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The effectiveness of online education heavily depends on student engagement, which is often challenging to assess and sustain in virtual learning environments compared to traditional classrooms. This dissertation explores the use of smart wearable devices, specifically the BITalino platform, to monitor physiological signals such as electrocardiogram (ECG) and electrodermal activity (EDA) for real-time assessment of student engagement during remote learning. By integrating these signals with a custom biofeedback application, the study proposes a novel system to provide immediate feedback to students, promoting self-regulation and adaptive teaching strategies.
Experimental results from a sample of 20 students demonstrate significant correlations between physiological metrics (e.g., heart rate variability and skin conductance responses) and self-reported engagement levels, validating the system’s efficacy. The findings contribute to educational technology by offering a low-cost, transparent, and scalable solution for enhancing engagement in virtual learning, with implications for fostering human-technology collaboration in Education 5.0. Ethical considerations, including data privacy and informed consent, are also addressed to ensure responsible implementation.