CHAMP- Concept Hierarchy Aggregator, Mapper and Projector
Learning how to self-learn is difficult. Most self-learners don’t know where to start, what to cover and what concepts relate to each other, and how. Usually, it's only truly acquired during Ph.D.
CHAMP is a NLP concept hierarchy module capable of analyzing every skill, breaking it to sub-skills, guide a user how to climb from skill to skill, assessing user's proficiency in a given skill, asses the quality of the user's work using a given skill and its topic coverage, as well as its match to intended audience.
It can check if work is of progress over existing knowledge, and refer it to experts should the writer want it, detecting talent early regardless of location, gender or class.
It can examine the learning plans made by ministries, and give the minister a cover in topics the minister has no expertise.
This is CHAMP, the future of training.
The problem I'm trying to solve is making self-learning an ordered process rather than controlled trial-and-error chaos.
The problem is innate to the process of organizing knowledge, and can be avoided using learning frames such as universities, which have experts that arrange the material and offer guidance should one fail to understand anything.
However, many cannot afford university, and even those who can leave it at some point and must fend for themselves in the free market. There, most fail to acquire new skills as they don’t know how to arrange them, and lose their job when the market changes.
There probably isn’t one human being that isn’t affected by it, but my focus is on under-privileged populations, such as those who can't afford education (moneywise or time-wise, the effect over their lives is the same), as they benefit most from it and are most likely to adopt it.
Both in Latin America and the Caribbean, there are traditionally under- privileged populations, with no fault of their own. These people usually have little resources available, and cannot propel themselves higher as a result of that. Societal prejudice, which is also present, isn’t helping their situation either.
CHAMP can change that.
I serve any human being wishing to gain or improve a skill demanding theoretical knowledge.
The solution addresses their needs by mapping for them what they need to do to get to their goal, from their current point of proficiency, throughout time and for free.
They will no longer need to read material to understand if it deals with the skill they wish to learn- CHAMP can classify it for them, letting them focus on what actually matters- learning what THEY need to know to acquire skill, without missing or repeating material.
The nature of the problem is no different going up the educational and socioeconomic ladders, but the impact is- a person with computer sciences bachelor degree in the US not knowing what to train further to get ahead at work is facing the same problem as an underprivileged person not knowing how propel to better future in Haiti, and the solution can help them both. That being stated, the dreamer in Haiti is far more likely to use it, as the incentive is higher.
My solution is a novel NLP software module, working in the following fashion:
Conceptual hierarchy mapping is the use of the links between concepts to understand their relation.
It does it by determining how many concepts are needed to be known in advance in order to define the current concept, and how many concepts rely on it, or in other words, how abstract and how fundamental a concept is.
As skills are concepts by definition, the method can be applied on them.
The method is relatively simple and effective, can automate the process of knowledge arrangement, and can analyze the novelty element in written work.
Abstraction is the ability to use concepts to define more complex concepts, enabling humans to deliver huge amounts of information in one sentence, or even one word.
For example, we use the concept dexterity to describe coordination of small muscles, and particularly fingers, with human eyes. Each of these concepts is also defined by other concepts, until raw sensual data (precept) is reached.
The amount of concept layers needed to define a concept is called “abstraction level”. The higher the abstraction level, the more complex the concept is and the harder it is to learn.
The more concepts rely on another concept, the more fundamental it is.
Complex skills are found by analyzing abstraction level, i.e. how many concepts are needed to define the examined skill.
Foundational skills are found by analyzing reliance level, i.e. how many other skills need to use the examined skill in order to be defined.
In CHAMP's current implementation, it is done by mapping concept prevalence in lexical dataset.
Adjusting for previously learned concepts is rather straightforward- if a concept has been learned, its abstraction level will set to be zero.
When a new piece of knowledge is introduced, it can be mapped in the same fashion and compared to existing concept mapping. Contradiction in the concept maps will indicate wrong data, and additions nonexistent in it will indicate new data.
A skill containing only zero-level abstraction concepts is considered learned.
By subtracting sub-concepts of previously-learned skills that overlap those of the desired skill, a map of the exact additional concepts one needs to learn to acquire the new skill is made.
This creates roadmap from any skill to another, similar in function to skills trees in computer games, but for real people and with real data.
- Deploy new and alternative learning models that broaden pathways for employment and teach entrepreneurial, technical, language, and soft skills
- Provide equitable access to learning and training programs regardless of location, income, or connectivity throughout Latin America and the Caribbean
- Prototype