EXP: Attention-Aware Cyberlearning to Detect and Combat Inattentiveness During Learning

专注于任务的能力对学习至关重要。本项目将开发注意力感知网络学习，作为一种新的学习技术，自动检测和响应学生的注意力状态。特别是，该项目将实施检测思维徘徊（MW）的技术，即注意力从任务相关的想法转移到任务无关的想法。MW已经在复杂的理解任务的背景下进行了研究，并且已经发现，高程度的MW导致较差的性能。然而，没有在学习技术的背景下研究兆瓦，也没有提出减少兆瓦的技术解决方案。该项目在技术学习的背景下解决MW问题。MW的检测是通过使用廉价的眼睛跟踪设备。这些设备将与软件集成，以检测MW，而学生则通过一个名为Guru的交互式系统学习高中生物。一旦检测到MW，软件策略将用于使学生返回到学习任务。主要研究将是MW检测算法的开发和测试，以及减少MW的策略的开发和测试。更详细地说，注意力感知大师将包括一个集成的眼动仪，一个自动化的基于凝视的MW检测器，以及通过减轻MW成本来改善学习的干预策略。这项研究将在北方印第安纳州的9年级生物教室进行，在那里核心技术组件将被形成性地研究，迭代地改进，并进行总结性评估。在项目的每个阶段都将确定可推广的见解，以促进研究结果向未来注意力感知技术的转移，从而帮助学生充分发挥其潜力。总之，拟议的注意力感知Guru技术将用于推进基础研究，重点是发现：（1）在使用技术学习期间MW的发生率，（2）MW与学习之间的关系，（3）诊断MW的眼睛注视模式，（4）重新定向注意力和减少MW有害影响的自动干预策略，以及（5）促进未来实现注意力感知网络学习的可推广见解。

The ability to concentrate on tasks is critical to learning. This project will develop attention-aware cyberlearning as a new genre of learning technologies that automatically detect and respond to students' attentional states. In particular, this project will implement technology that will detect mind wandering (MW) which is when attention shifts from task-related thoughts to task-unrelated thoughts. MW has been studied in the context of complex comprehension tasks and it has been found that a high degree of MW leads to inferior performance. However MW has not been studied in the context of learning with technology and technology solutions have not been proposed to reduce MW. This project addresses MW in the context of learning with technology. The detection of MW is through the use of inexpensive eye-tracking devices. The devices will be integrated with software to detect MW while students are engaged in learning high school biology through an interactive system called Guru. Once MW is detected, software strategies will be used to enable the students return to the learning task. The primary research will be in the development and testing of MW detection algorithms and in the development and testing of strategies to reduce MW.In more detail, the attention-aware Guru will include an integrated eye tracker, an automated gaze-based MW detector, and intervention strategies to improve learning by mitigating the costs of MW. The research will be conducted in 9th grade biology classrooms in Northern Indiana, where the core technological components will be formatively studied, iteratively refined, and summatively evaluated. Generalizable insights will be identified at every stage of the project in order to promote transferability of the findings to future attention-aware technologies, thereby helping students learn to their fullest potential. In summary, the proposed attention-aware Guru technology will be used to advance fundamental research focused on uncovering: (1) the incidence of MW during learning with technology, (2) relationships between MW and learning, (3) patterns of eye-gaze that are diagnostic of MW, (4) automated intervention strategies to reorient attention and reduce the detrimental effects of MW, and (5) generalizable insights to catalyze future implementations of attention-aware cyberlearning.