# 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
```bash
# From repository root
python3 analyze_modules.py --all

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

### Analyze Specific Module
```bash
python3 analyze_modules.py --module 02_activations --save
```

### Compare Modules
```bash
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. 