Attention is a key factor for successful learning, with research indicating strong associations between (in)attention and learning outcomes. This dissertation advanced the field by focusing on the automated detection of attention-related processes using eye tracking, computer vision, and machine learning, offering a more objective, continuous, and scalable assessment than traditional methods such as self-reports or observations. It introduced novel computational approaches for assessing various dimensions of (in)attention in online and classroom learning settings and addressing the challenges of precise fine-granular assessment, generalizability, and in-the-wild data quality. First, this dissertation explored the automated detection of mind-wandering, a shift in attention away from the learning task. Aware and unaware mind wandering were distinguished employing a novel multimodal approach that integrated eye tracking, video, and physiological data. Further, the generalizability of scalable webcam-based detection across diverse tasks, settings, and target groups was examined. Second, this thesis investigated attention indicators during online learning. Eye-tracking analyses revealed significantly greater gaze synchronization among attentive learners. Third, it addressed attention-related processes in classroom learning by detecting hand-raising as an indicator of behavioral engagement using a novel view-invariant and occlusion-robust skeleton-based approach. This thesis advanced the automated assessment of attention-related processes within educational settings by developing and refining methods for detecting mind wandering, on-task behavior, and behavioral engagement. It bridges educational theory with advanced methods from computer science, enhancing our understanding of attention-related processes that significantly impact learning outcomes and educational practices.

翻译：注意力是成功学习的关键因素，研究表明（非）注意力与学习成果之间存在强关联。本论文通过聚焦于利用眼动追踪、计算机视觉和机器学习实现注意力相关过程的自动检测，推动了该领域的发展，相较于自我报告或观察等传统方法，提供了更客观、连续且可扩展的评估方式。论文引入了新颖的计算方法，用于评估在线和课堂学习环境中（非）注意力的多个维度，并应对精确细粒度评估、可推广性以及真实场景数据质量等挑战。首先，本论文探索了走神（注意力从学习任务上转移）的自动检测。通过整合眼动追踪、视频和生理数据的新型多模态方法，区分了有意识与无意识的走神。此外，还检验了基于可扩展网络摄像头检测方法在不同任务、环境和目标群体中的可推广性。其次，本论文研究了在线学习过程中的注意力指标。眼动追踪分析表明，注意力集中的学习者之间存在显著更高的注视同步性。第三，论文通过检测举手行为作为行为投入的指标，探讨了课堂学习中的注意力相关过程，采用了一种新颖的视角不变且对遮挡鲁棒的基于骨架的方法。本论文通过开发和改进检测走神、任务行为和行为投入的方法，推进了教育环境中注意力相关过程的自动评估。它架起了教育理论与计算机科学先进方法之间的桥梁，加深了我们对显著影响学习成果和教育实践的注意力相关过程的理解。