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