Eye-tracking perspectives of students’ learning trough MOOCs

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Reinforcement learning (RL) is inherently rife with non-stationarity since the states and rewards the agent observes during training depend on its changing policy. Therefore, networks in deep RL must be capable of adapting to new observations and fitting n ...

Computer simulations are often used as support material for science education, as they can engage students through inquiry-based learning, promote their active interaction in the experimentation phase, and help them visualize abstract concepts. For instanc ...

Finding optimal bidding strategies for generation units in electricity markets would result in higher profit. However, it is a challenging problem due to the system uncertainty which is due to the lack of knowledge of the strategies of other generation uni ...

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Over the past few decades, the debates have shifted from whether to how Computer Science (CS) should be introduced into formal education. Given the diverse ways to introduce CS into formal education, and the struggles many countries have faced, considerabl ...

The paper presents ICAP (interactive, constructive, active and passive) as the theoretical framework to understand the role of informal learning spaces as an active learning tool when students have informal meetings to work on projects. Students in our En ...

Inclusive teaching is the intentional practice of recognising biases, working to mitigate their impact, and ensuring that students have equitable learning opportunities. In addition to improving students' sense of belonging and self efficacy, inclusive tea ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments ...

This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-tr ...

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

Addressing the complex challenges of sustainability demands for good teamwork abilities for future technicians and engineers. In our three institutions we adopted project-based learning to facilitate the development of these skills – but is this enough? Si ...

Background Early exposure to Computer Science (CS) and Computational Thinking (CT) for all is critical to broaden participation and promote equity in the field. But how does the introduction of CS and CT into primary school curricula impact learning, perce ...

Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for ...

Although adults and children differ in self-vs.-other perception, a developmental perspective on this discriminative ability at the brain level is missing. This study examined neural activation for self-vs.-other in a sample of 39 participants spanning fou ...

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

Eye-tracking perspectives of students’ learning trough MOOCs

Chattez avec Graph Search

No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO

Teacher-Mediated and Student-Led Interaction with a Physics Simulation: Effects on the Learning Experience

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Co-constructing an Effective, Sustainable and Scalable Primary School Computer Science Curricular Reform

Work in progress: Imagining / Designing informal spaces for learning

Inclusive engineering classrooms: student teaching assistants' perspectives

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Networked Signal and Information Processing: Learning by multiagent systems

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

FROM GROUP WORK TO TEAM WORK: COMPARATIVE ANALYSIS IN THREE EUROPEAN INSTITUTIONS

How are primary school computer science curricular reforms contributing to equity? Impact on student learning, perception of the discipline, and gender gaps

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Me, Myself, and I: Neural Activity for Self versus Other across Development

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations