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.

翻译：大规模开放在线课程（MOOC）让优质教育触手可及。然而，缺乏面对面互动使得教师难以获取学习者表现的反馈，更难提供有效的教学指导。传统的分析方法（如点击流日志或测验分数）仅能捕捉粗粒度的学习成果，对学习者实时认知状态的洞察十分有限。本研究提出COIVis——一个基于眼动追踪的可视分析系统，支持对MOOC视频中学习过程进行概念级探索。COIVis首先从多模态视频内容中提取课程概念，并将其与讲座的时间结构和屏幕空间对齐，定义"兴趣概念"（COIs），将抽象概念锚定于特定时空区域。学习者的注视轨迹被转换为COI序列，并基于眼动指标在COI层级计算五种可解释的学习者状态特征——注意力、认知负荷、兴趣、偏好和同步性。基于这些表征，COIVis提供多视图叙事可视化，使教师能够从群体层面概览过渡到个体学习路径，快速定位问题概念，并比较不同学习策略。我们通过两项案例研究和深度用户反馈访谈对COIVis进行评估。结果表明，COIVis能有效帮助教师洞察学习者学习模式的一致性与异常，从而支持对学习者进行及时、个性化的干预，并优化教学设计。