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

TL;DR

This paper introduces the IMPROVE dataset, a comprehensive multimodal resource capturing behavioral, biometric, and academic data to study mobile phone impacts on remote online learners, facilitating advanced research in educational technology.

Contribution

The work presents a novel, publicly available multimodal dataset with synchronized sensor data and detailed annotations to analyze mobile phone effects in online education settings.

Findings

Biometric changes linked to phone usage identified

Dataset validated with high-quality signal recordings

Mobile phone interaction influences learner engagement

Abstract

This work presents the IMPROVE dataset, a multimodal resource designed to evaluate the effects 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 different 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 multiple-choice tests. Mobile phone usage events, including both controlled interventions and uncontrolled interactions, were labeled by supervisors and refined through a semi-supervised re-labeling process. Technical validation confirmed 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.