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Knowledge Dissemination and Sustainable Use of Knowledge Networks
Cognitive and social processes of creating, developing, maintaining, and
dismantling knowledge networks
Intellectual property, privacy, confidentiality and credibility of information
and of participants in knowledge networks
Adapting knowledge networks to human needs, preferences, and abilities,
including cognitive, cultural, economic, and educational differences in the
access, use, and benefit from knowledge networks
Social Integration and Impacts of Knowledge Networking
New methodologies, metrics, and investigations of the scientific, technical,
economic, and human performance capabilities and the social, organizational, and
economic impacts of knowledge networks
Ethical, social, political, legal, and economic processes that influence the
creation, use, ownership, and governance of knowledge networks
Creation, distribution, life course, and other characteristics of "knowledge
capital"
Introduction
Efforts to understand the nature of learning and intelligence, and the
realization of these capacities in the human mind, are among the most
fundamental activities of science. The goal of LIS is to stimulate research that
will advance and integrate concepts of learning and intelligence emerging from
theoretical and experimental work in a variety of disciplines, including
education, cognitive science, computer science, neuroscience, engineering,
social science, and physical science. Accordingly, LIS encompasses studies of
learning and intelligence in a wide range of systems, including (but not limited
to) the nervous systems of humans or other animals; networks of computers
performing complex computations; robotic devices that interact with their
environments; social systems of human or non-human species; and, formal and
informal learning situations. LIS also includes research that promotes the
development and use of learning technologies across a broad range of fields.
Development of new scientific knowledge on learning and intelligent systems, and
its creative application to education and learning technologies, are integral
parts of this solicitation.
There are two parallel and compelling reasons for focusing on the general area
of learning and intelligent systems:
First, there has been a convergence of techniques and ideas addressing questions
in cognitive science and behavior of intelligent systems. For example, there has
been a growing use of neural networks, pattern recognition, visualization,
simulation, nonlinear dynamical systems analysis, and probabilistic and
statistical learning theory in these fields. As another example, researchers in
many disciplines -- including biochemistry, biophysics, neuroscience, and
cognitive science -- are studying how the nervous system changes as a result of
experience, at levels ranging from individual synapses, to neural circuits, to
brain systems subserving complex perceptual and cognitive functions. Although
concepts and methods differ across levels of analysis, a growing integration
across levels is creating fruitful theoretical frameworks and rich bodies of
data for advancing our understanding of learning and intelligent systems.
Second, as our knowledge and understanding of learning, intelligent systems, and
information technologies grows, so does the need to integrate and apply this
understanding within a broad social context. Research on associated technologies
and systems can and has enabled better understanding of learning and cognition
and has led to better classroom practice. Integrating research with prototyping
in these critical areas promises rapid advances in both theory and application.
For information regarding proposals funded by LIS in FY 1997 see http://www.ehr.nsf.gov/lis/award97.htm
Research Emphases for FY 1998
The research emphases for LIS in FY 1998 are essentially the same as in FY 1997.
Specifically, LIS seeks projects that propose:
To identify, investigate, and model the ways natural and artificial systems
operate in order to arrive at unifying principles that explain:
How learning and intelligent behavior occur in humans, in other natural systems,
and in artificial systems
The types of learning tasks and decision making that are best suited for each
The kinds of information and decisions each characteristically produces or
creates
The impact of interactions among alternative interactive learning environments,
social contexts and experiences
To enhance the ability of students and researchers to learn and to create by
developing a comprehensive set of learning and research tools, methods and
technologies that use biological, behavioral, cognitive, linguistic, social, and
educational concepts with interactive, collaborative, and multisensory
technologies, and are accessible to people with varied abilities, knowledge, and
expectations
To further basic research designed to develop fundamental knowledge concerning
the nature of learning and intelligence in natural or artificial systems, and to
apply such knowledge in a variety of situations such as education, learning
technologies, design of robotic devices and smart instrumentation, and networks
of computer systems.
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