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There is much confusion globally over distinctions between and definitions of distributed learning, distance education, open learning, e-learning, blended learning and other related terms. Many terms are used more commonly in particular geographies. Distinctions can arise when the chosen model focuses on either or both time and geographic ...
The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael Kearns , Yishay Mansour , Dana Ron , Ronitt Rubinfeld , Robert Schapire and Linda Sellie in 1994 [ 1 ] and it was inspired from the PAC-framework introduced by Leslie Valiant .
e-Learning (e.g., traditional web-based courseware) Mobile learning (m-Learning) and associated mobile performance support; Web-based virtual worlds and simulations; Learning analytics and performance modeling; Associated learning theory (e.g., pedagogy, andragogy, instructional design) Distributed learning interoperability specifications
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets.
It adopts a relational stance in which learning takes place both in relation to others and in relation to learning resources. [1] In design and practice, networked learning is intended to facilitate evolving sets of connections between learners and their interpersonal communities, knowledge contexts, and digital technologies. [2]
Distributed networking, used in distributed computing, is the network system over which computer programming, software, and its data are spread out across more than one computer, but communicate complex messages through their nodes (computers), and are dependent upon each other. The goal of a distributed network is to share resources, typically ...
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The LIDA (Learning Intelligent Decision Agent) cognitive architecture, previously Learning Intelligent Distribution Agent for its origins in IDA, attempts to model a broad spectrum of cognition in biological systems, from low-level perception/action to high-level reasoning.