LOGIN

REGISTER
Seeker

The problem of learning retention

Select the language:

This video is only available to students who have purchased the course.

Transcription The problem of learning retention


One of the biggest challenges in any learning process is information retention.

Our mind is not a hard drive that permanently stores data.

In fact, forgetting is a natural and surprisingly rapid process.

This phenomenon was first studied by psychologist Hermann Ebbinghaus, who described what we now know as the "forgetting curve."

This curve shows that we forget information exponentially: most of what we forget, we forget very soon after learning it.

For example, we may only retain 60% of the information 90 minutes into a lecture, and by the end of the day, that number may have dropped to just over 30%.

After a month, without any review, we may only remember about 20% of the material.

Understanding this reality is essential to approaching learning strategically and not get frustrated by our natural tendency to forget.

Spaced Retrieva Strategies

The good news is that we can effectively combat the forgetting curve.

The key isn't studying harder all at once (known as "cramming"), but studying smarter.

The most powerful technique for improving retention is spaced retrieva.

It involves actively retrieving information from our memory at increasing intervals of time.

Instead of simply rereading our notes, we need to force our brains to "remember" the information.

We can do this in a variety of ways: by giving ourselves quizzes, asking a friend to quiz us, or trying to explain the concept to someone else.

Each time we effortfully retrieve information, the neural connection strengthens.

Spacing is equally important: it's more effective review information several times throughout the week rather than several times in a single day.

Factors That Boost Memory

Besides spaced retrieva, other factors influence our ability to retain what we learn.


the problem of learning retention

Recent publications by emotional intelligence

Are there any errors or improvements?

Where is the error?

What is the error?