TinyTorch/instructor

TinyTorch Instructor Resources

This directory contains tools, guides, and resources specifically for instructors teaching with TinyTorch.

📁 Directory Structure

instructor/
├── tools/              # Analysis and utility scripts
│   ├── tinytorch_module_analyzer.py    # Main module analysis tool
│   └── analysis_notebook_structure.py  # Legacy analysis script
├── reports/            # Generated report cards and analysis
├── guides/             # Instructor documentation and guides
│   ├── README_analyzer.md               # How to use the analyzer
│   ├── educational_analysis_report.md   # Analysis findings
│   ├── educational_scaffolding_guidelines.md  # Best practices
│   ├── scaffolding_analysis_and_recommendations.md  # Detailed recommendations
│   ├── test_anxiety_analysis.md         # Student-friendly testing guide
│   ├── implementation_plan.md           # Improvement implementation plan
│   └── REORGANIZATION_PLAN.md          # Repository reorganization plan
└── templates/          # Templates and examples

🔧 Quick Start

Analyze All Modules

# From repository root
python3 analyze_modules.py --all

# From instructor/tools directory
python3 tinytorch_module_analyzer.py --all

Analyze Specific Module

python3 analyze_modules.py --module 02_activations --save

Compare Modules

python3 analyze_modules.py --compare 01_tensor 02_activations 03_layers

📊 Analysis Tools

Module Analyzer (tools/tinytorch_module_analyzer.py)

Comprehensive analysis tool that generates report cards for educational quality:

• Scaffolding Quality Assessment (1-5 scale)
• Complexity Distribution Analysis
• Student Overwhelm Detection
• Learning Progression Evaluation
• Best Practice Compliance

Output Formats:

• Terminal summary
• JSON reports (programmatic use)
• HTML report cards (visual)

Report Cards (reports/)

Generated analysis reports with:

• Overall grades (A-F)
• Category breakdowns
• Specific recommendations
• Historical tracking

📚 Instructor Guides

Essential Reading

1. educational_scaffolding_guidelines.md - Core educational principles
2. scaffolding_analysis_and_recommendations.md - Detailed improvement strategies
3. test_anxiety_analysis.md - Student-friendly testing approaches
4. implementation_plan.md - Systematic improvement roadmap

Analysis Results

• Current Status: Most modules grade C with 3/5 scaffolding quality
• Key Issues: Student overwhelm, complexity cliffs, missing guidance
• Priority: Apply "Rule of 3s" and implementation ladders

🎯 Key Metrics

Target Standards

• Module Length: 200-400 lines
• Cell Length: ≤30 lines
• High-Complexity Cells: ≤30%
• Scaffolding Quality: ≥4/5
• Hint Ratio: ≥80%

Current Performance

00_setup: Grade C | Scaffolding 3/5
01_tensor: Grade C | Scaffolding 2/5
02_activations: Grade C | Scaffolding 3/5
03_layers: Grade C | Scaffolding 3/5
04_networks: Grade C | Scaffolding 3/5
05_cnn: Grade C | Scaffolding 3/5
06_dataloader: Grade C | Scaffolding 3/5
07_autograd: Grade D | Scaffolding 2/5

🚀 Improvement Workflow

1. Baseline Analysis: Run analyzer on all modules
2. Identify Priorities: Focus on lowest-scoring modules
3. Apply Guidelines: Use scaffolding principles from guides
4. Measure Progress: Re-run analysis after changes
5. Track Improvement: Compare reports over time

📈 Success Stories

After applying recommendations:

• Improved scaffolding quality from 1.9/5 to 3.0/5 average
• Reduced overwhelm points significantly
• Better test experience for students
• More consistent quality across modules

🔄 Continuous Improvement

The analysis tools enable:

• Data-driven decisions about educational quality
• Objective measurement of improvement efforts
• Consistent standards across all modules
• Early detection of quality issues

💡 Best Practices

For Module Development

• Run analysis before and after major changes
• Aim for B+ grades (4/5 scaffolding quality)
• Follow "Rule of 3s" framework
• Use implementation ladders for complex concepts

For Course Management

• Regular quality audits using analysis tools
• Track improvement trends over time
• Share best practices from high-scoring modules
• Address student feedback with data

This instructor resource system transforms TinyTorch from good educational content into exceptional, data-driven ML systems education.