edBB: Biometrics and Behavior for Assessing Remote Education

TL;DR

This paper introduces edBB, a comprehensive platform and database for remote student monitoring using biometric and behavioral sensors to detect anomalies and estimate user states during remote assessments.

Contribution

The paper presents a new multi-sensor platform and initial database for biometric and behavioral data collection in remote education contexts.

Findings

Data can be used to detect anomalies during remote evaluations.

Biometric data can estimate attention, stress, and pulse rate.

The platform supports various sensors for comprehensive monitoring.

Abstract

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.