edBB: Biometrics and Behavior for Assessing Remote Education
This work addresses the challenge of remote education monitoring for educators and institutions, but it is incremental as it builds on existing biometric and behavioral sensing methods.
The authors tackled the problem of automatic student monitoring in remote education by developing a platform that collects biometric and behavioral data from sensors like webcams, microphones, and EEG bands, and they released an initial database with data from 20 users to analyze anomaly detection and estimate user states such as attention and stress.
We present a platform for student monitoring in remote education consisting of a collection of sensors and software that capture biometric and behavioral data. We define a collection of tasks to acquire behavioral data that can be useful for facing the existing challenges in automatic student monitoring during remote evaluation. Additionally, we release an initial database including data from 20 different users completing these tasks with a set of basic sensors: webcam, microphone, mouse, and keyboard; and also from more advanced sensors: NIR camera, smartwatch, additional RGB cameras, and an EEG band. Information from the computer (e.g. system logs, MAC, IP, or web browsing history) is also stored. During each acquisition session each user completed three different types of tasks generating data of different nature: mouse and keystroke dynamics, face data, and audio data among others. The tasks have been designed with two main goals in mind: i) analyse the capacity of such biometric and behavioral data for detecting anomalies during remote evaluation, and ii) study the capability of these data, i.e. EEG, ECG, or NIR video, for estimating other information about the users such as their attention level, the presence of stress, or their pulse rate.