论文标题
用于协作学习分析的深度神经网络：使用学生凝视点预测评估团队合作
Deep neural networks for collaborative learning analytics: Evaluating team collaborations using student gaze point prediction
论文作者
论文摘要
在协作任务中对团队绩效的自动评估和评估是学习分析和计算机支持的合作工作研究的关键。人们对使用面向目光的线索来评估团队的协作和合作感越来越感兴趣。但是，由于时间和成本限制，使用眼线笔收集目光数据并不总是可行的。在本文中，我们根据凝视点和关注视觉关注（JVA）信息引入了一个自动团队评估工具，该信息由计算机视觉解决方案提取。然后，我们将团队在本科解剖学学习活动（n = 60，30团队）中的合作进行评估，作为测试用户研究。结果表明，较高的JVA与学生学习成果呈正相关（R（30）= 0.50，p <0.005）。此外，参与两个实验组并使用交互式3-D解剖模型的团队的JVA（F（1,28）= 6.65，P <0.05）和更好的知识保留率（F（1,28）= 7.56，P <0.05）比对照组的JVA更高。同样，基于JVA对不同性别组成的不同性别组成，没有观察到显着差异。这项工作的发现通过提供一种基于相互注意的新型措施来客观地评估团队协作动态，从而为学习科学和协作计算提供了含义。
Automatic assessment and evaluation of team performance during collaborative tasks is key to the learning analytics and computer-supported cooperative work research. There is a 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 extracted by computer vision solutions. We then evaluate 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 3-D 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 offer implications in learning sciences and collaborative computing by providing a novel mutual attention-based measure to objectively evaluate team collaboration dynamics.
