Studying Webcam-based Gaze Estimation and Mouse Coordination for Cognitive Inferences
Type
conference paper
Date Issued
2026-06-01
Author(s)
Abstract
Adaptive e-learning systems require scalable signals for inferring learners’ cognitive states (e.g., attention, engagement, and cognitive load), yet webcam-based gaze estimation remains sensitive to calibration demands and real-world performance degradation. Mouse input is ubiquitous and calibration-free; however, cursor trajectories may only weakly reflect moment-to-moment visual attention. This work presents a preliminary research design that pairs a mouse-contingent blur interaction with a co-observation modeling view of cognitive state to make gaze–mouse data more useful under realistic constraints. First, we propose a mouse-contingent blur paradigm (i.e., delayed blur after mouse inactivity) and compare it with no blur and mouse-contingent blur to study how they affect gaze–mouse coordination and usability. Second, we frame webcam-based eye tracking and mouse input as cross-modal observations and motivate their fusion as a practical strategy to assess the changes in cognitive state of learners.
Language
English (United States)
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
A
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
2026_Doubabi Bektas Andaloussi et al ETRA_LBW_Studying webcam based gaze estimation.pdf
Size
503.59 KB
Format
Adobe PDF
Checksum (MD5)
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