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.

Previous studies have used the Fitts’ Law to predict the performance of pointing task on touch screens. It was found that moving distance, target width, and the direction of motion would affect the task performance. The present research aimed to investigate the impact of the initial and terminal positions (ITPs) on pointing performance in two Exps. ITPs were divided into two categories: center of a screen and outer of a screen (center ↔ outer) pointing, and outer of a screen to outer of a screen (outer→outer) pointing. In Exp 1, 30 participants performed the center ↔ outer pointing tasks with 8 directions. The results showed that the outer → center movement was significantly faster than the center → outer at 45°, 90°, and 180°. In Exp 2, 30 participants performed the outer → outer pointing tasks with eight directions. The current study revealed that the ITPs influenced the performance of the pointing task, possibly due to human biomechanical characteristics associated with different movements.