Concept Sequencing

Concept Sequencing

Just tagging isn’t enough. 

While simple tagging can help learners find courses based on keyword, and help AI – simply tagging alone doesn’t take into account the complex nature of what talent actually is.

Not only are keywords not interoperable, because they cannot be translated between organizations, because they are removed from scale, or association. 

 

Plato’s idea of a dialogue or, “Flow of meaning” believed that no idea existed in isolation, but existed through a flow and interaction with other things and actions. 

 

That is why all languages have a subject and a predicate, because it conveys a flow of meaning. 

 

Hagle expanded this further with his dialects, introducing us to the fact that every idea comprises of a theses an anti-thesis and a synthesis, which is the meaning of the two combined, which creates something new. 

 

In order to accurately create interoperability of ideas and concepts, we need to be able to use data science to transcribe metaphysics into graph data… To do this, we follow the laws of nature, starting giving it, not just a single data point, but a point that is connected to another point across a plane of time, that has depth of overlapping layers that grow over time. 

 

Our sequencing algorithm is controlled by the LMS admin settings that identify the structure front end and back end structure of the LMS to create a map of how each course scales. 

 

Simple Dashboard & visual selector tool for admins to label the LMS & page structure to map course categories or departments in 5-10 minutes. 

During “concept sequencing”, individual lessons are measured and “bifurcated” to scale.

LMS & Page elements create the structure to label and link concepts that are identified with the “Ontological Spectrometer”

 

The categories and hierarchies are noted, and format the file’s architecture/clustering. 

 

On page course description elements such as time, lesson types & experience points set the scale & identification of ratios for each Course Map File.

 

Our sequencing algorithm creates a linked data file (JSON LD) that annotates scale, context & connections of the ideas described in the modules & lesson titles. 

 

They are scaled according to the “course anatomy” & labeled according to the elemental concepts identified by the “ontological spectrometer.” 

 

Finally, the ideas are linked in context by subject/predicate, reading left to right, top to bottom. 

 

Lesson weights are each divided by the anatomy of the course already identified. 

 

Descriptions add weight and details to key concepts identified in the titles.

The Self-Generated Ontology then is used to identify the key terms in the text, which are linked together at scale using our sequencing algorithm. 

By using the ontology like a periodic table of elements to identify and label the sequence of connected concepts, you create a sequence of the knowledge and skills grown in a certain amount of learning done on any LM, creating a graph file that can be filtered to resemble DNA: 

This then creates the raw data attachment, that powers the addition to the admin screen on the course page: 

These course maps not only help learners visualize the knowledge and skills in their courses, but also attach to their certificates for decentralized ownership. 

See other parts of Gobekli's science & tech: ​

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Self-Organizing Ontologies

We provide keywords families with clear meaning through Self-Organizing Ontologies

Concept Sequencing

We map knowledge and skills observed in courses through Concept Sequencing, creating Linked Talent Data

Verified Human Knowledge Maps

We distribute verified talent maps to individuals using Verified Credentials & blockchain networks.

Self-Soverign Verified Talent Data

We leverage human distributed Solid Pods to give the user ownership and control to combine and share their data, creating a Human Distributed Talent Ecosystem