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

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.

Published in Scientific Data (Nature). GitHub repository of the dataset at: https://github.com/BiDAlab/IMPROVE

References in corpus (7)

• Array Programming with NumPy
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
• DeepFace-Attention: Multimodal Face Biometrics for Attention Estimation with Application to e-Learning
• ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning
• mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detection
• MATT: Multimodal Attention Level Estimation for e-learning Platforms

Cited by in corpus (2)

• SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality
• Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos