Real-Time Multimodal Data Collection Using Smartwatches and Its Visualization in Education
Alvaro Becerra, Pablo Villegas, Ruth Cobos

TL;DR
This paper introduces two tools, Watch-DMLT and ViSeDOPS, for real-time multimodal data collection and visualization using smartwatches in educational settings, enabling scalable analysis of physiological and behavioral data.
Contribution
The paper presents novel tools for synchronized multimodal data acquisition and visualization in education, addressing scalability and real-time analysis challenges.
Findings
Successful deployment with 65 students and 16 smartwatches
Collected data includes heart rate, motion, gaze, and video
Demonstrated system's feasibility and utility in real classrooms
Abstract
Wearable sensors, such as smartwatches, have become increasingly prevalent across domains like healthcare, sports, and education, enabling continuous monitoring of physiological and behavioral data. In the context of education, these technologies offer new opportunities to study cognitive and affective processes such as engagement, attention, and performance. However, the lack of scalable, synchronized, and high-resolution tools for multimodal data acquisition continues to be a significant barrier to the widespread adoption of Multimodal Learning Analytics in real-world educational settings. This paper presents two complementary tools developed to address these challenges: Watch-DMLT, a data acquisition application for Fitbit Sense 2 smartwatches that enables real-time, multi-user monitoring of physiological and motion signals; and ViSeDOPS, a dashboard-based visualization system for…
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Taxonomy
TopicsMobile Learning in Education · Visual and Cognitive Learning Processes · Emotion and Mood Recognition
