Learning Modals
How to define Learning?
Learning is the process of acquiring new knowledge, skills, behaviors, or attitudes through experience, study, or training. Learning can take place in a variety of contexts, including in formal educational settings, such as schools or universities, or informally, through life experiences or self-directed study.
There are many different approaches to learning, and people learn in different ways. Some people may learn more effectively through hands-on experience, while others may learn better through reading or listening to lectures.
Learning can be a continuous process that occurs throughout a person's life, as they encounter new situations and experiences that challenge them to learn and adapt. It can also be a deliberate process, as people may seek out learning opportunities in order to acquire new knowledge or skills.
Overall, learning is a fundamental aspect of human development and is essential for personal and professional growth and success.
The Use of Open Badges with Webizen
[[OpenBadges]] can be used to provide recognition of prior learning for knowledge workers via systems that support Webizen AI agents in a number of ways:
- Recognition of skills and expertise: AI agents can be used to assess the skills and expertise of knowledge workers and issue Open Badges to recognize their learning and achievements. This can help to provide evidence of skills and abilities that may be difficult to demonstrate through traditional methods.
- Verification of learning: Open Badges can be used to verify the learning and achievements of knowledge workers, helping to ensure that their skills and expertise are accurately represented.
- Portability of learning: Open Badges can be easily shared and displayed on a variety of online platforms, making it easier for knowledge workers to share their learning and achievements with others. This can be particularly useful for knowledge workers who may work in a variety of different settings or who may be seeking to demonstrate their skills to potential employers or clients.
Overall, Open Badges can be a useful tool for providing recognition of prior learning for knowledge workers via systems that support AI agents, helping to provide evidence of skills and expertise and making it easier to share learning and achievements with others.
Objective
The objective is to improve the way people are able to be supported by recgonition of prior learning applications of CognitiveAI tooling. These systems will seek to develop an evidence base to support a conclusion that a person can be asserted to have knowledge of concepts, things, fields of expertise and other factors of importance in relation to the performance of new work activities, as is in-turn made possible by the prior performance of activities.
In-part, what this constituency seeks to form a means to address, relates to innovation in particular; but also, an array of other socio-economic and governance factors.
Within the field of innovation, people work on new ideas that form new 'things', of various types. Yet traditional / historical means of qualification have been mostly focused upon the use of academic facilities; who do not offer qualifications about the knowledge of a field that is presently being invented. The consequence is that the people who were instrumental to the creation of a new field of expertise, are subsequently not considered to be qualified in the area and consequentially; both, discriminated against due to not having done a course relating to the field (only available later); and in-turn somewhat forced to pay 'rents' to obtain qualifications for an area that they may have been involved in creating / inventing!.
The commonly accepted solution for this problem, is to simply pay the 'rents' or suffer the consequences.
The other area illustrated, about improving ESG support (in-effect); is that projects that are run by groups may in-turn nominate a person to take-on a role that they are not competently equipped to perform; and as a consequence of these sorts of practices, harms may in-turn be incurred by others - that may well be of a kind, that has no appropriate available lawful remedy.
In a similar yet different area; a person may be charged with the responsibility of providing an analysis of a situation and acting or failing to act in a circumstance that has material consequences upon others; but they've not got the skills to comprehend some sort of important facts, due to a lack of skills or knowledge in that area. Consequentially the person may act negligently and/or engage in - what is in-effect - an act of violence, and then seek protection at a later time, irraspective of the consequences put upon others. At which stage, the situation may in-turn become adverserial and the victim is unlikely to be resourced with lawful remedy, compensation and/or even simply an otherwise appropriate apology and whatever further actions may need to take place in-order to resolve the consequences of that persons mistakes; as was in-turn put, upon others. These sorts of situations undermine 'good faith' relations and in-turn often invoke further wrong-doings as the mistakes of one person, may in-turn lead to an array of other persons seeking to cover up and/or protect the wrong-doer, irraspective of whether it was an honest mistake or a willful act of violence causing injury.
So the learning modals, seek to support sense-making to more rapidly produce contextually useful insights and related links that support a means to process the circumstances of a persons learning and seek to mitigate risks of events that may lead to disputes and/or SocialAttackVectors that may negatively impact a legal entities ESG and/or insurability status.
Additionally, the objective is to produce systems that can provide categorised qualification [[SemWebOntologies]] and TemporalSemantics that is in-turn intended to support CognitiveAI processes (noting the importance of: HumanCentricDigitalIdentity ) and thereby providing support for [[VerifiableClaims&Credentials]] in relation to this field of WebScience and its means to address various [[SocialFactors]].