📄 Full text: arXiv:2605.01238 · local
Sensor-based momentary assessment of engagement in self-guided video learning environments.
The Engagement Problem in Video Learning
EduGage (Leng et al., 2026) addresses a core challenge: in online/video-based learning, learners must self-regulate their engagement with instructional materials.
Dimensions of Engagement
| Dimension | Measurement | Relevance to Learning |
|---|---|---|
| Attentional | Eye tracking, gaze patterns | Sustained focus on content |
| Emotional | Facial expression, sentiment | Positive affect supports persistence |
| Cognitive | Physiological signals, task performance | Deep processing vs. superficial viewing |
Sensor-Based Momentary Assessment
Traditional engagement measures:
- Post-hoc surveys: Retrospective bias, low temporal resolution
- Self-reports: Introspection difficulty, social desirability bias
EduGage contributes: Real-time sensor fusion for momentary assessment during video learning.
Technical Approach
- Sensors: Webcam (facial analysis), interaction logs (pause, rewind, speed)
- Assessment: Momentary (in-the-moment) vs. retrospective
- Feedback loop: Real-time reflection prompts based on engagement state
Connection to Adaptive Learning
This enables adaptive interventions in video learning: 1. Detect disengagement (gaze diversion, prolonged pauses) 2. Trigger scaffolds (reflection prompt, content re-summarization) 3. Close loop: Learner reflects → re-engages → improved outcomes
This aligns with adaptive-learning-systems principles: real-time learner modeling → personalized intervention.
Implications for ITS
Intelligent tutoring systems increasingly include video components (e.g., worked examples, concept explanations). EduGage's approach enables:
- Multimodal engagement tracking (cf. multimodal-ai-tutoring, affective-tutoring)
- Just-in-time scaffolds when engagement drops
- Self-regulated learning support (self-regulated-learning)
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Sources
- Leng et al. (2026). EduGage: Methods and Dataset for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning. arXiv:2605.01238. PDF