COIVis: Eye tracking-based Visual Exploration of Concept Learning in MOOC Videos

2025-12-07

AI Summary

MOOCs lack real-time learning feedback, and conventional analytical methods struggle to model learners’ dynamic cognitive states. To address this, we propose COIVis, a visual analytics system grounded in eye-tracking data. COIVis is the first to spatially and temporally anchor eye movements to conceptual units within instructional videos, defining “Concepts of Interest” (COIs) and extracting five interpretable state features: attention, cognitive load, interest, preference, and synchrony. The system integrates multimodal video understanding, eye-movement trajectory modeling, temporal alignment, and narrative-driven multi-view visualization to support fine-grained analysis—from population-level patterns to individual learning pathways. Through case studies and interviews with educators, COIVis demonstrates effectiveness in identifying learning consistency and anomalous behaviors, thereby enabling timely pedagogical interventions and evidence-based course design improvements.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Cognitive Modeling & Cognitive Systems: Analogy
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Develops a visual analytics system using eye tracking
• Explores concept-level learning processes in MOOC videos
• Supports instructors in identifying and addressing learning issues

Innovation

• Eye tracking extracts learner cognitive states
• Concepts of Interest align content with gaze data
• Multi-view visualization enables personalized intervention analysis