Learning Graph for Forensic Science¶

Open Learning Graph Viewer Fullscreen

This section contains the learning graph for the Forensic Science intelligent 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 — only inbound edges from other concepts that depend on them. 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.

Summary Statistics¶

Total Concepts: 258
Taxonomy Categories: 14 (corresponding to 15 chapters — Ch. 11 and Ch. 12 split)
Foundational Concepts (no prerequisites): 3
Total Dependency Edges: 322
Maximum Learning Path Length: 14 concepts
Valid DAG: ✅ No cycles detected

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. The 258 concepts span all 15 chapters across 6 thematic modules.

Concept Dependency List¶

We 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:

A CSV file with columns: ConceptID, ConceptLabel, Dependencies (pipe-delimited), TaxonomyID
A JSON file that uses the vis-network JavaScript library format

The vis-network format uses nodes, edges, groups, and metadata elements with edges containing from and to properties.

Analysis & Documentation¶

Course Description Quality Assessment¶

This report rates the overall quality of the course description for the purpose of generating a learning graph.

Quality score: 94 / 100 ✅
Verified complete Bloom's Taxonomy coverage (all six levels)
All required fields present: title, audience, prerequisites, outcomes

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 258 concepts connected in a single component
DAG validation — no cycles detected ✅
3 foundational entry-point concepts
Average dependencies per concept: 1.26
Maximum dependency chain: 14 steps

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. This textbook uses 14 pedagogically-aligned taxonomy categories:

14 categories corresponding to the 15 course chapters
All categories between 5.8% and 10.9% — well within the 30% maximum threshold
Clear 3–6 letter abbreviations (FOUND, CSI, PRINTS, TRACE, SERO, BPA, DNA, CHEM, FIRE, ANTHRO, ENTOM, ARMS, DOCX, DIGIT)