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Learning Graph for Beginning Electronics

Overview

This learning graph represents a comprehensive knowledge structure for the Beginning Electronics course, mapping 200 interconnected concepts with their dependencies and categorical organization.

Purpose

The learning graph serves as foundational infrastructure for an intelligent textbook that supports:

  • Personalized Learning Pathways: Students can navigate concepts based on their current knowledge
  • Prerequisite Tracking: Clear visualization of concept dependencies
  • Progress Monitoring: Track mastery of concepts across the curriculum
  • Adaptive Content: Customize learning experiences based on student needs

Graph Statistics

  • Total Concepts: 200
  • Total Dependencies: 268
  • Root Concepts: 6 (foundation concepts with no prerequisites)
  • Maximum Depth: 6 levels
  • Taxonomy Categories: 9

Files

Core Data Files

  • concepts-dependencies.csv - Original two-column CSV (Concept, Dependency)
  • concepts-with-taxonomy.csv - Enhanced CSV with taxonomy IDs (Concept, Dependency, TaxonomyID)
  • learning-graph.json - vis.js network format for visualization

Analysis and Reports

  • step-01-course-assessment.md - Course description quality analysis
  • step-02-concepts.md - Complete list of 200 concepts with categorization
  • step-04-quality-analysis.md - Graph validation report (cycles, orphans, connectivity)
  • step-05-taxonomy.md - Taxonomy structure and category definitions
  • step-07-distribution-report.md - Taxonomy balance analysis

Python Scripts

  • csv-to-json.py - Convert CSV to vis.js JSON format
  • analyze-graph.py - Validate graph quality (DAG, connectivity, cycles)
  • add-taxonomy.py - Add taxonomy IDs to concept CSV
  • taxonomy-distribution.py - Generate category distribution report

Taxonomy Categories

ID Category Count Percentage Description
FUND Fundamentals 40 20.0% Core electrical concepts and laws
PASS Passive Components 30 15.0% Resistors, capacitors, diodes
IO Input/Output 30 15.0% Sensors, buttons, LEDs, motors
ACT Active Components 25 12.5% Transistors and integrated circuits
BREAD Breadboarding 25 12.5% Prototyping and circuit construction
MEAS Measurement 15 7.5% Testing, debugging, multimeter use
PWR Power Systems 15 7.5% Power supplies, regulation, efficiency
DIG Digital Logic 12 6.0% Boolean logic, gates, truth tables
ADV Advanced Circuits 8 4.0% Complex projects and applications

Foundation Concepts (Root Nodes)

These 6 concepts have no prerequisites and form the foundation of the curriculum:

  1. Boolean Logic - Basis for digital logic concepts
  2. Component Lead - Physical structure of components
  3. Electric Current - Fundamental electrical phenomenon
  4. Multimeter - Essential measurement tool
  5. Resistance - Opposition to current flow
  6. Voltage - Electrical potential difference

Most Central Concepts (High In-Degree)

These concepts are depended upon by many other concepts:

  1. Voltage - 24 dependents
  2. Electric Current - 20 dependents
  3. Resistor - 13 dependents
  4. Circuit - 11 dependents
  5. Polarity - 11 dependents
  6. Component Lead - 11 dependents
  7. Transistor - 10 dependents

Graph Quality

✓ Valid DAG: No cycles detected

✓ Fully Connected: Single connected component

✓ No Orphans: All concepts integrated into graph

✓ Balanced Distribution: All categories < 30%

Depth Distribution

Depth Level Concept Count Description
0 6 Foundation concepts (no dependencies)
1 43 First-level concepts
2 62 Second-level concepts
3 46 Third-level concepts
4 23 Fourth-level concepts
5 15 Fifth-level concepts
6 5 Most advanced concepts

Using the Learning Graph

For Students

The learning graph helps you:

  • Understand what concepts you need to master first
  • See how concepts build upon each other
  • Track your progress through the curriculum
  • Find gaps in your knowledge

For Instructors

The learning graph enables you to:

  • Design optimal learning sequences
  • Identify prerequisite knowledge for each lesson
  • Create customized learning paths for different students
  • Assess student readiness for advanced topics

For Developers

The graph data can be used to:

  • Build interactive visualization tools
  • Create adaptive learning systems
  • Generate personalized study plans
  • Track learning analytics

Visualization

The learning-graph.json file can be visualized using vis.js or similar network visualization libraries. The JSON structure includes:

  • Nodes: Each concept with ID, label, and depth level
  • Edges: Directed edges showing dependencies (prerequisite → dependent concept)

Course Alignment

This learning graph aligns with the Beginning Electronics course structure:

  • Bloom's Taxonomy: Concepts progress from Remember/Understand through Create
  • Hands-On Focus: Emphasis on practical breadboarding and testing skills
  • Low-Cost Components: Focus on accessible, affordable parts
  • Interactive Learning: Integration with MicroSims and simulations

Maintenance

To update the learning graph:

  1. Edit concepts-dependencies.csv to add/modify concepts
  2. Run python3 csv-to-json.py to regenerate JSON
  3. Run python3 analyze-graph.py to validate quality
  4. Run python3 add-taxonomy.py to update taxonomy assignments
  5. Run python3 taxonomy-distribution.py to check balance

References

Contact

For questions about the learning graph structure or usage, see the main course contact page.

Generated using the learning-graph-generator skill Last updated: 2025-10-31