Learning Graph for Mountainash Rules¶
This section contains the learning graph for the mountainash-rules package. A learning graph is a graph of concepts where each concept is represented by a node in a network graph. Concepts are connected by directed edges that indicate what concepts each node depends on before that concept is understood by the learner.
A learning graph is the foundational data structure for intelligent textbooks that can recommend learning paths. A learning graph is like a roadmap of concepts to help learners arrive at their learning goals.
At the left of the learning graph are prerequisite or foundational concepts. They have no outbound edges. They only have inbound edges for other concepts that depend on understanding these foundational prerequisite concepts. At the far right we have the most advanced concepts. To master these concepts you must understand all the concepts that they point to.
Course Description¶
We use the Package Overview as the source document for the concepts that are included in this package. The course description uses the 2001 Bloom taxonomy to order learning objectives.
List of Concepts¶
We use generative AI to convert the course description into a Concept List. Each concept is in the form of a short Title Case label with most labels under 32 characters long.
Concept Dependency List¶
We next use generative AI to create a Directed Acyclic Graph (DAG). DAGs do not have cycles where
concepts depend on themselves. We provide the DAG in two formats. One is a CSV file and the other
format is a JSON file that uses the vis-network JavaScript library format. The vis-network format uses nodes, edges and metadata
elements with edges containing from and to properties.
Analysis & Documentation¶
Learning Graph Quality Validation¶
View the Learning Graph Quality Validation