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。该项目将开发注意力感知网络学习作为一种新的学习技术类型，自动检测和响应学生的注意力状态。特别是，该项目将采用检测走神（MW）的技术，即注意力从与任务相关的想法转移到与任务无关的想法。在复杂理解任务的背景下，研究人员已经发现，高程度的理解会导致较差的表现。然而，并没有在技术学习的背景下研究MW，也没有提出降低MW的技术解决方案。该项目在技术学习的背景下解决了MW问题。通过使用廉价的眼球追踪设备来检测多发性骨髓瘤。当学生们通过一个名为Guru的互动系统学习高中生物时，这些设备将与检测MW的软件集成在一起。一旦检测到MW，将使用软件策略使学生返回到学习任务中。主要的研究将是开发和测试毫瓦检测算法，以及开发和测试降低毫瓦的策略。更详细地说，注意力感知大师将包括一个集成的眼动追踪器，一个基于注视的自动脑容量检测器，以及通过降低脑容量成本来改善学习的干预策略。这项研究将在印第安纳州北部的九年级生物教室进行，在那里将对核心技术组件进行形成性研究，迭代改进和总结性评估。将在项目的每个阶段确定可概括的见解，以促进研究结果在未来注意力感知技术中的可转移性，从而帮助学生充分发挥其潜力。总之，所提出的注意力感知大师技术将用于推进基础研究，重点是揭示：(1)使用技术学习时的注意力转移发生率，(2)注意力转移与学习之间的关系，(3)诊断注意力转移的眼睛凝视模式，(4)重新定向注意力和减少注意力转移有害影响的自动化干预策略，以及(5)催化注意力感知网络学习未来实施的可推广的见解。

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.