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Did You Get That? Predicting Learners' Comprehension of a Video Lecture from Visualizations of Their Gaze Data
Ellen M Kok1,2, Halszka Jarodzka2, Matt Sibbald3
1Department of Education, Utrecht University.
Cognitive Science
|February 6, 2023
Summary
Eye-tracking visualizations can help teachers gauge online student engagement and comprehension. Gaze data accurately predicts learning outcomes, offering insights for improving remote education strategies.
Area of Science:
- Educational Technology
- Human-Computer Interaction
- Cognitive Science
Background:
- Online learning environments lack nonverbal cues crucial for assessing student engagement and comprehension.
- Eye-tracking technology offers a potential solution by capturing and visualizing student gaze data.
Purpose of the Study:
- To investigate if "with-me-ness" (gaze on relevant information) correlates with comprehension in online lectures.
- To determine if untrained individuals can predict student comprehension from eye-tracking visualizations.
- To evaluate the impact of different visualization techniques on predicting comprehension.
Main Methods:
- Study 1: Eye-tracking 36 students during a video lecture, correlating gaze patterns with comprehension scores.
- Study 2: 50 participants predicting student comprehension using six gaze visualization techniques (scanpaths, heatmaps, focus maps).
Main Results:
- Gaze measures, including looking at relevant information and synchronized gaze with the instructor, correlated with lecture comprehension.
- Participants could predict student comprehension from gaze visualizations above chance level.
- Minor differences were observed in prediction accuracy across various visualization techniques.
Conclusions:
- Eye-tracking visualizations are a viable tool for inferring student comprehension in online learning settings.
- Further research is needed to explore how teachers can utilize this information to enhance student learning outcomes.
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