What is Spaced Repetition?
Spaced repetition is a highly effective method of learning, providing learners with higher retention rate, 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. EdApp has incorporated this element into its learning platform through Brain Boost; a powerful, algorithm-based feature that automatically feeds concepts to learners if they haven’t yet completed the concept successfully.
Spaced Repetition and Brain Boost provide 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, giving their brains the opportunity to assess and absorb key concepts without flooding them with too much material.
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 timetable, recall easily diminishes over time.
How is Spaced Repetition implemented in EdApp?
Based on the SuperMemo SM-2 algorithm, EdApp implements the concept of spaced repetition through the built-in Brain Boost feature. Brain Boost does the work for you, by automatically assessing which concepts need to be revisited and simply feeding that material back to the learner intermittently. This works perfectly with EdApp’s microlearning format, as any concepts that learners find difficult can be easily repeated or revisited at a later date. Without microlearning, spaced repetition would almost be impossible to implement as courses would become too lengthy and the learner would be overwhelmed with information.
A spaced repetition app (like EdApp) incorporates the technique of providing content to learners in strategic intervals for maximum absorption and retention of essential content. Spaced active recall is particularly important for the embedment of knowledge in the long-term memory. This can be achieved through the development of your spaced learning strategy revolved around the technique of releasing content in short bursts. As a result, learners will be able to absorb and retain more information in a shorter amount of time.
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.
Why do you need a Spaced Repetition schedule?
If you repeat spaced 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 mimic the way a human brain functions. The Leitner system was designed with boxes of flashcards 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.
How to study better with EdApp spaced repetition system?
If you’d like to know more about EdApp’s spaced repetition system – 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.
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