Learning Graph Generator Session Log

Skill Version: 0.05 Date: 2026-06-02 Course: Semiconductor Physics: Fundamentals to Advanced Applications

Session Summary

Generated a 600-concept learning graph for the semiconductor physics course. The course description had a quality score of 95/100, so the course description assessment step was skipped to save tokens.

Steps Completed

Step 0: Setup

Verified project structure: docs/ directory and mkdocs.yml present
Created docs/learning-graph/ directory (already existed)
Copied Python scripts from skill package

Step 1: Course Description Quality Assessment

Skipped — quality score of 95/100 found in course description YAML front matter (above 85 threshold)

Step 2: Concept Labels

Generated 600 concepts covering all major topic areas in semiconductor physics
Saved to concept-list.md
Categories covered: foundations, crystal structure, quantum mechanics, band theory, carrier statistics, transport, generation-recombination, p-n junctions, MOS structures, BJTs, FETs, optoelectronics, advanced materials, power/microwave devices, fabrication, and characterization/modeling

Step 3: Dependency Graph

Generated learning-graph.csv with 600 concepts and pipe-delimited dependency columns
Fixed 3 initial cycle errors detected by analysis:
Saturation Region MOSFET ↔ Pinch-Off Point (mutual dependency)
Impact Ionization ↔ Avalanche Breakdown (mutual dependency)
Photodiode Responsivity ↔ Quantum Efficiency Photodiode (mutual dependency)
Fixed 3 connectivity issues (Metallic Bonding, Maxwell-Boltzmann, Bose-Einstein isolated)

Step 4: Graph Quality Validation

Tool: analyze-graph.py (skill package)
Result: Valid DAG ✅
Total concepts: 600
Foundational concepts: 12
Terminal nodes: 215 (35.8%)
Orphaned nodes: 0
Connected components: 1
Max dependency chain: 32 hops
Longest path: Wave-Particle Duality → Tandem Solar Cell

Step 5: Concept Taxonomy

Created 16 taxonomy categories
Saved to concept-taxonomy.md

Step 5b: Taxonomy Names JSON

Created taxonomy-names.json with human-readable names for all 16 categories

Step 6: Taxonomy Added to CSV

TaxonomyID column added directly during CSV generation

Step 7: Metadata JSON

Created metadata.json with Dublin Core fields
Title, description, creator (Dan Mccreary), date (2026-06-02), version 1.0

Step 8: Color Config

Created color-config.json with 16 named CSS colors
Each category assigned a distinct color from the recommended palette

Step 9: Learning Graph JSON

Tool: csv-to-json.py v0.04
Command: python csv-to-json.py learning-graph.csv learning-graph.json color-config.json metadata.json taxonomy-names.json
Result: learning-graph.json generated with 600 nodes, 1075 edges, 16 groups

Step 10: Taxonomy Distribution Report

Tool: taxonomy-distribution.py (skill package)
Result: taxonomy-distribution.md
All 16 categories under 30% threshold
Largest: FET (9.5%), Smallest: FOUND (1.8%)
Balance spread: 7.7% — Excellent ✅

Step 11: Index Page

Created index.md from template, customized for semiconductor physics course

Added Learning Graph section to mkdocs.yml nav

Files Created

File	Description
`concept-list.md`	600 numbered concept labels
`learning-graph.csv`	Full dependency graph with taxonomy (600 rows)
`taxonomy-names.json`	Taxonomy ID → human-readable name mapping
`metadata.json`	Dublin Core metadata for the learning graph
`color-config.json`	Taxonomy ID → CSS color mapping
`learning-graph.json`	Complete vis-network JSON (600 nodes, 1075 edges)
`concept-taxonomy.md`	16 taxonomy category definitions
`quality-metrics.md`	DAG quality validation report
`taxonomy-distribution.md`	Category distribution analysis
`index.md`	Learning graph section introduction page

Graph Statistics

Metric	Value
Total Concepts	600
Total Dependency Edges	1,075
Taxonomy Categories	16
Foundational Concepts	12
Terminal Nodes	215
Max Chain Length	32
Connected Components	1
Valid DAG	✅ Yes
Cycles	0
Orphaned Nodes	0

Python Tools Used

Tool	Version
`csv-to-json.py`	v0.04
`analyze-graph.py`	(skill package)
`taxonomy-distribution.py`	(skill package)