AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning
This addresses the problem of maintaining learner engagement in online education by providing a method to detect distractions, though it appears to be an incremental application of existing multimodal biometric techniques to a specific domain.
This work tackled the problem of detecting smartphone distractions during online learning by developing an AI-based multimodal biometric system that combines physiological signals and head pose data. The results showed that a multimodal model achieved 91% accuracy in detecting phone use, outperforming single biometric approaches.
This work investigates the use of multimodal biometrics to detect distractions caused by smartphone use during tasks that require sustained attention, with a focus on computer-based online learning. Although the methods are applicable to various domains, such as autonomous driving, we concentrate on the challenges learners face in maintaining engagement amid internal (e.g., motivation), system-related (e.g., course design) and contextual (e.g., smartphone use) factors. Traditional learning platforms often lack detailed behavioral data, but Multimodal Learning Analytics (MMLA) and biosensors provide new insights into learner attention. We propose an AI-based approach that leverages physiological signals and head pose data to detect phone use. Our results show that single biometric signals, such as brain waves or heart rate, offer limited accuracy, while head pose alone achieves 87%. A multimodal model combining all signals reaches 91% accuracy, highlighting the benefits of integration. We conclude by discussing the implications and limitations of deploying these models for real-time support in online learning environments.