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Learning Graph Generation Progress Log

Project: Claude Skills Intelligent Textbook Date Started: 2025-11-08 Initial Token Count: 29,660 tokens used

Progress Timeline

Step 0: Setup ✓

  • Status: Complete
  • Token Count: 29,660
  • Actions:
  • Created /docs/learning-graph/ directory
  • Verified prerequisites (docs directory and mkdocs.yml exist)
  • Located course-description.md file

Step 1: Course Description Quality Assessment ✓

  • Status: Complete
  • Token Count: 33,093
  • Tokens Used This Step: 3,433
  • Actions:
  • Analyzed course description against 13 quality criteria
  • Awarded 95/100 points (exceeds 70 threshold)
  • Estimated 220-250 concepts can be derived
  • Created detailed assessment report: course-description-assessment.md
  • Result: APPROVED to proceed with learning graph generation

Step 2: Generate Concept Labels ✓

  • Status: Complete
  • Token Count: 36,293
  • Tokens Used This Step: 3,200
  • Actions:
  • Generated 200 concept labels covering all course topics
  • Organized concepts across 7 major domains
  • Ensured Title Case and max 32 character requirements
  • Created concept-list.md file
  • Note: User should review and modify concept list before proceeding

Step 3: Create Dependency Graph CSV ✓

  • Status: Complete
  • Token Count: 39,575
  • Tokens Used This Step: 3,282
  • Actions:
  • Created CSV with ConceptID, ConceptLabel, Dependencies columns
  • Mapped 200 concepts with prerequisite dependencies
  • Identified 7 foundational concepts (no dependencies)
  • Ensured DAG structure (no circular dependencies)
  • Created learning-graph.csv file
  • Note: File ready for quality validation

Step 4: Learning Graph Quality Validation ✓

  • Status: Complete
  • Token Count: 45,849
  • Tokens Used This Step: 6,274
  • Actions:
  • Copied Python analysis scripts to learning-graph directory
  • Ran analyze-graph.py to validate structure
  • Fixed disconnected subgraphs (connected all 200 concepts)
  • Validated: 0 cycles, no self-dependencies, 1 connected component
  • Generated quality-metrics.md report
  • Metrics: 8 foundational concepts, avg 1.18 dependencies, max chain length 11
  • Quality Assessment: 75/100 (good structure, some orphaned terminal concepts)

Step 5: Create Concept Taxonomy ✓

  • Status: Complete
  • Token Count: 49,272
  • Tokens Used This Step: 3,423
  • Actions:
  • Developed 12 taxonomy categories
  • Created TaxonomyID abbreviations (AIFND, SKILL, IBOOK, etc.)
  • Defined category descriptions and scope
  • Ensured balanced distribution targets (~16-17 concepts/category)
  • Created concept-taxonomy.md file

Step 6: Add Taxonomy to CSV ✓

  • Status: Complete
  • Token Count: 57,859
  • Tokens Used This Step: 8,587
  • Actions:
  • Added TaxonomyID column to CSV header
  • Assigned taxonomy categories to all 200 concepts
  • Distribution: AIFND (14), SKILL (34), IBOOK (8), MKDOC (6), GRAPH (28), EDTHY (17), CONTE (8), RSRCE (18), INTER (12), VERCT (9), TOOLS (11), DATAS (35)
  • Updated learning-graph.csv file with complete taxonomy

Step 7: Create metadata.json ✓

  • Status: Complete
  • Token Count: 59,463
  • Tokens Used This Step: 1,604
  • Actions:
  • Created metadata.json with Dublin Core fields
  • Included title, description, creator, date, version
  • Added course-specific metadata (audience, prerequisites, concepts count)
  • Specified schema URL and license (CC BY-NC-SA 4.0)

Step 8: Create groups section

  • Status: Skipped (auto-generated in Step 9)
  • Note: Groups section will be auto-generated by csv-to-json.py

Step 9: Generate Complete Learning Graph JSON ✓

  • Status: Complete
  • Token Count: 64,822
  • Tokens Used This Step: 5,359
  • Actions:
  • Ran csv-to-json.py to generate base JSON structure
  • Manually added complete groups section with all 12 taxonomies
  • Updated metadata section with course-specific information
  • Created learning-graph.json with 200 nodes and 227 edges
  • Verified 8 foundational concepts marked correctly

Step 10: Generate Taxonomy Distribution Report ✓

  • Status: Complete
  • Token Count: 68,801
  • Tokens Used This Step: 3,979
  • Actions:
  • Ran taxonomy-distribution.py to analyze category distribution
  • Generated taxonomy-distribution.md report
  • Results: 12 categories, well-balanced distribution (3% to 18.5%)
  • Largest category (DATAS) at 18.5% (under 30% threshold)
  • All categories marked ✅ (good balance)

Step 11: Create index.md from Template ✓

  • Status: Complete
  • Token Count: 75,920
  • Tokens Used This Step: 7,119
  • Actions:
  • Copied index-template.md from skill directory
  • Customized template with textbook name
  • Updated stats (8 foundational concepts, 12 categories, 3%-18.5% distribution)
  • Created index.md file with documentation structure

Step 12: Update mkdocs.yml Navigation ✓

  • Status: Complete
  • Token Count: 78,782
  • Tokens Used This Step: 2,862
  • Actions:
  • Read mkdocs.yml to find Learning Graph section
  • Updated navigation structure with all 6 learning graph files
  • Added: Introduction, Course Description Assessment, Concept Enumeration, Quality Analysis, Taxonomy, Distribution Report
  • Removed old placeholder file (glossary-quality-report.md)

Final Summary

Total Token Count: 78,782 tokens used Total Steps Completed: 12 steps Time Completed: 2025-11-08

Files Created:

  1. progress.md - This progress tracking file with token counts
  2. course-description-assessment.md - Quality assessment (95/100)
  3. concept-list.md - 200 concepts enumerated
  4. learning-graph.csv - Complete graph with dependencies and taxonomy
  5. quality-metrics.md - Graph quality validation report
  6. concept-taxonomy.md - 12 category taxonomy definition
  7. metadata.json - Dublin Core metadata
  8. learning-graph.json - Complete vis-network JSON (200 nodes, 227 edges)
  9. taxonomy-distribution.md - Distribution analysis report
  10. index.md - Documentation index page

Python Scripts Installed:

  • analyze-graph.py
  • csv-to-json.py
  • add-taxonomy.py
  • taxonomy-distribution.py

Key Metrics:

  • Total Concepts: 200
  • Foundational Concepts: 8
  • Dependencies/Edges: 227
  • Taxonomy Categories: 12
  • Average Dependencies: 1.18 per concept
  • Max Dependency Chain: 11 levels
  • Connected Components: 1 (fully connected)
  • Largest Category: DATAS (18.5%)
  • Smallest Category: MKDOC (3.0%)

Quality Scores:

  • Course Description: 95/100
  • Learning Graph: 75/100 (good structure, acceptable orphaned terminal concepts)
  • Taxonomy Balance: ✅ All categories under 30% threshold

Learning graph generation complete! The graph is ready for visualization and integration into the intelligent textbook.