1,751 research outputs found
SciTech News Volume 71, No. 1 (2017)
Columns and Reports From the Editor 3
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Computers for learning : an empirical modelling perspective
In this thesis, we explore the extent to which computers can provide support for domain
learning. Computer support for domain learning is prominent in two main areas: in education,
through model building and the use of educational software; and in the workplace, where
models such as spreadsheets and prototypes are constructed. We shall argue that computerbased
learning has only realised a fraction of its full potential due to the limited scope for
combining domain learning with conventional computer programming. In this thesis, we
identify some of the limitations in the current support that computers offer for learning, and
propose Empirical Modelling (EM) as a way of overcoming them.
We shall argue that, if computers are to be successfully used for learning, they must support
the widest possible range of learning activities. We introduce an Experiential Framework for
Learning (EFL) within which to characterise learning activities that range from the private to
the public, from the empirical to the theoretical, and from the concrete to the abstract. The
term ‘experiential’ reflects a view of knowledge as rooted in personal experience. We discuss
the merits of computer-based modelling methods with reference to a broad constructionist
perspective on learning that encompasses bricolage and situated learning. We conclude that
traditional programming practice is not well-suited to supporting bricolage and situated
learning since the principles of program development inhibit the essential cognitive model
building activity that informs domain learning. In contrast, the EM approach to model
construction directly targets the semantic relation between the computer model and its
domain referent and exploits principles that are closely related to the modeller’s emerging
understanding or construal. In this way, EM serves as a uniform modelling approach to
support and integrate learning activities across the entire spectrum of the EFL. This quality
makes EM a particularly suitable approach for computer-based model construction to support
domain learning.
In the concluding chapters of the thesis, we demonstrate the qualities of EM for educational
technology with reference to practical case studies. These include: a range of EM models that
have advantages over conventional educational software due to their particularly open-ended
and adaptable nature and that serve to illustrate a variety of ways in which learning activities
across the EFL can be supported and scaffolded
Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In
recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence
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