Spaced Repetition Learning Web Application
Spaced Repetition Learning Web Application
An intelligent learning platform that leverages scientifically-proven spaced repetition algorithms to help students optimize their study sessions and improve long-term retention of academic material.
Status: In Progress
GitHub: aysajanez/spaced-repetition-app
Demo: spaced-repetition.aysajaneziz.com
Project Overview
This web application is designed to address the challenges students face when trying to memorize and retain complex academic concepts, particularly in fields like operations research, statistics, and business analytics. The system uses adaptive algorithms to schedule review sessions at optimal intervals, maximizing learning efficiency.
Key Features
Intelligent Scheduling Algorithm
The core of the application implements a sophisticated spaced repetition algorithm based on research in cognitive science:
interface CardSchedule {
interval: number; // Days until next review
repetition: number; // Number of successful recalls
efactor: number; // Ease factor (retention difficulty)
nextReview: Date; // Scheduled review date
}
function calculateNextReview(
card: CardSchedule,
quality: number // 0-5 quality of recall
): CardSchedule {
// SuperMemo 2 algorithm implementation
if (quality >= 3) {
if (card.repetition === 0) {
card.interval = 1;
} else if (card.repetition === 1) {
card.interval = 6;
} else {
card.interval = Math.round(card.interval * card.efactor);
}
card.repetition++;
} else {
card.repetition = 0;
card.interval = 1;
}
// Update ease factor based on performance
card.efactor = card.efactor + (0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02));
if (card.efactor < 1.3) {
card.efactor = 1.3;
}
card.nextReview = new Date(Date.now() + card.interval * 24 * 60 * 60 * 1000);
return card;
}
Adaptive Learning Paths
- Difficulty Assessment: The system analyzes user performance to identify challenging concepts
- Personalized Scheduling: Review intervals adapt based on individual learning patterns
- Progress Tracking: Comprehensive analytics show learning progress and retention rates
Rich Content Support
- Mathematical Notation: Full LaTeX/MathJax support for complex equations
- Code Highlighting: Syntax highlighting for programming concepts
- Media Integration: Support for images, audio, and video content
- Multi-format Cards: Traditional flashcards, cloze deletion, and multiple choice
Technical Implementation
Architecture
The application follows a modern full-stack architecture:
- Frontend: Next.js 14 with TypeScript and Tailwind CSS
- Backend: Next.js API routes with serverless functions
- Database: PostgreSQL with Prisma ORM for type-safe database access
- Authentication: NextAuth.js with multiple provider support
- Deployment: Vercel for seamless CI/CD and global edge deployment
Learning Science Foundation
The application is built on established research in cognitive psychology:
Hermann Ebbinghaus's Forgetting Curve
The spacing algorithm accounts for the exponential decay of memory retention:
Where:
- = Retention probability
- = Time since learning
- = Memory strength
Spaced Repetition Benefits
Research shows spaced repetition can improve retention by:
- 300% improvement in long-term recall compared to massed practice
- 50% reduction in study time needed for same retention level
- Optimal intervals that maximize memory consolidation
Current Development Status
Completed Features ✅
- User authentication and profile management
- Deck creation and card authoring interface
- Basic spaced repetition algorithm implementation
- Mathematical notation support with KaTeX
- Responsive design for mobile and desktop
- Progress tracking and statistics dashboard
In Progress 🚧
- Advanced analytics and learning insights
- Social features (deck sharing, collaborative learning)
- Mobile application development
- Integration with academic content APIs
- Advanced card types (audio, video, interactive)
Planned Features 📋
- Machine learning-powered difficulty prediction
- Integration with learning management systems (LMS)
- Gamification elements and achievement systems
- Bulk import from popular flashcard formats
- Advanced study session customization
Impact and Usage
Target Audience
- Students in STEM fields requiring memorization of complex concepts
- Professionals learning new technical skills and certifications
- Academics preparing for conferences and research presentations
- Language learners building vocabulary and grammar knowledge
Educational Applications
The system is particularly effective for:
- Mathematical formulas and theorems
- Statistical concepts and definitions
- Business terminology and frameworks
- Programming syntax and algorithms
- Foreign language vocabulary
This project represents the intersection of educational psychology, software engineering, and data science - combining rigorous academic research with practical technological implementation to enhance human learning.