Quantifying Gaze-Based Strategic Patterns in Physics Vector Field Divergence

We instructed a group of 20 undergraduate physics students with an instruction to visually interpret divergence of vector field with integral and differential strategies. We designed two distinct sets of 10 tasks and recorded the students’ eye gaze while they completed the task. In this study, we first developed Attentive Region Clustering (ARC), a novel unsupervised approach to analyze and evaluate the fixations and saccadic movements of the participants. Secondly, a linear Support Vector Machine model was used to classify the two problem-solving strategies in the vector field domain. The results revealed the implication of vector flow orientation in the eye movement patterns. We achieved an accuracy 10-fold cross-validation, we achieved \(81.2\%\) \((11\%)\) accuracy by evaluating a linear Support Vector Machine model to classify which strategy was applied by the student to comprehend the divergence of a vector field problem. The outcome of this work is useful to monitor the student visual performance on similar tasks. Besides, advances in Human-Computer Interaction empower students by getting objective feedback on their progress by visual clues in a vector field problems.