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Dynamics of Visual Attention in Multiparty Collaborative Problem Solving Using Multidimensional Recurrence Quantification Analysis

Conference Paper/Proceedings
Dynamics of Visual Attention in Multiparty Collaborative Problem Solving Using Multidimensional Recurrence Quantification Analysis
Publication Year:
2019
Publication Source:
Chi 2019: Proceedings Of The 2019 Chi Conference On Human Factors In Computing Systems
Funding Type:
ECR:Core
Author(s):
Vrzakova, Hana; Amon, Mary Jean; Stewart, Angela E. B.; D'Mello, Sidney K.
Supporting Project(s):

Multiparty collaborative problem solving-an increasingly important context in the 21st century workforce-suffers from a degradation of social and behavioral signals when attempted remotely, resulting in suboptimal outcomes. We investigate teams' multidimensional patterns of visual attention during a collaborative problem-solving task with an eye for leveraging insights to improve collaborative interfaces. Fifty-seven novices (forming 19 triads) engaged in a challenging programming task (Minecraft Hour of Code) using videoconferencing software with screen sharing. To discover patterns of individual-level gaze-UI coupling (coordination of a teammate's attention with respect to changes in the user interface) and team-level gaze-UI regularity (dynamics of teams' collective attention in context with changes in the user interface), we applied cross- and multidimensional recurrence quantification analyses, respectively. Individuals' eye gaze was significantly coupled with the ongoing screen activity whereas teams displayed significant patterns of gaze regularity, suggesting repetitive patterns in teams' attention. These measures predicted expert-coded collaborative processes of constructing shared knowledge and negotiation and coordination (but not maintaining team function) and correlated with task score (r = .425). They also predicted individually assessed subjective perceptions of team performance and the collaboration process, but not individual's learning or team's task scores. We discuss implications of our findings for the design of intelligent collaborative interfaces.