  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.
