Efe Bozkir

Since the COVID-19 pandemic, online courses have expanded access to education, yet the absence of direct instructor support challenges learners' ability to self-regulate attention and engagement. Mind wandering and disengagement can be detrimental to learning outcomes, making their automated detection via video-based indicators a promising approach for real-time learner support. However, machine learning-based approaches often require sharing sensitive data, raising privacy concerns. Federated learning offers a privacy-preserving alternative by enabling decentralized model training while also distributing computational load. We propose a framework exploiting cross-device federated learning to address different manifestations of behavioral and cognitive disengagement during remote learning, specifically behavioral disengagement, mind wandering, and boredom. We fit video-based cognitive disengagement detection models using facial expressions and gaze features. By adopting federated learning, we safeguard users' data privacy through privacy-by-design and introduce a novel solution with the potential for real-time learner support. We further address challenges posed by eyeglasses by incorporating related features, enhancing overall model performance. To validate the performance of our approach, we conduct extensive experiments on five datasets and benchmark multiple federated learning algorithms. Our results show great promise for privacy-preserving educational technologies promoting learner engagement.

The latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life. Eye tracking offers not only a hands-free way of interaction but also the possibility of a deeper understanding of human visual attention and cognitive processes in VR. Despite these possibilities, eye-tracking data also reveal users’ privacy-sensitive attributes when combined with the information about the presented stimulus. To address all, this survey first covers major works in eye tracking, VR, and privacy areas between 2012 and 2022. While eye tracking in VR part covers the computational eye-tracking pipeline from pupil detection and gaze estimation to offline data analysis, for privacy and security, we focus on eye-based authentication as well as computational methods to preserve the privacy of individuals and their eye-tracking data in VR. Later, we outline three main directions by focusing on privacy. In summary, this survey presents an extensive literature review of the utmost possibilities of eye tracking in VR and their privacy implications.

Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.

Split learning emerges as a promising paradigm for collaborative distributed model training, akin to federated learning, by partitioning neural networks between clients and a server without raw data exchange. However, sequential split learning suffers from poor scalability, while parallel variants like parallel split learning and split federated learning often incur high server resource overhead due to model duplication and aggregation, and generally exhibit reduced model performance and convergence owing to factors like client drift and lag. To address these limitations, we introduce CycleSL, a novel aggregation-free split learning framework that enhances scalability and performance and can be seamlessly integrated with existing methods. Inspired by alternating block coordinate descent, CycleSL treats server-side training as an independent higher-level machine learning task, resampling client-extracted features (smashed data) to mitigate heterogeneity and drift. It then performs cyclical updates, namely optimizing the server model first, followed by client updates using the updated server for gradient computation. We integrate CycleSL into previous algorithms and benchmark them on five publicly available datasets with non-iid data distribution and partial client attendance. Our empirical findings highlight the effectiveness of CycleSL in enhancing model performance. Our source code is available at https://gitlab.lrz.de/hctl/CycleSL.

Attention is crucial for learners to enhance their learning and build knowledge. Sustaining prolonged attention requires students to self-regulate their behavioral, emotional, and cognitive processes. At the same time, learners engage in mind wandering approximately 30% of the time spent in educational activities, leading to a deterioration in learning outcomes. Despite its prevalence, the integration of mind-wandering research with self-regulated learning (SRL) frameworks remains limited. To address this gap, we first conceptually integrated research on mind wandering and SRL. Second, we assessed the extent to which this integration has been empirically explored by situating research on mind wandering during learning within the self-regulated learning process, multimodal data, and analysis (SMA) grid. To this end, we conducted a preregistered systematic literature review. We reviewed 134 studies from 2010 to 2024, analyzing the employed definitions of mind wandering, especially with regard to meta-awareness, the data streams used for measuring mind wandering, and whether mind wandering was investigated in relation to or in combination with cognitive, affective, metacognitive, and motivational (CAMM) SRL processes. Our analysis revealed that a significant number of studies have examined mind wandering alongside cognitive and metacognitive SRL processes and predominantly using self-report measures. An equal number of studies unobtrusively measured mind wandering through multimodal data streams (e.g., eye tracking, physiology, reaction time), with 27% utilizing machine learning (ML) techniques for assessment. However, most of these reports did not include SRL procedures and only a small number of studies used an integrated analytic approach, combining multimodal assessment and including SRL processes. Overall, affective and motivational processes received little to no attention. This review demonstrates how integrating research on mind wandering into established SRL frameworks can enhance our understanding of their complex interaction and role in the learning process. It revealed a blind spot in studying mind wandering in educational settings in conjunction with SRL processes using multimodal data streams, especially regarding affective and motivational SRL processes. Additionally, the results suggest that this integration can improve the effectiveness of real-time interventions based on automated mind-wandering detection in attention-aware learning technologies, thereby supporting SRL.