Enhanced Flashcard Learning Tool - "Ankara"

An intelligent, data-driven vocabulary learning application with spaced repetition and comprehensive analytics

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🚀 Overview

Ankara is a next-generation vocabulary learning application that tries to combine proven educational techniques with modern data analytics. Unlike traditional flashcard applications, this tool features three-sided cards (word, meaning, example) and collects comprehensive learning data for AI research applications ( and this feature is completely for fun and experimental).

🎯 Key Innovation

While most flashcard tools limit you to two-sided cards, our application provides a unique three-stage learning approach:

1. Word → See the vocabulary term
2. Example → Context before definition (optional intermediate step)
3. Meaning → Final definition reveal

This progression mimics natural language acquisition and allows learners to build context before seeing direct translations, at least for me this approach helped and I like to think its better approach.

✨ Features

🧠 Advanced Learning System

• Spaced Repetition Algorithm: Intelligently schedules reviews based on your performance
• Three-Sided Flashcards: Word → Example → Meaning progression
• Vim-Inspired Keybindings: Efficient navigation for power users, plus helps you learning touch typing
• Multi-Language Support: Automatic language detection for pronunciation

📊 Data Analytics & AI Integration

• Comprehensive Learning Metrics: Track study time, accuracy, and progress patterns
• Machine Learning Ready: Generates datasets suitable for memory research
• Performance Analytics: Session statistics and long-term progress tracking
• Export Capabilities: Data ready for neural network training

🛡️ Robust Data Management

• Automatic Backups: Scheduled backups with configurable retention
• CSV-Based Storage: Lightweight, portable, and version-controllable
• Data Recovery: Built-in backup restoration system
• Performance Optimized: Handles large vocabularies efficiently

🎨 User Experience

• Intuitive Interface: Clean, distraction-free design
• Real-Time Pronunciation: Text-to-speech with automatic language detection
• Progress Visualization: Session stats and accuracy tracking
• Responsive Controls: Both keyboard and mouse support

🛠️ Installation

Prerequisites

Python 3.8+

Quick Setup

1. Clone the repository

   git clone https://github.com/Andebugulin/ankara.git
   cd ankara

2. Create virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate  # Linux/macOS
   # or
   venv\Scripts\activate     # Windows

3. Install dependencies

   pip install -r requirements.txt

4. Run the application

   python ankara.py

Dependencies

• pygame - GUI framework
• pandas - Data manipulation
• gtts - Text-to-speech functionality
• pathlib - Modern path handling
• langdetect - Language detection

📖 Usage Guide

Initial Setup

1. Prepare your vocabulary data in CSV format:

   word,meaning,example
   serendipity,pleasant surprise,Finding that book was pure serendipity
   ephemeral,lasting very briefly,The ephemeral beauty of cherry blossoms
   

2. Place the CSV file in the application directory in flashcard_data/words.csv

3. Launch the application and follow the on-screen instructions

Keyboard Controls

Navigation

• N - Next card
• P - Previous card
• 0 - Skip 10 cards forward
• < - Switch language for pronunciation manually
• > - Switch language for pronunciation manually

Card Views

• F - Show word
• D - Show meaning
• S - Show example
• A - Play pronunciation (auto-detects language)

Evaluation (Spaced Repetition)

• H - Don't remember (1)
• J - Hard (2)
• K - Normal (3)
• L - Nice (4)
• I - Very impressive (5)

System

• ESC - Save and exit

Optimal Hand Positioning

For maximum efficiency, position your hands like this:

Left Hand: A S D F (ring → index fingers)
Right Hand: H J K L (index → pinky fingers)

This Vim-inspired layout minimizes hand movement and maximizes learning speed.

📁 Project Structure

ankara.py                  # Main application script
requirements.txt          # Python dependencies
flashcard_data/
├── words.csv              # Main vocabulary database
├── config.json           # Application settings
└── backups/              # Automatic backup storage
    ├── words_backup_20241201_120000.csv
    └── words_backup_20241130_120000.csv

📊 Data Collection & AI Applications

Learning Analytics

The application collects comprehensive learning metrics:

# Per-word analytics
- study_sessions: int      # Times studied
- total_study_time: float  # Cumulative study time
- correct_answers: int     # Successful recalls
- total_answers: int       # Total attempts
- accuracy_rate: float     # Success percentage
- class_progression: list  # Spaced repetition history

AI Research Applications

This data structure enables research into:

• Memory retention patterns
• Optimal review scheduling
• Individual learning differences
• Vocabulary acquisition modeling

⚙️ Configuration

Backup Settings

Edit flashcard_data/config.json:

{
  "auto_backup": true,
  "backup_interval_days": 7,
  "max_backups": 10,
  "last_backup": "20250912_143722",
  "pronunciation_language": "en"
}

Performance Tuning

For large vocabularies (10,000+ words):

• Increase chunk size in data loading
• Adjust backup frequency
• Consider SSD storage for better I/O performance

🔬 Research Applications

This tool is designed for academic research in:

• Cognitive Science: Memory and learning pattern analysis
• Educational Technology: Adaptive learning algorithm development
• Natural Language Processing: Vocabulary acquisition modeling
• Human-Computer Interaction: Interface optimization studies

Data Sharing Protocol

For research collaboration:

1. Export anonymized learning data
2. Follow ethical guidelines for educational data
3. Contribute to open learning research initiatives

🤝 Contributing

We welcome contributions! Here's how to get started:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Guidelines

• Follow PEP 8 style guidelines
• Add unit tests for new features
• Update documentation for API changes
• Ensure cross-platform compatibility

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Inspired by proven spaced repetition research
• Built for the open-source learning community
• Designed with accessibility and efficiency in mind

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Made with ❤️ for learners and researchers worldwide