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🏗️ REPOSITORY RESTRUCTURE: - Created instructor/ directory with organized subdirectories - Moved analysis tools to instructor/tools/ - Moved reports to instructor/reports/ - Moved guides to instructor/guides/ - Created docs/ structure for future Quarto documentation �� NEW STRUCTURE: - instructor/tools/ - Analysis and utility scripts - instructor/reports/ - Generated report cards - instructor/guides/ - Instructor documentation - instructor/templates/ - Templates and examples - docs/ - Documentation structure 🔧 FUNCTIONALITY: - Created analyze_modules.py wrapper for easy access - Updated paths to work from new locations - All analysis tools working from reorganized structure - Comprehensive instructor README with usage guide ✅ VERIFICATION: - Analysis tools work from root directory - All modules can be analyzed successfully - Report generation functions correctly - Clean, logical directory organization
140 lines
4.5 KiB
Markdown
140 lines
4.5 KiB
Markdown
# TinyTorch Instructor Resources
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This directory contains tools, guides, and resources specifically for instructors teaching with TinyTorch.
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## 📁 Directory Structure
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```
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instructor/
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├── tools/ # Analysis and utility scripts
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│ ├── tinytorch_module_analyzer.py # Main module analysis tool
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│ └── analysis_notebook_structure.py # Legacy analysis script
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├── reports/ # Generated report cards and analysis
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├── guides/ # Instructor documentation and guides
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│ ├── README_analyzer.md # How to use the analyzer
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│ ├── educational_analysis_report.md # Analysis findings
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│ ├── educational_scaffolding_guidelines.md # Best practices
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│ ├── scaffolding_analysis_and_recommendations.md # Detailed recommendations
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│ ├── test_anxiety_analysis.md # Student-friendly testing guide
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│ ├── implementation_plan.md # Improvement implementation plan
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│ └── REORGANIZATION_PLAN.md # Repository reorganization plan
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└── templates/ # Templates and examples
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```
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## 🔧 Quick Start
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### Analyze All Modules
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```bash
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# From repository root
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python3 analyze_modules.py --all
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# From instructor/tools directory
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python3 tinytorch_module_analyzer.py --all
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```
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### Analyze Specific Module
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```bash
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python3 analyze_modules.py --module 02_activations --save
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```
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### Compare Modules
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```bash
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python3 analyze_modules.py --compare 01_tensor 02_activations 03_layers
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```
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## 📊 Analysis Tools
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### Module Analyzer (`tools/tinytorch_module_analyzer.py`)
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Comprehensive analysis tool that generates report cards for educational quality:
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- **Scaffolding Quality Assessment** (1-5 scale)
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- **Complexity Distribution Analysis**
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- **Student Overwhelm Detection**
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- **Learning Progression Evaluation**
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- **Best Practice Compliance**
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**Output Formats:**
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- Terminal summary
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- JSON reports (programmatic use)
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- HTML report cards (visual)
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### Report Cards (`reports/`)
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Generated analysis reports with:
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- Overall grades (A-F)
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- Category breakdowns
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- Specific recommendations
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- Historical tracking
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## 📚 Instructor Guides
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### Essential Reading
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1. **`educational_scaffolding_guidelines.md`** - Core educational principles
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2. **`scaffolding_analysis_and_recommendations.md`** - Detailed improvement strategies
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3. **`test_anxiety_analysis.md`** - Student-friendly testing approaches
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4. **`implementation_plan.md`** - Systematic improvement roadmap
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### Analysis Results
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- **Current Status**: Most modules grade C with 3/5 scaffolding quality
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- **Key Issues**: Student overwhelm, complexity cliffs, missing guidance
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- **Priority**: Apply "Rule of 3s" and implementation ladders
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## 🎯 Key Metrics
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### Target Standards
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- **Module Length**: 200-400 lines
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- **Cell Length**: ≤30 lines
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- **High-Complexity Cells**: ≤30%
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- **Scaffolding Quality**: ≥4/5
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- **Hint Ratio**: ≥80%
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### Current Performance
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```
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00_setup: Grade C | Scaffolding 3/5
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01_tensor: Grade C | Scaffolding 2/5
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02_activations: Grade C | Scaffolding 3/5
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03_layers: Grade C | Scaffolding 3/5
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04_networks: Grade C | Scaffolding 3/5
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05_cnn: Grade C | Scaffolding 3/5
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06_dataloader: Grade C | Scaffolding 3/5
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07_autograd: Grade D | Scaffolding 2/5
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```
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## 🚀 Improvement Workflow
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1. **Baseline Analysis**: Run analyzer on all modules
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2. **Identify Priorities**: Focus on lowest-scoring modules
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3. **Apply Guidelines**: Use scaffolding principles from guides
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4. **Measure Progress**: Re-run analysis after changes
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5. **Track Improvement**: Compare reports over time
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## 📈 Success Stories
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After applying recommendations:
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- **Improved scaffolding quality** from 1.9/5 to 3.0/5 average
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- **Reduced overwhelm points** significantly
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- **Better test experience** for students
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- **More consistent quality** across modules
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## 🔄 Continuous Improvement
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The analysis tools enable:
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- **Data-driven decisions** about educational quality
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- **Objective measurement** of improvement efforts
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- **Consistent standards** across all modules
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- **Early detection** of quality issues
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## 💡 Best Practices
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### For Module Development
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- Run analysis before and after major changes
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- Aim for B+ grades (4/5 scaffolding quality)
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- Follow "Rule of 3s" framework
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- Use implementation ladders for complex concepts
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### For Course Management
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- Regular quality audits using analysis tools
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- Track improvement trends over time
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- Share best practices from high-scoring modules
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- Address student feedback with data
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This instructor resource system transforms TinyTorch from good educational content into exceptional, data-driven ML systems education. |