Abstract
This work presents the IMPROVE dataset, designed to evaluate the effects of mobile phone usage on learners during online education. The dataset not only assesses academic performance and subjective learner feedback but also captures biometric, behavioral, and physiological signals, providing a comprehensive analysis of the impact of mobile phone use on learning. Multimodal data were collected from 120 learners in three groups with different phone interaction levels. A setup involving 16 sensors was implemented to collect data that have proven to be effective indicators for understanding learner behavior and cognition, including electroencephalography waves, videos, eye tracker, etc. The dataset includes metadata from the processed videos like face bounding boxes, facial landmarks, and Euler angles for head pose estimation. In addition, learner performance data and self-reported forms are included. Phone usage events were labeled, covering both supervisor-triggered and uncontrolled events. A semi-manual re-labeling system, using head pose and eye tracker data, is proposed to improve labeling accuracy. Technical validation confirmed signal quality, with statistical analyses revealing biometric changes during phone use.
Abstract (translated)
本研究介绍了IMPROVE数据集，该数据集旨在评估移动电话使用对在线教育中学习者的影响。除了评估学术表现和主观的学习者反馈外，该数据集还捕捉了生物特征、行为和生理信号，从而全面分析手机使用对学习的综合影响。来自三个不同手机互动水平组别的120名学习者的多模态数据被收集。通过实施包含16个传感器的设置来收集已被证明能有效指示理解学习者行为与认知的数据，包括脑电波、视频和眼动追踪等。该数据集包含了处理后的视频元数据，如面部边界框、面部特征点以及用于头部姿态估计的欧拉角。此外，还包括了学习者的成绩数据和个人报告表单。手机使用事件被标记，涵盖了由监督者触发的和未控制的事件。提出了一种半自动重新标注系统，利用头部姿态和眼动追踪数据来提高标注准确性。技术验证确认了信号质量，并通过统计分析揭示了在使用手机时生物特征的变化。
URL
https://arxiv.org/abs/2412.14195
