Learning Graph for Ancient History: Origins to 1200 CE
Open Learning Graph Viewer Fullscreen
This section contains the learning graph for the Ancient History: Origins to 1200 CE course. 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 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 course. To master these concepts you must understand all the concepts that they point to.
Currency of Evidence
This learning graph explicitly incorporates archaeological, paleoanthropological, and paleogenomic findings published in the last fifteen years as first-class concept nodes, not as footnotes to the canonical pre-2010 textbook content. Examples include Göbekli Tepe and the broader Tas Tepeler complex (monumental religion before settled agriculture), Lomekwi 3 stone tools, Sahelanthropus and Ardipithecus, Homo Naledi, the Jebel Irhoud finds, Doggerland and Sundaland-Sahul, Madjedbebe, the White Sands footprints, Sulawesi cave art, Sanxingdui, Liangzhu Culture, Caral-Supe, Cahokia, Chaco Canyon, the Aguada Fenix LIDAR-revealed Maya complex, the ancient-DNA-driven understanding of Yamnaya migration and Proto-Indo-European, and climate-driven causal events (4.2 ka Event, Late Antique Little Ice Age, Justinianic Plague). See logs/revised-concepts-list.md for the full revision history.
Here are other files used by the learning graph.
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 of 297 concepts organized by Big Era, plus dedicated sections for recent paleoanthropological evidence, the Paleolithic visual culture cluster, recent paradigm-shifting archaeology, submerged landscapes, African archaeology, recent East Asian excavations, the pre-Columbian Americas, European megaliths, climate and disease drivers, and the Yamnaya cultural and linguistic legacy. 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
The current course description scored 100 / 100. 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: 2 entry points (Big Era Framework, Big Bang)
- Indegree distribution analysis
- Longest dependency chains (max chain length: 56)
- 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 about a dozen categories for our concepts and then place each concept into a single primary classifier.
- 13 categories tuned to the structure of an ancient-history survey: methodology, three thematic axes, the five UCLA Big Eras, the Axial Age and Islamic civilization (split out for visibility), the pre-Columbian Americas, and the bridge unit
- Balanced distribution (largest category 14.5%, all under the 30% cap)
- Clear 3–6 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 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