相关论文: Context, input and process as critical elements fo…

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks. However, the context shift problem arises due to the distribution discrepancy between the…

In today's rapidly evolving technological landscape, organizations face the challenge of integrating external insights into their decision-making processes to stay competitive. To address this issue, this study proposes a method that…

The outbreak of COVID-19 has highlighted the intricate interplay between public health and economic stability on a global scale. This study proposes a novel reinforcement learning framework designed to optimize health and economic outcomes…

M-learning (mobile learning) can take various forms. We are interested in contextualized M-learning, i.e. the training related to the situation physically or logically localized. Contextualization and pervasivity are important aspects of…

Distance education has expanded significantly over the last decade, but the natural sciences have lagged in the implementation of this instructional mode. The abrupt onset of the COVID-19 pandemic left educational institutions scrambling to…

Severe acute respiratory disease SARS-CoV-2 has had a found impact on public health systems and healthcare emergency response especially with respect to making decisions on the most effective measures to be taken at any given time. As…

The prevailing paradigm for improving large language models relies on offline training with human annotations or simulated environments, leaving the rich experience accumulated during real-world deployment entirely unexploited. We propose…

Causal representation learning (CRL) and traditional representation learning have largely developed along different trajectories. Traditional representation learning has been driven mainly by applications and empirical objectives, whereas…

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models…

We present an investigation regarding the challenges faced by student teams across four consecutive iterations of a team-focused, project-based course in software engineering. The studied period includes the switch to fully online…

Large Language Models (LLMs) have gained traction in educational settings, often framed as virtual tutors or teaching assistants. Following early skepticism and bans, many schools and universities have begun integrating these systems into…

Understanding human--environment interactions from egocentric vision is essential for assistive robotics and embodied intelligent agents, yet existing multimodal large language models (MLLMs) still struggle with accurate interaction…

The COVID-19 pandemic has thrown natural life out of gear across the globe. Strict measures are deployed to curb the spread of the virus that is causing it, and the most effective of them have been social isolation. This has led to…

We formalize a new concept for LLMs, context-enhanced learning. It involves standard gradient-based learning on text except that the context is enhanced with additional data on which no auto-regressive gradients are computed. This setting…

The COVID-19 pandemic has underscored the importance of blended learning in contemporary physics and, more generally, STEM education. In this contribution, we summarize current pedagogical models of blended learning, such as rotational and…

In-context reinforcement learning (ICRL) refers to the ability of RL agents to adapt to new tasks at inference time without parameter updates by conditioning on additional context. Recent empirical studies further demonstrate that…

Many studies in educational data mining address specific learner groups, such as first-in-family to attend Higher Education, or focus on differences in characteristics such as gender or ethnicity, with the aim of predicting performance and…

Computational models can advance affective science by shedding light onto the interplay between cognition and emotion from an information processing point of view. We propose a computational model of emotion that integrates reinforcement…

Engaging in interactions with peers is important for student learning. Many studies have quantified patterns of student interactions in in-person physics courses using social network analysis, finding different network structures between…

Conferences are a key place for training and education. Attendees learn about the state of the art of their field, about relevant methods, and acquire networking skills that can support their work. In the world that was changed by the…