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Published on: October 18, 2018
COIVis: Eye-Tracking-Based Visual Exploration of Concept Learning in MOOC Videos
Summary
COIVis, an eye-tracking system, enhances Massive Open Online Courses (MOOCs) by visualizing learner cognitive states. This allows instructors to identify learning challenges and personalize instruction for better outcomes.
Area of Science:
- Educational Technology
- Human-Computer Interaction
- Learning Analytics
Background:
- Massive Open Online Courses (MOOCs) offer accessible education but lack direct learner feedback mechanisms.
- Traditional analytics (clickstreams, quizzes) provide limited insight into real-time cognitive states.
- Instructors struggle to gauge learner engagement and cognitive load during online video lectures.
Purpose of the Study:
- To introduce COIVis, a novel eye-tracking-based visual analytics system for exploring MOOC learning processes.
- To enable concept-level analysis of learner behavior within MOOC video content.
- To support instructors in understanding and intervening in learner difficulties.
Main Methods:
- COIVis extracts concepts from video content, defining Concepts of Interest (COIs) linked to spatiotemporal regions.
- Learner gaze data is transformed into COI sequences to compute learner-state features (Attention, Cognitive Load, Interest, Preference, Synchronicity).
- A multi-view visualization system allows cohort and individual learning path analysis.
Main Results:
- COIVis effectively visualizes learner cognitive states at the concept level.
- The system identifies patterns, inconsistencies, and anomalies in learner engagement.
- Case studies and user feedback confirm the system's utility for instructors.
Conclusions:
- COIVis provides valuable, granular insights into learner cognitive processes in MOOCs.
- The system supports timely, personalized interventions and instructional design optimization.
- Eye-tracking data, when analyzed with COIVis, offers a powerful tool for understanding online learning.
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