BiDAlab/mEBAL2
mEBAL2 is a multimodal database for eyeblink detection and attention level estimation.
This project provides a large dataset of student behavior to help researchers understand attention and engagement in online learning. It contains video footage (RGB and infrared) and brain activity (EEG) from 180 students as they complete e-learning tasks or MOOC lessons. Educators, educational researchers, and AI developers can use this data to build better systems for assessing student attention and preventing cheating.
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Use this if you are developing or researching methods to automatically detect student attention levels or engagement in e-learning environments.
Not ideal if you need real-time, in-the-moment feedback on individual student attention without building a computational model first.
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May 13, 2025
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