EXP: Linking Eye Movements with Vvisual Attention to Enhance Cyberlearning

“网络学习和未来学习技术计划”资助的工作旨在支持展望学习技术的未来，并推进我们对人们如何在技术丰富的环境中学习的了解。网络学习探索（EXP）项目通过设计和构建新型学习技术并研究其促进学习的可能性和有效使用它们的挑战来探索新型学习技术的可行性。这个项目将为将眼球运动融入网络学习奠定必要的基础。尽管硬件和软件解决方案正在迅速提高检测和跟踪网络学习者眼球运动的能力，但对这些眼球运动与实际学习之间联系的科学理解仍然是初步的。这个问题特别重要，因为研究表明人们接受的视觉信息有惊人的局限性：即使可以证明他们看过一些东西，这也不能保证学习者获得他们所看到的知识。这个项目将从两个方面解决这个问题。首先，研究人员将开发一种认知理论，可以帮助说明眼球运动如何揭示网络学习者在观看和与基于技术的学习系统互动时吸收了什么。其次，研究人员将开发一种新颖的软件应用程序，帮助网络学习内容创建者将眼球运动评估纳入他们的实践中。这些项目将汇聚在一起，不仅可以发展认知理论，帮助网络学习者实现更有效的互动，还可以通过现实世界的网络学习实践者的输入来丰富认知理论，这些实践者每天都在努力理解向学习者展示某些东西与学习者理解和记住他们所看到的东西的实际能力之间的联系，这种联系有时令人困惑。特别是，研究者假设固定模式与学习之间的联系是由视觉模式介导的，视觉模式改变了视觉属性的具体编码与对因果关系和行动目标的抽象关注之间的关系。该项目将包括一些实验，在这些实验中，当学习者观看屏幕捕捉的信息技术课程时，他们的眼睛会被跟踪。一些学习者会被诱导采用一种“编码”模式，在这种模式下，他们专注于完成任务所需的特定步骤序列，而其他学习者则会使用一种“因果”模式，在这种模式下，他们专注于课程背后的概念。最初的研究已经证明了这些任务中固定模式的显著差异（其中最强的是学习者在编码模式下更密切地跟随教练的鼠标运动），当前的项目将测试这些模式是否与视觉和概念学习的不同模式有关。该项目将利用这些结果，将模式揭示分析整合到一个新颖的软件应用程序中，该应用程序允许内容创建者在记录自己的眼球运动的同时录制他们的课程屏幕捕捉视频。此外，一组观众将配备自己的眼动仪，观看内容创作者的课程。观看者的眼球运动将返回给内容创建者，内容创建者将能够查看应用程序中的注视模式，以及基于视觉模式实验结果的分析。该原型系统将集成现有的学习技术，即名为“贝蒂的大脑”的计算机科学教育课件，并部署在正式和非正式的学习环境中，包括纳什维尔冒险科学中心。

The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. This project will lay the groundwork necessary for incorporating eye movements into cyberlearning. Although hardware and software solutions are rapidly advancing the ability to detect and track cyberlearners' eye movements, the scientific understanding of the link between these eye movements and actual learning remains tentative. This issue is particularly important because research demonstrates surprising limits to the visual information that people take in: Even when it can be demonstrated that they have looked at something, this is no guarantee that learners gain knowledge of what they have seen. This project will address this problem in two ways. First, the researchers will develop a cognitive theory that can help specify how eye movements reveal what cyberlearners have absorbed when they view and interact with technology-based learning systems. Second, the researchers will develop a novel software application that helps cyberlearning content creators to incorporate assessment of eye movements into their practice. These projects will converge not only to develop cognitive theory that can help cyberlearners achieve more effective interactions, but also to enrich cognitive theory with input from real-world cyberlearning practitioners who struggle every day with the need to understand the sometimes confounding link between showing a learner something and learners' actual ability to understand and remember what they have seen. In particular, the investigators hypothesize that the link between fixation patterns and learning is mediated by visual modes that vary the relationship between concrete coding of visual properties and abstract focus on causal relationships and the goals of actions. The project will include experiments in which learners have their eyes tracked while they view a screen-captured information technology lesson. Some learners will be induced to deploy an "encoding" mode in which they focus on the specific sequence of steps needed to complete the task, while other learners will view the same materials using a "causal" mode in which they focus on the concepts underlying the lesson. Initial research has demonstrated significant differences in fixation patterns in these tasks (the strongest of these is that learners follow the instructor's mouse movements more closely in the encoding mode), and the current project will test whether these modes are associated with different patterns of visual and conceptual learning. The project will leverage these results by incorporating mode-revealing analytics into a novel software application that allows content creators to record screen-capture videos of their lessons while recording their own eye movements. In addition, a panel of viewers will be equipped with their own eye trackers and will view the content creators' lessons. Viewer eye movements will be returned to content creators who will be able view fixation patterns in the application, along with analytics based on findings from the visual mode experiments. The prototype system will be integrated with an existing learning technology, courseware for computer science education titled "Betty's Brain," and deployed in both formal and informal learning environments, including the Nashville Adventure Science Center.