Spaced repetition algorithms like the one used in Anki are designed to help you retain information over time by scheduling reviews at increasing intervals. The basic idea is simple: after you learn a new fact or concept, you review it shortly afterward, then again after a slightly longer period, and so on, with the intervals growing based on how well you remember the material. This approach leverages the psychological spacing effect, where information is better retained when it's reviewed at carefully spaced intervals rather than crammed all at once.
Anki’s algorithm, known as SM2, works by assigning intervals to cards based on your response to each review. When you review a card, you can choose from several options (e.g., "Again," "Hard," "Good," "Easy"), and Anki adjusts the next review interval accordingly. If you struggle with a card, it will be shown again soon, while easier cards will be shown less frequently. The intervals grow exponentially after each successful recall, meaning the time between reviews increases dramatically as you demonstrate strong retention of the material.
While this system is effective for many learners, it has limitations. The intervals are fixed based on predefined models and don't adapt to your unique memory patterns. For example, the algorithm doesn't account for how certain types of information might be easier or harder for you to remember. More sophisticated algorithms, like FSRS, address this by using machine learning to model memory retention more accurately, dynamically adjusting the intervals for each user and each item.
Spaced repetition is particularly useful for studying complex subjects. On PracticeProblems.org, we’re using these techniques to build decks for subjects like calculus and system design, ensuring that students can retain key concepts for the long term while minimizing unnecessary repetition. Whether you're tackling challenging calculus topics or preparing for technical interviews, spaced repetition ensures that you're reviewing the right material at the right time to maximize retention.
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