  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.
