ADR 0002: SM-2 Algorithm for Spaced Repetition

Status

Accepted

Context

We need a scheduling algorithm for flashcard reviews. The app targets users who want Anki-style spaced repetition on a handheld device.

Options considered:

Algorithm	Pros	Cons
SM-2 (SuperMemo-2)	Proven, simple, same as Anki	Less sophisticated than newer algorithms
FSRS (Free Spaced Repetition Scheduler)	Machine learning, more accurate	Complex, needs training data, harder to implement
SM-17 (SuperMemo-17)	Most advanced	Proprietary, extremely complex
Custom interval	Simple	No optimization, poor retention
Leitner system	Simple, visual	Less efficient than SM-2

Decision

Use SM-2.

Consequences

Positive

Same algorithm as Anki — users already understand it
Simple to implement (~30 lines of Rust)
No machine learning dependencies
Well-studied, proven effective for decades
Easy to debug and reason about

Negative

Doesn’t adapt to individual user patterns as well as FSRS
Fixed EF change formula; newer research suggests improvements
No optimization for specific card types

Implementation

#![allow(unused)]
fn main() {
pub fn review(&mut self, quality: u8, now: DateTime<Utc>) {
    let q = quality.clamp(0, 5);

    if q < 3 {
        self.repetitions = 0;
        self.interval_days = 1;
    } else {
        self.repetitions += 1;
        self.interval_days = match self.repetitions {
            1 => 1,
            2 => 6,
            _ => (self.interval_days as f64 * self.easiness_factor).round() as i64,
        };
    }

    let ef_change = 0.1 - (5 - q) as f64 * (0.08 + (5 - q) as f64 * 0.02);
    self.easiness_factor = (self.easiness_factor + ef_change).max(1.3);
    self.due_date = now + Duration::days(self.interval_days);
    self.last_reviewed = Some(now);
}
}

Future work

Could migrate to FSRS later if user demand exists. SM-2 data is compatible — we’d just recompute intervals with the new algorithm.

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