COIVis: Eye-tracking-based Visual Exploration of Concept Learning in MOOC Videos
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911617907687424 |
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| author | Zhou, Zhiguang Yu, Ruiqi Ma, Yuming Ni, Hao Li, Guojun Ye, Li Wang, Xiaoying Li, Yize Wang, Yigang Wang, Yong |
| author_facet | Zhou, Zhiguang Yu, Ruiqi Ma, Yuming Ni, Hao Li, Guojun Ye, Li Wang, Xiaoying Li, Yize Wang, Yigang Wang, Yong |
| contents | Massive Open Online Courses (MOOCs) make high-quality instruction accessible. However, the lack of face-to-face interaction makes it difficult for instructors to obtain feedback on learners' performance and provide more effective instructional guidance. Traditional analytical approaches, such as clickstream logs or quiz scores, capture only coarse-grained learning outcomes and offer limited insight into learners' moment-to-moment cognitive states. In this study, we propose COIVis, an eye tracking-based visual analytics system that supports concept-level exploration of learning processes in MOOC videos. COIVis first extracts course concepts from multimodal video content and aligns them with the temporal structure and screen space of the lecture, defining Concepts of Interest (COIs), which anchor abstract concepts to specific spatiotemporal regions. Learners' gaze trajectories are transformed into COI sequences, and five interpretable learner-state features -- Attention, Cognitive Load, Interest, Preference, and Synchronicity -- are computed at the COI level based on eye tracking metrics. Building on these representations, COIVis provides a narrative, multi-view visualization enabling instructors to move from cohort-level overviews to individual learning paths, quickly locate problematic concepts, and compare diverse learning strategies. We evaluate COIVis through two case studies and in-depth user-feedback interviews. The results demonstrate that COIVis effectively provides instructors with valuable insights into the consistency and anomalies of learners' learning patterns, thereby supporting timely and personalized interventions for learners and optimizing instructional design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | COIVis: Eye-tracking-based Visual Exploration of Concept Learning in MOOC Videos Zhou, Zhiguang Yu, Ruiqi Ma, Yuming Ni, Hao Li, Guojun Ye, Li Wang, Xiaoying Li, Yize Wang, Yigang Wang, Yong Human-Computer Interaction Graphics Massive Open Online Courses (MOOCs) make high-quality instruction accessible. However, the lack of face-to-face interaction makes it difficult for instructors to obtain feedback on learners' performance and provide more effective instructional guidance. Traditional analytical approaches, such as clickstream logs or quiz scores, capture only coarse-grained learning outcomes and offer limited insight into learners' moment-to-moment cognitive states. In this study, we propose COIVis, an eye tracking-based visual analytics system that supports concept-level exploration of learning processes in MOOC videos. COIVis first extracts course concepts from multimodal video content and aligns them with the temporal structure and screen space of the lecture, defining Concepts of Interest (COIs), which anchor abstract concepts to specific spatiotemporal regions. Learners' gaze trajectories are transformed into COI sequences, and five interpretable learner-state features -- Attention, Cognitive Load, Interest, Preference, and Synchronicity -- are computed at the COI level based on eye tracking metrics. Building on these representations, COIVis provides a narrative, multi-view visualization enabling instructors to move from cohort-level overviews to individual learning paths, quickly locate problematic concepts, and compare diverse learning strategies. We evaluate COIVis through two case studies and in-depth user-feedback interviews. The results demonstrate that COIVis effectively provides instructors with valuable insights into the consistency and anomalies of learners' learning patterns, thereby supporting timely and personalized interventions for learners and optimizing instructional design. |
| title | COIVis: Eye-tracking-based Visual Exploration of Concept Learning in MOOC Videos |
| topic | Human-Computer Interaction Graphics |
| url | https://arxiv.org/abs/2512.06834 |