Spaced repetition is a study technique grounded in the spacing effect — a robust finding from cognitive psychology that information is retained far better when reviews are distributed over time rather than massed in a single session. The classic algorithm, formalized by Piotr Wozniak in the SM-2 algorithm (1987) and implemented in tools like Anki, computes a growing inter-review interval for each item based on the learner's recall performance: easy recall widens the gap; failed recall resets it. For learning video specifically, spaced repetition changes the product architecture in important ways: rather than a learner watching a course once and moving on, the platform must schedule and surface review content — brief recall questions, short re-watch clips, or flashcard-style prompts — days and weeks after initial exposure. This requires a persistent learner model that tracks which concepts each individual has seen and how well they remembered them, plus a notification or scheduling mechanism to bring learners back at the right interval. The combination with microlearning is powerful: a microlearning library of short clips becomes a spaced-repetition system when the platform knows which clips relate to which concepts and re-surfaces them on a schedule. The engineering challenge is retention of learner context across sessions: the system must persist individual item-level memory state, which is richer than the simple completion flags that SCORM tracks. xAPI is the natural fit here, as each recall attempt can be recorded as a statement with timestamp, performance, and item identifier. The practical trade-off is engagement: spaced repetition requires learners to return repeatedly, and push notifications or email nudges are necessary to sustain the behaviour, adding a marketing-automation dimension to the platform.
