Deep neural networks for collaborative learning analytics: Evaluating team collaborations using student gaze point prediction

作者：Zhang Guo, R. Barmaki · 发表于：Australasian Journal of Educational Technology · 年份：2020 · DOI：10.14742/ajet.6436 · 被引用次数：26 · 研究领域：Computer Science

Automatic assessment and evaluation of team performance during collaborative tasks is key to the research on learning analytics and computer-supported cooperative work. There is growing interest in the use of gaze-oriented cues for evaluating the collaboration and cooperativeness of teams. However, collecting gaze data using eye-trackers is not always feasible due to time and cost constraints. In this paper, we introduce an automated team assessment tool based on gaze points and joint visual attention (JVA) information drawn from computer vision solutions. We evaluated team collaborations in an undergraduate anatomy learning activity (N = 60, 30 teams) as a test user study. The results indicate that higher JVA was positively associated with student learning outcomes (r(30) = 0.50, p < 0.005). Moreover, teams who participated in two experimental groups and used interactive 3D anatomy models, had higher JVA (F(1,28) = 6.65, p < 0.05) and better knowledge retention (F(1,28) = 7.56, p < 0.05) than those in the control group. Also, no significant difference was observed based on JVA for different gender compositions of teams. The findings from this work have implications in learning sciences and collaborative computing by providing a novel joint attention-based measure to objectively evaluate team collaboration dynamics. Implications for practice or policy: Student learning outcomes can be improved by receiving constructive feedback about team performances using our gaze-based...