2771-1463ISSN (Online)
2771-1471ISSN (Print)
Volume 10 , Issue 2 , PP: 12-22, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud A. Zaher 1 * , Nabil M. Eldakhly 2
Doi: https://doi.org/10.54216/JCHCI.100202
Attention allocation, efficiency of interactions and the formation of errors during human-computer interaction (HCI) are directly influenced by cognitive workload. Eye tracking provides a feasible, non-invasive source of evidence to estimate workload since the behavior of gaze is strongly correlated with visual search, task processing and decision effort. The paper explores explainable cognitive workload classification based on explainable cognitive workload on the public COLET dataset; eye-tracking recordings of 47 subjects completing interactive search tasks of the visual-search with workload labels based on NASA-TLX. The five supervised learning models are tested on binary and four-class problems, and the most successful setup is analyzed via SHAP-based feature attribution. In both tasks, boosting-based ensembles are best at predictive behavior, with XGBoost scoring highest on the overall and binary low-v-high discrimination scores in the best range of performance reported in the original COLET benchmark. The feature analysis attribute shows that the most significant variables are gaze entropy, fixation time, pupil changes, and saccadic movements. The results are consistent with the application of explainable gaze-based models to adaptive interfaces that can adapt to a rising mental load by making the content simpler to present, varying the pacing, or attentive to important information.
Cognitive workload , Eye tracking , Human-computer interaction , Explainable artificial intelligence , XGBoost , Adaptive interfaces
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