What Is Spaced Repetition?
Spaced repetition is a highly effective method of learning, providing learners with higher retention rates. Spaced Repetition is an effective method of learning, where lesson content or lesson exercises are retaken by the learner at increasing intervals until the material is fully embedded in the learner’s long term memory. This provides higher retention rates and ultimately leads to better learning experiences. Based on a proven algorithm, spaced repetition only provides the learner with information that they haven’t quite mastered yet.
What is a Spaced Repetition Schedule?
A spaced repetition schedule dictates how frequently lessons should be re-taken in order to boost retention and embed knowledge. It governs the duration of the increasing gaps used by the spaced repetition learning methodology which counters the Ebbinghaus forgetting curve. Popular schedules include SuperMemo SM-2 and Mnemosyne. Without a sophisticated spaced repetition schedule, recall easily diminishes over time.
Why do you need a Spaced Repetition Schedule?
If you repeat learning at the wrong frequency, long-term recall and embedding are diminished. If you repeat learning too frequently, retention improvements are redundant as the memory simply hasn’t had a chance to decay. If it’s repeated too infrequently then retention and recall lapse and learning needs to be started again.
What does a Spaced Repetition Schedule look like?
Gwern offers great analysis on spaced repetition frequency, whereby their following graphs are instructive. They illustrate why cramming is ineffective for anything other than short-term retention but that correct spacing is necessary for the brain to best retain information:
Spaced Repetition Algorithms
Spaced repetition algorithms detect optimal content scheduling for reinforcing information. There are several different groups of algorithms surrounding spaced repetition, including but not limited to ones that are neural networks based, the Leitner system, and the SM-family of algorithms.
Created in 1943, the neural network was one of the first algorithms designed around spaced repetition. The neural network assesses data and recognises underlying relationships that mimics the way a human brain functions. The Leitner system was designed with boxes of flash cards in mind, allowing the learner to advance to the next card box when they correctly answered the question. On the other hand, an incorrect answer would set the learner back to the previous box. In 1990, P.A. Wozniak published a Master’s Thesis called “Optimization of Learning” in which he describes an algorithm based called SuperMemo-2 – SM-2 for short. It’s derived from a trial-and error approach that took years to perfect. He says of it:
“During the first year of using the SM-2 algorithm (learning English vocabulary), I memorized 10,255 items. The time required for creating the database and for repetitions amounted to 41 minutes per day. This corresponds to the acquisition rate of 270 items/year/min. The overall retention was… 92%.
“Separating items previously grouped in pages and introducing E-Factors were the two major components of the improved algorithm. Constructed by means of the trial-and-error approach, the SM-2 algorithm proved in practice the correctness of nearly all basic assumptions that led to its conception.”
You can read more about the math behind SM-2, the numbers behind its spaced repetition schedule and e-Factors, here.
In the succeeding years, SM-2 has been the basis of many algorithm tweaks (such as Mnemosyne) and “improvements,” however, few have proved popular often due to increased complexity that hinders measurement. As such the elegant simplicity of SM-2 makes a compelling case to be the de facto spaced repetition schedule of choice.
If you’d like to know more about EdApp’s implementation of spaced repetition – called Brain Boost (which is based upon SM-2) – and how it enhances institutional and corporate training, get in touch at email@example.com. You can also try EdApp’s Mobile LMS for free by signing up here or in the box below.
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