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What is Spaced Repetition?

Spaced repetition is a learning technique that involves reviewing information at increasing intervals. This method leverages the psychological spacing effect to combat the forgetting curve and improve long-term retention. Forgetting Curve

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How FlashMind Implements Spaced Repetition

FlashMind uses a sophisticated algorithm based on the SM-2 algorithm, enhanced with modern improvements:

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The Algorithm

1

Initial Learning

When you first encounter a card, it starts with a short interval (1 day)
2

Quality Assessment

After each review, you rate your recall quality from 0-5:
  • 0: Complete blackout
  • 1: Incorrect response, correct one remembered
  • 2: Incorrect response, correct one seemed familiar
  • 3: Correct response recalled with serious difficulty
  • 4: Correct response after hesitation
  • 5: Perfect response
3

Interval Calculation

Based on your rating, the algorithm calculates the next review interval:
if quality < 3:
    interval = 1 day (reset)
else:
    interval = interval × ease_factor
4

Ease Factor Adjustment

The ease factor is adjusted based on performance:
ease_factor = ease_factor + (0.1 - (5 - quality) × (0.08 + (5 - quality) × 0.02))

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Key Features

Adaptive Intervals

Intervals automatically adjust based on your performance

Difficulty Tracking

Cards that are harder get reviewed more frequently

Forgetting Curve

Algorithm accounts for natural memory decay

Optimal Timing

Reviews scheduled just before you’re likely to forget

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Benefits of Spaced Repetition

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Scientific Backing

Research has consistently shown that spaced repetition:
  • Increases retention by 200-400% compared to massed practice
  • Reduces study time by focusing on difficult material
  • Improves long-term memory through strategic timing
  • Prevents overlearning of easy material

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Real-World Applications

Perfect for vocabulary acquisition and grammar patterns. Studies show 90%+ retention rates for vocabulary learned with spaced repetition.
Medical students using spaced repetition show 25% better performance on board exams.
Programming concepts, formulas, and procedures benefit greatly from spaced review.
History dates, scientific facts, and theoretical concepts are ideal for spaced repetition.

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Optimizing Your Study Sessions

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Best Practices

Consistency is key: Regular daily reviews, even for 10-15 minutes, are more effective than long cramming sessions.
  1. Be honest with ratings: Accurate self-assessment ensures optimal scheduling
  2. Study daily: The algorithm works best with consistent daily reviews
  3. Focus on understanding: Don’t just memorize; understand the concepts
  4. Use mnemonics: Memory aids work well with spaced repetition

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Study Timing

The algorithm schedules reviews based on optimal timing:

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Performance Analytics

FlashMind tracks your progress with detailed analytics:
  • Retention rate: Percentage of cards answered correctly
  • Study streak: Consecutive days of study
  • Cards mastered: Number of cards with long intervals
  • Time saved: Efficiency gains from the algorithm
The spaced repetition algorithm adapts to your individual learning patterns, becoming more accurate over time as it learns from your performance data.

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Customizing the Algorithm

Advanced users can adjust algorithm parameters:

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Interval Modifiers

  • Learning steps: Customize initial learning intervals
  • Graduating interval: Set the interval after successful learning
  • Maximum interval: Cap the longest possible interval

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Ease Factor Settings

  • Starting ease: Initial ease factor for new cards
  • Minimum ease: Lowest possible ease factor
  • Ease bonus: Reward for easy cards
const defaultSettings = {
  learningSteps: [1, 10], // minutes
  graduatingInterval: 1, // days
  maximumInterval: 36500, // days (~100 years)
  startingEase: 2.5,
  minimumEase: 1.3,
  easeBonus: 0.15,
};
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