Talk:Meta University

Logic Model (Meta University) Template:LogicModel 02 20, 2022
Abstract Specification Context As Moore's Law shifted the paradigm of data processing by claiming that data processing capabilities will grow at an exponential pace. This exponential growth assumption has not only guided the development of technologies, but also challenged social and economic believes around the world. It also becomes a theory that shapes our perception of the half-life of useful knowledge. Since all knowledge must be representable in some form of processable data, the functions of universities in general are about creating intuitive data presentation, effective data processing, and providing of highly-available data content. Goal The goal of MU is to enable all learnable organizations to become self-aware through the following tri-fold knowledge organization framework: Establish a sound cognitive foundation grounded on data: Data Structures and Algorithms to process the data. Support decisions with effective methods: Teach computational thinking to improve upon mental models and the awareness and skills to use computing tools. Improve upon the system of knowledge: Publish knowledge content and create data manipulation methods and tools to attenuate unnecessary information asymmetry. Success Criteria Organizations and individuals that contribute to MU-based learning activities must be able to exchange data in a consistent format that is compliant with a common data exchange format defined by the MU community. MU-related data content should be labeled by a common time-stamping system that defines the partial ordering sequences of data updates across all MU data sets. The degree of MU success will be continuously measured by the amount of published knowledge content that reached consensus according to the consensus reaching process defined by MU community.
Concrete Implementation Given Inputs When Process is executed... Then, we get Outputs PKC the software package Computing resources for running PKC MU Participants Participant Signed Constitution Budget and Endowment of resources Publicly witness time progression (witness by blockchains). Common PKC DevOps Cycles. Team-based cycles of MU program execution. Organizationally-independent PKC-bound Data Assets including PKC DevOps Records Organization-specific Data interpreted given PKC Data Assets Accomplish observable societal impact according to organizational consensus
Boundary/Safety Conditions of Meta University The adoption of PKC as a common knowledge/ data repository All participants have access to at least one shared instance of PKC. Data and Network Security mechanisms employed by PKCs are not breached.

Logic Model (Capacity of Learning (as a grade) or CpL) Template:LogicModel 02 20, 2022
Abstract Specification Context Capacity of Learning (as a grade) or CpL/Context Goal Capacity of Learning (as a grade) or CpL/Goal Success Criteria Capacity of Learning (as a grade) or CpL/Criteria
Concrete Implementation Given Inputs When Process is executed... Then, we get Outputs Capacity of Learning (as a grade) or CpL/Input Capacity of Learning (as a grade) or CpL/Process Capacity of Learning (as a grade) or CpL/Output
Boundary/Safety Conditions of Capacity of Learning (as a grade) or CpL Capacity of Learning (as a grade) or CpL/Boundary