EduGage dataset

Product or Programme · All products and programmes
Evidence · 6 claims (6 for) · 2 studies (2 design), q2 · 0 of 2 report an effect size · 6 claims rest on one study

Description

A multimodal learning-engagement dataset released by Zikang Leng and colleagues, containing synchronized sensor streams, engagement-probe ratings, video metadata, and quiz responses from self-guided video-learning sessions for participant-independent engagement-modeling research.

Components

Claims

• Videos experienced as higher attention difficulty were associated with lower normalized quiz learning gains, providing convergent evidence that the probe captures an educationally meaningful aspect of momentary engagement [+W]
• A modality-aware reference model outperforms sensor-free, statistical, deep temporal, foundation-model, and LLM-based baselines for estimating minute-level attention difficulty from wearable sensing [+M]
• Chest-worn ECG provides the strongest cardiac-sensing performance for attention-difficulty prediction, and fusing all cardiac streams does not improve upon chest ECG alone [+M]
• Predictive performance of sensor configurations does not change monotonically with the number of sensing streams; preferred configuration depends on target metric and sensing cost [+M]
• A head-confined multimodal configuration (EEG, eye tracking, forehead EDA) predicts held-out learners' five-level engagement ratings at 75.0% balanced 1-off accuracy, 12.0 points above the mode baseline [+M]
• One-minute-ahead direct forecasting performs similarly to contemporaneous estimation, but performance degrades at longer horizons and a carry-forward baseline outperforms the sensor model at every future horizon [+W]

Related Products and Programmes

• DIPSER multimodal classroom dataset for in-person student engagement and attention recognition

Key Sources

• Zikang Leng, Edan Eyal, Yingtian Shi, Jiaman He, Yaqi Liu, and Thomas Plötz. (2026). EduGage: A Multimodal Dataset and Benchmark for Sensor-Based Momentary Assessment of Engagement in Self-Guided Video Learning. Proc. ACM Interact. Mob. Wearable Ubiquitous Ubiquitous Technol. https://arxiv.org/abs/2605.01238

Page metadata

Field	Value
type	product
id	edugage-dataset
title	EduGage dataset
description	A multimodal learning-engagement dataset released by Zikang Leng and colleagues, containing synchronized sensor streams, engagement-probe ratings, video metadata, and quiz responses from self-guided video-learning sessions for participant-independent engagement-modeling research.
status	review
product_kind	dataset
generated	by process:settle-candidates on 2026-10-10
trust	unverified — no one has confirmed this page's content

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