Learning Graph for Architecture Tradeoff Analysis Method¶
Open Learning Graph Viewer Fullscreen
This section contains the learning graph for this textbook. A learning graph is a graph of concepts used in this textbook. 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 can be 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 course. To master these concepts you must understand all the concepts that they point to.
This learning graph contains 350 concepts across 12 taxonomic categories, with 471 dependency edges, 13 foundational concepts, and a longest learning path of 14 steps (from Software Quality Definition through to Leaf-Level Scenario).
Course Description¶
We use the Course Description as the source document for the concepts that are included in this course. 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 200 concepts
- Compares course description against similar courses
- 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 350 concepts connected in a single component
- DAG validation — 0 cycles detected
- Foundational concepts: 13 entry points
- Indegree distribution analysis
- Longest dependency chains (max 14 steps)
- Connectivity: 100% 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 about a dozen categories for our concepts and then place each concept into a single primary classifier.
- 12 taxonomic categories from Foundation Concepts through AI and Emerging Systems
- Category organization — foundational elements first, emerging applications last
- Balanced categories (6.3% – 12.9% each)
- All categories under 30% threshold
- Clear 4-letter abbreviations (FOUND, PROC, STKE, QUAL, SCEN, TACT, RISK, DIST, CLOU, SECP, PERF, AIEM)
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 an equal 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