Gaze-Driven Design Insights to Amplify Debugging Skills: A Learner-Centered Analysis Approach

Kshitij Sharma
Michail Giannakos
Hallvard Trætteberg
Pierre Dillenbourg


This study investigates how multimodal user-generated data can be used to reinforce learner reflection, improve teaching practices, and close the learning analytics loop. In particular, the aim of the study is to utilize user gaze and action-based data to examine the role of a mirroring tool (i.e., Exercise View in Eclipse) in orchestrating basic behavioural regulation during debugging. The results demonstrated that students who processed the information presented in the Exercise View and acted upon it, improved their performance and achieved a higher level of success than those who failed to do so. The findings shed light on what constitutes relevant data within a particular learning context in programming using gaze patterns. Moreover, these findings could guide the collection of essential learner-centred analytics for designing usable, modular learning environments based on data-driven approaches.

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