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S. Downes: http://www.blip.tv/file/840097 2 approaches to learning - tradiotional (AI): old artifitial technology. Expert system organises. Old managnement systems. Focus on: - Goal orientated. - Competencies. - Efficency (from A to B in the most efficient). Requieres: - an expert - knowledge representation (VS. Siemens: the knowledge that we have CAN'T be represented) for expl. language -- Problem: it creates a simplification of the knowledge. - learning activities are set up by an expert. -network approach: (???IDF). Conectivism (born 40 years ago Pappert &?). Computational system is NOT set up as a representational system BUT is set up as a NETWORK (like a brain). The connectivist system: - is unnorganized - is unstructured (previously) - looks messy and unorganised - can NOT be predicted HOw Knowledge is represented in the system? DISTRIBUTED. Our concept of X is not a symbolic representation but a set up of active connections also in a neuronal level (?) Model of learning NOt based in deduction and inference BUT on ASSOCIATION based on: - concurrency. - proximity. - back propagation (economics: supply and demand market is based on that) - ???Amealing the way form networks/community in society work in THE SAME WAY that they do in a neuronal level and a personal level. Communities ARE networks that work through distributed connections. How should be the network? - DIVERSITY (wide representation of different points of views) Knowledge in a network is: EMERGENT - AUTONOMY : each individual is self-directed. Each individual works as his own guide. - CONNECTEDNESS (or interactivities). Knowledge produced by mechanism of interaction is produced by the nature/properties of the network. The way/organization of connections are formed is essential. - OPENESS (there's no inside/outside the "system"). Connection FLOWS freely. RECOGNITION of patterns (clustter). LEARNERS: Learners have different things they want to learn and the system

S. Downes: http://www.blip.tv/file/840097 NOtes (need to be double checked) 2 approaches to learning 1. traditional (AI): old artifitial technology. Expert system organises. Old managnement systems. Focus on: - Goal orientated. - Competencies. - Efficency (from A to B in the most efficient). Requieres: - an expert - knowledge representation (VS. Siemens: the knowledge that we have CAN'T be represented) for expl. language -- Problem: it creates a simplification of the knowledge. - learning activities are set up by an expert. 2.-network approach: (???IDF). Conectivism (born 40 years ago Pappert &?). Computational system is NOT set up as a representational system BUT is set up as a NETWORK (like a brain). The connectivist system: - is unnorganized - is unstructured (previously) - looks messy and unorganised - can NOT be predicted HOw Knowledge is represented in the system? DISTRIBUTED. Our concept of X is not a symbolic representation but a set up of active connections also in a neuronal level (?) Model of learning NOt based in deduction and inference BUT on ASSOCIATION based on: - concurrency. - proximity. - back propagation (economics: supply and demand market is based on that) - ???Amealing the way form networks/community in society work in THE SAME WAY that they do in a neuronal level and a personal level. Communities ARE networks that work through distributed connections. How should be the network? - DIVERSITY (wide representation of different points of views) Knowledge in a network is: EMERGENT - AUTONOMY : each individual is self-directed. Each individual works as his own guide. - CONNECTEDNESS (or interactivities). Knowledge produced by mechanism of interaction is produced by the nature/properties of the network. The way/organization of connections are formed is essential. - OPENESS (there's no inside/outside the "system"). Connection FLOWS freely. RECOGNITION of patterns (clustter). LEARNERS: Learners have different thin