2,986 research outputs found

    Affordances and the new political ecology

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    Affordances for learning in a non-linear narrative medium

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    A multimedia CD makes an impressive resource for the scholar-researcher, but students unfamiliar with the subject-matter may not always work so effectively with such a resource. Without any narrative structure, how does the novice cope? The paper describes how we are investigating the design features that 'afford' activities that generate learning: What are the design features that encourage students to practise the role of the scholar? What encourages them to explore, but also to reflect on their analysis of the data they find? What kind of learning takes place when students are allowed to explore at will? The paper goes on to compare the learning experiences of students using commercial CDs with those using material with contrasting designs, in an attempt to identify the design features that afford constructive learning activities. It concludes with an interpretation of the findings, comparing them with work in related educational media, and situating the findings in the context of a conversational framework for learning

    Higher education curriculum ecosystem design

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    This study focuses on the development of a Design Framework for Higher Education Curriculum Ecosystem design. The study views the world as a digital ecosystem where the physical and the virtual are fully intertwined and function through integrated social and technical architecture working together in a seamless mesh that is persistent and pervasive. This digital ecosystem is an open, flexible, demand driven, self-organising, collaborative environment. It has enhanced individuals’ abilities to connect with other people, share ideas, work collaboratively and form communities. This has inevitably impacted on educational practice in Higher Education. The thesis draws together educational theories, curriculum designs, and concepts drawn from ecological psychology, cognitive apprenticeship, distributed cognition and activity theory, and extends them through the application of a Complexity Science lens. A Complexity Science perspective views the world as comprised of Complex Adaptive Systems. This study explores how authentic learning processes can be scaffolded within a Complex Adaptive System. The iterative development and refinement, through three iterations over six years, of a curriculum ecosystem for a Built Environment Degree Program is used as a case study for the development of a Higher Education curriculum ecosystem exemplar. A Design Framework for a Curriculum Ecosystem for Higher Education which has emerged through this process is presented

    How could a rational analysis model explain?

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    Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances

    Multi-Object Graph Affordance Network: Enabling Goal-Oriented Planning through Compound Object Affordances

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    Learning object affordances is an effective tool in the field of robot learning. While the data-driven models delve into the exploration of affordances of single or paired objects, there is a notable gap in the investigation of affordances of compound objects that are composed of an arbitrary number of objects with complex shapes. In this study, we propose Multi-Object Graph Affordance Network (MOGAN) that models compound object affordances and predicts the effect of placing new objects on top of the existing compound. Given different tasks, such as building towers of specific heights or properties, we used a search based planning to find the sequence of stack actions with the objects of suitable affordances. We showed that our system was able to correctly model the affordances of very complex compound objects that include stacked spheres and cups, poles, and rings that enclose the poles. We demonstrated the applicability of our system in both simulated and real-world environments, comparing our systems with a baseline model to highlight its advantages

    Discovering Affordances Through Perception and Manipulation

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    International audienceConsidering perception as an observation process only is the very reason for which robotic perception methods are to date unable to provide a general capacity of scene understanding. Related work in neuroscience has shown that there is a strong relationship between perception and action. We believe that considering perception in relation to action requires to interpret the scene in terms of the agent's own potential capabilities. In this paper, we propose a Bayesian approach for learning sensorimotor representations through the interaction between action and observation capabilities. We represent the notion of affordance as a probabilistic relation between three elements: objects, actions and effects. Experiments for affordances discovery were performed on a real robotic platform in an unsupervised way assuming a limited set of innate capabilities. Results show dependency relations that connect the three elements in a common frame: affordances. The increasing number of interactions and observations results in a Bayesian network that captures the relationships between them. The learned representation can be used for prediction tasks
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