Model and store spaced repetition scheduling data for a language learning application

Steps

1. For each learnable item, store: item_id, user_id, ease_factor (a float representing difficulty, starting around 2.5), interval_days (days until next review), repetition_count, and next_review_date
2. After each review, apply the SM-2 algorithm: if the user's performance score (0-5) is below 3, reset interval to 1 day and repetition_count to 0; otherwise calculate new_interval = previous_interval * ease_factor, and update ease_factor = ease_factor + (0.1 - (5 - score) * (0.08 + (5 - score) * 0.02))
3. Clamp ease_factor to a minimum of 1.3 to prevent intervals from collapsing to near-zero for consistently difficult items
4. Set next_review_date = today + new_interval_days and persist the updated record
5. At session start, query for all items where next_review_date <= today for the user, prioritizing items with the oldest next_review_date to handle overdue cards first