E3Sense¶
Product or Programme · All products and programmes
Evidence · 2 claims (2 for) · 1 study (1 design),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
E3Sense is a research platform developed by Sidharth Anupkrishnan et al. that synchronizes EEG, eye-tracking, and forehead-EDA streams to support engagement sensing during video learning.
Components¶
Claims¶
- 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]
- Model-family rankings depend on the prediction target: AdaBoost leads the five-level ordinal task while LightGBM holds the highest binary macro-F1 [+M]
Related Products and Programmes¶
-
Key Sources¶
- Sidharth Anupkrishnan, Itir Sayar, Jeongah Lee, Sri Harsha Musunuri, Guan-Ming Su, Madeline Endres, Ravi Karkar, Phuc Nguyen. (2026). E3Sense: Head-Confined Multimodal Sensing of Learner Engagement. arXiv. https://arxiv.org/abs/2609.26569
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
e3sense |
title |
E3Sense |
description |
E3Sense is a research platform developed by Sidharth Anupkrishnan et al. |
status |
review |
product_kind |
software |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
Frontmatter is the page's machine-readable record — see the Schema & Guide.