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

TL;DR

COIVis targets a gap in MOOC analytics by grounding eye-tracking data in Concepts of Interest (COIs) that align with multimodal video content. It defines five COI-level learner-state features (Attention, Cognitive Load, Interest, Preference, Synchronicity) and provides an end-to-end pipeline for video-based concept extraction, gaze-to-COI mapping, and narrative, multi-view visualization to support instructor-led interventions. Through Case Studies and instructor interviews in smart classrooms, COIVis demonstrates its ability to reveal patterns of engagement and identify problematic concepts for targeted instructional design. The work advances concept-level, semantically integrated visual analytics for online learning and sets the stage for real-time, scalable deployment in diverse educational contexts.

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.

Figures (8)

• Figure 1: An instructor uses COIVis to explore learners' performance in the MOOC video Definitions and Terminology of Graphs from a COI (Concept of Interest) perspective. The video and eye tracking data are imported via the Control Panel (a), processed in the backend, and displayed in the Detailed View (c), which shows the instructor’s explanation of each COI along with the original video. The Main View (d) shows all learners’ interactions with the COIs (d1), while the Feature Panel (b) filters and highlights key COIs using bubbles and icons (d2). Individual learner data can be selected in the Projection View (d). The Slide View (e) breaks down the video content by COI, aligning with the Main View to provide contextual semantic supplementation.
• Figure 2: Illustration of the COI notion using Concept 1--3 "Relationship representation": (a) the textual definition (COI 1--3--0) and two diagrams (COI 1--3--1/2) as concept-driven AOIs within one instructional episode; (b) COI 1--3--1 illustrating the three components of a COI: the concept identifier, instructional episode (slide interval and narration), and the concept-related gaze trace aggregated within the AOI.
• Figure 3: The pipeline of COIVis consists of three modules: COI definition and eye tracking integration, COI-based learner-state feature analysis, and visual analytics design.
• Figure 4: Visual design of COIs, relationships, and learner-state features. (a) shows how COIs are linked through four relationships; (b) illustrates how the five icons map to their respective learner-state features.
• Figure 5: (a) and (b) represent alternative designs of COI pathways and learner-state features. (c) shows our final design.