ModusPractica

Smart Music Practice Management System

🇳🇱 Nederlandse versie beschikbaar: README-NL.md

ModusPractica is an advanced, scientifically-grounded music practice management system based on the Ebbinghaus memory curve and spaced repetition learning. It uses adaptive algorithms to optimize learning intervals and practice intensity for individual musicians.

🎯 Key Features

• Spaced Repetition Engine - Based on Ebbinghaus forgetting curve
• Adaptive Learning - Personalizes intervals based on your performance
• Memory Stability Tracking - Advanced metrics inspired by SuperMemo SM-17+
• Interleaved Practice - Optimal mixing of different musical pieces
• Practice Intensity Optimization - Adapts session length and difficulty
• Cross-Platform - Windows, macOS, Linux via Electron
• Offline-First - Full functionality without internet connection
• Privacy-First - All data stays on your computer

📚 How It Works

ModusPractica implements sophisticated learning algorithms:

1. Ebbinghaus Memory Curve - Scientifically-proven spacing intervals
2. Personalized Memory Calibration - Learns your individual forgetting patterns
3. Adaptive Tau Manager - Unified system coordinating all adaptive mechanisms
4. Memory Stability Manager - Tracks retention and difficulty per piece
5. Intensity Module - Optimizes practice duration and intensity

Learn more: DEVELOPERS.md

🚀 Quick Start

Prerequisites

• Node.js 16+
• npm or yarn

Installation

# Clone repository
git clone https://github.com/frankyzip/ModusPractica.git
cd ModusPractica

# Install dependencies
npm install

# Run development
npm start

# Build for distribution
npm run build:win    # Windows
npm run build:mac    # macOS
npm run build:linux  # Linux

See README-ELECTRON.md for detailed development instructions.

📖 Documentation

Document	Purpose
README-ELECTRON.md	Development setup & commands
DEVELOPERS.md	Full development history, architecture, algorithms
LICENSING.md	Dual licensing: AGPL-3.0 (open) & Commercial
CONTRIBUTORS.md	How to contribute, contributor guidelines
NOTICES	Copyright and attribution notices
_dev-docs/	Technical algorithm documentation

📋 Architecture

Core Algorithms

EbbinghausEngine.js
├── Core spaced repetition scheduling
├── Forgetting curve modeling
└── Interval calculations

AdaptiveTauManager.js
├── Coordinates all adaptive systems
├── PersonalizedMemoryCalibration
├── MemoryStabilityManager
└── Performance-based adjustments

IntensityModule.js
├── Practice duration optimization
└── Difficulty scaling

InterleavedSessionManager.js
├── Practice session randomization
└── Circuit review management

Technology Stack

• Frontend: HTML5, CSS3, JavaScript (ES6+)
• Desktop: Electron 28+
• Backend: Node.js
• Storage: localStorage, IndexedDB

🔐 License

ModusPractica is Proprietary Freeware

✅ Free for Personal & Educational Use

• ✓ Free to use for personal music practice
• ✓ Free for educational purposes
• ✓ No subscription or payment required

🔒 Source Code Ownership

• All source code and intellectual property remain the exclusive property of Frank De Baere (Partura Music™)
• You may NOT copy, modify, reverse engineer, or redistribute the code
• You may NOT use for commercial purposes without permission

💼 Commercial License Available

For commercial use, contact Frank De Baere (Partura Music™)

📄 License Documents:

• LICENSE-FREEWARE - Current license (free for personal/educational use)
• LICENSE-INFO.md - Easy-to-read license summary
• LICENSE-COMMERCIAL - Commercial licensing terms

👥 Contributing

Note: This is proprietary software. Direct code contributions are not accepted at this time.

You can help by:

• 🐛 Reporting bugs via GitHub Issues
• 💡 Suggesting features via GitHub Discussions
• 📖 Improving documentation
• 🌍 Providing translations
• ⭐ Starring the repository

See CONTRIBUTORS.md for more details.

• Sign commits with your name

🔬 Scientific Foundation

ModusPractica is built on peer-reviewed research:

• Ebbinghaus (1885): Forgetting curve theory
• Cepeda et al. (2006): Meta-analysis of spacing effects
• Dunlosky et al. (2013): Improving students' learning with effective techniques
• SuperMemo research: SM-15, SM-17+ algorithms
• Cognitive psychology: Interleaving and spacing effects in music learning

📊 Algorithm Examples

Ebbinghaus Interval Calculation

// Core formula
nextInterval = τ * (difficulty / targetRetention);

// Where:
// τ = tau parameter (personalized)
// difficulty = piece difficulty estimate
// targetRetention = target success rate (typically 0.9)

Adaptive Tau Learning

// Bayesian updates from performance
If (actual_success > expected):
    increase_tau  // Increase intervals
Else:
    decrease_tau  // Decrease intervals

More details: DEVELOPERS.md

🎓 Use Cases

For Musicians

• Optimize practice sessions
• Reduce practice time while improving retention
• Track progress scientifically
• Personalized learning recommendations

For Music Educators

• Student practice optimization
• Understanding learning patterns
• Research on effective practice
• Educational software reference

For Researchers

• Music learning algorithm research
• Spaced repetition studies
• Personalized learning effectiveness
• Cognitive science applications

🐛 Issues & Support

Found a bug? Have a suggestion?

• Bug Reports: GitHub Issues
• Feature Requests: GitHub Discussions
• Commercial Support: Contact Frank De Baere

🗺️ Roadmap

Short Term (Q1 2026)

• Community feedback integration
• Performance optimization
• Additional statistical reports

Medium Term (2026)

• Mobile applications (iOS/Android)
• Cloud synchronization
• Multi-device support
• Advanced analytics dashboard

Long Term

• AI-powered practice recommendations
• Collaboration features
• Teacher/student management system
• API for third-party integration
• Research partnership tools

See DEVELOPERS.md for detailed roadmap.

📄 Copyright

Copyright © 2024-2025 Frank De Baere - Partura Music™

All source code, algorithms, and intellectual property in this repository are original works by Frank De Baere.

Third-party libraries and attributions: See NOTICES

🙏 Acknowledgments

• Ebbinghaus - Memory research foundation
• SuperMemo community - SM algorithms and research
• Electron team - Cross-platform framework
• Music education researchers - Learning methodology
• Open source community - Tools and inspiration

📞 Contact

Frank De Baere
Partura Music™

• GitHub: @frankyzip
• Email: [Your email]
• Website: [Your website]

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🌟 Star this project if you find it useful!

Let's make music learning scientific, personalized, and effective. 🎵

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Last Updated: December 15, 2025