Difference between revisions of "Meta University"
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Meta University can be considered as a kind of [[Decentralized Autonomous Organization]]([[DAO]]). It aims at Offering egalitarian access to knowledge is the foundation of equality, where all learning activities and organizations should be universally allowed to leverage public data sources and data interpretation tools. The opportunity to manipulate public data with effective tools should only depend on the willingness to learn, and should be independent of social status and financial standing. Meta University([[MU]]), as an idealized universities<ref>Please refer to [[Data Governance]] page</ref><ref>{{:Book/The Idea of a University}}</ref><ref>{{:Paper/The Life and Works of Luca Pacioli}}</ref> helps participants to acquire [[open source]] [[data-centric knowledge|knowledge]] through [[Data Science]], which is universally applicable to all knowledge domains and all learning organizations. [[MU]] as a way to [[knowledge organization|organized knowledge]] is freely available to anyone and welcomes contributions from everyone! | |||
To accommodate the uniqueness in everyone's learning trajectory, [[MU]] | To accommodate the uniqueness in everyone's learning trajectory, [[MU]] utilizes the [[Personal Knowledge Container]]([[PKC]]) technology platform to deploy up-to-date software and hardware solutions to accommodate personal variances. To organizing collective learning experience, [[MU]] defines data structures to accumulate learning experience and organize curriculum content structures. To enable institutional exchanges, [[MU]] allows different organizations to communicate and exchange data content using features built-in [[PKC]]s to streamline and to authenticate exchanged data content. This data-centric strategy of intellectual framing, will help operationalize the verification procedures of [[truth|logical truth]] based on observable data, which can be recursively pursued and validated using the transcending notion of [[Universality]]! | ||
= What is the purpose of [[MU]]? = | = What is the purpose of [[MU]]? = | ||
The purpose of [[MU]] is to | The purpose of [[MU]] is to scale-up learning activities by guiding people to apply [[data-centric knowledge]]s to wide ranges of activities. [[MU]] utilizes [[PKC]] as a general-purpose data-management tool, lowering the entry barrier for arbitrary project-related activities to have a baseline data-driven learning infrastructure. [[MU]] helps individuals and organizations to learn with data by providing the methodology to shape [[individualized curricula]], and provide the data security infrastructures to enable [[privacy-respecting learning assessments]]. Professionally, [[MU]] will orchestrate participants to engage in practice runs of [[Inter-Organizational Workflow]]s, and deploy [[Industry-strength automated services]] that helps them to become more effective contributors in their professional careers. | ||
==The Meta Purpose== | |||
[[MU]] considers the foundational skill in [[Data Science]] is the mastery of a [[Common Namespace Management Model]]. This model provides a base vocabulary that helps [[MU]] participants to leverage prior work on [[existing resources on the Internet]] and maximize the chance to enable [[namespace management automation]]. | |||
= How does [[MU]] work? = | = How does [[MU]] work? = | ||
The societal trend of [[digital transformation]], sometimes called the [[4th Industrial Revolution]], are begging for a major update to existing educational practices, because a broad range of disciplinary knowledge and skills must be made available to a much larger bigger crowd, at a much faster rate, with much lower initial costs. | The societal trend of [[digital transformation]], sometimes called the [[4th Industrial Revolution]], are begging for a major update to existing educational practices, because a broad range of disciplinary knowledge and skills must be made available to a much larger bigger crowd, at a much faster rate, with much lower initial costs. | ||
==Core Curriculum of MU== | ==Core Curriculum of MU== | ||
A university must be able to provide a coherent [[core curriculum]]. In the post-pandemic societal context, teaching and learning must be adaptable to a mixture of online and offline contexts. [[MU]] offers the following areas of learning supports: | |||
=== | # Balanced Knowledge Content: Avoid tunnel vision induced by this overtly competitive world, participants will be exposed to well-rounded knowledge to see the world in a context beyond a singular viewpoint. | ||
# Pacing and Reinforce healthy patterns: Promote sportsmanship, performance record keeping, and pacing training efforts are required supports for reaching excellence. | |||
# Community forming and socialized learning: Creating platforms for content sharing, exchanges of learning tips, and garner friendship/commradarieship. | |||
===Evolving Content improved by [[MU]] administered Data=== | |||
For common skills like arithmetic and simple logical inference techniques, the amount and intensity of repetition, as well as the sequence of content presentation, may benefit from having access to experiential teaching data, including test batteries and tutorial materials. Therefore, [[MU]] uses [[PKC]] to capture the learning experience, and allowing individuals and teams to publish their strategies and anecdotal records in how learning hurdles could be tackled. Moreover, effective learning practices are often context sensitive, and therefore unique to each individuals. Given the law of large numbers, the same kind of learning obstacles might have already been explored in the past. Therefore [[MU]] serves as an digitally-enabled online data agency to help identify data sources from other agencies, and share learning data to tackle this problem. As an example, [[MU]] will use [[Gasing Method]], a large collection of math and science curricular content, combined with hands-on practice problems to specifically address the operationalized strategies in helping individuals to practice the scientific reasoning skill. The same strategy would also apply to knowledge content that evolves rapidly. To leverage learning experience in a rapidly evolving content field, automated data collection tools for teaching and learning, such as [[PKC]] will be particularly valuable for accelerating the speed of content and teaching strategy evolution. | |||
==Learning Assessment and Certification== | |||
An essential function of a school/university, is to serve as a neutral agency for assessing the qualification of intellectual or professional skills. A knowledge dissemination institution should have a qualification procedure that is generally reputable, yet adoptable to various disciplines that are being taught by that institution. Therefore, using reliable, fair, and yet affordable means to conduct tests for participants' professional skills and knowledge levels would be an important feature of [[MU]]. Underlying all possible variations of certification processes, there is a [[universal test pattern]] that could be applied to all certification processes of skills and knowledge levels. It allows [[MU]] to confer certificates to its participants in digital forms that can be verify and validated anywhere, at anytime. On top of all, testing and reflecting on the test results would be a data-driven mechanism to close the loop of any [[learning processes]]. | |||
===Data Content Ownership=== | ===Data Content Ownership=== | ||
Having this | When test results of individuals can be stored as data records, these records become valuable data assets and immediately have risks of leaking them. Having this data security issue in mind, [[MU]] is a [[decentralized governance protocol]] designed to organize intellectual consensus based on [[data-centric knowledge]]. Operates in a decentralized fashion, everyone may administer one's own [[Personal Knowledge Container]]([[PKC]]) as a consensus-shaping semi-automated agent to offer data services with the following properties: [[Intuitive data presentation|data representation]], [[effective data processing|data processing]], and [[High Availability|high-availability service]]. These features implies that all users will have the access to program their own instances of [[PKC]] to reveal the type of data content to the external world in their own chosen formats and content areas. | ||
===Representing Knowledge in terms of Recursively Structured Dictionary=== | |||
The external presentation of [[MU]] is a human and machine processable language, represented by a set of [[hyperlink|relationally defined vocabulary]] that are universally accessible through an online library, that offers publicly updatable content-sharing services. The process of engaging everyone, including students and teachers around the world to collaboratively refine a publishable space of [[data-centric knowledge]] should help improve knowledge dissemination and content accumulation universally. This universal and simple data type: [[key-value pairs]], or just [[dictionary]], will enable a computable way to qualitatively and quantitatively analyze the data content. | |||
= Who can attend [[MU]]? = | = Who can attend [[MU]]? = | ||
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= What can it become professionally = | = What can it become professionally = | ||
All activities conducted in [[MU]] are considered to be transactional data, and can be selectively recorded. It informs participants to recognize what parts of inputs/outputs are shown in public, and what parts of the data can be protected in privately-owned data storage. In either cases, [[MU]] can be considered as a playground or sandbox for people to practice organizational leadership. The persons or teams that have more active inputs/outputs are going to be recorded and therefore acknowledged by the historical data. This also meant that for all the work that has been done by individuals in various instances of [[PKC]]s can be published and shared in the [[MU]] community, therefore, the implications of data transaction can be utilized to accumulate operational experience and extract knowledge from these data records. This is how [[MU]] engages with its participants in a professional manner. Please note that data transactions of test network and production network will have almost identical physical footprints. People who are competent or experienced in test networks, will have almost | All activities conducted in [[MU]] are considered to be transactional data, and can be selectively recorded. It informs participants to recognize what parts of inputs/outputs are shown in public, and what parts of the data can be protected in privately-owned data storage. In either cases, [[MU]] can be considered as a playground or sandbox for people to practice organizational leadership. The persons or teams that have more active inputs/outputs are going to be recorded and therefore acknowledged by the historical data. This also meant that for all the work that has been done by individuals in various instances of [[PKC]]s can be published and shared in the [[MU]] community, therefore, the implications of data transaction can be utilized to accumulate operational experience and extract knowledge from these data records. This is how [[MU]] engages with its participants in a professional manner. Please note that data transactions of test network and production network will have almost identical physical footprints. People who are competent or experienced in test networks, will have almost exactly the same experience in the '''real''' world. In other words, [[MU]] provides a mechanism to help individuals to seamlessly practice their professional skills in an organizationally contained environment. Only users who behaves adequately and professionally will possess the usage history that qualifies them to their desirable professional status. | ||
==Talents: The Most | ===Professional Certification=== | ||
Based on the [[data-centric knowledge]] accumulation practice, [[MU]] participants are equipped with an operational infrastructure to help capture their daily contribution and routine data transaction activities committed to relevant instances of [[PKC]]s. These data set will become a powerful set of evidence to assess their learning results, and provide guidance to help them attain proper professional certification. [[MU]] can also be operated by institutions, such as medical schools or sport training facilities to certify the professional skills and behavioral habits of persons who wish to be certified by a third party agency. [[MU]] provides the data witnessing mechanism and institutional role to act as the certifier of their devoted profession. | |||
===Talents: The Most Valuable Resource in a Community=== | |||
Joining a learning community, such as a university, is about having access to its talent pool. Professionally speaking, associating oneself with a community is to become an qualifying agent in a community of talents. In a community that commits to transparent witnessing of learning progress, is like participating in a popularly witnessed sporting event. The persons or teams that demonstrates higher performance will be noticed and that witnessed data is the indicator of one's accomplishment. Therefore, strategically joining and exposing certain aspects of learning progress data is a generic mechanism for identifying and filtering high-performance [[human resources]]. Since [[MU]] provides a universal data reasoning infrastructure to encourage knowledge reuse across domains, adopting [[MU]]'s domain-neutral approach, allows for individuals and organizations to organize their knowledge portfolio beyond [[domain-specific data set]]s as defined by the status-quo of legacy professional practices. [[MU]] as a generic professional community data management infrastructure will aim to serve as the most general form of professional talent measuring stick. | Joining a learning community, such as a university, is about having access to its talent pool. Professionally speaking, associating oneself with a community is to become an qualifying agent in a community of talents. In a community that commits to transparent witnessing of learning progress, is like participating in a popularly witnessed sporting event. The persons or teams that demonstrates higher performance will be noticed and that witnessed data is the indicator of one's accomplishment. Therefore, strategically joining and exposing certain aspects of learning progress data is a generic mechanism for identifying and filtering high-performance [[human resources]]. Since [[MU]] provides a universal data reasoning infrastructure to encourage knowledge reuse across domains, adopting [[MU]]'s domain-neutral approach, allows for individuals and organizations to organize their knowledge portfolio beyond [[domain-specific data set]]s as defined by the status-quo of legacy professional practices. [[MU]] as a generic professional community data management infrastructure will aim to serve as the most general form of professional talent measuring stick. | ||
= How can I learn more about MU's Learning practices = | = How can I learn more about MU's Learning practices = | ||
| Line 163: | Line 174: | ||
[[Category:Gamification]] | [[Category:Gamification]] | ||
[[Category:Evangelism]] | [[Category:Evangelism]] | ||
[[Category:Monad]] | |||
[[Category:Education Practice]] | |||
</noinclude> | </noinclude> | ||
Latest revision as of 03:03, 23 January 2023
Meta University can be considered as a kind of Decentralized Autonomous Organization(DAO). It aims at Offering egalitarian access to knowledge is the foundation of equality, where all learning activities and organizations should be universally allowed to leverage public data sources and data interpretation tools. The opportunity to manipulate public data with effective tools should only depend on the willingness to learn, and should be independent of social status and financial standing. Meta University(MU), as an idealized universities[1][2][3] helps participants to acquire open source knowledge through Data Science, which is universally applicable to all knowledge domains and all learning organizations. MU as a way to organized knowledge is freely available to anyone and welcomes contributions from everyone!
To accommodate the uniqueness in everyone's learning trajectory, MU utilizes the Personal Knowledge Container(PKC) technology platform to deploy up-to-date software and hardware solutions to accommodate personal variances. To organizing collective learning experience, MU defines data structures to accumulate learning experience and organize curriculum content structures. To enable institutional exchanges, MU allows different organizations to communicate and exchange data content using features built-in PKCs to streamline and to authenticate exchanged data content. This data-centric strategy of intellectual framing, will help operationalize the verification procedures of logical truth based on observable data, which can be recursively pursued and validated using the transcending notion of Universality!
What is the purpose of MU?
The purpose of MU is to scale-up learning activities by guiding people to apply data-centric knowledges to wide ranges of activities. MU utilizes PKC as a general-purpose data-management tool, lowering the entry barrier for arbitrary project-related activities to have a baseline data-driven learning infrastructure. MU helps individuals and organizations to learn with data by providing the methodology to shape individualized curricula, and provide the data security infrastructures to enable privacy-respecting learning assessments. Professionally, MU will orchestrate participants to engage in practice runs of Inter-Organizational Workflows, and deploy Industry-strength automated services that helps them to become more effective contributors in their professional careers.
The Meta Purpose
MU considers the foundational skill in Data Science is the mastery of a Common Namespace Management Model. This model provides a base vocabulary that helps MU participants to leverage prior work on existing resources on the Internet and maximize the chance to enable namespace management automation.
How does MU work?
The societal trend of digital transformation, sometimes called the 4th Industrial Revolution, are begging for a major update to existing educational practices, because a broad range of disciplinary knowledge and skills must be made available to a much larger bigger crowd, at a much faster rate, with much lower initial costs.
Core Curriculum of MU
A university must be able to provide a coherent core curriculum. In the post-pandemic societal context, teaching and learning must be adaptable to a mixture of online and offline contexts. MU offers the following areas of learning supports:
- Balanced Knowledge Content: Avoid tunnel vision induced by this overtly competitive world, participants will be exposed to well-rounded knowledge to see the world in a context beyond a singular viewpoint.
- Pacing and Reinforce healthy patterns: Promote sportsmanship, performance record keeping, and pacing training efforts are required supports for reaching excellence.
- Community forming and socialized learning: Creating platforms for content sharing, exchanges of learning tips, and garner friendship/commradarieship.
Evolving Content improved by MU administered Data
For common skills like arithmetic and simple logical inference techniques, the amount and intensity of repetition, as well as the sequence of content presentation, may benefit from having access to experiential teaching data, including test batteries and tutorial materials. Therefore, MU uses PKC to capture the learning experience, and allowing individuals and teams to publish their strategies and anecdotal records in how learning hurdles could be tackled. Moreover, effective learning practices are often context sensitive, and therefore unique to each individuals. Given the law of large numbers, the same kind of learning obstacles might have already been explored in the past. Therefore MU serves as an digitally-enabled online data agency to help identify data sources from other agencies, and share learning data to tackle this problem. As an example, MU will use Gasing Method, a large collection of math and science curricular content, combined with hands-on practice problems to specifically address the operationalized strategies in helping individuals to practice the scientific reasoning skill. The same strategy would also apply to knowledge content that evolves rapidly. To leverage learning experience in a rapidly evolving content field, automated data collection tools for teaching and learning, such as PKC will be particularly valuable for accelerating the speed of content and teaching strategy evolution.
Learning Assessment and Certification
An essential function of a school/university, is to serve as a neutral agency for assessing the qualification of intellectual or professional skills. A knowledge dissemination institution should have a qualification procedure that is generally reputable, yet adoptable to various disciplines that are being taught by that institution. Therefore, using reliable, fair, and yet affordable means to conduct tests for participants' professional skills and knowledge levels would be an important feature of MU. Underlying all possible variations of certification processes, there is a universal test pattern that could be applied to all certification processes of skills and knowledge levels. It allows MU to confer certificates to its participants in digital forms that can be verify and validated anywhere, at anytime. On top of all, testing and reflecting on the test results would be a data-driven mechanism to close the loop of any learning processes.
Data Content Ownership
When test results of individuals can be stored as data records, these records become valuable data assets and immediately have risks of leaking them. Having this data security issue in mind, MU is a decentralized governance protocol designed to organize intellectual consensus based on data-centric knowledge. Operates in a decentralized fashion, everyone may administer one's own Personal Knowledge Container(PKC) as a consensus-shaping semi-automated agent to offer data services with the following properties: data representation, data processing, and high-availability service. These features implies that all users will have the access to program their own instances of PKC to reveal the type of data content to the external world in their own chosen formats and content areas.
Representing Knowledge in terms of Recursively Structured Dictionary
The external presentation of MU is a human and machine processable language, represented by a set of relationally defined vocabulary that are universally accessible through an online library, that offers publicly updatable content-sharing services. The process of engaging everyone, including students and teachers around the world to collaboratively refine a publishable space of data-centric knowledge should help improve knowledge dissemination and content accumulation universally. This universal and simple data type: key-value pairs, or just dictionary, will enable a computable way to qualitatively and quantitatively analyze the data content.
Who can attend MU?
MU subscribes to Open Source Software (OSS) principles, and utilizes the code base and namespace definitions of MediaWiki/Semantic MediaWiki to operationalize metadata management, so anyone who registers an account on PKC-compliant data service can engage immediately. The learning process allows anyone to make progress on their own, while allowing cross-reference across other personified avatars or agencies using time as the global variable to measure learning progress, encouraging independent discovery, self-driven merits, and objectively-traceable experience, in contrast to traditionally-administered tests and subjective grades. More specifically, users of different technical literacy scales and capacity of learning can all find their respective entry points to start engaging with the learning activities with MU. MU's participant categorization is shown below:
- Non-Digital Native: Any person who has the potential to use popular web apps and smartphone apps for daily operations.
- Content Creator: All participants will have read/write access to at least one dedicated instances of PKC. Since all content are automatically version controlled, we will allow dedicated content organizers/librarians in your own teams to maintain data integrity.
- Self-Service Agent: All participants will be given instruction material to learn to operate their own PKC instance for personal knowledge management. However, installation and operation of this personal data instrument are not required for all participants, shared instances of PKCs are encouraged.
What can be achieved by joining MU? How does it differ from existing Universities?
By managing knowledge through personally-administrated data exchange tools, MU helps individuals and organizations to acquire data and content knowledge relating to merits, capacity of learning, self-governance/self-discipline, social exchanges, and personal scope that existing education system have not been able to formulate at the Internet-scale. In contrast, MU enables individuals and organizations to gain automated access to unrestricted collection of data content, and retain content within privately operated knowledge containers. This knowledge dissemination and storage architecture will strive to become a faithful provider to preserve free-will and respect privacy in a technically verifiable manner.
What can it become professionally
All activities conducted in MU are considered to be transactional data, and can be selectively recorded. It informs participants to recognize what parts of inputs/outputs are shown in public, and what parts of the data can be protected in privately-owned data storage. In either cases, MU can be considered as a playground or sandbox for people to practice organizational leadership. The persons or teams that have more active inputs/outputs are going to be recorded and therefore acknowledged by the historical data. This also meant that for all the work that has been done by individuals in various instances of PKCs can be published and shared in the MU community, therefore, the implications of data transaction can be utilized to accumulate operational experience and extract knowledge from these data records. This is how MU engages with its participants in a professional manner. Please note that data transactions of test network and production network will have almost identical physical footprints. People who are competent or experienced in test networks, will have almost exactly the same experience in the real world. In other words, MU provides a mechanism to help individuals to seamlessly practice their professional skills in an organizationally contained environment. Only users who behaves adequately and professionally will possess the usage history that qualifies them to their desirable professional status.
Professional Certification
Based on the data-centric knowledge accumulation practice, MU participants are equipped with an operational infrastructure to help capture their daily contribution and routine data transaction activities committed to relevant instances of PKCs. These data set will become a powerful set of evidence to assess their learning results, and provide guidance to help them attain proper professional certification. MU can also be operated by institutions, such as medical schools or sport training facilities to certify the professional skills and behavioral habits of persons who wish to be certified by a third party agency. MU provides the data witnessing mechanism and institutional role to act as the certifier of their devoted profession.
Talents: The Most Valuable Resource in a Community
Joining a learning community, such as a university, is about having access to its talent pool. Professionally speaking, associating oneself with a community is to become an qualifying agent in a community of talents. In a community that commits to transparent witnessing of learning progress, is like participating in a popularly witnessed sporting event. The persons or teams that demonstrates higher performance will be noticed and that witnessed data is the indicator of one's accomplishment. Therefore, strategically joining and exposing certain aspects of learning progress data is a generic mechanism for identifying and filtering high-performance human resources. Since MU provides a universal data reasoning infrastructure to encourage knowledge reuse across domains, adopting MU's domain-neutral approach, allows for individuals and organizations to organize their knowledge portfolio beyond domain-specific data sets as defined by the status-quo of legacy professional practices. MU as a generic professional community data management infrastructure will aim to serve as the most general form of professional talent measuring stick.
How can I learn more about MU's Learning practices
Please start learning by registering onto or operating your own instance of PKC, now.
The Framing of MU
MU's emergence comes from imminent societal needs. The following logic model tries to capture the essence of why we must create MU now.
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Implementation Strategy
To make this happen, we decide to leverage the Web Tech Stack as an operational platform and leverage the resources and tools in this open-ended platform to govern the evolutionary trends of MU. MU has three explicit functions:
- MU is a data container for Learning Activities, it captures physical activities in concrete data elements for the organization.
- The Data Content of MU is governed by the participants of these learning activities, implemented as smart contracts authorized by the said participants.
- MU invites verification and validation, through namespace management, time stamping and data linkages, MU associates physical meaning to data by encoding content knowledge based on the observable patterns of the physical universe.
The goal statement mentioned above also indicates that MU will provide a curriculum that enables a minimal footprint to the core content knowledge while covering the broadest possible content areas. In other words, MU's existence can be thought of as the core/kernel of an operating system of knowledge acquisition in this data-intensive era, and it will help organize learning activities in a structure that best adheres to this fast-changing world. Specifically, MU will offer a curriculum structure that follows the above assumptions and try to link all knowledge content using a unifying data management strategy.
Individuals and their Societies
Knowing that all content knowledge cannot exist in isolation. MU is intended to be a social learning tool that allows individuals to maintain one's private content while maximizing their ease of exchange with other interacting parties. Therefore, MU will explicitly spell out project management tools and practices for both individuals and team members and offer data governing policies for all participants. The logic of how data evolve is the foundation of computing science, and that is where all projects on MU-supported projects will leverage its project management/governance features. This requires users to be highly aware of data security and data privacy-related issues.
In layman's terms, MU will organize content knowledge in the units of languages, starting with the foundation of all languages, the meta language, or the logic of data in general. Then, MU will introduce domain-specific languages, including linkages to natural languages as ways to demonstrate the power of information compression in languages. Therefore, MU participants can continuously leverage a single repository of languages, to organize their own knowledge content in privately controlled data storage containers.
A Universal Container
The first principle of MU is to assume that certain universal principle exists. The principle that we will introduce to all MU students is the notion that all data can be represented using the same type, a type called: "Lattice" or bounded "Partially ordered set"(POSet). This universal data type can be mapped onto human's daily experience in terms of space, time, and energy. The reason that we must introduce this common notion of data structure is to offer a universal data framework that will be applicable to situations across all spatial and temporal contexts. To guarantee this universality, some logical assumptions must be made, and we assume that all spacetime complex follows the logical boundaries of POSet.
Foundational References
MU differs from other universities by starting its curriculum in a sequence that is almost opposite of what most popular curricula are designed. This is suggested by Gregory Chaitin, that many accomplished mathematicians are taught the most advanced mathematics from a young age and in a sequence that delights their interest to pursue mathematical content. When properly administered, this strategy may apply to learn and teach content knowledge of any kind. For example, we will provide a universal data structure[5], a.k.a. lattice[6][7], to approximate the boundary of our logical reasoning scopes.
Prior work
A Google Document on Meta University that lead to this document is available[8].
MetaUniversity
Programs in MU will be organized in languages, as in managing vocabulary, syntactical rules, and pragmatic uses. This classification will enable a unifying framework and data analytics tools for learning outcome assessments as well as enabling the compositional opportunities of knowledge content.
The Notion of Universality
Universality is a operationalized definition according to logicians.
A Generalized Process for Knowledge Acquisition
A university is an agency that embodies collective Intelligence. The process model for such intelligence can be modeled after ideas originated in Carl Jung and Thomas Kuhn's theories.
Containers for all Knowledge
Using Wikidata as an example.
- Leverage Existing Data and Learning Assets on the Internet
- Capture visitation data for all these assets
- Provide annotation and create new content
- Publish it using PKC
Conceptual Space and Repeatability
As I talking with 郭逸舟， he mentioned Gutenberg can make more impact was due to the size of alphabet in European languages are smaller than Chinese. So that he was able to leverage his work to create Renaisance.
Functional Roles in Society
The initial vocabulary of Roles in MU will be based on two camps of knowledge, Holacracy's roles and Ansible's roles.
Data as the Medium
Core Curriculum:Reliable Data as the Medium
Data identity is given measurable degrees of trust worthiness based on timestamps and namespace references.
According to Prof. Gautam Dasgupta, this curriculum will be organized in various methods of counting:
- Arithemtic:Counting in Numbers (See Gasing Counting)
- Geometry:Counting in Space
- Music:Counting in Time
- Astronomy and Geography:Counting in Spacetime
Skill Mastery
A univeresity is also a place where skills and knowledge of persons get refined to a point of mastery. To ensure this experience of incremental improvements and shaping of good habits, while avoiding addiction, we need a general framework to observe the process of entering mastery. The program will be initiated with past experience accumulated in the previous programs:
Domain Specialties: Disciplinary Specific Data
Smart Contracts to operationalize accountability
All students in Meta University must use Smart Contracts to organize their shared tasks.
Measuring Metrics: All learning activities will be accounted for
Learning activities will be registered using common databases. Detail actions for each participants will be tracked by Matomo.
Working with Concurrent Data Events at Scale
Teach all students the basic notion of sets, orderings, and compositions in precise languages. Moreover, allow them to present the data and information content in tools and platforms that would matter to their daily lives.
- Content-wise Linked Open Data
- Processing Capacity Scale Up and Scale Out
An Exchange Platform
There are some existing literature[9] that already covers content related to this proposition. Financial and labor market places will be set up to enable exchanges. Ideally, some form of financial rewards should be directly written into smart contracts for people who are competent to conduct their work professionally.
Organizational Sustainability
Participation model,Reaching Consensus, and Failure Handling
Actor Model, Decentralized Identity Authentication, and Game Theoretic Decision-Making.
Branching and Exiting
Since all types of organizations must evolve and change over time, mention Thomas Kuhn and his book[10].
Merging and Joining
All organizations evolves in a similar way, mention Kuhn's Cycle and his book[10].
Data is the Asset
To help students assess the values of knowledge content, value of data content is dynamically evaluated in covertable currencies. MU will provide an exchange market place for students to verify and validate the transaction of data content, while measuring these transactional activities to assess the social value of individual data assets. Both factual and counterfactual data content can be computationally synthesized to create a single source of truth.
Score Keeping: Manage incentives
Offering currencies to quantify data asset transactions can be an incentive mechanism for learning. It is operationally feasible to record all data transactions between users in MU. In the universe of data, any data exchange could cause a cascading effect throughout the community. Therefore, score keeping should be managed by sustainable governing mechanisms. Data from the past, present, and future should all be given credits and therefore, create a perpetual incentive system for all. Instrumentation such as PKC will keep these transactional data records as a form of evidence for accumulative efforts or contributions. In educational practices, they are often called degree certificate/badges or gamification. Put it simply, it is one of many scoring, or assessment mechanism in traditional teaching. The most important aspect is to establish a formalized metric to continuously assess learning accomplishments using highly-available and consistent data.
Namespaces: Managing Data Dictionary over Time
To ensure all data content are tracked within MU, a set of highly-available, automated data dictionaries will be utilized to track the above mentioned transactional activities in terms of frequencies and amount of data exchange. This transactional activity log will provide a mechanism to measure the focus of interests of the utilizing parties.
Time:the symmetry-breaker
Become Productive and Offer Services to Society
All the above mentioned knowledge should enhance the participants' capability to create better products and services in their respective society. MU intend to have all participants to be productive in offering data, information, knowledge, and actual services/products while they are learning at MU. The data-intensive environment articulated earlier will enable people to contribute to a data-linked supply chain of services and products. More importantly, it should reduce the unnecessary barriers and middle persons in the economic process. MU will enhance students' and the overall MU community's productivity by offering a number of foundational guidance:
- Governance Template in terms of Constitution
- Economic Template in terms of Exchange Marketplaces and Currencies
- Technology Template in terms of DevOps and Linked Open Data APIs
Value assessment workflow
MU also provide reference implementation and experimental programs to verify and validate approaches to turn data into valuable assets. These are considered to be workflows that includes explicit speficiations of tools and data content.
Conclusion
Toward the Science of Self-Governance
References
- ↑ Please refer to Data Governance page
- ↑ Newman, John (1986). The Idea of a University (University of Notre Dame Press edition 1982 ed.). local page: University of Notre Dame Press. ISBN 0-268-01150-8.
- ↑ Sangster, Alan (2021). "The Life and Works of Luca Pacioli (1446/7–1517), Humanist Educator". Abacus: A Journal of Accounting, Finance and Business Studies. local page: University of Sydney. 57.
- ↑ Gordon, Moore E. (Apr 19, 1965). Cramming more components onto integrated circuits (PDF). local page: Electronics Magazine.
- ↑ Scott, Dana (January 1, 1970). "Outline of a Mathematical Theory of Computation". local page: Oxford University Computing Laboratory Programming Research Group.
- ↑ Cousot, Patrick; Cousot, Radhia (1977). Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints (PDF). 4th POPL. local page: ACM Press. p. 238-252.
- ↑ Cousot, Patrick (Sep 2021). Principles of Abstract Interpretation. local page: ACM Press.
- ↑ If you have editorial access to the Meta University Google document, click here
- ↑ Crawley, Edward; Hegarty, John; Edström, Kristina; Sanchez, Juan Cristobal Garcia (2020). Universities as Engines of Economic Development. local page: Springer. ISBN 978-3-030-47549-9.
- ↑ 10.0 10.1 Kuhn, Thomas (2012). The Structure of Scientific Revolutions (50th Anniversary ed.). local page: University of Chicago Press. ISBN 978-0-226-45811-3.
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Has Logic Model:True, Was created on:01 23, 2023