A multimodal dataset for understanding the impact of mobile phones on remote online virtual education

Roberto Daza, Alvaro Becerra, Ruth Cobos, Julian Fierrez, Aythami Morales

Abstract:

This work presents the IMPROVE dataset, a multimodal resource designed to evaluate the efects
of mobile phone usage on learners during online education. It includes behavioral, biometric,
physiological, and academic performance data collected from 120 learners divided into three groups
with diferent levels of phone interaction, enabling the analysis of the impact of mobile phone usage
and related phenomena such as nomophobia. A setup involving 16 synchronized sensors—including
EEG, eye tracking, video cameras, smartwatches, and keystroke dynamics—was used to monitor learner
activity during 30-minute sessions involving educational videos, document reading, and multiplechoice tests. Mobile phone usage events, including both controlled interventions and uncontrolled
interactions, were labeled by supervisors and refned through a semi-supervised re-labeling process.
Technical validation confrmed signal quality, and statistical analyses revealed biometric changes
associated with phone usage. The dataset is publicly available for research through GitHub and Science
Data Bank, with synchronized recordings from three platforms (edBB, edX, and LOGGE), provided in
standard formats (.csv, .mp4, .wav, and .tsv), and accompanied by a detailed guide.