Spaced repetition is a technique where individuals review lessons at increasing intervals to memorize information. Spaced repetition is based on the premise that the brain learns more effectively when the individual “spaces out” the learning process. Thus, it can be used as a mnemonic technique to transform short-term memory into long-term memory.
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Understanding spaced repetition
To understand the idea of spaced repetition, first imagine that you’re a fitness fanatic who wants stronger arms.
To achieve this, you would not walk into the gym and start with 100-pound dumbbells.
You may instead start with the 10-pound dumbbells and allow your body to rest and adjust before moving to something heavier.
In other words, you would repeat the process and move up to 20, 30, 40, and finally 100-pound dumbbells.
Spaced repetition works in much the same way, except this time, replace stronger arms with increased knowledge and dumbbells with lesson-based information.
This method of learning trains the brain to store information in long-term memory for a longer period of time.
Spaced repetition is based on the premise that the brain learns more effectively when the individual “spaces out” the learning process.
Note that the lessons themselves do not need to be identical. What’s important is that a sufficient amount of time passes between each.
When this condition is respected, spaced repetition is far more effective at improving long-term memory than other methods such as rote learning or last-minute cramming.
It also increases the likelihood that the individual not only acquires information but can use it in other contexts.
What is the optimal time between lessons?
The subject of optimal spacing in spaced repetition is somewhat contentious.
In 2014, Polish researcher Dr. Piotr Wozniak developed SuperMemo, the first software-based learning system using algorithms to determine optimal intervals:
- First repetition (lesson) – 1 day.
- Second repetition – 7 days.
- Third repetition – 16 days.
- Fourth repetition – 35 days.
While there are now many other spaced repetition apps that use increasingly sophisticated algorithms, the intervals they suggest are guides at best.
This is because brain and memory science are rather nuanced and it can be difficult to make specific recommendations that will suit everyone.
Having said that, the interval can be increased or decreased depending on how easily pieces of information are recalled or learned.
Spaced repetition and Ebbinghaus’s forgetting curve
The Ebbinghaus forgetting curve is a graphical representation of how information is forgotten with time.
The curve is exponential such that it starts with near-perfect recall on Day 0 before a precipitous drop in retention over the next few days.
After a week or two, the individual will remember very little of the information.
Spaced repetition can be used to recall information at certain times over this period and ensure the forgetting curve never approaches zero.
In other words, spaced repetition forces the learner to review the information stored in their brain before everything is lost.
As we touched on earlier, our memory becomes stronger when we revisit information after some of it is forgotten.
This idea is explained by the theory of disuse, which posits that well-learned information may be difficult to retrieve from our memory if there is interference from other information or contexts.
To increase our ability to retrieve the right information and make it more accessible, we must be able to recall that piece of information repeatedly.
Key takeaways:
- Spaced repetition is a technique where individuals review lessons at increasing intervals to memorize information.
- Spaced repetition is based on the premise that the brain learns more effectively when the individual “spaces out” the learning process. Whilst there is some debate around how much time should pass between lessons, a software-based learning system found that learning on the 1st, 7th, 16th, and 35th days was optimal.
- Spaced repetition can be used in conjunction with Ebbinghaus’s forgetting curve to recall information and ensure the learning curve never approaches the bottom of the graph.
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