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Learning Graph Quality Metrics Report

Overview

  • Total Concepts: 300
  • Foundational Concepts (no dependencies): 2
  • Concepts with Dependencies: 298
  • Average Dependencies per Concept: 1.47

Graph Structure Validation

  • Valid DAG Structure: ❌ No
  • Self-Dependencies: None detected ✅
  • Cycles Detected: 0

Foundational Concepts

These concepts have no prerequisites:

  • 1: Data Science
  • 2: Python Programming

Dependency Chain Analysis

  • Maximum Dependency Chain Length: 22

Longest Learning Path:

  1. Data Science (ID: 1)
  2. Data (ID: 4)
  3. Data Types (ID: 6)
  4. Categorical Data (ID: 8)
  5. Ordinal Data (ID: 9)
  6. Measurement Scales (ID: 11)
  7. Descriptive Statistics (ID: 101)
  8. Correlation (ID: 126)
  9. Regression Analysis (ID: 131)
  10. Linear Regression (ID: 132)
  11. Simple Linear Regression (ID: 133)
  12. Prediction (ID: 143)
  13. Residuals (ID: 138)
  14. Sum of Squared Errors (ID: 139)
  15. Least Squares Method (ID: 137)
  16. Cost Function (ID: 239)
  17. Loss Function (ID: 238)
  18. Loss Functions PyTorch (ID: 275)
  19. Training Loop (ID: 279)
  20. Model Saving (ID: 283)
  21. Model Loading (ID: 284)
  22. Transfer Learning (ID: 285)

Orphaned Nodes Analysis

  • Total Orphaned Nodes: 121

Concepts that are not prerequisites for any other concept:

  • 15: Observation
  • 20: Data Collection
  • 23: Pip
  • 24: Conda Environment
  • 25: Virtual Environment
  • 27: VS Code
  • 30: Markdown Cell
  • 32: Kernel
  • 37: Dictionaries
  • 38: Tuples
  • 42: Series
  • 45: Row
  • 48: Read CSV
  • 50: Head Method
  • 51: Tail Method
  • 52: Shape Attribute
  • 53: Info Method
  • 54: Describe Method
  • 59: Dropna Method
  • 61: Imputation

...and 101 more

Connected Components

  • Number of Connected Components: 1

✅ All concepts are connected in a single graph.

Indegree Analysis

Top 10 concepts that are prerequisites for the most other concepts:

Rank Concept ID Concept Label Indegree
1 41 DataFrame 14
2 101 Descriptive Statistics 10
3 156 Model Performance 9
4 4 Data 8
5 132 Linear Regression 8
6 5 Variables 7
7 14 Dataset 7
8 79 Axes 7
9 138 Residuals 7
10 181 Multiple Linear Regression 7

Outdegree Distribution

Dependencies Number of Concepts
0 2
1 169
2 119
3 8
4 2

Recommendations

  • ⚠️ Many orphaned nodes (121): Consider if these should be prerequisites for advanced concepts
  • ℹ️ Long dependency chains (22): Ensure students can follow extended learning paths
  • ℹ️ Consider adding cross-dependencies: More connections could create richer learning pathways

Report generated by learning-graph-reports/analyze_graph.py