Build a spaced repetition system

Building a spaced repetition system (SRS) involves designing a method to help users efficiently review information by spacing out review sessions based on their learning progress. Here’s a comprehensive overview of how to build a functional SRS, including core concepts, algorithms, and implementation ideas.

Core Concepts of Spaced Repetition

1. Forgetting Curve: Learners tend to forget information over time unless it is reviewed at optimal intervals.

2. Spaced Repetition: Reviewing material just before you forget it strengthens memory and increases retention.

3. Intervals: The gap between review sessions increases each time the learner successfully recalls the information.

4. Active Recall: Testing yourself by retrieving information rather than passively reviewing enhances learning.

Key Components of a Spaced Repetition System

1. Item Database: Collection of learning items (cards, facts, questions).

2. User Profiles: Store progress and review schedules per user.

3. Scheduling Algorithm: Determines when an item should be reviewed next.

4. Review Interface: Shows items to the user, captures recall performance.

5. Performance Tracking: Records how well the user recalled each item.

Popular Algorithms

• Leitner System: Uses boxes to separate cards based on recall success.

• SuperMemo SM-2 Algorithm: Widely used algorithm for scheduling based on quality of recall.

The SM-2 Algorithm Explained

The SM-2 algorithm adjusts intervals according to how well the user recalls each item.

Variables:

• EF (Easiness Factor): Starts at 2.5, adjusts based on recall.

• Interval: Days until next review.

• Repetition: Number of consecutive correct recalls.

Steps per item review:

1. User rates recall quality (0–5).

2. If quality < 3:

   • Reset repetition to 0.

   • Set interval to 1.

3. Else:

   • Increase repetition by 1.

   • Calculate next interval:

     • If repetition = 1: interval = 1 day

     • If repetition = 2: interval = 6 days

     • Else: interval = previous interval × EF

4. Update EF:

   makefile
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   EF = EF + (0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02))
   EF cannot be less than 1.3
   

5. Schedule next review by current date + interval.

Implementation Example (Python)

python
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from datetime import datetime, timedelta

class Flashcard:
    def __init__(self, content):
        self.content = content
        self.ef = 2.5
        self.interval = 0
        self.repetition = 0
        self.next_review = datetime.now()

    def review(self, quality):
        if quality < 3:
            self.repetition = 0
            self.interval = 1
        else:
            self.repetition += 1
            if self.repetition == 1:
                self.interval = 1
            elif self.repetition == 2:
                self.interval = 6
            else:
                self.interval = int(self.interval * self.ef)

        self.ef = max(1.3, self.ef + (0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02)))
        self.next_review = datetime.now() + timedelta(days=self.interval)

    def is_due(self):
        return datetime.now() >= self.next_review

# Example usage:
card = Flashcard("What is the capital of France?")
print(f"Next review: {card.next_review}")

# Simulate user recalling the card with quality 4
card.review(quality=4)
print(f"Next review scheduled for: {card.next_review}")
print(f"Interval: {card.interval} days, EF: {card.ef:.2f}, Repetition: {card.repetition}")

Features to Enhance the System

• User Authentication: To save personalized progress.

• Multiple Decks: Organize flashcards by subjects.

• Review History: Track past performance.

• Notifications: Remind users when cards are due.

• Statistics Dashboard: Show learning progress.

Web-Based SRS Considerations

• Use a backend framework (Django, Flask, Node.js) for user and card management.

• Frontend UI with interactive review cards.

• Database to store user data and review schedules.

• API endpoints for review submissions and scheduling updates.

Summary

Building a spaced repetition system requires implementing an algorithm like SM-2 to schedule reviews, managing user data, and providing a review interface. The system dynamically adjusts review intervals based on recall quality to optimize learning efficiency and long-term retention.