feedback and highlights the limitations of relying solely on self-reported measures. The findings also suggest that feedback designed for conciseness may increase cognitive processing demands for novices when insufficient scaffolding is provided. Overall, this study offers design implications for developing feedback systems that prioritise clarity and support novice learning more effectively.