A taxonomy of surprise definitions

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Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Traditional example-based learning methods are often limited by static, expert-created content. Hence, they face challenges in scalability, engagement, and effectiveness, as some learners might struggle to relate to the examples or find them relevant. To a ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

Information collected through sensor measurements has the potential to improve knowledge of complex-system behavior, leading to better decisions related to system management. In this situation, and particularly when using digital twins, the quality of sens ...

The local physical properties - such as shape and flexibility - of the DNA double-helix is today widely believed to be influenced by nucleic acid sequence in a non-trivial way. Furthermore, there is strong evidence that these properties play a role in many ...

The balancing of mechanisms consists in distributing their moving masses, inertias, and elastic components in order to achieve key mechanical properties, such as the elimination of the shaking forces and moments exported onto their supporting structure or ...

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 ...

Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Other ...

The trend towards generalized blended and active learning approaches in engineering education is redefining the requirements and the deployment schemes for digital education platforms. This paper presents a quadriptych techno- pedagogical model highlightin ...

The learning process depends on the nature of the learning environment, particularly in the case of open-ended learning environments, where the learning process is considered to be non-linear. In this paper, we report on the findings of employing a multimo ...

In this thesis, we present a theory of value for the design and analysis of service ecosystems. The theory is based on general systems thinking. The concept of a system is used for relating knowledge from different disciplines (such as software engineering ...

Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...

Due to conservative approaches in construction design and practice, infrastructure often has hidden reserve capacity. When quantified, this reserve has potential to improve decisions related to asset management. Field measurements, collected through load t ...

Orchestrating technology-enhanced learning is a difficult task, especially in demanding pedagogical approaches like inquiry-based learning (IBL). To foster effective teacher adoption, both the complexity of designing IBL activities and the uncertainty abou ...

Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivat ...

In developed countries, the management of existing civil infrastructure is challenging due to evolving functional requirements, aging and climate change. Due to conservative approaches in construction design and practice, infrastructure often has hidden re ...

This paper reports on a study carried out in the framework of the Go-lab Goes Africa project, in which teachers implemented online and offline Inquiry Learning Spaces (ILS) in their classes using the Go-lab platform. After a brief description of the Inquir ...

Driving is a very challenging task to automatize despite how naturally and efficiently it may come to experienced human drivers. The complexity stems from the need to (i) understand the surrounding context and forecast how it is likely to evolve, (ii) plan ...

Social practices are well-known mediators in the adoption of educational innovations during professional learning, as postulated by the Knowledge Appropriation Model (KAM). However, understanding how teachers adopt new pedagogical approaches at scale is of ...

A taxonomy of surprise definitions

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Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Social Opinion Formation and Decision Making Under Communication Trends

GELEX: Generative AI-Hybrid System for Example-Based Learning

Unveiling the complexity of learning and decision-making

Methodology for selecting measurement points that optimize information gain for model updating

Landscapes of DNA Mechanics and Genomes

Mechanism balancing taxonomy

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

A Functional Perspective on Information Measures

Integrated Model for Comprehensive Digital Education Platforms

Temporal Pathways to Learning: How Learning Emerges in an Open-ended Collaborative Activity

A Theory of Value for Service Ecosystems

Learning in Volatile Environments With the Bayes Factor Surprise

A Comparison of optimal measurement-system design with engineering judgement for bridge load testing

ADA for IBL: Lessons Learned in Aligning Learning Design and Analytics for Inquiry-Based Learning Orchestration

Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain

Measurement-system design for structural identification

GO-GA: Class Fxperiences with Offline Inquiry Learning Spaces in Go-Lab

Motion planning for CAVs in mixed traffic, a study on roundabouts

Combining the Knowledge Appropriation Model and epistemic networks to understand co-creation and adoption of learning designs using log data