Let’s start with understanding what “Learning Efficiency” is, as until this week, I had not encountered this term before. Learning Efficiency is the “positive relation between learning rate (speed of learning) and retention (amount remembered after a delay).” That definition gave me reason to investigate further. Simply, a more efficient learner can acquire information faster and remember more of it over time. Two important variable for teaching.
Next step was the McDermott and Zerr (2019) paper which discusses potential characteristics of “efficient learners” and considers future directions for research.
McDermott and Zerr (2019) swiftly remind us that, more generally, “conditions of learning that make performance improve rapidly often fail to support long-term retention and transfer, whereas conditions that create challenges and slow the rate of apparent learning often optimize long-term retention and transfer.” Bjork & Bjork, (2015). Next, and here was my route way to the paper, McDermott and Zerr (2019) link back to Karpicke and Roediger (2007).
Participants were instructed to recall as many of those words as possible on each test. In the dropout condition, participants studied only words they had not yet recalled on a prior test and needed to recall only words they had studied in the immediately preceding cycle. As would be expected, both groups improved with repeated encounters, although the dropout group appeared to learn more quickly. That is – they reported learning the 60 words faster.
Of most interest is memory a week later. The proportion of words recalled in the standard condition (.55) greatly exceeded that in the dropout condition (.21).
The RememberMore designer in me would add that the data shows the frailty of learning decision making and when to drop cards. Kornell and Bjork (2008) cover this at length.
Zerr et al., (2018) explored this further, with repeated tests where only on the items previously missed were repeated. Taking tests until each of the pairs had been recalled exactly once. They then restudied all 45 pairs once before taking a final cued-recall test covering all items.
They looked at:
- initial performance
- the number of tests a person took until all items had been recalled (tests to criterion),
- final-test performance at the end of the study
There findings showing a strong relation between the number of tests to criterion (an index of a person’s speed of learning) and performance on the final test. The fastest learners required only 5.06 test cycles (on average) before completing their initial learning phase, whereas the slowest learners took 12.26 test cycles.
What do we learn? There are strong intercorrelations among recall, tests to criterion, and final-test performance – a case for efficient learning (i.e., quick, durable learning) as a construct. In fact Learning efficiency scores from one day to the next were quite consistent, highly consistent over 3 years. (r = .70; Zerr et al., 2018, Study 2).
However McDermott and Zerr (2019) highlight that learning and memory studies rarely examine reliability of measures or stability of performance over time. Where as they did, examining whether a person’s learning efficiency is stable when assessed at two time points.
Let’s take a closer look at efficient learners
Zerr et al. (2018) reported that more efficient learners had higher intelligence scores, faster processing speed, and better performance on a variety of cognitive tasks. But that is far from a sufficient answer.
Might instructing learners on strategies before hand improve learning efficiency overall and perhaps even close the performance gap? Evidence is mixed at best.
Is it prior knowledge important? Bors and MacLeod (1996) concluded that individuals “who know more learn better, whether the knowledge and learning are in a specific domain or more global.” Possibly?
My current teaching soap box. The ability to control one’s attention is also thought to be a major determinant of working memory capacity and fluid intelligence, two constructs that predict performance on a wide variety of real world cognitive tasks. Individuals with higher working memory capacity, in turn, implement more effective knowledge-based strategies during learning, whereas those with lower working memory capacity frequently fail to use any particular strategy. Each of these factors presumably aids in making learning more efficient, and the factors likely interact to allow efficient learners to both acquire information more quickly and remember it more successfully.
As with most interesting diversions – it keeps going. As Kathleen McDermott @Kat_McD_ tweeted.
And, when including 3 essential design elements (large N, testing different items at each delay, and testing each participant at multiple timepoints – e.g., 1 min to 1-month delays), faster learning corresponds to slower forgetting.
Faster learners demonstrate better retention, even though they have less exposure to the to-be-remembered information. Replicated the effect across days (Study 1) and years (Study 2).
Learning efficiency – an area of research to keep a track of.
Bjork, E. L., Bjork, R. A. (2015). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In Gernsbacher, M. A., Pomerantz, J. R. (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (2nd ed., pp. 55–64). New York, NY: Worth.
Kornell, Nate & Bjork, Robert. (2008). Optimising self-regulated study: The benefits-and costs-of dropping flashcards. Memory (Hove, England). 16. 125-36. 10.1080/09658210701763899.
Karpicke, J. D., Roediger, H. L.. (2007). Repeated retrieval during learning is the key to long-term retention. Journal of Memory and Language, 57, 151–162.
McDermott, K. B., & Zerr, C. L. (2019). Individual Differences in Learning Efficiency. Current Directions in Psychological Science, 28(6), 607–613. https://doi.org/10.1177/0963721419869005
Zerr, C. L., Berg, J. J., Nelson, S. M., Fishell, A. K., Savalia, N. K., McDermott, K. B. (2018). Learning efficiency: Identifying individual differences in learning rate and retention in healthy adults. Psychological Science, 29, 1436–1450.