Learning Analytics
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Toward a Metaphysics of Learning Analytics: Ontological Positioning of Data, Inference, and Normativity
arXiv:2606.06851v1 Announce Type: new Abstract: The Learning Analytics (LA) community has undergone rapid development over the 15 years since the first LAK conference was held. However, while epistemological and ethical debates regarding the philosophical foundations of LA have been vigorous, metaphysical discussions have been sparse, signifying a lack of effort to derive the identity of LA from its internal principles. In this paper, we attempt to establish a metaphysics of LA by addressing...
Toward Scalable Co-located Practical Learning: Assisting with Computer Vision and Multimodal Analytics
Announce Type: replace Abstract: Co-located practical learning leaves evidence in visible actions around patients, task resources and room zones, but these traces are often recovered through live observation or retrospective video review. Fixed wide-angle video could reduce sensing burden, yet a debriefing pipeline must do more than detect behaviours: it must maintain detection after small camera-position shifts, relate the detector-derived behaviour trace to instructor-labelled outcomes and...
Quantum feature-map learning with reduced resource overhead
Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature-maps, which embed classical data into the state space of qubits. We introduce Quantum Feature-Map Learning via Analytic Iterative Reconstructions (Q-FLAIR), an algorithm that reduces quantum resource overhead in iterative feature-map circuit construction.
NEA taps surveillance tech, video analytics to boost fight against rat infestations
NEA taps surveillance tech, video analytics to boost fight against rat infestations As rats are nocturnal and tend to avoid humans, surveillance tools can help authorities better understand their behaviour and movement patterns, said the agency. SINGAPORE: The National Environment Agency (NEA) is exploring the use of more advanced technologies such as video analytics and machine learning to strengthen efforts against rat infestations. The agency currently deploys thermal and passive infrared...
Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation
arXiv:2509.24467v3 Announce Type: replace Abstract: Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necessitating domain-specific explanations. We introduce KREPES, a unified framework to analytically interpret the learned representations of SSL objectives, including SimCLR, BYOL, and VICReg. By bridging empirical neural tangent kernel approximations of neural networks with the Representer Theorem...
Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation
Announce Type: replace Abstract: Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necessitating domain-specific explanations. We introduce KREPES, a unified framework to analytically interpret the learned representations of SSL objectives, including SimCLR, BYOL, and VICReg. By bridging empirical neural tangent kernel approximations of neural networks with the Representer Theorem for kernels, we...
Advanced Mathematics Learning Behavior Prediction and Academic Early Warning Model Based on Multimodal Data Analysis
Announce Type: new Abstract: Early detection of at-risk students and timely academic intervention pose major challenges in advanced mathematics education, where complex conceptual hierarchies and nonlinear learning trajectories often hold back students' academic performance. This study adopts multimodal data analytics to build a dynamic framework for learning behavior prediction and academic early warning. It constructs a hierarchical knowledge graph ontology, realizes adaptive edge...
SQLite is all you need for durable workflows
Here is the summary: SQLite is a powerful and versatile database that can be used for durable workflows. It is a self-contained, serverless, and zero-configuration database that can be easily embedded into applications. SQLite is also highly reliable and can handle large amounts of data. It is a great choice for applications that require durable workflows, such as data processing, analytics, and machine learning. SQLite is also easy to use and can be integrated with other technologies, such as Python and JavaScript. With its powerful features and ease of use, SQLite is an ideal choice for durable workflows. ## Welcome to [Stack Overflow](https://stackoverflow.com/) This is a Q&A forum for computer programming. The [automatic community-generated FAQ](https://meta.stackexchange.com/questions/7931/faq-proposed-faq-for-stack-exchange-sites) has answers to many common questions. Read the [tour](https://stackoverflow.com/tour) to learn more about us. Some questions to help you get started: * [What is Stack Overflow?](https://meta.stackexchange.com/questions/92107/what-is-stack-overflow) * [How do I ask a good question?](https://stackoverflow.com/help/how-to-ask) * [How to create a Minimal, Complete, and Verifiable example
Explainable deep-learning detection of microplastic fibers via polarization-resolved holographic microscopy
arXiv:2601.15769v3 Announce Type: replace Abstract: Reliable identification of microplastic fibers is crucial for environmental monitoring but remains analytically challenging. We report an explainable deep-learning framework for classifying microplastic and natural microfibers using polarization-resolved digital holographic microscopy. From multiplexed holograms, the complex Jones matrix of each fiber was reconstructed to extract polarization eigen-parameters describing optical anisotropy.
Powering An Ecosystem Of Pedagogical AI Agents: A Validation Strategy For A Unified Data Architecture
Announce Type: new Abstract: The application of AI in education has evolved from monolithic intelligent tutoring systems to a diverse ecosystem of pedagogical agents, including conversational assistants, virtual coaches, and adaptive tutors. This shift requires a unified and scalable data architecture to manage the complex information feedback loops between human instructors, learners, and the varied AI agents. The design, development, and deployment of the data architecture in turn raises a...