Becerra, Alvaro; Daza, Roberto; Cobos, Ruth; Morales, Aythami; Cukurova, Mutlu; Fierrez, Julian; (2025) AI-Based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning. In: Tammets, Kairit and Sosnovsky, Sergey and Ferreira Mello, Rafael and Pishtari, Gerti and Nazaretsky, Tanya, (eds.) Two Decades of TEL. From Lessons Learnt to Challenges Ahead (EC-TEL 2025). (pp. pp. 31-46). Springer: Cham, Switzerland.

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Abstract

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 focus 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 that combines all signals reaches accuracy 91%, 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.

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