Learning Graph — Grade 3¶
This section contains the learning graph for the Grade 3 band of the Health Education intelligent textbook. A learning graph is a graph of concepts used in this band. 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 student.
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 students 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 in the band. To master these concepts you must understand all the concepts that they point to.
This band's graph is intentionally smaller than a general-purpose graph — 57 concepts rather than 200 — because Grade 3 health benchmarks are describe/explain-level content, with the first genuine evaluate-level reasoning and the first explicit puberty topic, for 8-9 year olds (see the Course Description Assessment for the rationale). It builds directly on the Kindergarten (45-concept), Grade 1 (55-concept), and Grade 2 (55-concept) bands' graphs and uses the same 7-category taxonomy for cross-band comparability.
Explore the Interactive Learning Graph¶
Use the interactive viewer to explore this band's concepts and their dependencies. Drag to pan, scroll to zoom, and click a concept to highlight its connections. The viewer opens with the Grade 3 graph loaded; you can switch to any other grade band from the dropdown.
Open the Grade 3 Learning Graph Viewer
Course Description¶
We use the Course Description as the source document for the concepts that are included in this band. 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. This makes it easy for you to view and edit the learning
graph using an editor built with the vis-network tools.
Analysis & Documentation¶
Course Description Quality Assessment¶
This report rates the overall quality of the course description for the purpose of generating a learning graph.
- Course description fields and content depth analysis
- Validates course description has sufficient depth for generating a right-sized concept set for this band
- Compares course description against similar bands
- Identifies content gaps and strengths
- Suggests areas of improvement
View the Course Description Quality Assessment
Learning Graph Quality Validation¶
This report gives you an overall assessment of the quality of the learning graph. It uses graph algorithms to look for specific quality patterns in the graph.
- Graph structure validation - all concepts are connected
- DAG validation (no cycles detected)
- Foundational concepts: 1 entry point (Health)
- Indegree distribution analysis
- Longest dependency chains
- Connectivity: percent of nodes connected to the main cluster
View the Learning Graph Quality Validation
Concept Taxonomy¶
In order to see patterns in the learning graph, it is useful to assign colors to each concept based on the concept type. We use generative AI to create categories for our concepts and then place each concept into a single primary classifier. This band uses 7 categories, seeded from its five content strands plus a cross-cutting Skill Standards group — the same scheme used for Kindergarten, Grade 1, and Grade 2.
- A concept classifier taxonomy with 7 categories
- Category organization - foundational elements first
- Balanced categories (1.8% - 24.6% each)
- All categories under the 30% threshold
- Clear 3-5 letter abbreviations for use in the CSV file
Taxonomy Distribution¶
This report shows how many concepts fit into each category of the taxonomy. Our goal is a somewhat balanced taxonomy where each category holds a reasonable number of concepts. We also don't want any category to contain over 30% of our concepts.
- Statistical breakdown
- Detailed concept listing by category
- Visual distribution table
- Balance verification