54,413 research outputs found

    Data Brushes: Interactive Style Transfer for Data Art

    Get PDF

    What is an affordance and can it help us understand the use of ICT in education?

    Get PDF
    This paper revisits the concept of affordance and explores its contribution to an understanding of the use of ICT for teaching and learning. It looks at Gibson‟s original idea of affordance and at some of the difficulties long associated with the use of the word. It goes on to describe the translation of the concept of affordance into the field of design through the work, in particular, of Norman. The concept has since been translated into research concerning ICT and further opportunities and difficulties emerge. The paper locates key points of divergence within the usage of „affordance‟, as involving direct perception, invariant properties and complementarity. It concludes by arguing that affordance offers a distinctive perspective on the use of ICT in education because of its focus on possibilities for action

    The Design Postgraduate Journeyman: Mapping the relationship between design thinking and doing with skills acquisition for skilful practice

    Get PDF
    The relationship between knowing, doing and skillful practice resonate in industry and design education. The connection between creativity, design and successful innovation practices in industry has been debated much recently, heightened by realization in academe and governments that 'we need a different way of thinking and doing if we are to live well and prosper in the future' This paper addresses the question; how to understand more about the relationship of design thinking and doing with learning. It describes research to correlate design knowledge and skill with the pedagogy of skilful practice, thereby supporting pedagogical theory for the design practitioner learner. The research correlates Sennett's review of craftsmanship as skillful thinking and doing, with Dreyfus and dreyfus's model of mental activitiesin the transition of novice to masterful states of skilful practice. It concludes by illustrating the critical transition points to inform educational practice

    Key factor for hastening the strategic issue diagnosis process: a within organisational model

    Get PDF
    Previous research on Strategic Issue Diagnosis (SID) had focused on the complexity and novelty associated with the decision-making process in a turbulent environment. What had not been previously addressed in the extant literature is the requirement for speed inherent within the SID process, especially that is related to the gathering of information and facts through an organisation’s environmental scanning procedures. Since proactive management techniques, nimble processes, and systems that allow an organisation to be responsive and build rapid decision-making capabilities are important determinants of success in a turbulent environment, the element of speed associated with SID is an important factor. Our paper identifi es a series of propositions focusing att ention on elements of the environmental scanning processes and management hierarchies that are intended to counteract the recursiveness and redundancy inherent in SID systems and ultimately hasten the strategic decision-making process

    The Intuitive Appeal of Explainable Machines

    Get PDF
    Algorithmic decision-making has become synonymous with inexplicable decision-making, but what makes algorithms so difficult to explain? This Article examines what sets machine learning apart from other ways of developing rules for decision-making and the problem these properties pose for explanation. We show that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties. Calls for explanation have treated these problems as one and the same, but disentangling the two reveals that they demand very different responses. Dealing with inscrutability requires providing a sensible description of the rules; addressing nonintuitiveness requires providing a satisfying explanation for why the rules are what they are. Existing laws like the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA), and the General Data Protection Regulation (GDPR), as well as techniques within machine learning, are focused almost entirely on the problem of inscrutability. While such techniques could allow a machine learning system to comply with existing law, doing so may not help if the goal is to assess whether the basis for decision-making is normatively defensible. In most cases, intuition serves as the unacknowledged bridge between a descriptive account and a normative evaluation. But because machine learning is often valued for its ability to uncover statistical relationships that defy intuition, relying on intuition is not a satisfying approach. This Article thus argues for other mechanisms for normative evaluation. To know why the rules are what they are, one must seek explanations of the process behind a model’s development, not just explanations of the model itself
    • 

    corecore