Learning Graph for From Neuron to Bedside¶
Open the Learning Graph Viewer Fullscreen
This section contains the learning graph for From Neuron to Bedside. A learning graph is a map of the concepts taught in this textbook. Each concept is a node in a network graph. Concepts are connected by arrows (directed edges) that show which concepts you need to understand first. For example, the arrow from Action Potential points to Threshold, Voltage-Gated Channel, and Depolarization, because you need those ideas before an action potential makes sense.
A learning graph is the foundational data structure for an intelligent textbook. It works like a roadmap: it shows the paths a student can take from basic ideas, like Cell Membrane and Ion, all the way to the bedside, like diagnosing Parkinson's Disease or treating a Stroke.
The foundational concepts sit at the start of the graph. They have no prerequisites of their own. Only arrows from other concepts point into them. The most advanced concepts, such as Differential Diagnosis and Lesion Localization, sit at the far end. To master them you need every concept along the paths they point to.
Course Description¶
The concepts come from the course description for From Neuron to Bedside, which follows the topics tested in the Brain Bee and adds a clinical case to every chapter. The course description uses the 2001 Bloom's Taxonomy (Remember, Understand, Apply, Analyze, Evaluate, Create) to order its learning objectives.
List of Concepts¶
The course description was turned into a Concept List of 253 concepts, grouped by chapter. Each concept has a short Title Case label of at most 32 characters, so it fits on a node in the graph.
Concept Dependency List¶
Next, the dependencies between concepts were mapped as a Directed Acyclic Graph (DAG). A DAG has no cycles, so no concept ever ends up depending on itself. Every prerequisite comes from the same chapter or an earlier one, so reading the book in order never asks you to use an idea before it has been taught.
The graph is available in two formats:
- A CSV file with the columns
ConceptID,ConceptLabel,Dependencies, andTaxonomyID. - A JSON file in the vis-network format, with
metadata,groups,nodes, andedgessections. Each edge has afromconcept and thetoconcept it depends on.
Analysis and Documentation¶
Learning Graph Quality Validation¶
This report checks the overall quality of the learning graph using graph algorithms.
- Graph structure validation: every concept is connected
- DAG validation: no cycles
- Foundational concepts: 5 entry points
- Indegree analysis: which concepts the most other concepts depend on
- Longest dependency chains
View the Learning Graph Quality Validation
Concept Taxonomy¶
To see patterns in the graph, each concept is assigned to one of 12 categories, such as Neuroanatomy, Sensory Systems and Pain, or Disorders and Diseases. Each category has its own color in the graph viewer.
- 12 categories with short codes (for example
SIGfor Electrical and Chemical Signaling) - Categories ordered from foundational biology to clinical practice and research
- No category holds more than 30% of the concepts
Taxonomy Distribution¶
This report shows how many concepts fall into each category, with the full list of concepts in every category.
- Statistical breakdown
- Detailed concept listing by category
- Visual distribution table
- Balance check