Eye to Eye: Gaze Patterns Predict Remote Collaborative Problem Solving Behaviors in Triads

We investigated the feasibility of using eye gaze to model collaborative problem solving (CPS) behaviors in 96 triads (N = 288) who used videoconferencing to remotely collaborate on an educational physics game. Trained human raters coded spoken utterances based on a theoretically grounded framework consisting of three CPS facets: constructing shared knowledge, negotiation/coordination, and maintaining team function. We then trained random forest classifiers to identify each of the CPS facets using eye gaze features pertaining to each individual (e.g., number of fixations) and/or shared across individuals (e.g., eye gaze distance between collaborators) in conjunction with information about the unfolding task context. We found that the individual gaze features outperformed the shared features, and together yielded between 6% to 8% improvements in classification accuracy above task-context baseline models, using a cross-validation scheme that generalized across teams. We discuss how our findings support CPS theories and the development of real-time intervention systems that provide actionable feedback to improve collaboration.