# EduGage: A Multimodal Dataset and Benchmark for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning

arXiv: https://arxiv.org/abs/2605.01238 · HTML: https://mlsanity.com/paper/2605.01238/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 103 references that are in the map.
- First posted on arXiv 2 May 2026.

## Builds on (most cited first, 11 of 11)

- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals](https://mlsanity.com/paper/2409.00101/) (2024)
- [DAiSEE: Towards User Engagement Recognition in the Wild](https://mlsanity.com/paper/1609.01885/) (2016)
- [Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings](https://mlsanity.com/paper/2502.01108/) (2025)
- [Prediction and Localization of Student Engagement in the Wild](https://mlsanity.com/paper/1804.00858/) (2018)
- [Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals](https://mlsanity.com/paper/2412.09758/) (2024)
- [Deep ConvLSTM with self-attention for human activity decoding using wearables](https://mlsanity.com/paper/2005.00698/) (2020)
- [CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels](https://mlsanity.com/paper/2312.09066/) (2023)
- [τ-Ring: A Smart Ring Platform for Multimodal Physiological and Behavioral Sensing](https://mlsanity.com/paper/2508.00778/) (2025)
- [Characterizing Personality from Eye-Tracking: The Role of Gaze and Its Absence in Interactive Search Environments](https://mlsanity.com/paper/2601.08287/) (2026)

## Abstract (arXiv)

Engagement, which links to attentional, emotional, and cognitive dimensions, plays an important role in learning. In online and video-based learning environments, learners often need to regulate their own interactions with instructional materials. Measuring and reflecting on engagement can therefore support both learners and adaptive learning systems. In this study, we use wearable and camera-based sensing devices to collect physiological and motion signals, including PPG, ECG, EDA, EEG, IMU, heart rate, temperature, and eye-tracking data, to estimate learner engagement. We conducted a user study with 16 participants in a video-based learning scenario, where participants completed learning tasks and provided repeated in-situ engagement-probe ratings on a 1-5 scale, reporting how difficult it was to pay attention during the preceding minute. We establish a benchmark for engagement estimation, compare different sensing modalities, and further analyze the feasibility and effectiveness of multimodal modeling for characterizing learner engagement. Across participant-based cross-validation, the modality-aware reference model achieves an MAE of 0.80, 83.18% within-1 accuracy, 70.96% binary accuracy, and 62.90% binary Macro-F1, outperforming sensor-free, statistical, deep temporal, foundation-model, and LLM-based baselines. Our results suggest that fine-grained engagement estimation is feasible but inherently noisy, and that the preferred sensing configuration depends on the target metric and practical sensing burden. We release the EduGage dataset as the primary contribution of this work, including synchronized multimodal sensor signals, probe-aligned engagement-probe ratings, video metadata, quizzes, and study materials, to support reproducible research on fine-grained sensor-based engagement modeling in self-guided learning.