Psychology article review

Focus&Questions& Article&4:&“Increasing&Retention&Without&Increasing&Study&Time”&

By&D.&Rohrer&&&H.&Pashler& & Remember:'You'do'not'have'to'answer'all'of'these'questions'in'your'summary'and' application'essays.'An'understanding'of'these'focus'questions'will'help'you'synthesize'your' responses'to'the'essay'questions'in'the'quiz.' ' As'you'read'the'Rohrer&&&Pashler'article'and'write'your'summary'consider:'

• What'is'the'main'point'of'the'article?' • What'are'the'benefits'and'limitations'of'overlearning,'according'to'Rohrer'&'

Pashler?' • How'does'the'spacing'of'learning'influence'the'retention'of'the'information'learned?' • What'does'research'on'mathematics'learning'tell'us'about'overlearning'and'massing'

as'strategies'fore'teaching/learning'math?'What'other'strategies'do'Rohrer'&' Pashler'suggest?' '

As'you'read'the'Rohrer&&&Pashler'article'and'write'your'application'consider:' • Refer'to'Chapter'9:'The'Knowing'Mind:'Memory,'what'type'of'memory'are'Rohrer'&'

Oashler'most'concerned'with'in'this'review'of'the'literature'on'learning'and' retention?'

• What'else'did'you'learn'in'Chapter'9:'The'Knowing'Mind:'Memory,'about'the' following'concepts'that'relate'to'the'article:'relearning,'distributed'versus'massed' practice,'the'neural'basis'of'memory'storage?'

• How'might'you'change'your'studying'habits'based'on'what'you'have'learned'from' this'article?'

• What'other'aspects'of'this'article'remind'you'of'topics'addressed'(or'to'be' addressed'in'class?'(Tip:'Review'relevant'material'covered'in'Chapter'4:'The' Biological'Mind:'The'Physical'Basis'of'Behavior,'Chapter'9:'The'Knowing'Mind:' Memory,'and'Chapter'8:'The'Adaptive'Mind:'Learning).'

' Please&note:&References&to&chapter&numbers&above&are&specific&to&the&OSU&custom&edition&of&

the&Cacioppo&&&Freberg&2e&(2015)&textbook&and&may&not&correspond&to&the&textbook&used&in&

the&Honors&sections&of&Psychology&1100.&

' '

Increasing Retention Without Increasing Study Time Doug Rohrer1 and Harold Pashler2

1University of South Florida and 2University of California, San Diego

ABSTRACT—Because people forget much of what they learn, students could benefit from learning strategies that yield long-lasting knowledge. Yet surprisingly little is known about how long-term retention is most efficiently achieved.Herewe examinehowretention is affected by two variables: the duration of a study session and the temporal distribution of study time across multiple sessions. Our results suggest that a single session devoted to the study of some material should continue long enough to ensure that mastery is achieved but that immediate further study of the same material is an inefficient use of time. Our data also show that the benefit of distributing a fixed amount of study time across two study sessions—the spacing effect—depends jointly on the interval between study sessions and the interval between study and test. We discuss the practical implications ofbothfindings, especially inregard tomathematics learning.

KEYWORDS—spacing; overlearning; education; learning; mathematics

Although most people have spent thousands of hours in the

classroom, the result of this effort is often surprisingly disap- pointing. Indeed, both the popular press and the academic lit-

erature are replete with examples of educational failure among students and recent graduates. In one assessment of U.S. eighth

graders, only 50% were able to correctly multiply !5 and !7 (Reese, Miller, Mazzeo, & Dossey, 1997), and a recent survey of young adults in the United States revealed that most could not

identify the continent in which Sudan is located (National Geographic, 2006). While such findings are partly explained by

the fact that some students never learned the information in the first place, we believe that forgetting is often the cause.

For this reason, it seems important to define learning strate- gies that can promote long-lasting retention. Yet surprisingly little is known about the long-term effectiveness of most learning

strategies. We have been conducting learning experiments in which subjects are tested as much as 1 year after the final study

session. In a further nod to ecological validity, our subjects learn the kinds of material that people often try to learn, such as vo-

cabulary, geography, foreign languages, and mathematics (e.g., Pashler, Rohrer, Cepeda, & Carpenter, 2007). In this review, we

focus on two decisions that all learners face: How long should one study the same material before quitting or shifting to different material, and how should a fixed amount of study time

be distributed across study sessions?

OVERLEARNING

When learners choose to devote an uninterrupted period of time to learning some material or a skill, they must decide when to quit, regardless of whether they later return to the samematerial.

For example, once a student has cycled through a list of vo- cabulary words until each definition has been correctly recalled

exactly one time, the student must decide whether to cycle again through the same list. The continuation of study immediately

after the student has achieved error-free performance is known as overlearning. Many educators argue that overlearning is an effective way to boost long-term retention, and overlearning

appears to be quite common in schools. In mathematics courses, for instance, assignments typically include many problems of

the same kind, thereby ensuring that students devote much of their study time to overlearning.

Does Overlearning Produce Long-Lasting Benefits? At first glance, the heavy reliance on overlearning might be seen as consistent with the results of nearly 80 years of empirical

literature. In these experiments, subjects either quit or contin- ued studying after some criterion was reached, and the addi-

tional study typically boosted subsequent test performance (see Driskell, Willis, & Copper, 1992, for a meta-analysis). Yet a closer examination of the literature led us to wonder whether

the benefits of overlearning might be short lived. In most over- learning studies, the test was given within a week of the study

session, and in many cases, within an hour. To determine how

Address correspondence to Doug Rohrer, Department of Psychology, PCD4118G, University of South Florida, Tampa, FL 33620; e-mail: [email protected].

CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE

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the benefits of overlearning hold up over meaningful periods of time, we have been measuring the effects of overlearning after

various retention intervals (RIs)—the amount of time between study and test. For example, in one of our experiments (Rohrer, Taylor, Pashler, Wixted, & Cepeda, 2005), subjects learned

vocabulary by cycling through a list of word–definition pairs (e.g., cicatrix–scar) by repeatedly testing themselves (cicatrix–? . . . scar), as one would do with flash cards. They completed either 5 learning trials (adequate learning) or 10 learning trials (over-

learning). Adequate learners generally had no more than one perfect study trial, whereas most overlearners achieved at least three perfect trials. Subjects were tested either 1 or 4 weeks later.

As shown in Figure 1, overlearning provided noticeable gains at 1 week, but these gains were almost undetectable after 4 weeks.

Other studies of ours have confirmed this pattern of declining overlearning benefits, although the length of time over which

gains remain detectable varies with the details of the procedure (e.g., Rohrer et al., 2005; Rohrer & Taylor, 2006). In summary, then, we see that while overlearning often increases performance

for a short while, the benefit diminishes sharply over time.

Implications In thinking through the practical implications of our over-

learning results, it probably makes sense to focus on the relative efficiency of overlearning versus alternative strategies. Because

overlearning requires more study time than does the avoidance of overlearning, the critical question is how the benefits of overlearning compare to the benefits resulting from some alter-

native use of the same time period. As we will see in the second part of this article, it seems very likely that devoting this study

time to the review of materials studied weeks, months, or even years earlier will typically pay far greater dividends than the continued study of material learned just a moment ago. In es-

sence, overlearning simply provides very little bang for the buck, as each additional unit of uninterrupted study time provides an

ever smaller return on the investment of study time. (We hope it

is clear that in questioning the utility of overlearning, we are not

suggesting that students reduce their study time, nor are we disparaging the use of drill and practice. Rather, we question the

wisdom of providing continued practice onmaterial immediately after error-free performance has been achieved.)

There are, however, situations in which overlearning is de- sirable. For instance, overlearning appears to be effective in the short term and therefore might be a fine choice for learners who

do not seek long-term retention. In addition, there are situations in which an error or even a delayed response might have dire

consequences—say, emergency routines performed by pilots, soldiers, or nurses—and here, overlearning is probably advis-

able and perhaps even necessary.

SPACING OF LEARNING

Overlearning speaks to one aspect of the broader question of how distribution of study time affects learning. This area has been the

focus of research for more than a century (see Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006, for a recent review). In most re- search on this topic, a fixed amount of study time is divided

across two sessions that are separated by an intersession interval (ISI). If the ISI equals zero, study time is said to be massed. Importantly, the retention interval is always measured from the second study session. When tested later, performance is usually

much better if the study time is spaced rather than massed—a finding known as the spacing effect (e.g., Bahrick, 1979; Bjork, 1979). There are numerous theoretical explanations for the

spacing effect, but these are beyond the scope of this article (see Dempster, 1989, for a review).

While the superiority of spacing overmassing iswell established, less is known about how far apart the study sessions should be spaced to optimize long-term retention. For instance, does the

duration of the ISI affectmemory, and, if so, how?Wehave begun to seek answers to these questions with experiments using long RIs.

Varying the ISI In our first set of spacing experiments, we varied the ISI sepa- rating the two study sessions, and the RI was fixed (Cepeda et al.,

2007). In the first of these studies, students studied Swahili– English word pairs. The ISI ranged from 5 minutes to 14 days, and the RI was 10 days. ISI had a very large effect on test scores,

with the 1-day ISI yielding the best recall (Fig. 2). In a second experiment in which subjects learned the names of some obscure

objects, we used a 6-month RI, and varied ISI from 5 minutes to 6 months. Effects were even bigger than in the first study, but

the optimal ISI was roughly 1 month (Fig. 2).

The Interaction of ISI and RI In comparing the results of the two experiments just described (Fig. 2), one sees that the increase inRI from 10 days to 6months

resulted in an increase in the optimal ISI from about 1 day to

Retention Interval (weeks) 1 4

T es

t S co

re

0%

100%

Overlearning (10 list cycles)

Adequate Learning (5 list cycles)

Fig. 1. The diminishing benefits of overlearning over time. Students learned 10 word–definition pairs (e.g., cicatrix–scar) by cycling through the list 5 or 10 times via testing with feedback (cicatrix–? . . . scar). On the subsequent test, the benefit for the 10-trial conditionwas large after 1week but undetectable after 4 weeks.

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about 1 month. The results are consistent with an idea that has

long been suspected based on studies with short time intervals (Crowder, 1976): that the optimal ISI varies with the RI. To as- sess this possibility within a single experiment, we are currently

conducting a Web-based experiment in which we simulta- neously vary both ISI (up to 15 weeks) and RI (as long as 50

weeks). Preliminary results from about 1,300 subjects indicate that the optimal ISI is indeed varying as expected with RI, with

the optimal ISI lying at a value of roughly 10 to 30% of the RI. The character of this rather intriguing interaction between ISI

and RI is illustrated by the surface in Fig. 3. Here, the vertical

axis shows the test score, with the other two axes representing ISI and RI. Three features are noteworthy. First, for any value of ISI,

an increase in RI leads to a decline in test score—the expected forgetting curve. Second, for any value of RI, an increase in ISI causes the test score to first increase and then decrease (like the

data in Fig. 2). Third, as RI is increased, optimal ISI increases as well, generating a ‘‘mountain ridge’’ that moves gradually out-

ward from the RI axis as RI increases.

Implications Our experiments demonstrate that powerful spacing effects oc-

cur over practically meaningful time periods. Furthermore, final test performance depends heavily on the duration of the spacing

gap, with too-brief gaps causing poorer performance than ex- cessively long gaps. Moreover, spacing effects generally seem to get bigger, not smaller, when one examines longer-term reten-

tion. The results have widespread implications for instruction at many levels, of which we will offer just a few examples. Many

elementary- and middle-school teachers present a different set of spelling or vocabulary words each week, but their students might be far better served if material was distributed sporadi-

cally across many months. At the college level, instructors often fail to give cumulative final exams that likely would induce re-

study of material. In the realm of life-long learning, immersion-

style foreign-language courses are popular, yet their brevity, which prevents sufficient spacing, should produce deceptively high initial levels of learning followed by rapid forgetting.

MATHEMATICS LEARNING

Because the experiments described thus far required subjects to learn concrete facts, it is natural to wonder whether the results

of these studies will generalize to tasks requiring more abstract kinds of learning. To begin to explore this question, we have been assessing the effects of overlearning and spacing inmathematics

learning. For example, in one experiment (Rohrer & Taylor, 2006), students were taught a permutation task and then as-

signed either three or nine practice problems. The additional six problems, which ensured heavy overlearning, had no detectable effect on test scores after 1 or 4 weeks. In another experiment

with the same task (Rohrer & Taylor, in press), a group of ‘‘spacers’’ divided four practice problems across two sessions

separated by 1 week, whereas a group of ‘‘massers’’ worked the same four problems in one session.When tested 1 week later, the

spacers outscored the massers (74% vs. 49%). This apparent ineffectiveness of overlearning and massing is

troubling, as these two strategies are fostered by most mathe-

matics textbooks. In these texts, each set of practice problems consists almost entirely of problems relating solely to the im-

mediately preceding material. The concentration of all similar problems into the same practice set constitutes massing, and the sheer number of similar problems within each practice set

guarantees overlearning. Alternatively, mathematics textbooks could easily adopt a format that engenders spacing. With such a

shuffled format, practice problems relating to a given lessonwould

ISI 0.0 RI 0.5 RI 1.0 RI 1.5 RI

T es

t S co

re

0%

100%

RI = 10 days (Swahili)

RI = 6 months (Objects)

ISI = 1 day = 10% of RI

ISI = 1 month = 17% of RI

Fig. 2. Effect of varying intersession interval (ISI). In the Swahili exper- iment, two study sessions were separated by an ISI of 0, 1, 2, 4, 7, or 14 days, followed by a 10-day retention interval (RI). In the object-naming experiment, an ISI of 0, 1, 7, 28, 84, or 168 days was followed by a 6-month RI. In both studies, the optimal ISI was about 10 to 20% of the RI.

Fig. 3. Interaction between intersession interval (ISI) and retention in- terval (RI). Test score is shown as a function of ISI andRI. For any value of ISI, an increase in RI causes test score to decline. For any value of RI, an increase in ISI causes test score to first increase and then decrease. The optimal ISI values, which lie along the ‘‘mountain ridge’’ of the surface, increase as RI increases, producing amountain ridge thatmoves gradually outward from the RI axis.

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be distributed throughout the remainder of the textbook. For

example, a lesson on parabolas would be followed by a practice set with the usual number of problems, but only a few of these

problems would relate to parabolas. Other parabola problems would be distributed throughout the remaining practice sets.

The shuffled format not only provides a spaced temporal dis- tribution but also confronts the learner with a variety of problem types within each set, whichmay itself enhance learning.With the

standard format, a lesson on the one-sample t-test, for example, is followed by nothing but one-sample t-test problems. This provides no discrimination learning to help students determine which features of a problem indicate the appropriate choiceof procedure.

With a shuffled format, however, problem types are mixed, and students must learn how to select the appropriate procedure for each problem. This benefit seems to be independent of the

spacing effect (Rohrer & Taylor, in press).

THE BIGGER PICTURE

Although this brief review has focused on the optimal timing and duration of study, there are, of course, many other decisions

learners must make. For example, when preparing for an exam, should students self-test (casa–?) before seeing the answer (house), or is itmore effective to restudy the answer (casa–house)?A sizable body of evidence suggests that self-testing, or retrieval practice, is usually the wiser strategy (e.g., Roediger & Karpicke, 2006), with the caveat that learners receive the correct answer after an error (Pashler, Cepeda, Wixted, & Rohrer, 2005).

Oddly, these kinds of practical questions have mostly been ignored by experimental psychologists over the years (although

Harry Bahrick and Robert Bjork are two notable exceptions). Happily, however, there has been a resurgence of interest in this domain in the last few years (see Recommended Reading), and

efforts are underway in various places to try to cull the empirical research for simple, concrete principles that can be communi-

cated directly to learners and teachers. Research of this sort should also have spinoffs for educational software. For example,

although computer-based instruction typically provides exten- sive retrieval practice and rapid feedback, it offers a currently unexploited opportunity to schedule study sessions in ways that

optimize long-term retention. The various developments cur- rently underway should all help bring us closer to the time when

educational practice will rely chiefly on empirical evidence rather than a combination of tradition and fads.

Recommended Reading Bjork, R.A. (1979). (See References)

McDaniel, M.A., Roediger, H.L., & McDermott, K.B. (2007). Gener- alizing test-enhanced learning from the laboratory to the class- room. Psychonomic Bulletin & Review, 14, 200–206.

Metcalfe, J., Kornell, N, & Son, L.K. (in press). A cognitive-science based program to enhance study efficacy in a high- and low-risk setting. European Journal of Cognitive Psychology.

Pashler, H., Rohrer, D., Cepeda, N.J., & Carpenter, S.K. (2007). (See References)

Acknowledgments—Our collaborators include Shana Car- penter, Nicholas Cepeda, Noriko Coburn, Michael Mozer, Kelli Taylor, Edward Vol, and John Wixted. This research was sup- ported by the Institute of Education Sciences, U.S. Department of Education (Grant #R305H040108).

REFERENCES

Bahrick, H.P. (1979). Maintenance of knowledge: Questions about memory we forgot to ask. Journal of Experimental Psychology: General, 108, 296–308.

Bjork, R.A. (1979). Information-processing analysis of college teach- ing. Educational Psychologist, 14, 15–23.

Cepeda, N.J., Mozer, M.C., Coburn, N., Rohrer, D., Wixted, J.T., & Pashler, H. (2007). Optimizing distributed practice: Theoretical analysis and practical implications. Unpublished manuscript.

Cepeda, N.J., Pashler, H., Vul, E., Wixted, J.T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quanti- tative synthesis. Psychological Bulletin, 132, 354–380.

Crowder, R.G. (1976).Principles of learning andmemory. Hillsdale, NJ: Erlbaum.

Dempster, F.N. (1989). Spacing effects and their implications for theory and practice. Educational Psychology Review, 1, 309–330.

Driskell, J.E., Willis, R.P., & Copper, C. (1992). Effect of overlearning on retention. Journal of Applied Psychology, 77, 615–622.

National Geographic. (2006). 2006 Geographic Literacy Study. Wash- ington, DC: National Geographic Society.

Pashler, H., Cepeda, N.J., Wixted, J.T., & Rohrer, D. (2005).When does feedback facilitate learning of words? Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 3–8.

Pashler, H., Rohrer, D., Cepeda, N.J., & Carpenter, S.K. (2007). Enhancing learning and retarding forgetting: Choices and conse- quences. Psychonomic Bulletin & Review, 14, 187–193.

Reese, C.M., Miller, K.E., Mazzeo, J., & Dossey, J.A. (1997). NAEP 1996 Mathematics Report Card for the Nation and the States. Washington, DC: National Center for Education Statistics.

Roediger, H.L., & Karpicke, J.D. (2006). The power of testing memory: Basic research and implications for educational practice. Per- spectives on Psychological Science, 1, 181–210.

Rohrer, D., & Taylor, K. (2006). The effects of overlearning and dis- tributed practice on the retention of mathematics knowledge. Applied Cognitive Psychology, 20, 1209–1224.

Rohrer, D., & Taylor, K. (in press) The shuffling of mathematics prob- lems improves learning. Instructional Science.

Rohrer, D., Taylor, K., Pashler, H., Wixted, J.T, & Cepeda, N.J. (2005). The effect of overlearning on long-term retention. Applied Cogni- tive Psychology, 19, 361–374.

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