When is the best time to recall something? How do we determine scientifically and accurately that an information is likely to fade out from our mind?
Spaced Repetition is an effective tool for learners, it helps in finding the most optimum time and revision pattern for a learner to maximize the learning and memorization experience. Spaced repetition has been applied in almost all major foreign language learning tools, corporate training tools and educational games.
Hundreds of studies in cognitive and educational psychology have shown that spacing out repeated contacts with the information across time results in improved long-term learning compared to massed repetitions. Incorporating assessments into spaced practice further enhances the advantages. Multiple types of learning, including as memory, problem solving, and generalization to new settings, benefit from spaced review or practice. Spaced practice is a practical and cost-effective technique to improve learning effectiveness and efficiency, with enormous potential to improve educational outcomes.
Algorithms for spaced repetition
All algorithms attempt to solve these, using cloud based apps, graphs and flashcards they achieve following common goals:
- Creating a personalized pattern for re-presenting the data for prolonging the retention
- Forecasting the likelihood of forgetfulness of a specific concept or information.
- Feedback based improvement and grouping of relevant concepts so that its easy to group “least to most” vulnerable concepts for recalling.
Supermemo
The SM-2, created by Wozniak in the late 1980s, was the most well-known version of the SuperMemo algorithm. The SM-2 is probably best known as the spaced repetition algorithm that underpins the open-source Anki software. The most recent SuperMemo algorithm, as of the writing of this page, is the SM-18, which was released in 2019. The algorithm, however, requires licensing, and you can only get it by purchasing a licence (or taking one of the SuperMemo language learning courses).
The capacity of the SuperMemo spaced repetition algorithms to deliver correct difficulty estimations for flashcards, as well as the algorithms’ ability to adjust to the learner based on the individuality of their brain and memory, are two of the algorithms’ advantages. The SM series of spaced repetition algorithms appears to be very promising for power learners, but its application is restricted due to licencing requirements and an old user interface.
Anki
While the Anki algorithms aren’t as fine-tuned and optimised as the SM-series, the user-friendliness and variety of decks make Anki a very enticing option. The Anki algorithm, which is based on Piotr Wozniak’s original SM-2 algorithm, is an open-source system. Medical students, language learners, and other students who need to memorize vast amounts of facts and ideas are increasingly turning to the Anki system. Because the Anki app is available for both iOS and Android devices, the system is also useful for folks who wish to review their flashcards on a mobile device. Anki also lets you make custom flashcards with custom scheduling, which comes in handy when you need to adjust your calendar based on forthcoming test dates and free time.
Leitner System
Modern spaced repetition algorithms offer more elements that are more configurable depending on your needs as a learner, whereas the original Leitner System simply employs three boxes with fixed intervals.
Regardless, if you’re a student who prefers to write flashcards by hand and feel the physical touch of your flashcards, the Leitner System has a great spaced repetition algorithm to get you started.
This method divides flashcards into groups based on how well the student understands each one in Leitner’s learning box. The students attempt to recollect the solution from a flashcard. They send the card to the next group if they succeed. They send it back to the first group if they fail. The student gets a longer time before having to revisit the cards in each subsequent group. The size of the partitions in the learning box determined the schedule of repetition in Leitner’s original approach, which he described in his book.
Half-Life Regression
HLR (half-life regression) is a spaced repetition algorithm developed by Duolingo language learning website and app experts. The half-life regression model was developed using data from Duolingo’s 40+ million monthly active users in order to improve learner retention, recall rates, and engagement. Duolingo is an excellent tool for learning a second or third language in a simple software package.
Duolingo’s research on the model’s success resulted in a published paper, which can be read for more information. Duolingo’s algorithms, on the other hand, are well worth investigating for language learning objectives.