IMPROVE: Impact of Mobile Phones on Remote Online Virtual Education

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.