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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2407.05803 |
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| _version_ | 1866913421002276864 |
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| author | Bühler, Babette |
| author_facet | Bühler, Babette |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal Machine Learning for Automated Assessment of Attention-Related Processes during Learning Bühler, Babette Human-Computer Interaction 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. |
| title | Multimodal Machine Learning for Automated Assessment of Attention-Related Processes during Learning |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2407.05803 |