ArticlePDF Available

The shuffling of mathematics problems improves learning

Authors:

Abstract and Figures

In most mathematics textbooks, each set of practice problems is comprised almost entirely of problems corresponding to the immediately previous lesson. By contrast, in a small number of textbooks, the practice problems are systematically shuffled so that each practice set includes a variety of problems drawn from many previous lessons. The standard and shuffled formats differ in two critical ways, and each was the focus of an experiment reported here. In Experiment 1, college students learned to solve one kind of problem, and subsequent practice problems were either massed in a single session (as in the standard format) or spaced across multiple sessions (as in the shuffled format). When tested 1week later, performance was much greater after spaced practice. In Experiment 2, students first learned to solve multiple types of problems, and practice problems were either blocked by type (as in the standard format) or randomly mixed (as in the shuffled format). When tested 1week later, performance was vastly superior after mixed practice. Thus, the results of both experiments favored the shuffled format over the standard format.
Content may be subject to copyright.
The shuffling of mathematics problems improves
learning
Doug Rohrer ÆKelli Taylor
Received: 29 August 2006 / Accepted: 3 January 2007 / Published online: 19 April 2007
Springer Science+Business Media, Inc. 2007
Abstract In most mathematics textbooks, each set of practice problems is comprised
almost entirely of problems corresponding to the immediately previous lesson. By contrast,
in a small number of textbooks, the practice problems are systematically shuffled so that
each practice set includes a variety of problems drawn from many previous lessons. The
standard and shuffled formats differ in two critical ways, and each was the focus of an
experiment reported here. In Experiment 1, college students learned to solve one kind of
problem, and subsequent practice problems were either massed in a single session (as in the
standard format) or spaced across multiple sessions (as in the shuffled format). When tested
1 week later, performance was much greater after spaced practice. In Experiment 2,
students first learned to solve multiple types of problems, and practice problems were
either blocked by type (as in the standard format) or randomly mixed (as in the shuffled
format). When tested 1 week later, performance was vastly superior after mixed practice.
Thus, the results of both experiments favored the shuffled format over the standard format.
Keywords Mathematics Practice Distribute Mass Block Mix
Interleave Spacing
Introduction
The effort to improve mathematics learning has focused primarily on the manner in which
material is taught, with far less attention given to the role of practice problems. Yet, for
many students, the majority of their mathematics learning effort is devoted to practice
problems (rather than, say, reading). While many aspects of practice are worthy of
investigation, the two experiments presented here focused primarily on the effects of
varying either the temporal distribution of practice problems or the order in which
D. Rohrer (&)K. Taylor
Department of Psychology, PCD 4118G, University of South Florida, Tampa, FL 33620, USA
e-mail: drohrer@cas.usf.edu
123
Instr Sci (2007) 35:481–498
DOI 10.1007/s11251-007-9015-8
problems are solved. Neither manipulation required an increase in the total number of
practice problems, yet both experiments revealed large boosts in subsequent test
performance. That is, merely altering the timing of practice led to large gains in test
performance.
The arrangement of practice problems in most mathematics textbooks is one that most
readers will recognize. Each set of practice problems, or practice set, consists almost
entirely of problems corresponding to the immediately preceding lesson (e.g., Glencoe,
2001). For example, a lesson on the addition or subtraction of fractions (e.g., 5/6–4/5) is
followed immediately by perhaps a few dozen problems, all of which require the addition
or subtraction of fractions. In brief, each set of practice problems is devoted to the most
recent lesson. Moreover, problems of the same type are usually in blocks (e.g., 12 fraction
addition problems, followed by 12 fraction subtraction problems). This format also is the
modal format of computer-aided instructional packages, and, therefore, the data reported
herein apply to this instructional medium as well.
The standard practice format has two features that are examined here. First, most or all
of the problems relating to a given lesson are concentrated or massed into the immediately
following practice set instead of being distributed or spaced across multiple practice sets.
For example, in the standard format, virtually all of the quadratic formula problems within
the textbook appear in the practice set that appears immediately after the lesson on the
quadratic formula. The second feature of the standard format is that the problems within
each practice set are usually blocked by topic and not mixed across topics. For example,
after a lesson explaining how to find the least common multiple and the greatest common
factor of two integers, a practice set includes a block of least common multiple problems
followed by a block of greatest common factor problems. Notably, it is possible for a
textbook to use massed practice but not blocked practice, but, in our experience, these two
features usually co-occur.
By contrast, a very small number of mathematics textbooks use what we call a shuffled
format (e.g., Saxon, 1997). A textbook with a shuffled format may have lessons identical to
those in the standard format, and moreover, the two formats need not differ in either the
number of practice sets within the text or the number of practice problems per practice set.
But, with the shuffled format, the practice problems are systematically arranged so that
practice problems are both distributed and mixed. For example, after a lesson on the
quadratic formula, the immediately following practice set would include no more than a
few quadratic formula problems, with other quadratic formula problems appearing in
subsequent practice sets with decreasing frequency. Thus, the practice problems of a given
type are systematically spaced throughout the textbook. This spacing intrinsically ensures
that the problems within each practice set include a mixture of different types, as there are
no more than one or two practice problems of each kind within each practice set. In order
to achieve such variety in the early portion of the textbook, the first several practice sets
can include problems relating to topics covered in previous years.
In summary, virtually all mathematics textbooks use one of two formats that differ with
regard to two variables. First, the problems of a given type are either massed in a single
practice set (as in the standard format) or spaced across multiple practice sets (as in the
shuffled format). Second, problems of different types are either blocked by type (as in the
standard format) or randomly mixed (as in the shuffled format). The massed vs. spaced
variable was examined in Experiment 1, and the blocked vs. mixed variable was examined
in Experiment 2. A third variable—light versus heavy massed practice—was also
examined in Experiment 1, for reasons described below. The remainder of the Introduction
is devoted to the relevant literature.
482 D. Rohrer, K. Taylor
123
Massed versus spaced practice
In an experiment comparing the benefits of massed and spaced practice, a given amount of
practice is either massed into a single session or spaced across multiple sessions. For
example, four practice problems (relating to the same skill or concept) might be assigned in
a single session or divided evenly across two sessions separated by 1 week. The retention
interval equals the period of time between the last practice problem and the test. For
example, if a skill is practiced on Monday and Tuesday and tested on Friday, the retention
interval equals 3 days.
Test performance is generally superior after practice that is spaced rather than massed—
a finding known as the spacing effect (e.g., Bahrick, Bahrick, Bahrick, & Bahrick, 1993;
Bjork, 1979,1988,1994; Bloom & Shuell, 1981; Carpenter & DeLosh, 2005; Reynolds &
Glaser, 1964; Smith & Rothkopf, 1984). Exactly how spacing of practice produces this
benefit is the focus of much unresolved debate (for a review, see Dempster, 1989), but, for
the present purposes, it is sufficient to simply note that spaced practice boosts test per-
formance. For this reason, many previous authors have advocated that learners space their
study (Bahrick et al., 1993; Bjork, 1979,1988,1994; Bloom & Shuell, 1981; Cepeda,
Pashler, Vul, Wixted, & Rohrer, 2006; Dempster, 1989; Pashler, Rohrer, Cepeda, &
Carpenter, 2007; Reynolds & Glaser, 1964; Schmidt & Bjork, 1992; Smith & Rothkopf,
1984).
While only a few of the hundreds of spacing experiments have used mathematics tasks,
these few findings have shown benefits of spacing mathematics practice. For instance,
Smith and Rothkopf (1984) observed a spacing effect if several statistics lectures were
spaced across 4 days rather than massed into one session. More recently, Rohrer and
Taylor (2006) found a benefit of spacing mathematics practice for students who were tested
4 weeks after their last practice problem. Finally, Rea and Modigliani (1985) found a
spacing effect with young children who were asked to memorize five multiplication facts
(e.g., 8 ·5 = 40), although this kind of task is better described as verbal memory rather
than mathematical learning (which is not to say that such facts are not sometimes useful).
Incidentally, several mathematics learning experiments that purport to show a spacing
effect were, in fact, confounded in favor of the spacing effect. In Grote (1995), for in-
stance, students either massed their practice on Day 1 or spaced their practice across Days
1 through 22, but every student was tested on Day 36. Thus, the spaced practice condition
benefited from a far shorter retention interval. Nevertheless, the results of the few non-
confounded studies support the view that the long-term retention of mathematical
knowledge is enhanced by distributing the corresponding practice problems across multiple
practice sessions. This effect is revisited in Experiment 1.
Light versus heavy massed practice
One explanation for the preponderance of massed practice within mathematics textbooks is
the oft-cited belief that material is retained longer if study or practice continues imme-
diately after the material is understood. This kind of massed practice is formally known as
an overlearning strategy. For example, after a student has correctly solved one mathe-
matics problem (or perhaps two problems of the same type in order to rule out the
possibility that the first correct answer was due to chance), additional problems of the same
type, if attempted immediately, constitute an overlearning strategy. It must be clarified,
incidentally, that the term overlearning describes a strategy and not the degree of learning.
Shuffling of mathematics problems 483
123
In fact, one can achieve a very high degree of learning without using an overlearning
strategy. For example, most everyone has mastered the names of the calendar months, but
few did so by the use of an overlearning strategy (i.e., immediate post-criterion practice).
Thus, we are not evaluating the utility of knowing material very well but rather the utility
of learning by the strategy of post-criterion practice.
Overlearning experiments include a condition that ensures overlearning and a condition
in which overlearning is avoided or at least minimized. The great majority of these
experiments have found that the overlearning condition produces greater subsequent test
performance (e.g., Gilbert, 1957; Krueger, 1929; Postman, 1962), and such a benefit was
confirmed by a meta-analysis reported by Driskell et al. (1992). In brief, although a few
studies have found little or no benefit of overlearning (e.g., Reynolds & Glaser, 1964;
Rohrer, Taylor, Pashler, Wixted, & Cepeda, 2005), most results find overlearning to boost
subsequent test performance. These empirical findings perhaps explain the widespread
support for overlearning as a learning strategy (e.g., Fitts, 1965; Foriska, 1993; Hall, 1989;
Jahnke & Nowaczyk, 1998; Radvasky, 2006).
Yet there is reason to be cautious about the utility of overlearning in the mathematics
classroom. Only one previous overlearning experiment has used a mathematics task, and it
found no effect of overlearning on subsequent test performance. In an experiment reported
by Rohrer and Taylor (2006), students learned a single procedure and then immediately
worked either three or nine practice problems. The threefold increase in practice had no
effect on test scores at either the 1-week or 4-week tests.
Thus, this single experiment raises the possibility that mathematics overlearning is a
waste of time, and the implications of this finding are troubling because many mathe-
matics assignments demand a large degree of overlearning. For example, in the standard
(massed-blocked) format described at the outset of this Introduction, practice sets often
include as many as a dozen or more problems of the same kind. Thus, if overlearning is
ineffective, most mathematics students are devoting a sizeable proportion of their
practice to a learning strategy with little or no benefit. The benefits of overlearning are
revisited in Experiment 1.
Blocked versus mixed practice
Practice problems within mathematics textbooks are usually blocked by topic and not
mixed together, as described at the outset of this Introduction, but there appears to be little
direct evidence supporting either strategy for mathematics tasks. For motor tasks, the data
suggest that subsequent test performance is greater after mixed practice (see Bjork, 1994,
for a review). In Carson and Wiegand (1979), for instance, young children learned to throw
bean bags of different weights at a target, and their subsequent test performance was
greater when the practice throws for each particular weight were intermixed and not
blocked by weight.
For mathematics learning, however, we are unaware of any experiments comparing
mixed and blocked practice. Some previous studies have compared practice schedules that
differ with regard to the extent of mixture, but these experimental comparisons have been
confounded. For example, in an experiment reported by Mayfield and Chase (2002), one
group of subjects relied on mixed, spaced practice while another group underwent blocked,
massed practice. Thus, it was impossible to assess the specific effect of mixture. In
Experiment 2 of the present paper, students are randomly assigned to either a mixed or
blocked practice schedule, and the practice problems for both groups are spaced across two
484 D. Rohrer, K. Taylor
123
sessions. This way, we were able to assess whether mixed practice provides benefits above
and beyond the benefit of spaced practice.
There is good reason to expect that a mixture of problem types will benefit subsequent
test performance. If a practice set includes a randomly arranged variety of problem types,
students learn to pair each kind of problem with the appropriate procedure. In other words,
a mixed practice schedule requires that students learn not only how to perform each
procedure but also which procedure is appropriate for each kind of problem (e.g., Kester,
Kirschner, & Van Merrie
¨nboer, 2004). For example, when a lesson on the repeated-
measures t-test is followed immediately by a practice set comprised solely of repeated-
measures t-test problems, the choice of procedure is obvious to students. Thus, they can
complete this block of practice problems without learning why each problem requires this
particular procedure. Consequently, when these students receive a repeated-measures t-test
problem on a later exam that includes a variety of problem types, each requiring that they
‘‘assess statistical significance,’’ they are faced with a task they have not practiced:
knowing which statistical test is appropriate for each type of problem. In fact, knowing
which procedure is appropriate is arguably more important than knowing how to perform
the procedure.
Learning to pair problem types and procedures is especially challenging in mathematics
because different problem types are often superficially similar. For example, the solution of
a single equation with a single variable is a rather narrow subset of problems, but even this
subset of problem types subsumes different procedures. For example, the equation,
x
3
3x
2
2x= 0, is solved by factoring the left-hand expression, but the equation,
x
2
x1 = 0, cannot be solved by factoring and instead requires the quadratic formula.
Likewise, integral problems share a similar appearance, but students must learn which
integration technique is appropriate for each of the subtly different kinds. Such superficial
similarity is ubiquitous in mathematics, and this is why students need discrimination
training.
The link between superficial similarity and the importance of this discrimination learning
has been demonstrated by VanderStoep and Seifert (1993). In their first experiment, for
instance, students learned to solve two kinds of mathematics problems that were either
similar or different in appearance. Some students saw a tutorial emphasizing how to solve
each kind of problem, and others saw a tutorial emphasizing which of two procedures was
appropriate for each kind of problem. The learning-which tutorial proved more effective
than the learning-how tutorial when the two kinds of problems were similar, but the tutorials
were equally effective when the kinds of problems did not resemble each other. Thus,
discrimination training proved useful when problems were similar in appearance.
In summary, while the importance of discrimination training provides one reason to
suspect that the mixture or interleaving of problem types will produce better subsequent
test performance, it appears that no prior experiments have directly compared mixed and
blocked practice. This was the aim of Experiment 2. If mixed practice is, in fact, superior
to blocked practice for mathematics learning, it would suggest that the widespread reliance
on blocked practice needs reevaluation.
Experiment 1
The first experiment assessed the effects of temporal distribution (spaced vs. massed
practice) and overlearning (massed practice vs. light massed practice) of mathematics
practice. College students were taught how to calculate the number of permutations of a
Shuffling of mathematics problems 485
123
letter sequence with at least one repeated letter (e.g., aabccc), and they then practiced this
procedure according to one of three schedules. Spacers worked two practice problems in
each of two sessions separated by 1 week; Massers worked the same four practice problems
in a single session; and Light Massers worked just two practice problems in one session.
All students were tested 1 week after their final practice problem. The procedure is
summarized in Fig. 2a.
Two critical comparisons are made. First, we assessed the effect of spacing practice by
comparing the test performance of Spacers and Massers. Second, we assessed the effect of
overlearning by comparing the test performance of Massers and Light Massers. As detailed
in the Introduction, the standard format relies predominantly on practice sets that are
massed, and the sheer number of problems within these practice sets ensures overlearning.
By contrast, the shuffled format incorporates spaced practice.
Method
Participants
All three sessions were completed by 66 undergraduates (51 women) at the University of
South Florida. An additional 14 students completed the first session but failed to attend
either the second or third session.
Task
Students calculated the number of unique orderings (i.e., permutations) of a letter sequence
with at least one repeated letter. For example, the sequence abbccc has 60 permutations,
including abccbc, accbcb, bbaccc, and so forth. Every letter sequence was four to eight
letters in length, and the number of unique letters in each sequence equaled two (aand b)
or three (a,b, and c). No sequence had more than 90 permutations. The number of
permutations for any sequence is given by a formula that is illustrated in the Appendix, but
students were not shown this formula because we believed that it would prove too complex
for some of our students Instead, we taught students with examples that were presented
exactly as shown in Fig. 1.
Base rate survey
Although we were confident that this particular kind of permutation problem was unknown
to our participant pool, we verified this by testing a sample of 50 students (with 43 women)
from the same participant pool, none of whom participated in either Experiments 1 or 2.
Each student was given 3 min to find the number of permutations for three of the practice
problems used in Experiment 1.
None of the surveyed students correctly answered any of the problems, and none of their
written solutions exhibited any evidence of the appropriate procedure. Some attempted to
simply list every permutation, but none succeeded, probably because of the time constraint.
Hence, this survey showed that this task is virtually, if not entirely, unknown to our
participant pool. Furthermore, to the extent that any relevant pre-experimental knowledge
did exist, it would not confound the experiment because of random assignment and the law
of large numbers.
486 D. Rohrer, K. Taylor
123
Procedure
Each student attended three sessions spaced 1 week apart. At the beginning of the first
session, each student was assigned to the group of Spacers, Massers, or Light Massers. At
no point were students told what to expect in subsequent sessions.
All students simultaneously observed a 3-min tutorial at the beginning of the first
session. The tutorial included a single projected visual slide with some explanatory
information and a sample problem, accompanied by oral explanation. The slide also
included the solution to the sample problem, which was presented exactly as shown in
Fig. 1. Immediately after the tutorial, every student completed the first practice set. The
Light Massers worked only the first practice set. The Massers worked both practice sets in
session one. The Spacers worked the first practice set in session one and the second
practice set in session two.
Each practice set included two examples and two practice problems, all of which were
presented in a test booklet. Students were given 45 s to solve each example, and each
example was followed immediately by a 15-s visual projection of its solution (which, like
the tutorial sample problem, was presented as shown in Fig. 1). The two practice problems
were also allotted 45 s each but were not followed by feedback. The selection and order of
the example and practice problems did not vary across students.
The test was given to the Massers and Light Massers in session two (1 week after their
final practice problem), and the Spacers were tested in session three (1 week after their
final practice problem), as illustrated in Fig. 2a. The test consisted of a single piece of
paper with five novel problems, and all students saw the same five problems in the same
order. Students were asked to solve all five problems in 225 s (which averages to 45 s per
problem). Students were required to sit for the entire time period, and feedback was not
provided.
Critically, although the Massers and Light Massers were tested in the second session,
they were required to attend the third session. If they had been allowed to skip the third
Problem
In how many ways can the letters abbccc be arranged?
Solution
6 letters
232
23456
skip a, because it
does not repeat b appears 2 times c appears 3 times
=232
23456 =60
Fig. 1 Permutation task. This example illustrates the format of the solutions presented to students during
the tutorial and the feedback after each example
Shuffling of mathematics problems 487
123
session, the test scores of the Massers and Light Massers would have included subjects
who might have not attended the third session if it had been required. This would have
confounded the experiment because subjects who fail to show for a follow-up session
perform worse, on average, than those who show. Thus, allowing Massers and Light
Massers to skip the third session would have confounded the experiment in favor of the
Spacers. Indeed, the present experiment included three Massers who failed to attend the
third session, and their average test score was, in fact, lower than the average score of the
Massers who attended all three sessions. Thus, by requiring every student to attend the
third session, the observed spacing effect was not exaggerated.
Resultsand discussion
Inclusion criterion
Because one aim of this study was to assess the benefits of overlearning by comparing the
Massers and Light Massers, it was important that Light Massers provide at least one correct
response during practice. This is because overlearning requires that students continue
practice beyond criterion, and, consequently, the benefits of overlearning cannot be as-
sessed unless the control group reaches criterion. Therefore, we restricted our analyses to
those students who correctly answered at least one of the first two practice problems
(which were the only two practice problems attempted by all students). This eliminated six
of the 66 students. The exclusion of these six students slightly increased the mean test
scores of each group, but it had no effect on the findings.
A Practice Procedure
week 1 week 2 week 3
Spacers 2 problems 2 problems test
Massers 4 problems test filler tas
k
Light Massers 2 problems test filler task
B Test Performance
S
p
acers Massers Li
g
ht Massers
Accuracy
0%
100%
74%
49% 46%
Fig. 2 Experiment 1. aPractice
procedure. Each pair of practice
problems was preceded by two
examples. Students saw a single
tutorial immediately before the
first example. Practice session
performance did not differ
reliably between groups. bTest
performance. Error bars reflect
±1 SE
488 D. Rohrer, K. Taylor
123
Practice performance
Mean accuracy for the first two problems equaled 95% (SE = 2%). Naturally, there were no
reliable differences between the three groups on these first two problems (p> 0.05) because
these two practice problems were completed before the procedures for the three groups
diverged.
For the second set of two practice problems, the timing was manipulated, as it was
begun immediately after the first practice set (Massers) or 1 week later (Spacers). Yet
despite the delay imposed upon the Spacers, their second practice set mean accuracy of
83% (SE = 6%) was about equal to the Massers’ average of 82% (SE = 7%), t< 1. Thus, a
1-week delay did not impair performance on the second practice set, and this was probably
due to the fact that each practice set began with two solved examples. Notably, though, this
was not a confound because both Massers and Spacers saw the same two examples just
before the second practice set. In summary, practice strategy did not significantly affect
practice performance.
Test performance
Practice strategy affected test performance. As shown in Figure 2a, the Spacers’ mean test
accuracy of 74% (SE = 8%) exceeded both the Massers’ average of 49% (SE = 10%) and
the Light Massers’ average of 46% (SE = 7%). An analysis of variance revealed a reliable
difference between the groups, F(2, 57) = 3.59, p< 0.05, g
p
2
= 0.11. Subsequent Holm–
Sidak comparisons revealed that the Spacers outscored both the Massers (p< 0.05) and the
Light Massers (p< 0.05), but the Massers and the Light Massers did not differ reliably
(p= 0.8).
Summary
Two key findings were observed. First, despite a twofold different in the amount of massed
practice assigned to Massers and Light Massers, there was not detectable difference in their
test scores. Thus, because the Light Massers correctly answered at least one practice
problem (as all analyses excluded subjects who did not correctly answer any practice
problems), this finding constitutes a null effect of overlearning (i.e., immediate post-
criterion study). Admittedly, overlearning might have significantly boosted test scores if
the number of massed practice problems had varied by a factor of, say, 10 and not just two.
However, any such effect would need to be extremely large before it would justify the
tenfold increase in study time. This is because learners have a finite amount of study time,
and they should invest this time in strategies that provide a good return on their investment.
Thus, while an extremely large amount of overlearning might boost test scores, it would
probably not be efficient. Finally, and as noted in the Introduction, a null effect of
mathematics overlearning was observed previously (Rohrer & Taylor, 2006). However, the
present finding is the first in which the null effect cannot be attributed to an artificial
constraint on test performance. That is, the inability of the Massers to outscore the Light
Massers cannot be attributed to an inherent ceiling effect because the Massers were vastly
outscored by the Spacers. This superiority of Spacers over Massers–a spacing effect—is
the second key finding of this study. Both findings—the null effect of overlearning and the
superiority of spacing over massing—favor the shuffled format, which uses spaced prac-
tice, over the more commonly used standard format, which induces massing and over-
learning.
Shuffling of mathematics problems 489
123
Experiment 2
In the second experiment, students worked a set of practice problems that were either
blocked by problem type or mixed together. College students were taught how to find the
volume of the four obscure geometric solids shown in Fig. 3a and then completed one of
two randomly assigned practice schedules. Each group worked the same practice problems,
but the practice problems were either blocked (e.g., four problems for one solid, then four
problems for another solid) or systematically mixed. Both the Mixers and the Blockers
completed two practice sessions, separated by 1 week, and were tested 1 week after their
second practice session, as shown in Fig. 4a. As detailed in the Introduction, mixed
practice requires that students learn to pair a type of problem with its appropriate proce-
dure, and, for that reason, we suspected that the Mixers would outscore Blockers at test.
Method
Participants
Three sessions were completed by 18 undergraduates (13 women) at the University of
South Florida. An additional 15 students completed the first session but failed to attend
either the second or third session. None participated in Experiment 1. Although the sample
size was small, statistical power was not a concern because of effect sizes were large.
Task
The students learned to calculate the volume of four geometric solids. Formal definitions of
the four solids are given in the Appendix, but students instead saw the illustrations and
descriptions shown in Fig. 3a. The volume of each solid depends solely on its radius (r) and
height (h). In every problem presented during practice or test, the radius and height equaled
a positive integer of seven or less. Problems and solutions were presented in the format
shown in Fig. 3b. Of note, students were asked to write the appropriate formula in a
preprinted box and write the volume in a preprinted oval.
Base rate survey
To verify that the volume formulas were virtually unknown to the participant pool used in
Experiment 2, we tested a sample of 25 students (14 women) from the same pool, none of
whom participated in either experiment. Each student was given 8 min to solve the eight
test problems given in Experiment 2, and these included two problems for each of the four
solids. None of the students correctly answered any of the problems. As in Experiment 1,
concerns about pre-experimental knowledge are further tempered by random assignment
and the law of large numbers.
Procedure
The students attended three sessions spaced 1 week apart. At the beginning of the first
session, each student was randomly assigned to the group of Mixers or Blockers. For both
groups, the first and second sessions were practice sessions, and the third session included
the test.
490 D. Rohrer, K. Taylor
123
Each of the two practice sessions included four tutorials and 16 practice problems. The
Mixers read all four tutorials before beginning the practice problems, and the 16 practice
problems were randomly ordered with the constraint that each set of four practice problems
(e.g., 1-4, 5–8, etc.) included one problem for each of the four solids. For the Blockers,
each tutorial on a given solid was followed immediately by the four problems relating to
that solid (e.g., the wedge tutorial was followed by four wedge problems, the spherical
A
B
Awedge is the boldfaced portion of the tube.
Its bottom is a circle, and its top is a slanted oval.
Its volume equals
2
2hr
Aspherical cone is the boldfaced part of the sphere.
Its bottom is at the center of the sphere.
The rim of the cone is on the surface of the sphere.
Its volume equals
3
22hr
Problem
Find the volume of a wedge with r = 2 and h = 3.
Write the formula in the box; write the answer in the oval.
Solution
2
2hr
=
2
322
= 6
Aspheroid is similar to a sphere.
But its height has been squeezed or stretched.
Its volume equals
3
42hr
Ahalf cone is the bottom half of a cone.
Both its top and bottom are circles.
Its volume equals
3
72hr
Fig. 3 Volume task. aThe illustrations and descriptions are identical to those shown to the students.
Formal definitions of each shape are given in the Appendix. bA sample problem. This example
illustrates the format of the solutions presented during the tutorial and the feedback after each practice
problem
Shuffling of mathematics problems 491
123
cone tutorial was followed by four spherical cone problems, and so forth). Within each
condition, the order of the problems did not vary across students, and no problem appeared
in both practice sessions. Most importantly, both groups saw the same tutorials and the
same practice problems in each session.
Students were given 45 s to read each tutorial, which consisted of the illustration and
written description in Fig. 3a and one solved example like that shown in Fig. 3b. Students
were allotted 40 s for each practice problem, and each practice problem was followed
immediately by a 10-s visual presentation of the solution. Each practice problem and its
subsequent solution were presented in the format shown in Fig. 3b.
One week after the second session (and their last practice problem), students were
tested. Eight novel problems, with two problems for each solid, were presented simulta-
neously in a random order. All students saw the same problems in the same order. Students
were allotted 8 min and were required to sit for the entire time period. Feedback was not
provided.
Results and discussion
Inclusion criterion
Every student correctly answered at least one practice problem in each practice session.
Consequently, every student was included in all further analyses.
Practice performance
Practice session performance was impeded by mixture (Fig. 4b), as the Blockers’ average
of 89% (SE = 4%) statistically exceeded the Mixers’ average of 60% (SE = 7%), t
(16) = 3.14, p< 0.01,d= 1.06. This superiority of Blockers was due primarily to the
difference in their scores during the first session (87 vs. 43%), t(16) = 3.88, p< 0.01, d=
0.53. In the second practice session, the Blockers’ superiority was more moderate and not
statistically significant (91 vs. 78%), t(16) = 1.58, p> 0.05.
Test performance
By contrast, the mean test performance of Mixers (63%, SE = 12%) was far greater than
that of the Blockers (20%, SE = 9%), t(14) = 2.64, p< 0.05, d= 1.34, as shown in Fig. 4c.
Thus, mixed practice produced superior test performance and inferior practice performance
(compared to blocked practice), as evidenced by a statistically significant interaction be-
tween practice strategy (mixed vs. blocked) and experiment phase (practice vs. test), F(1,
16) = 35.08, p< 0.001.
In a secondary analysis of test performance, we tabulated the number of test problems
for which students provided the correct formula but not the correct answer. Across all
students and all test problems, this happened only twice: once for a Mixer and once for a
Blocker. Thus, if the correct formula was recalled, the correct answer was almost always
found. This means that Blockers (and Mixers) knew how to solve each kind of problem at
the time of test, and, consequently, their poor performance was due to their inability to
recall the correct formula for each problem. Thus, as fully detailed in the Introduction, it
appears that students received the necessary discrimination training only when practice
problems were mixed by type.
492 D. Rohrer, K. Taylor
123
Finally, although it might seem that the superior test scores of Mixers could be
attributed to the fact that the test problems were mixed rather than blocked, we believe this
is unlikely for two reasons. First, if it is assumed that the Blockers’ poor test performance
stemmed from their inability to pair each kind of problem with the appropriate formula, as
suggested by the analysis in the paragraph immediately above, the order of the test
problems is logically inconsequential. Second, because the test included only two problems
of each type, the difference between a blocked and mixed format would have been slight.
Summary
While blocked practice proved superior to mixed practice during the practice session,
subsequent test scores were much greater when practice was mixed rather than blocked.
The superior test performance after mixed practice is, in our view, attributed to the fact that
students in this condition were required to know not only how to solve each kind of
problem but also which procedure (i.e., formula) was appropriate for each kind of problem
APractice Procedure
week week 3
Mixers Set 1 Set 2 test
interleaved interleaved
Blockers Set 1 Set 2 test
grouped grouped
CTest Performance
Mixers Blockers
Accuracy
0%
100%
63%
20%
BPractice Performance
Mixers Blockers
Accuracy
0%
100%
89%
60%
1week 2
Fig. 4 Experiment 2 aPractice
procedure. bPractice session
performance. Error bars reflect
±1 SE. Data are averaged across
the two practice sessions. See
text for details about performance
on each specific practice session.
cTest performance. Error bars
reflect ±1 SE
Shuffling of mathematics problems 493
123
(i.e., solid). This possibility is also consistent with the finding that virtually every test error
was due to the selection of the wrong formula.
General discussion
Test performance in both experiments benefited from altering either the timing or the serial
order of practice problems. In Experiment 1, test performance increased sharply if a given
set of practice problems was spaced across two sessions separated by 1 week, as compared
to the massing of these problems within a single session. In addition, there was no dec-
rement in test performance when the number of massed practice problems was reduced by
half, which is to say that there was a null effect of the strategy known as overlearning. In
Experiment 2, test performance improved 250% when practice problems of different types
were mixed together and not blocked by type. In brief, while an increase in the number of
massed practice problems did not reliably affect test scores (Experiment 1), large gains in
test performance were achieved by the use of spacing or mixing, even though neither of
these strategies required additional practice problems.
The two experiments also demonstrated that a learning strategy which provides superior
test performance is not necessarily the one that optimizes practice performance. In
Experiment 1, the spacing of practice, which boosted test performance, had no effect on
practice performance. In Experiment 2, the mixture of problem types, which boosted test
performance, actually impeded practice performance. Bjork and his colleagues have
observed similar dissociations between practice and test performance, leading them to
describe these initially costly but ultimately beneficial strategies as desirable difficul-
ties(e.g., Bjork, 1994; Christina & Bjork, 1991; Schmidt & Bjork, 1992).
Caveats
Several limitations apply to the generality of these findings. First, our subjects were college
students, and it is possible that the effects observed here might be muted or even absent
with much younger students. Second, the experiments reported here relied on a test that
required students to solve problems exactly like those shown in practice, and it is not
known whether our findings would obtain with measures requiring transfer. Third, our
experiments were laboratory based, and future research will be needed to determine if the
findings will replicate in a classroom setting. Fourth, the tasks used in our experiments are
procedural rather than conceptual (e.g., Rittle-Johnson & Alibali, 1999; Rittle-Johnson,
Siegler, & Alibali, 2001), and it remains unknown whether the benefits of spaced and
mixed practice would hold for more abstract, conceptual tasks. In brief, our results leave
open the possibility that our findings may not generalize to different subjects, tasks, and
settings, yet, at the same time, we know of no reason why they would not.
Practical implications
The present results cast doubt on the utility of the standard practice format used in most
mathematics textbooks because this format is characterized by massed practice and
blocked practice—the very two strategies that proved here to be deficient long-term
learning strategies. Likewise, the present findings suggest that the shuffled format, with its
494 D. Rohrer, K. Taylor
123
reliance on spaced and mixed practice, deserves further consideration by researchers,
teachers, educators, and authors.
We should emphasize that the shuffled format can be adopted without any change in the
nature or the order of the lessons. It does mean, however, that, if a lesson is omitted, one
must be careful to also omit corresponding problems throughout the remainder of the
textbook. Fortunately, this task is made easy if the textbook includes an index listing every
practice problem and its corresponding lesson, allowing the instructor to easily avoid
assigning problems relating to omitted topics. Such an index also means that a student
can find the lesson corresponding to a problem that he or she cannot solve. In fact, the
lesson number for each problem could be provided immediately adjacent to each practice
problem.
Perhaps the most well known example of the shuffled format is the Saxon line of
mathematics textbooks (e.g., Saxon, 1997). In these textbooks, no more than two or three
problems within each practice set are drawn from the immediately preceding lesson, and
the remaining one or two dozen problems are drawn from many different lessons. We are
not aware of any published, controlled experiments comparing a Saxon and non-Saxon
textbook, but such an experiment may not be particularly informative because it would be
confounded by the numerous differences between any two such texts. That is, regardless of
the outcome of an experimental comparison of a shuffled textbook and a standard textbook,
any observed differences in, say, final exam performance might reflect differences in the
lessons rather than practice format.
Such confounds would be avoided, however, if two groups of students were presented
with the same lessons and different practice sets. For example, each group of students
could receive a packet that includes the lessons from a traditional textbook, and these
lessons would appear in the same order for both groups. Both groups would also see the
same practice problems, but the problems would be arranged in either a standard format
or shuffled format. By way of disclosure, neither author has an affiliation with a
publishing company or mathematics textbook, although the first author is a former
mathematics teacher who has taught with textbooks from many different publishers,
including Saxon.
Additional advantages of a shuffled format
There may be additional benefits of a shuffled format not addressed by Experiments 1 and
2. For example, when practice problems relating to a given topic are spaced across multiple
practice sets, a student who fails to understand a lesson (or fails to attend a lesson) will still
be able to solve most of the problems within the following practice set, whereas a massed
practice set ensures that this student will have little or no success. Likewise, if that student
achieves better understanding of the topic in a subsequent class meeting (perhaps by
observing other students solve the previously assigned practice problems in class), a
shuffled format provides opportunities to practice these new skills in the future.
Finally, the logistical demands and the financial costs of adopting a shuffled practice
format are relatively small. Instructors can incorporate a shuffled format regardless of
their adopted textbook by merely shuffling practice problems from multiple practice sets.
Ideally, though, the shuffled format would be incorporated by textbooks and instructional
software packages. Notably, the adoption of this new format could be accomplished with
little trouble or expense, as authors and publishers could merely rearrange the practice
problems in the next edition.
Shuffling of mathematics problems 495
123
Acknowledgments This research was supported by a grant from the Institute of Education Sciences, US
Department of Education. We thank Kristina Martinez and Erica Porch for their assistance with data
collection.
Appendix
Permutations
If a sequence of items includes nitems and kunique items, the number of permutations of
the sequence equals n!/(n
1
!n
2
! ... n
k
!), where n
i
equals the number of occurrences of item i.
Thus, for the sequence abbccc, the number of permutations equals 6!/(1! 2! 3!), or 60.
Wedge
A wedge is obtained by the truncation of a cylinder by two planes if exactly one of the
planes is perpendicular to the cylinder and if the linear intersection of the two planes
includes exactly one point on the cylindrical surface. If the latter constraint is relaxed so
that the linear intersection may intersect the cylindrical surface at either one or two points,
the solid is a cylindrical wedge. This is the shape shown in Fig. 3a. We chose the term
wedge for this specific case because we do not know of an accepted term. Its volume
equals r
2
hp/2, where requals the radius of its circular base and hequals its maximum
height
Spherical cone
A spherical cone is obtained by removing a conical section of a sphere provided that the
vertex of the cone is at the sphere’s center and the base of the cone is on the sphere’s
surface, as shown in Fig. 3a. Its volume is given by 2r
2
hp/3, where requals the radius of
the sphere and hequals the difference of the sphere’s radius and the cone’s height
Spheroid
A spheroid is obtained by the rotation of an ellipse about one of its axes. The spheroid in
Fig. 3a, for example, is rotated about its vertical axis. Its volume equals 4r
2
hp/3, where r
equals the ‘‘equatorial radius’’ and hequals the ‘‘polar radius.’’ The values of rand halso
equal one-half of the major and minor lengths of the rotated ellipse.
Half cone
A half cone is a cone truncated by a plane parallel to its base so that the truncation reduces
the cone’s height by half. Its volume equals 7r
2
hp/3, where requals the radius of the upper
base and hequals the height of the truncated cone, as illustrated in Fig. 3a. The half cone is
a specific instance of a conical frustum, which has a height equal to any proportion of the
cone’s height. We chose the term ‘‘half cone’’ to describe a conical frustrum with height
equal to exactly half of the cone’s height.
496 D. Rohrer, K. Taylor
123
References
Bahrick, H. P., Bahrick, L. E., Bahrick, A. S., & Bahrick, P. E. (1993). Maintenance of foreign-language
vocabulary and the spacing effect. Psychological Science, 4, 316–321.
Bjork, R. A. (1979). Information-processing analysis of college teaching. Educational Psychologist, 14, 15–
23.
Bjork, R. A. (1988). Retrieval practice and the maintenance of knowledge. In M.M. Gruneberg, P.E., Morris,
& R.N. Sykes (Eds.), Practical aspects of memory II (pp. 391–401). London: Wiley.
Bjork, R. A. (1994). Memory and meta-memory considerations in the training of human beings. In J.
Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185–205). Cambridge:
MIT.
Bloom, K. C., & Shuell, T. J. (1981). Effects of massed and distributed practice on the learning and retention
of second-language vocabulary. Journal of Educational Research, 74, 245–248.
Carpenter, S. K., & DeLosh, E. L. (2005). Application of the testing and the spacing effects to name
learning. Applied Cognitive Psychology, 19, 619–636.
Carson, L. M., & Wiegand, R. L. (1979). Motor schema formation and retention in young children: A test of
Schmidt’s schema theory. Journal of Motor Behavior, 11, 247–251.
Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall
tasks: A review and quantitative synthesis. Psychological Bulletin, 132, 354–380.
Christina, R. W., Bjork, R. A. (1991). Optimizing long-term retention and transfer. In D. Druckman & R. A.
Bjork (Eds.), In the mind’s eye: Enhancing human performance (pp. 23–56). Washington DC: National
Academy Press.
Dempster, F. N. (1989). Spacing effects and their implications for theory and practice. Educational Psy-
chology 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.
Fitts, P. M. (1965). Factors in complex skill training. In R. Glaser (Ed.), Training research and education
(pp. 177–197). New York: Wiley.
Foriska, T. J. (1993). What every educator should know about learning. Schools in the Middle, 3, 39–44.
Gilbert, T. F. (1957). Overlearning and the retention of meaningful prose. Journal of General Psychology,
56, 281–289.
Glencoe (2001) Mathematics: Applications and Connections—Course 1. New York: Glencoe-McGraw Hill.
Grote, M. G. (1995). Distributed versus massed practice in high school physics. School Science and
Mathematics, 95, 97–101.
Hall, J. F. (1989). Learning and memory, 2nd Ed. Boston: Allyn & Bacon.
Jahnke, J.C., & Nowaczyk, R. H. (1998). Cognition. Upper Saddle River: Prentice Hall.
Kester, L., Kirschner, P. A., & Van Merrie
¨nboer, J. J. G. (2004). Timing of information presentation in
learning statistics. Instructional Science, 32, 233–252.
Krueger, W. C. F. (1929). The effect of overlearning on retention. Journal of Experimental Psychology, 12,
71–78.
Mayfield, K. H., & Chase, P. N. (2002). The effects of cumulative practice on mathematics problem solving.
Journal of Applied Behavior Analysis, 35, 105–123.
Pashler, H., Rohrer, D., Cepeda, N. J., & Carpenter, S. K. (2007). Enhancing learning and retarding
forgetting: Choices and consequences. Psychonomic Bulletin & Review (in press).
Postman, L. (1962). Retention as a function of degree of overlearning. Science, 135, 666–667.
Radvasky, G. (2006). Human memory. Boston: Pearson Education Group.
Rea, C. P., & Modigliani, V. (1985). The effect of expanded versus massed practice on the retention of
multiplication facts and spelling lists. Human Learning: Journal of Practical Research & Applications,
4, 11–18.
Reynolds, J. H., & Glaser, R. (1964). Effects of repetition and spaced review upon retention of a complex
learning task. Journal of Educational Psychology, 55, 297–308.
Rittle-Johnson, B. & Alibali, M. W. (1999). Conceptual and procedural knowledge of mathematics: Does
one lead to the other? Journal of Educational Psychology, 91, 175–189.
Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and
procedural skill in mathematics: An iterative process. Journal of Educational Psychology, 93, 346–
362.
Rohrer, D., & Taylor, K. (2006). The effects of overlearning and distributed practice on the retention of
mathematics knowledge. Applied Cognitive Psychology, 20, 1209–1224.
Rohrer, D., & Taylor, K. (2006). The effects of overlearning and distributed practice on the retention of
mathematics knowledge. Applied Cognitive Psychology, 20, 1209–1224.
Shuffling of mathematics problems 497
123
Saxon, J. (1997). Algebra I (3
rd
Ed.). Norman: Saxon Publishers.
Schmidt, R. A., & Bjork, R. A. (1992). New conceptualizations of practice: Common principles in three
paradigms suggest new concepts for training. Psychological Science, 3, 207–217.
Smith, S. M., & Rothkopf, E. Z. (1984). Contextual enrichment and distribution of practice in the classroom.
Cognition and Instruction, 1, 341–358.
VanderStoep, S. W., & Seifert, C. M. (1993). Learning ‘how’ versus learning ‘when’: Improving transfer of
problem-solving principles. Journal of the Learning Sciences. 3, 93–111.
498 D. Rohrer, K. Taylor
123
... The constructivist theory also recommends getting away from rote memorization and developing conceptual understanding that necessitates the examination and discussions of concepts at a finer level, considering all dimensions to create a knowledge system in which every piece makes sense (Hein, 1991). In chemistry, physics and mathematics education, it has been consistently observed that practice problems often have an emphasis on the quantitative aspect, algorithmic side, of problem solving rather than the qualitative facet, conceptual side (Nurrenbern and Pickering, 1987;Moseley, 2005;Rohrer and Taylor, 2007). In a study conducted by (1995), it was determined that while students were able to solve problems related to Boyle's law or Charles's law, about two-thirds of them were unable to explain critical aspects of gas behaviours. ...
... When it comes to implementing an interleaved study plan, there are many ways of preventing the traditional ''blocks'' of study material. In one particular study conducted by Rohrer and Taylor (2007), students were assessed across three categories, massed versus spaced, light versus heavy, and blocked versus mixed. While our study was primarily interested in blocked versus mixed version, the results from massed versus spaced was relevant in the design of our study. ...
... The results showed that the spaced practice group vastly outperformed the massed group, indicating that spaced practice is a more effective study strategy than massed practice. Rohrer and Taylor (2007) also investigated the success of the blocked versus mixed groups, where students from the blocked group practiced 4 similar problems in a row followed by another 4 similar problems, whereas the mixed groups had their questions shuffled. It was determined that the students in the mixed group scored worse on practice assignments, but higher on exams. ...
Article
The questions in the practice assignments given to students in the form of worksheets or other formats are often grouped by chapter, topic, or concepts. There is a great emphasis on categorization. Most of the end-of-chapter problems in chemistry textbooks are organized by sections. Although this was done with the intention of helping students navigate the assignments more easily and practice in order, it is not what they are expected to do during the tests. There is a mismatch between what they practice on and how they are tested. The goal of this study is to examine the influence of the structure of the assignments on students’ problem-solving performances. Two groups of students from chemistry classes were recruited to participate in this study. Each group had the same length of practice and identical questions with only one difference. The experimental group had assignments with mixed organization of questions, while the control group had traditional assignments with the questions organized around chapters and topics. Students completed three two-hour long problem-solving sessions during the weekends. Evaluation of their progress consisted of their solutions obtained from one pre-test and three post-tests, with one given after each problem-solving session. The study revealed that students in the experimental group increased their problem-solving success more than those in the control group starting from the first intervention. The achievement gap widened as the study progressed. It is recommended that educators and textbook publishers create and utilize assignments that contain more mixed questions on different topics and chapters.
... The AIED pedagogy model represents knowledge about effective teaching and learning approaches that have been elicited from teaching experts (and that constitute the learning sciences). This includes, for example, knowledge of instructional approaches (Bereiter and Scardamalia 1989), productive failure (Kapur 2008), guided discovery learning (Bruner 1961), collaborative learning (Dillenbourg 1999), the zone of proximal development (Vygotsky 1978), deliberate practice (Ericsson et al. 1993), interleaved practice (Rohrer and Taylor 2007), cognitive overload (Mayer and Moreno 2003), formative feedback (Shute 2008), uncertain rewards (Fiorillo 2003), and assessment for learning (Black 1986). ...
Chapter
Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies.
Article
Distributed practice improves learning by requiring the brain to expend extra effort retrieving prior learning after a time delay. I examine whether repeating the most troublesome homework question on the next assignment improves exam performance within one large upper-level undergraduate economics course. I compare exam outcomes of students enrolled in Fall 2017 as my control group (N = 136) with those of the intervention group in Spring 2018 (N =163). Adjusting for differences in student characteristics, the intervention was associated with a statistically significant (at the 90% level) increase of 2.44% in final exam scores, with raw average scores of 84.6% versus 81.7%. No difference was found post-intervention in overall course scores, while small increases for midterms. Subgroup analysis suggests the benefits may accrue more to the strongest and weakest performers. Findings suggest that repeating troublesome problems could improve learning in economics.
Article
Full-text available
After being taught how to perform a new mathematical operation, students are often given several practice problems in a single set, such as a homework assignment or quiz (i.e., massed practice). An alternative approach is to distribute problems across multiple homeworks or quizzes, increasing the temporal interval between practice (i.e., spaced practice). Spaced practice has been shown to increase the long-term retention of various types of mathematics knowledge. Less clear is whether spacing decreases performance during practice, with some studies indicating that it does and others indicating it does not. To increase clarity, we tested whether spacing produces long-term retention gains, but short-term practice costs, in a calculus course. On practice quizzes, students worked problems on various learning objectives in either massed fashion (3 problems on a single quiz) or spaced fashion (3 problems across 3 quizzes). Spacing increased retention of learning objectives on an end-of-semester test but reduced performance on the practice quizzes. The reduction in practice performance was nuanced: Spacing reduced performance only on the first two quiz questions, leaving performance on the third question unaffected. We interpret these findings as evidence that spacing led to more protracted, but ultimately more robust, learning. We, therefore, conclude that spacing imposes a desirable form of difficulty in calculus learning.
Article
Purpose: Residents have limited time and much to learn. Mounting evidence shows that Desirable Difficulty (DD) learning strategies can ease that imbalance, but few studies have specifically studied combinations of these strategies. Methods: We tested two different combinations of DD strategies: a double combination of distributed practice and retrieval practice and a triple combination additionally including interleaved practice. We compared residents' annual In-Training Exam (ITE) scores and graduates' board certification performance between both DD curricula and a historical baseline. Results: Average ITE scores rose from 149.06 in the historical baseline to 160.04 under the combined DD curricula (p < 0.001). Average ITE scores fell from 162.50 under the double combination to 155.11 under the triple combination (p = 0.03). There were no significant changes in graduates' board performance between any of the curricula. Conclusions: These results add to the evidence that DD strategies can enhance residents' learning. The drop in ITE scores under the triple DD combination may suggest that it pushed learners past beneficial desirable difficulty into detrimental overwhelming difficulty. Further research should apply this framework in larger and more diverse settings to clarify how these DD strategies can be optimally used to enhance residents' learning.
Article
Full-text available
Chemistry from subjects based on the cumulative construction of information. The study is aimed to develop the retention of learning among second-grade students in chemistry using mobile application based on spaced learning types (electronic-physical) and cognitive style (Leveling-sharpening). The number of participants 68 students divided between 22 (girls) and 46 (boys) between the ages of (16-17) years in a governmental language school in Egypt. The study is conducted to answer the following questions; what is the effect of Interaction between Mobile Application based on Spaced Learning types and Cognitive Style to improve retention in Chemistry for Secondary School Student? The research adopted a number of tools, achievement test (pre-post-follow up) testing consisting of (46) question and proposed program, a list of concepts for chemistry and a cognitive style scale. The results showed that the statistically significant difference at the function levels are (0.01), (0.05) between the average grades of students of the four experimental groups and post-test and follow up test. The magnitude of the impact showed that the effect of interaction between mobile application based on spaced learning types and cognitive style came in favor of the experimental groups as follows (electronic-sharpening) followed by experimental (physical-Leveling) followed by (physical-sharpening) and came experimental (electronic-Leveling) the least in the statistical function teams. The results can be explained and returned that to dividing the period into three sessions for a session of 20 minutes with a 10-minute break and diversity in the presentation of breaks between (electronic-physical).
Article
Previous research has demonstrated benefits of interleaved practice over blocked practice for learning mathematical formulas. This experiment tested whether the benefits from interleaved practice would generalize to more complex problems, where the problem type must be inferred from information in the problem. We compared delayed test performance of participants assigned to blocked practice to participants assigned to interleaved practice who had high or low practice performance. University students (Mage = 18.97, SDage = 1.50, 64% female) learned how to solve probability word problems in blocked practice, interleaved practice, or hybrid conditions that included both kinds of practice. Conditions that included some interleaved practice outperformed a condition that included only blocked practice at delayed test. Participants with high performance on interleaved practice problems outperformed participants assigned to blocked practice at delayed test. These results suggest that interleaved practice can confer learning advantages even for more complex problems.
Chapter
Full-text available
examine 2 . . . contributors to nonoptimal training: (1) the learner's own misreading of his or her progress and current state of knowledge during training, and (2) nonoptimal relationships between the conditions of training and the conditions that can be expected to prevail in the posttraining real-world environment / [explore memory and metamemory considerations in training] (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
Full-text available
This study examined relations between children's conceptual understanding of mathematical equivalence and their procedures for solving equivalence problems (e.g., 3 + 4 + 5 = 3 + 9). Students in 4th and 5th grades completed assessments of their conceptual and procedural knowledge of equivalence, both before and after a brief lesson. The instruction focused either on the concept of equivalence or on a correct procedure for solving equivalence problems. Conceptual instruction led to increased conceptual understanding and to generation and transfer of a correct procedure. Procedural instruction led to increased conceptual understanding and to adoption, but only limited transfer, of the instructed procedure. These findings highlight the causal relations between conceptual and procedural knowledge and suggest that conceptual knowledge may have a greater influence on procedural knowledge than the reverse. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
Full-text available
The authors propose that conceptual and procedural knowledge develop in an iterative fashion and that improved problem representation is 1 mechanism underlying the relations between them. Two experiments were conducted with 5th- and 6th-grade students learning about decimal fractions. In Experiment 1, children's initial conceptual knowledge predicted gains in procedural knowledge, and gains in procedural knowledge predicted improvements in conceptual knowledge. Correct problem representations mediated the relation between initial conceptual knowledge and improved procedural knowledge. In Experiment 2, amount of support for correct problem representation was experimentally manipulated, and the manipulations led to gains in procedural knowledge. Thus, conceptual and procedural knowledge develop iteratively, and improved problem representation is 1 mechanism in this process. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
Full-text available
This article constitutes an optimistic argument that basic research on human cognitive processes has yielded principles and phenomena that have considerable promise in guiding the design and execution of college instruction. To illustrate that point, four somewhat interrelated principles and phenomena arc outlined and some possible implications and applications of those principles and phenomena are put forward.
Article
The authors propose that conceptual and procedural knowledge develop in an iterative fashion and that improved problem representation is 1 mechanism underlying the relations between them. Two experiments were conducted with 5th- and 6th-grade students learning about decimal fractions. In Experiment 1, children's initial conceptual knowledge predicted gains in procedural knowledge, and gains in procedural knowledge predicted improvements in conceptual knowledge. Correct problem representations mediated the relation between initial conceptual knowledge and improved procedural knowledge. In Experiment 2, amount of support for correct problem representation was experimentally manipulated, and the manipulations led to gains in procedural knowledge. Thus, conceptual and procedural knowledge develop iteratively, and improved problem representation is 1 mechanism in this process.
Article
This study examined relations between children's conceptual understanding of mathematical equivalence and their procedures for solving equivalence problems (e.g., 3 + 4 + 5 = 3 + -). Students in 4th and 5th grades completed assessments of their conceptual and procedural knowledge of equivalence, both before and after a brief lesson. The instruction focused either on the concept of equivalence or on a correct procedure for solving equivalence problems. Conceptual instruction led to increased conceptual understanding and to generation and transfer of a correct procedure. Procedural instruction led to increased conceptual understanding and to adoption, but only limited transfer, of the instructed procedure. These findings highlight the causal relations between conceptual and procedural knowledge and suggest that conceptual knowledge may have a greater influence on procedural knowledge than the reverse.
Article
High school students enrolled in a French course learned vocabulary words under conditions of either massed or distributed practice as part of their regular class activities. Distributed practice consisted of three 10-minute units on each of three successive days; massed practice consisted of all three units being completed during a 30-minute period on a single day. Though performance of the two groups was virtually identical on a test given immediately after completion of study, the students who had learned the words by distributed practice did substantially better (35%) than the massed- practice students on a second test given 4 days later. The implications of the findings for classroom instruction and the need to distinguish between learning and memory are discussed.
Article
In a 9-year longitudinal investigation, 4 subjects learned and relearned 300 English-foreign language word pairs. Either 13 or 26 relearning sessions were administered at intervals of 14, 28, or 56 days. Retention was tested for 1.2.3. or 5 years after training terminated. The longer intersession intervals slowed down acquisition slightly, but this disadvantage during training was offset by substantially higher retention. Thirteen retraining sessions spaced at 56 days yielded retention comparable to 26 sessions spaced at 14 days. The retention benefit due to additional sessions was independent of the benefit due to spacing, and both variables facilitated retention of words regardless of difficulty level and of the consistency of retrieval during training. The benefits of spaced retrieval practice to long-term maintenance of access to academic knowledge areas are discussed.
Article
The variability-of-practice hypothesis, a major prediction of Schmidt's (1975) motor schema theory, was tested in an attempt to investigate motor-schema formation. In addition, schema retention was observed after a 2-week retention interval. The task involved preschool children in tossing a bean bag for appropriate distance. Four treatment groups received 100 practice trials equally divided over five days. Variation was provided by varying the weights of the bean bags. The testing situations involved tossing a criterion weighted bean bag as well as a novel weighted bean bag which none of the groups had experienced previously. In addition, all groups were tested on a new but similar task. The results supported the variability-of-practice hypothesis in terms of schema formation and transfer to novel tasks in the same movement class. After a two-week retention interval, loss in performance was significantly less for the group with variability of practice than all other groups.