Difference between revisions of "Extreme Learning Process"

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[[Category:Education]]
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Revision as of 17:06, 28 January 2022

Mathematical Semantic Model (Extreme Learning Process)


Abstract Specification
Context Given the power of Hyperlinked data infrastructure, learning processes must be continuously extended to utilize opportunities presented to users in their respective spatial/temporal contexts.
Goal Create a self-aware process to enable learning.
Success Criteria (Liveness)
  1. A highly available data system that can capture and measure the learning process data of relevant users.
  2. A set of learning assessment tools to reflect the conditions and quality of learning.
  3. A collection of intervention methods captured overtime, to document how to improve learning practices.
Concrete Implementation(s)
Outputs Process Inputs
A tensor-based representation of process.
Boundary Conditions of Extreme Learning Process/(Safety Conditions)
See Extreme Learning Process 1.43

Some Content on this website

 Category
BenkooXLP
Person
PKC
SoG
PKM
Challenge Design and Mission ExecutionXLP
ConvergenceXLP
DevOps
DemonstrationXLP
DevOps
Early SuccessXLP
DevOps
Extreme Learning Process 1.43Pages with broken file links
XLP
Education
Pedagogy
Crowd Learning
Gamification
Constitution
Fail Early Fail SafeXLP
DevOps
PKC on GithubXLP
DevOps
The Meta Process of XLPMeta Process
Video/An Affordable Supercomputing Testbed based on Raspberry PiXLP
Supercomputing
Super Computer
Raspberry Pi
Video/Raspberry Pi 3 Super Computing Cluster Part 1 - Hardware List and AssemblyXLP
Supercomputing
Super Computer
Raspberry Pi
Video/Toyhouse TV Extreme Learning Process Exploring an Island v3 2XLP

References

Related Pages