ArticlePDF Available

Abstract

Researchers’ and educators’ enthusiasm in applying cognitive principles to enhance educational practices has become more evident. Several published reviews have suggested that some potent strategies can help students learn more efficaciously. Unfortunately, for whatever reason, students do not report frequent reliance on these empirically supported techniques. In the present review, we take a novel approach, identifying study strategies for which students have strong preferences and assessing whether these preferred strategies have any merit given existing empirical evidence from the cognitive and educational literatures. Furthermore, we provide concrete recommendations for students, instructors, and psychologists. For students, we identify common pitfalls and tips for optimal implementation for each study strategy. For instructors, we provide recommendations for how they can assist students to more optimally implement these study strategies. For psychologists, we highlight promising avenues of research to help augment these study strategies.
https://doi.org/10.1177/1745691617710510
Perspectives on Psychological Science
2018, Vol. 13(3) 390 –407
© The Author(s) 2018
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1745691617710510
www.psychologicalscience.org/PPS
Being able to effectively regulate study is an essential
part of the educational experience. Indeed, it is plau-
sible that as much, if not more, learning takes place
outside the classroom as inside the classroom. As a
result, equipping students with effective study strategies
is vital to their educational success. Fortunately, recent
interest in applying cognitive principles to enhance
educational practices has produced a substantial litera-
ture on effective study strategies. A number of reviews
have been published on empirically supported tech-
niques by both cognitive and educational psychologists
(Dunlosky, Rawson, Marsh, Nathan, & Willingham,
2013; Fiorella & Mayer, 2015; Mayer, 2008; Roediger &
Pyc, 2012). Thus, the good news is that there are potent
study strategies that can help students learn more effi-
caciously. The bad news, however, is that students do
not often use the learning strategies that cognitive
researchers have identified as being effective (Hartwig
& Dunlosky, 2012; Karpicke, Butler, & Roediger, 2009;
Kornell & Bjork, 2007; Wissman, Rawson, & Pyc, 2012;
Yan, Thai, & Bjork, 2014). Self-report questionnaires
typically find that students are not aware of the effec-
tiveness of potent evidence-based study strategies and
thus do not regularly implement them (McCabe, 2011).
In addition, empirical studies suggest that students seek
conditions that maximize their perceived understanding
of the material at the moment of study rather than
conditions that maximize their learning as indexed by
later test results (for additional discussion on the distinc-
tion between performance and learning, see Soderstrom
& Bjork, 2015). That is, students want to see rapid gains
when they are studying and erroneously take these
gains as actual understanding, thus preferring whatever
study strategies support rapid gains.
Students appear to hold strong preferences for study
techniques that they have used throughout their edu-
cational careers; consequently, attempts to sell them on
new strategies may be met with resistance. Accordingly,
we suggest that a fruitful alternative approach is to
examine the effectiveness of the study strategies that
students regularly implement and identify ways to aug-
ment these preferred study strategies. As a first step,
we review the empirical evidence on the effectiveness
of the study strategies that students actually use. An
710510PPSXXX10.1177/1745691617710510Miyatsu et al.Five Popular Study Strategies
research-article2018
Corresponding Author:
Toshiya Miyatsu, Department of Psychological and Brain Sciences,
Washington University, 1 Brookings Dr., Campus Box 1125, St. Louis,
MO 63130
E-mail: tmiyatsu@wustl.edu
Five Popular Study Strategies: Their
Pitfalls and Optimal Implementations
Toshiya Miyatsu, Khuyen Nguyen, and Mark A. McDaniel
Department of Psychological and Brain Sciences, Washington University in St. Louis
Abstract
Researchers’ and educators’ enthusiasm in applying cognitive principles to enhance educational practices has become
more evident. Several published reviews have suggested that some potent strategies can help students learn more
efficaciously. Unfortunately, for whatever reason, students do not report frequent reliance on these empirically supported
techniques. In the present review, we take a novel approach, identifying study strategies for which students have strong
preferences and assessing whether these preferred strategies have any merit given existing empirical evidence from the
cognitive and educational literatures. Furthermore, we provide concrete recommendations for students, instructors, and
psychologists. For students, we identify common pitfalls and tips for optimal implementation for each study strategy.
For instructors, we provide recommendations for how they can assist students to more optimally implement these study
strategies. For psychologists, we highlight promising avenues of research to help augment these study strategies.
Keywords
study strategies, self-regulated learning, education
Five Popular Study Strategies 391
examination of students’ self-reported study strategies
yielded five popular strategies that students routinely
prefer: (a) rereading, (b) highlighting or underlining, (c)
note-taking, (d) outlining, and (e) using flash cards
(Hartwig & Dunlosky, 2012; Karpicke etal., 2009; Kornell
& Bjork, 2007; Wissman etal., 2012; Yan etal., 2014);
casual observation on any college campus confirms
these findings (for a summary of the frequency of use
of these top five strategies, see Table 1).
Although a couple of these strategies (i.e., rereading
and highlighting or underlining) have been reviewed
elsewhere, they have typically been assessed relative to
more effective strategies. For instance, Dunlosky etal.
(2013) gave rereading and highlighting or underlining
a low utility rating relative to other more potent tech-
niques, such as testing and spaced practice. To a casual
reader, this overall assessment suggests that rereading
and highlighting or underlining should not be used in
any educational context. Furthermore, because Dunlosky
etal. focused on the general utility of these study strate-
gies, they might have overlooked the possibility that
these study strategies may be particularly effective (or
commonly misused) in certain situations. Thus, less
emphasis has been placed on how effective these strate-
gies are relative to no study strategy. All five of the
popular study strategies being reviewed here can be
potent when properly used. Thus, a primary purpose
of the present review is to emphasize the conditions
under which these popular techniques do and do not
foster effective learning and highlight ways in which
they can be precisely prescribed.
We will cover each study strategy in turn, and each
study-strategy section is organized into three subsec-
tions: effective implementations, ineffective implementa-
tions, and educational recommendations. We anticipate
that this review will be beneficial for a wide audience,
including students, educators, and psychologists. For
students and educators, this review will shed some light
on how and when these popular study strategies should
be implemented to get the most from students’ study
time. With this purpose in mind, the Introduction and
the Educational Recommendation subsections of each
of the five sections are written with minimal technical
terminology. For psychologists, our hope is that this
review will create excitement about new lines of
research on study strategies that are both theoretically
interesting and—perhaps more important—educationally
motivated.
Rereading
A straightforward assumption is that rereading texts
should yield benefits of repetition similar to that gener-
ally found in list-learning experiments (i.e., multiple
exposures of words lead to better recall than a single
exposure; e.g., Tulving, 1966; but for an interesting
exception, see Mulligan & Peterson, 2015). Yet this
assumption has not been investigated as extensively as
one would imagine, given the popularity of rereading
as a study strategy (e.g., Karpicke etal., 2009). Since
the 1970s, cognitive and educational psychologists have
typically endorsed study strategies that require active
processing of complex materials such as texts (Segal,
Chipman, & Glaser, 1985; Weinstein, Goetz, & Alexander,
1988; Wittrock, 1974). Thus, research on rereading has
often taken a backseat to research focusing on more
active study strategies because rereading may be pas-
sive in nature, at least in the manner in which students
do it (for a different perspective based on laboratory
studies, see Stine-Morrow, Gagne, Morrow, & DeWall,
2004). We posit that this may be the same reason why
rereading is so popular among students. Because
rereading does not necessarily require active and effort-
ful processing of the text, it gives students the (false)
sense that they are effectively learning the text (Roediger
& Karpicke, 2006b; although see Rawson, Dunlosky, &
Thiede, 2000). That is, the second reading of a text feels
more fluent than the first reading, and the increased
fluency is perceived by students, accurately or inac-
curately, as an indication of successful learning (Rawson
& Dunlosky, 2002). Nevertheless, as highlighted next,
under certain conditions, rereading can benefit memory
and comprehension.
Effective implementation
Rereading is particularly effective when the first and
second readings are spaced out. A plethora of research
Table 1. Meta-Analyzed Frequency of the Use of the Five
Popular Study Strategies
Rereading
(n = 1,517)
Highlighting
(n = 1,517)
Note-taking
(n = 595)
Outlining
(n = 595)
Flash cards
(n = 842)
78% 53% 30% 23% 55%
Note: The percentage values were computed by meta-analyzing
Carrier (2003), Hartwig and Dunlosky (2012), Karpicke, Butler, and
Roediger (2009), Kornell and Bjork (2007), Wissman, Rawson, and
Pyc (2012), and Yan, Thai, and Bjork (2014). For each study strategy,
the number of participants who reported the use of the strategy
was divided by the total number of participants in the studies that
included a question about that study strategy. Carrier’s Exam 1 and
Exam 2 were treated as separate surveys. For Kornell and Bjork
(2007) and Yan etal. (2014), only the question “When you study, do
you typically read a textbook/article/other source material more than
once?” was considered. The answers “Yes, I reread whole chapters/
articles” and “Yes, I reread sections that I underlined/highlighted/
marked” were taken as the evidence of rereading, and “Yes, I reread
sections that I underlined/highlighted/marked” was taken as the
evidence of highlighting.
392 Miyatsu et al.
in the verbal-learning literature has shown that spaced
study (i.e., an intervening lag between study opportuni-
ties) produces superior memory performance relative
to massed study (i.e., no intervening lag between study
opportunities; for a recent review, see Cepeda, Pashler,
Vul, Wixted, & Rohrer, 2006). In addition, this finding
has been extended to text materials. For instance,
Glover and Corkill (1987) had students read short para-
graphs twice with either an immediate lag (massed
group) or a 30-min lag (spaced group). They found that
the spaced group was able to recall more of the content
than the massed group on an immediate free-recall test
(see also Krug, Davis, & Glover, 1990). However, Verkoeijen,
Rikers, and Ozsoy (2008) found that too much spacing can
be detrimental for text learning. Specifically, they com-
pared a massed reread group with both a shorter-spaced
reread group (i.e., 4 days) and a longer-spaced reread
group (i.e., 3.5 weeks) and found that only the shorter-
spaced reread group outperformed the massed reread
group on a free-recall test after a 48-hr delay. The longer-
spaced reread group was only able to recall as many
details as the massed reread group, which suggests that
the benefits of spacing diminish when the lag is too long.
To determine an optimal lag between the first and
second readings, one must consider the retention inter-
val. To this end, Rawson and Kintsch (2005) imple-
mented a factorial design manipulating both lag and
retention interval in four conditions: (a) immediate lag
and immediate retention interval, (b) immediate lag and
delayed retention interval, (c) long lag and immediate
retention interval, and (d) long lag and delayed reten-
tion interval (see also Rawson, 2012). Their results indi-
cated that long lags with a delayed retention interval
benefited test performance (relative to a single read-
ing), but long lags with an immediate retention interval
yielded no benefits. Instead, with an immediate reten-
tion interval, the immediate lag but not the long lag
condition yielded superior performance relative to a
single reading. These results suggest that long lags
between reading opportunities are vital for producing
more durable learning (i.e., performance on delayed
tests) relative to a single reading. However, under cer-
tain conditions (e.g., immediate tests), massed reread-
ing may actually be more beneficial than spaced
rereading.1
Ineffective implementation
As we have discussed above, spacing out the first and
second readings is beneficial for long-term retention of
the studied information. On the flip side, massing the
readings (or cramming) has been shown to produce
limited benefits (Rawson, 2012; Rawson & Kintsch,
2005). Unless the test is going to take place immediately
after the second reading (which is unlikely in educa-
tional settings), it is not recommended that students
engage in massed rereading.
Furthermore, although rereading has been shown to
produce robust benefits for free recall and cued-recall
tests (particularly if the readings are spaced apart), its
benefits for higher order assessments (e.g., application-
based, inference-based, or problem-solving tests) are
less clear. Callender and McDaniel (2009) had partici-
pants read authentic textbook chapters either once or
twice and found that (relative to a single reading)
rereading did not improve performance on short-
answer application questions or summary writing. How-
ever, Karpicke and Blunt (2011) compared groups that
had different numbers of reading opportunities (i.e.,
one or four) and found that rereading enhanced per-
formance for inference multiple-choice and short-
answer application tests relative to a single reading. It
is important to note that participants in the Karpicke
and Blunt study were allowed to restudy the passage
four times, and the passages were much shorter (i.e.,
approximately 300 words) than those typically assigned
in college classes. As a result, the Karpicke and Blunt
study may not have captured the complexity of typical
text comprehension at the college level.
Educational recommendations
The major advantage of rereading over most study strat-
egies is that it does not require training. However, its
benefits can be enhanced. For immediate tests, massed
rereading is an effective strategy for helping learners
pick up additional details and facts that might have
been missed during the first reading (Amlund, Kardash,
& Kulhavy, 1986; Barnett & Seefeldt, 1989; Glover &
Corkill, 1987; Haenggi & Perfetti, 1992; but see Callender
& McDaniel, 2009). Thus, for assessments that require
memorization of key information, rereading can be an
effective study strategy. However, for assessments that
require students to integrate key information and make
inferences from the texts, rereading appears to be less
effective. Another takeaway is that spaced rereading is
useful for producing durable learning in educational
settings; durable learning is important when studying
for comprehensive exams later in the semester, forming
a strong foundation for future coursework that builds
on prior classes, or preparing for a standardized test
that covers an array of content (e.g., SAT, MCAT, GRE).
On the other hand, if the goal is to do well on an
immediate exam, then rereading right before the exam
(cramming) may be sufficient.
In addition, retrieving the read contents before
rereading bolsters learning (McDaniel, Howard, &
Einstein, 2009), probably because the retrieval attempt
Five Popular Study Strategies 393
provides learners with feedback about what they know
and do not know (Little & McDaniel, 2015). This
increased metacognitive accuracy can guide more effec-
tive and focused rereading during further study com-
pared with additional retrieval practice (McDaniel,
Bugg, Liu, & Brick, 2015, Experiment 2). Simply recom-
mending to students that they incorporate retrieval
practice into their study routines may not prompt much
change in students’ study activities. For example, even
when an instructor set up an online quiz and told his
students about the benefit of practice testing, his stu-
dents’ participation in the practice testing was very poor
(Trumbo, Leiting, McDaniel, & Hodge, 2016). However,
encouraging students to incorporate retrieval practice
into their preexisting routine, such as rereading, might
meet with less resistance from students (see also the
General Discussion section).
The effectiveness of rereading might be further
enhanced by training students on reading strategies.
For instance, McNamara, O’Reilly, Best, and Ozuru
(2006) provided students with a computerized trainer
called Interactive Strategy Trainer for Active Reading
and Thinking (iSTART), which consisted of an interac-
tive agent training students on self-explanation and five
reading strategies (i.e., comprehension monitoring,
paraphrasing, prediction, elaboration, and bridging).
They found that iSTART improved students’ reading
comprehension relative to that of a group of students
who just received a brief demonstration of how to self-
explain texts. Although these results suggest that
rereading might not be necessary if the reader can
successfully comprehend the text the first time around,
we posit that rereading can be leveraged to improve
studying if students are more skilled at particular
aspects of reading, most notably metacomprehension.
That is, if students were able to accurately monitor their
comprehension while reading (e.g., after some training
such as iSTART), rereading would presumably be more
effective because it could target the missing gaps in the
student’s knowledge.
Underlining and Highlighting (Marking)
Underlining or highlighting parts of a text is one of the
most popular study strategies among students because
of its ease of use (Cioffi, 1986; Gurung, Weidert, & Jeske,
2010). The belief that this strategy can improve compre-
hension is so pervasive that even as early as the 5th grade,
students spontaneously underline (Brown & Smiley, 1978).
Both underlining and highlighting are believed to benefit
learning in two ways: (a) Selecting what is important in
the text elicits elaborative thinking (generative function),
and (b) underlining and highlighting important sections
makes it easier to identify them later (storage function).
Because underlining and highlighting essentially func-
tion through the same mechanism, we will hereafter refer
to them collectively as marking for brevity.
The literature on marking largely consists of two
types of experimental designs: learner-generated mark-
ing and experimenter-provided marking. In learner-
generated-marking experiments, participants mark texts
as they read, whereas in experimenter-generated-marking
experiments, participants read texts that are already
marked. We will focus primarily on the learner-generated
marking research to assess the effectiveness of marking as
a self-regulated study strategy, but we supplement our
literature review with research from experimenter-provided
and other marking research, where appropriate.
Effective implementation
Learner-generated marking has been shown to be effec-
tive for a variety of assessments, such as multiple-choice
tests (e.g., Leutner, Leopold, & Den Elzen-Rump, 2007),
free-recall tests (Rickards & August, 1975), short-answer
tests (Amer, 1994; Blanchard & Mikkelson, 1987), fill-
in-the-blank tests (Yue, Storm, Kornell, & Bjork, 2015),
and essay questions (Annis & Davis, 1978; Davis &
Annis, 1978; but see Idstein & Jenkins, 1972; Rickards
& Denner, 1979; Todd & Kessler, 1971).
There is strong evidence from experimenter-provided
marking studies that students can recall the marked
information better than unmarked information (Blalick,
Blalick, & Wark, 1977; Cashen & Leicht, 1970; Crouse
& Idstein, 1972; Hartley, Bartlett, & Branthwaite, 1980;
Klare, Mabry, & Gustafson, 1955; Leicht & Cashen, 1972;
Lorch, Lorch, & Klusewitz, 1995; Nist & Hogrebe, 1987).
Moreover, evidence from studies of learner-generated
marking is consistent with this trend (Blanchard &
Mikkelson, 1987; Fowler & Barker, 1974).
The question, then, is this: Can students accurately
select and mark important information? There seems to
be great variability in students’ abilities to effectively
mark texts. Generally speaking, students’ marking behav-
iors are largely ineffective; they often mark too little or
mark noncritical information (Nist & Kirby, 1989). How-
ever, high-skilled readers can more selectively mark rel-
evant information as determined by instructors’ marking
of the same text (Bell & Limber, 1938).
The good news is that students can be taught an effec-
tive marking strategy in as little as 60 min. Four published
studies (Amer, 1994; Dumke & Schäfer, 1986; Leutner
etal., 2007; Schnell & Rocchio, 1978) and an unpublished
dissertation (Willmore, 1966) have all shown benefits of
marking after training. These successful training programs
had students learn a particular marking strategy and then
practice applying that strategy. Although the marking
strategies taught in these studies varied slightly, one
394 Miyatsu et al.
critical feature among them was that students were
instructed not to mark the text during their first reading.
Rather, marking should occur after a learner finishes read-
ing at least a section of the text. Withholding marking
until after an initial read allows the learner to use the first
read to identify the key points to be marked, thus eliciting
active, elaborative processing of the text. Typically, mark-
ing training is conducted in a class format or as a group
lesson, but it can also be computerized (Leutner etal.,
2007). Although relatively extensive training (five 90-min
sessions) has been successfully conducted (Amer, 1994),
a 1- or 2-hr training session can be enough to produce
benefits (Leutner etal., 2007; Schnell & Rocchio, 1978).
Another approach to improve the effectiveness of
marking is through training focused on the text struc-
ture (i.e., how the text is organized). Meyer, Young, and
Bartlett (1989/2014) showed that teaching students how
to outline effectively by identifying various text struc-
tures made them able to mark more main ideas than
details, a tendency displayed by more expert readers
with high vocabulary and higher education (e.g., Meyer
& Rice, 1989). More detailed discussions on text struc-
ture and its relevance to study strategy training will be
provided later in the Outlining section.
There appears to be an interesting inverse relation-
ship between students’ inclination for marking and the
benefits they receive from marking. For example, Annis
and Davis (1978; Davis & Annis, 1978) found that stu-
dents who did not prefer marking as a study strategy
actually benefited more from marking. Likewise, Yue
etal. (2015) showed that students who were unsure
about the benefits of marking benefited more from
marking. A potential explanation for this finding is that
learners who are unaccustomed to marking put more
effort in selecting which information is important,
resulting in better retention. These findings are discour-
aging because they suggest that students who endorse
this popular study strategy and probably engage in it
more frequently are probably not using it optimally.
Ineffective implementation
Considering the storage function discussed earlier, one
might assume that the benefit of marking may be magni-
fied if a brief review of the marked text occurs before the
final test. In fact, reviewing of marked parts of the text is
extremely popular (Hartwig & Dunlosky, 2012; Kornell &
Bjork, 2007; Yan etal., 2014). However, the literature
indicates that reviewing does not add to the benefits of
marking. Many studies have shown the benefits of mark-
ing without a review (e.g., Fass & Schumacher, 1978;
Kulhavy, Dyer, & Silver, 1975; Rickards & August, 1975),
but several studies have failed to show benefits even with
a review (e.g., Hoon, 1974; Idstein & Jenkins, 1972; Nist
& Hogrebe, 1987). In these studies, the control condition
was to review unmarked material. Marking with a review
did not add benefits above and beyond the generative
benefits associated with marking during initial reading.
Younger students failed consistently to benefit from
marking without preexperiment training on how to
effectively mark texts. Two studies targeting students
who were younger than college age failed to show any
benefits (high school students: Mathews, 1938; 10-year-
olds: Rickards & Denner, 1979). Likewise, no published
study has shown that students younger than college age
benefit from marking without preexperiment training.
In addition, Schellings, Van Hout-Wolters, and Vermunt
(1996) asked 10th graders to mark texts either by main
points, points portrayed as important by the teacher,
or whatever they found interesting, and students were
largely unable to adjust their marking. In contrast,
experimenter-provided marking research has shown
benefits in younger students (high school students:
Blalick etal., 1977; 6th graders: Hartley etal., 1980).
This may suggest that important information cued by
marking is remembered better than nonmarked infor-
mation by younger students, but they are unable to
select important information from a passage on their
own.
In addition, whether marking can facilitate perfor-
mance on higher order assessments is questionable. For
example, a study using inference questions failed to
show benefits of marking (Peterson, 1992). Experimenter-
provided marking has also failed to enhance inference
performance (Christensen & Stordahl, 1955; Silvers &
Kreiner, 1997; Stordahl & Christensen, 1956).
Educational recommendations
Given the importance of reading in academic settings
and the perceived simplicity of marking as a study strat-
egy to enhance reading, marking will likely always be
a common study strategy among students. Thus, it might
be wise to teach students at various levels of education
how to effectively mark texts as they read. Research
suggests that training as brief as 60 min may be able to
enhance the effectiveness of marking. For example,
holding a workshop on how to mark text effectively for
college freshmen may enhance their learning during the
hundreds of hours they will spend reading throughout
their college career. In addition, given that marking can
promote retention of marked information, it can be used
successfully for memorizing terms and definitions. How-
ever, marking should not be students’ only preparation
method for higher-order assessments, such as problem-
solving in physics.
Five Popular Study Strategies 395
Note-Taking
Look around any college campus and you will see
students taking notes during lecture, jotting down notes
while reading their textbooks, and copying notes from
a fellow classmate. The popularity of note-taking stems
from the fact that it holds the appeal of both encoding
benefits and storage benefits. Similar to marking,
encoding benefits refer to the benefits associated with
the act of taking notes, whereas storage benefits refer
to the benefits associated with being able to review the
notes (Di Vesta & Gray, 1972). The majority of the research
conducted on note-taking has focused on the encoding
benefits, so the present review will mostly focus on these
benefits. Nevertheless, we will also discuss relevant find-
ings pertaining to the storage benefits and potential
interactions between the note-taking strategies and stor-
age function.
Effective implementation
Note-taking has been shown to be an effective strategy
for both text and lecture learning, particularly when
learners are engaging in generative processes while
taking notes. Summarizing, paraphrasing, organizing,
or outlining (see the Outlining section) the presented
content has been shown to be especially helpful for
the retention of target information (Bretzing & Kulhavy,
1979; Di Vesta & Gray, 1972; Einstein, Morris, & Smith,
1985; Peper & Mayer, 1978). For instance, Howe (1970)
found that students who took efficient notes (fewer
words to express critical ideas) were more likely to
recall the critical information than students who took
inefficient notes (more words to express critical ideas).
More recently, Bui, Myerson, and Hale (2013) demon-
strated that students who were instructed to take orga-
nized notes on a computer while viewing a lecture were
able to recall more information on a delayed test than
those who were instructed to transcribe (even though
more notes were recorded under transcription instruc-
tions). Mueller and Oppenheimer (2014) had partici-
pants listen to a TED talk and take notes by hand or
by computer. They found that those who took notes on
a computer produced a higher quantity of notes but
that these notes were more likely to be verbatim. As a
result, learners who took notes on a computer per-
formed worse on a final conceptual test than those who
took notes by hand.
It is important that this advantage of generative note-
taking over verbatim note-taking might not hold up
when students are allowed to review their notes, and
reviewing is crucial in reaping the full benefits of note-
taking (see the Educational Recommendations section).
On the one hand, the participants in Bui etal. (2013)
who were instructed to take verbatim notes outper-
formed another group of participants who were
instructed to take organized notes; both groups were
given an opportunity to review their notes. On the other
hand, Mueller and Oppenheimer (2014, Experiment 3)
showed that the advantage of longhand note-taking
(with presumably more generative processing) per-
sisted when they gave the participants an opportunity
to review their notes before a test after a 1-week delay.
This issue of whether generative note-taking or verba-
tim note-taking with greater quantity makes for better
storage function is an important issue that demands
further research, especially in the context of increas-
ingly popular note-taking on computers. With computer
note-taking, students can type more notes than when
they write, but their notes are more likely to be verba-
tim when typing (Mueller & Oppenheimer, 2014).
Unlike the marking research, where being able to
review the marked information yields minimal benefits,
being able to review notes is a potent addition to the note-
taking strategy (Howe, 1970; Kiewra, 1985; Kobayashi,
2006). Supporting this conclusion, a meta-analysis of 57
studies computed the average effect size of note-taking
without review to be relatively small (Cohen’s d of 0.22
(95% confidence interval, or CI = [0.17, 0.27]; Kobayashi,
2005). By contrast, another meta-analysis by the same
researcher found a large effect size of note-taking with
review (d = 0.75; 95% CI = [0.61, 0.89]; Kobayashi, 2006).
Some researchers have argued, in line with these meta-
analyses, that the storage benefits of note-taking are even
more robust than the encoding benefits (Kiewra, Dubois,
Christensen, Kim, & Lindberg, 1989).
Ineffective implementation
As discussed above, note-taking is an effective encod-
ing strategy when learners are engaging in generative
processing of the material (e.g., summarizing or para-
phrasing the presented material). On the flip side, when
learners do not engage in these generative processes,
note-taking has been shown to be a relatively ineffec-
tive learning strategy (perhaps unless the notes are
available for review). Bretzing and Kulhavy (1979)
instructed learners to take notes one of four different
ways: (a) summarize, (b) paraphrase, (c) copy verbatim,
or (d) search for certain letters. They found that copy-
ing verbatim did not yield any benefits relative to not
taking notes at all. However, it is important to note that
Bretzing and Kulhavy did not allow learners to go back
and review their notes before being tested.
Finally, note-taking may be less effective for audio-visual
presentations of content (e.g., lecture with PowerPoint).
In his meta-analysis, Kobayashi (2005) found that the
effect size for note-taking was significantly larger for
396 Miyatsu et al.
audio or text presentations than for audio-visual presenta-
tions. He reasoned that this might be because visual atten-
tion to handwriting (or typing) movements may be
interfering with the visual processing of the presented
information. No study has experimentally manipulated this
factor, however, so it is only speculative at the moment.
Nevertheless, it is an important finding that should be given
further consideration, given that most lectures consist pri-
marily of audio-visual presentations (e.g., particularly con-
texts in which the visual presentation is not just a repetition
of what the instructor is saying).
Educational recommendations
There are two recommendations that can be drawn
from the literature reviewed above. First, if there is no
time to review the notes, students should engage in
generative note-taking strategies such as summarizing
and outlining. The converse of this recommendation,
which may point to a pitfall of a note-taking strategy,
is that taking notes by simply transcribing verbatim and
not reviewing the notes is unlikely to benefit learning.
Even worse, shallow processing imposed by the verba-
tim transcription may actually hinder learning by pre-
venting the learners from engaging with the material
more meaningfully. Second, review the notes. As the
meta-analyses on the benefits of note-taking with or
without review showed (Kobayashi, 2005, 2006), the
beneficial effects of reviewing notes is substantial—
probably greater than the benefits of the act of taking
notes. Although the important issues of optimal strate-
gies (i.e., generative or transcribe as much as possible)
and the mode of note-taking (i.e., longhand or on com-
puter) in the context of reviewing remain inconclusive,
it is clear that reviewing is imperative in using note-
taking optimally.
Outlining
Outlining, a hierarchical representation of the main
points of material to be studied, is a long-standing study
technique endorsed by both educators and students. A
survey of more than 300 teachers in the 1920s found
that nearly half of them required their students to pre-
pare an outline while reading texts (Monroe, 1921). A
more recent survey in four classes from various disci-
plines indicated that nearly two thirds (65%) of the
students outlined at some point during the class
(Walvoord etal., 1995). The effectiveness of outlining
is so widely accepted that the most popular text reading
and editing programs, such as Microsoft Word and
Adobe Acrobat Reader, come with a function to edit or
display the outline of the text. One reason for outlin-
ing’s popularity might be that it offers an opportunity
for learners to engage in active learning through iden-
tification and structured organization of key information
(Mayer, 2008, Chapter 11). In addition, reading a pro-
vided outline of a lecture beforehand or in conjunction
with the lecture can facilitate students’ organization of
the information to be learned and result in better learn-
ing (e.g., Bui & McDaniel, 2015; advance organizers:
Mayer, 2008, Chapter 10).
The distinction between learner-generated and
instructor (or experimenter)-generated outlines is an
important one when evaluating the research on outlin-
ing. In experiments using learner-generated outlines,
participants construct their own outline from scratch as
they learn the material, whereas in experiments with
experimenter-generated outlines, learners are given an
outline prepared by the experimenter to guide their
study. There is also a hybrid of these two types of out-
lining, often called skeletal outlines (e.g., Barbetta &
Skaruppa, 1995; Collingwood & Hughes, 1978; Kiewra,
Benton, Kim, Risch, & Christensen, 1995; Montis, 2007;
for a review, see Larson, 2009), in which learners are
given parts of an outline (e.g., only the headings) and
asked to complete it as they study. Next, we review
studies on these different types of outlining separately
to draw out when they are (and are not) effective.
Effective implementation
Experimenter-generated outlines benefit students’
learning whether they are given before studying (see
also the information below on advance organizers;
Eggen, Kauchak, & Kirk, 1978; Eylon & Reif, 1984;
Glynn & Di Vesta, 1977; Hartley, 1976) or after studying
as a review aid (Kiewra, DuBois, Christian, & McShane,
1988; but see Glynn & Di Vesta, 1977). The benefits are
observed in text (e.g., Eylon & Reif, 1984) and lecture
material (Hartley, 1976; Kiewra etal., 1988), among
college (e.g., Hartley, 1976) and younger students (4th–
6th graders: Eggen etal., 1978), and in higher-order
assessments such as problem solving (Eylon & Reif,
1984) and transfer (Kiewra & Frank, 1988).
Skeletal outlines, which are incomplete outlines to
be filled out during study, can further enhance perfor-
mance (Cornelius & Owen-DeSchryver, 2008; Hartley,
1976; Russell, Caris, Harris, & Hendricson, 1983). Col-
lege students benefit from having a skeletal outline
when listening to lectures on a variety of educationally
relevant topics (psychology: Hartley, 1976; comparative
physiology: Klemm, 1976; types of creativity: Kiewra
etal., 1995; the mechanics of breaks and pumps: Bui
& McDaniel, 2015; for an experiment with medical stu-
dents, see also Russell etal., 1983), and the benefits
hold across various test types, including higher-order
assessments (application questions: Bui & McDaniel,
Five Popular Study Strategies 397
2015; Russell etal., 1983; conceptual questions: Cornelius
& Owen-DeSchryver, 2008) as well as at a surprise test
with a week’s delay (Klemm, 1976). Finally, a few stud-
ies have demonstrated the benefits of skeletal outlines
throughout an entire semester of a course (Austin, Lee,
Thibeault, Carr, & Bailey, 2002; Cornelius & Owen-
DeSchryver, 2008).
One form of outlining that we have not discussed
but that has received substantial attention is advance
organizers (e.g., Ausubel, 1960, 2012; Chapter 1). An
advance organizer is a presentation of the overarching
idea before learning from a text or a lecture, and it
often takes the form of an experimenter-generated out-
line with a pictorial representation of the concept. It
has been shown to benefit learning across a variety of
scientific concepts (e.g., computer science: Mayer, 1975;
mechanics of radars: Mayer, 1983) and on higher-order
assessments (e.g., Mayer, 1980, 1983; see also Corkill,
1992). Because advance organizers include pictorial
representations (e.g., illustrations, diagrams), teasing
apart the effects of outlining per se is difficult. There-
fore, we do not include an in depth review of this lit-
erature (for a review, see Mayer, 2008, Chapter 10; for
a meta-analysis on oral advance organizers, see Preiss
& Gayle, 2006). Nevertheless, this literature reinforces
the effectiveness of presenting an experimenter-
generated outline before the learning of the material.
Ineffective implementation
Learner-generated outlines, by contrast, show benefits
only when participants go through an outline training
process (Barton, 1930; Berkowitz, 1986; Taylor, 1982,
Experiment 1; Taylor & Beach, 1984; but see Taylor, 1982,
Experiment 2). Studies with college students (Tuckman,
1993) and younger students (Eggen etal., 1978) in which
participants were simply told to outline as they studied
(without any prior training) failed to show any benefit
of outlining relative to unguided note-taking or simply
reading. Sometimes younger students do not outline
effectively even with extensive training (Taylor, 1982,
Experiment 2). What is noteworthy here is that after
appropriate training (for details, see the next paragraph),
learner-generated outlining can enhance younger stu-
dents’ comprehension (middle and high school students:
Barton, 1930; 5th graders: Taylor, 1982, Experiment 1;
7th graders: Taylor & Beach, 1984) using educationally
authentic texts (history: Barton, 1930; health and social
studies: Taylor, 1982, Experiment 1; Taylor & Beach,
1984).2
Although the length of the outlining training used in
different experiments has varied (45 min a week for 6
weeks: Berkowitz, 1986; 1 hr a week for 7 weeks: Taylor,
1982; Taylor & Beach, 1984), a common feature is that
the training included regular sessions to practice the
skill over several weeks. Other important commonalities
among these successful outlining-training protocols
include an emphasis on identifying the main points
after reading through the whole section, identifying the
text structure, and retrieving the contents using the
outline as a cue. Correctly identifying the main points
of the text is the fundamental component of successful
outlining. This identification process starts from first
reading through the entire text (or a section, depending
on the length) before identifying the main points. Some
training programs also emphasized the importance of
identifying the text structure (Berkowitz, 1986; Taylor,
1982; Taylor & Beach, 1984). Correctly identifying how
the text is structured not only allows the learner to
identify the main points more easily but also gives the
learner a better understanding of how the ideas in the
text are organized. Finally, successful training also incor-
porated retrieval practice, which is one of the most
powerful learning techniques (e.g., Roediger & Karpicke,
2006a; see also the Using Flash Cards section), using
the outline as a cue. In some cases, learners were spe-
cifically taught to try to retrieve the supporting details
from the main idea headings (Barton, 1930; Berkowitz,
1986). In other cases, the retrieval with the generated
outline was incorporated into the discussion with the
teacher (Taylor, 1982) or peers (Taylor & Beach, 1984).
Educational recommendations
Students who take notes using a skeletal outline during
a lecture benefit in terms of understanding the presented
information, remembering the information at a delay,
and applying that information to solve problems. In addi-
tion, the majority of the studies investigating this issue
were authentic classroom studies (including a few
semester-long investigations)—and the remaining studies
at least compared outlining conditions to strong control
conditions such as note-taking—leaving little doubt on
the generalizability of the benefits of skeletal outlines.
Accordingly, instructors should make an effort to provide
a list of topics and subheadings before a lecture so that
students can use it as a skeletal outline. From a student’s
perspective, if an instructor structures a lecture in a pre-
dictable way (e.g., on the basis of the textbook), it is
advisable to take notes of the headings and subheadings
(i.e., main points and components) that will be covered
and prepare a skeletal outline before the lecture. This
will not only provide students with a better sense of
organization but also save students from wasting time in
copying the obvious headings, precious time they can
use to process the material in a more meaningful way.
398 Miyatsu et al.
Properly constructed outlining training (i.e., identify-
ing the main ideas after reading, identifying the text
structure, and using the constructed outline as a retrieval
cue) is strongly recommended, especially for younger
students who cannot effectively outline on their own.
There is evidence that training incorporating outlining
and text structure can improve reading comprehension
(Meyer etal., 1989/2014; Wijekumar, Meyer, & Lei, 2012,
2013). The extensive training (e.g., 1 hr a week for sev-
eral weeks) and the need for constant feedback during
these training sessions are costly but from our perspec-
tive, assuming that the benefits are relatively long last-
ing, that training expense is relatively minor.
Finally, the effectiveness of providing an experimenter-
generated outline before reading texts emphasizes the
importance of reading the table of contents or any out-
line before reading, which is often neglected. For exam-
ple, when reading textbooks, students may be tempted
to skip the table of contents, using it primarily when
they need to locate particular topics of interest. How-
ever, research reviewed above suggests that reading
through the table of contents and building an under-
standing of how the text is organized is likely to improve
comprehension and retention.
Using Flash Cards
Using flash cards is essentially the real-world applica-
tion of self-testing studies conducted in the laboratory
(Kornell, 2009; Pyc & Rawson, 2007, 2011; Rawson &
Dunlosky, 2011; Vaughn & Rawson, 2011). Thus, it
should be expected that using flash cards is an effective
study strategy, given that self-testing has been shown
to produce robust benefits on retention (for a review;
see Roediger & Karpicke, 2006a). However, it is impor-
tant to note that cognitive psychologists and college
students differ considerably in why they believe flash
cards are effective. Specifically, cognitive psychologists
posit that the retrieval component of using flash cards
is helping students learn and retain the target informa-
tion. Students, on the other hand, use flash cards
because they believe that it helps them gauge how well
they have learned the material (Kornell & Son, 2009;
Wissman etal., 2012). Although students commonly
report using flash cards as a study strategy, empirical
research on this topic is lacking. To our knowledge,
only a handful of studies have investigated the benefits
of using flash cards (or self-testing). As such, we will
borrow significantly from the experimenter-guided test-
ing literature to make this section a more informed
review. Because testing of associative material is essen-
tially the laboratory corollary of using flash cards, we
believe that the findings should generalize to self-testing
with flash cards in an authentic educational setting.
Thus, the present review makes the assumption that
students are actually engaging in self-testing rather than
just reading the flash cards.
Effective implementation
If the goal is to retain specific, detailed information,
using flash cards is one of the most effective study strate-
gies. Consequently, much of the research on experimenter-
guided testing has used associative learning materials,
such as foreign vocabulary (Carrier & Pashler, 1992;
Karpicke & Roediger, 2008; Pyc & Rawson, 2011), con-
cept definitions (Rawson & Dunlosky, 2011), and medical
terms (Schmidmaier etal., 2011), as the study material.
The benefits of testing have also been demonstrated in
more authentic educational contexts. For example,
Schmidmaier etal. (2011) found that medical students
who engaged in repeated testing with flash cards were
able to recall more medical terms on a test after a 1-week
delay than students who engaged in repeated studying.
Likewise, Golding, Wasarhaley, and Fletcher (2012)
found that students who reported using flash cards
more often scored higher on exams in an introductory
psychology course, and Senzaki, Hackathorn, Appleby,
and Gurung (2017) showed that students who were ran-
domly assigned to receive a short lecture on the use of
flash cards, incorporating generation of flash cards and
tying the content to their own experience, scored higher
in the term exams.
Although any amount of testing is beneficial, the
benefits can be further augmented by increasing the
amount of practice. Laboratory studies have shown that
recalling an item more than once can improve the likeli-
hood of future recall (Karpicke, 2009; Rawson & Dunlosky,
2011; Vaughn & Rawson, 2011). For instance, Vaughn and
Rawson had participants learn pairs of Lithuanian and
English words until they were correctly recalled a cer-
tain number of times; some items were recalled once,
whereas other items were recalled twice, three times,
four times, or five times. They found that after a 48-hr
delay, learners were able to recall only 31% of the items
that were originally recalled only once. However, learn-
ers were able to recall 71% of the items that were origi-
nally recalled four or five times. These results suggest
that students should continue to practice recalling the
target information even after they can get it correct the
first time.
Another factor that can enhance the benefits of self-
testing is the amount of lag or spacing between study-
ing an item. As we discussed in the Rereading section,
spacing study of the same information can have potent
benefits for long-term retention (for a review, see
Cepeda etal., 2006). It has also been shown that having
longer lags between the same items yields better
Five Popular Study Strategies 399
memory performance than shorter lags (Kornell, 2009;
Pyc & Rawson, 2007, 2011). For instance, Pyc and Rawson
(2011) had students learn pairs of Swahili and English
words that were separated by either 6 or 34 other items,
and they found that the longer lag condition led to better
retention (76% vs. 55% for test after a 25-min delay; 30%
vs. 5% for a test after a 1-week delay). Thus, it is impor-
tant for students not to just keep drilling the same item;
rather, they should take a break and come back to that
item later on.
Ineffective implementation
One potential pitfall associated with using flash cards
is knowing when to drop a flash card. Specifically, how
does one determine when an item has been learned?
Theoretically and intuitively speaking, dropping flash
cards that are well learned from study should allow
more study opportunities for yet-to-be-learned flash
cards and thus result in better overall learning. How-
ever, Kornell and Bjork (2008) found that allowing stu-
dents to self-regulate their own study by dropping flash
cards (relative to not dropping) had detrimental effects
on learning. Their explanation was that students lacked
the requisite metacognitive accuracy to effectively drop
flash cards from future study. This is not the entire story,
however. Even assuming that metacognitive accuracy
was perfect, dropping items from additional testing may
also have detrimental effects because the learner loses
out on additional practice. For instance, Karpicke and
Roediger (2007) dropped correctly recalled items from
further testing and found that it still had negative con-
sequences relative to repeated testing. A potential
explanation is that dropping correctly recalled items
diminished spacing, which has significant benefits for
retention of information (see Soderstrom, Kerr, & Bjork,
2016).
Another potential limitation of using flash cards is
that it is difficult to target higher order information.
Because flash cards were developed to learn specific,
detailed information, zero investigation has examined
whether flash cards can be used to learn complex infor-
mation that is not simply associative learning.
A final consideration of using flash cards optimally
concerns whether mixing (also referred to as interleav-
ing) flash cards from two different topics is beneficial
for long-term retention. Although a good deal of labora-
tory evidence suggests that mixing is beneficial in cat-
egory and math learning (e.g., Kang & Pashler, 2012;
Kornell & Bjork, 2008; Taylor & Rohrer, 2010; Wahlheim,
Dunlosky, & Jacoby, 2011), the story is less clear in flash
card studies. To address this question, Hausman and
Kornell (2014) varied whether learners studied flash
cards for two different topics (i.e., anatomical definitions
and Indonesian translations) in a mixed or a separate
condition. Their results indicated that mixing flash cards
from two different topics had no influence on long-term
retention, which suggests that students have much flex-
ibility in how they use flash cards to study for different
classes.
Educational recommendations
There are two important points to take away from the
present review on how to use flash cards effectively.
First, students should keep studying and testing them-
selves even after they get an item correct. Doing so
results in dual benefits: (a) The additional practice is
crucial for strengthening their memory for the target
information, and (b) they will be able to avoid the
pitfall of inaccurate metacognition (i.e., not discriminat-
ing between learned and unlearned information). This
is contrary to the conventional wisdom that once you
get an item correct, you should stop studying it. Second,
students should space out their studying of a given flash
card. According to Wissman etal. (2012), students do
not appear to recognize the importance of self-testing
with longer lags even though it has been shown empiri-
cally that spacing out an item between practice attempts
makes that item more likely to be recalled in the future.
General Discussion
There has been a growing interest in applying findings
from cognitive science to enhance educational prac-
tices. Recent reviews strongly suggest that there are
potent study strategies that foster effective learning
(Bjork, Dunlosky, & Kornell, 2013; Dunlosky etal.,
2013; Fiorella & Mayer, 2015; Roediger & Pyc, 2012).
Yet students’ study-strategy choices are largely driven
by their perceived understanding at the moment of
studying (Soderstrom & Bjork, 2015), and it appears
that students rarely use these strategies that truly pro-
mote learning. Accordingly, in the present review, using
existing empirical evidence from the cognitive and edu-
cational literatures, we explored whether students’ pre-
ferred study strategies might have merit. Given students’
strong preferences in using these strategies anyway,
illuminating when they do benefit learning—and when
they do not—may at least guide students to use the
strategies optimally. In Table 2, we summarize the main
outcomes of our review of five popular study strategies
(rereading, highlighting, note-taking, underlining, and
using flash cards) in terms of (a) common pitfalls, (b)
tips for optimal implementation, and (c) effectiveness
for different test types.
A few common themes have emerged about how to
appropriately leverage these five strategies to produce
400 Miyatsu et al.
optimal memory and comprehension performance. One
clear conclusion from our review is that students, when
left to their own devices, do not use these popular
strategies very effectively. Students are often unaware
of the pitfalls associated with these strategies, including
mistaking fluency for learning when rereading, high-
lighting too much, copying notes verbatim, and prema-
turely dropping flash cards from further study. However,
these pitfalls can be overcome once students are made
aware of them and are provided with explicit instruc-
tions to avoid them.
Aside from avoiding the pitfalls listed in Table 2, our
review suggests that there are potent ways to augment
these popular study strategies, including incorporating
cognitive-psychologist-endorsed strategies (e.g., retrieval
practice, distributed practice), hosting training, and pro-
viding instructor assistance.
First, in some of our recommendations, we suggested
the incorporation of study strategies that are strongly
endorsed by cognitive psychologists but rarely used by
students. One might wonder what is new about these
recommendations. For instance, in the case of reread-
ing, some might argue that recommending spacing the
readings or self-testing between readings is the same
as simply recommending distributed practice or retrieval
practice (which may not be fruitful in changing stu-
dents’ study behavior). However, our recommendations
in this regard are different from the previous recommen-
dations because they align with the principle that new
behaviors are attained most efficiently when they are
incorporated into preexisting behaviors. This principle is
well established in other fields, such as exercise interven-
tion (e.g., Lutes etal., 2012), and we think that it is likely
to also have bearing on study-strategy intervention. Spe-
cifically, students might not know how exactly to imple-
ment retrieval practice effectively, so identifying novel
occasions and implementing it properly takes effort,
thus potentially diminishing the likelihood that students
will actually implement retrieval practice. On the con-
trary, students already have a strong tendency to reread,
so having them take a moment to try to retrieve what
the read contents were before rereading would better
align with students’ established study habits and,
accordingly, might be more fruitful in making a change.
In this sense, some of our recommendations are
intended not only to make students’ preferred study
strategies more effective, but also to use these habitual
strategies as vehicles to introduce more effective but
rarely practiced strategies into students’ routines.
Second, training students on how to use the study
strategies can also produce more robust benefits on
learning outcomes. Research has demonstrated that
marking yields more robust benefits after learners have
been trained on effective marking strategies (Amer, 1994;
Dumke & Schäfer, 1986; Leutner etal., 2007; Schnell &
Rocchio, 1978) and learner-generated outlining is espe-
cially effective when participants receive training on how
to outline (Barton, 1930; Berkowitz, 1986; Taylor, 1982,
Experiment 1; Taylor & Beach, 1984). Thus, it is critical
that students receive some training on how to effectively
use these study strategies to reap the full benefits. Fur-
ther, because these training programs can benefit even
Table 2. Common Pitfalls, Tips for Optimal Implementation, and Effective Test Types
Test types
Strategy Common pitfalls Tips for optimal implementation Factual Application
Rereading × Mistaking the fluency associated
with a second reading as
having learned the material
successfully.
Space out the readings.
Test yourself in between the readings.
Yes No
Marking × Marking too little; marking
noncritical information.
× Mindless marking (frequent
users need to be careful).
Read through the text first before marking.
Pay attention to the text structure when
identifying important information to mark.
Yes No
Taking notes × Copying lecture notes verbatim
and not reviewing them.
Make sure to review the notes before an
exam.
Yes Yes
Outlining × Outline from scratch without
paying attention to the text
structure.
Identify the main points after reading
through the whole section.
Pay attention to the text structure.
Use skeletal outline as a guide.
Yes Yes
Flash cards × Dropping flash cards from study
after one successful retrieval.
Retrieve an item correctly at least three
times before dropping it from study.
Yes No
Note: Factual tests assess whether the learner can recall the studied information, whereas application tests assess whether the learner can apply
the studied information to a new context (e.g., problem solving). “Yes” and “No” indicate whether empirical evidence shows that a particular
strategy benefits learning assessed by different types of tests.
Five Popular Study Strategies 401
young students (as young as 5th graders in the case of
outlining training), starting the training at an early age
and ensuring that these students use the strategies
effectively for the years to come may confer significant
dividends for students and educators alike.
Finally, instructors can play an important role in assist-
ing students to use their preferred study strategies effec-
tively. For instance, it has been found that students of
all ages can benefit from outlining when provided with
a skeletal outline, whereas many students fail to benefit
from outlining from scratch (see the Outlining section).
A simple and effective solution would be for instructors
to provide their students with a skeletal outline before
the lecture starts. Another instance in which instructors
can assist students is by quizzing them on a given topic
on various occasions, which would indirectly require
students to engage in spaced review (e.g., rereading or
flash cards). Instructors may even incentivize these effec-
tive implementations by awarding a small portion of the
course grade to students for turning in their completed
outlines or performing well on the quizzes.
Individual differences
Although our review thus far has focused on the gen-
eral effectiveness of study strategies for all learners, it
would be a disservice not to briefly discuss how indi-
vidual differences may moderate the effectiveness of
these study strategies. Research on this issue is sorely
lacking, but for two of the strategies, there has been
preliminary research that is worth mentioning. First,
rereading seems to be a much more useful study strat-
egy for good comprehenders (as indexed by the Multi-
Media Comprehension Battery; Gernsbacher, Varner, &
Faust, 1990) than for poor comprehenders (Martin,
Nguyen, & McDaniel, 2016). In particular, Martin etal.
found that good comprehenders demonstrated superior
metacognitive control—the ability to effectively guide
their restudy opportunity. Specifically, good compre-
henders were more likely than poor comprehenders to
engage in a discrepancy-reduction strategy (i.e., allo-
cate more study time for information identified as less
well learned than information identified as well learned;
cf. Thiede & Dunlosky, 1999) during a second reading.
These results suggest that students who are already
good comprehenders can benefit from a variety of
study strategies, even those strategies that provide little
guidance. On the other hand, poor comprehenders
might benefit more from study strategies that provide
them with specific instructions on how to study.
Second, the benefits of note-taking may be moder-
ated by working memory ability. In particular, learners
with low working memory are better off transcribing
than synthesizing lecture notes (Bui & Myerson, 2014).
A plausible explanation is that learners with low work-
ing memory are unable to hold in mind and manipulate
different pieces of information while initially recording
notes. By contrast, learners with high working memory,
who have the capacity to hold and manipulate informa-
tion while recording notes, should attempt to organize
and synthesize the lecture content during note-taking
to develop a deeper understanding of the content.
Future research on individual differences in the
effectiveness of study strategies may benefit from con-
sidering two additional core skills, metacognition and
organization, that we believe underlie students’ ability
to use these popular study strategies efficaciously.
Metacognition plays a vital role in mediating successful
learning, especially in the cases of rereading and using
flash cards, during which learners need to be cognizant
of what they know and do not know to determine what
to focus on during a second reading or deciding when
to drop a flash card from further studying. Organization,
which is the ability to understand the relationship
between key points and grasp what is important, plays
an important role in identifying important information
to mark or constructing a coherent outline structure.
Future directions
One fruitful avenue for future research would be to
investigate the benefits of combining study strategies.
Much of the prior research has investigated various study
strategies in isolation and characterized their efficacy
and mechanisms that way. However, combining suitable
study strategies can be more effective and efficient than
using a single strategy. Basic research on this issue is in
its infancy, and only a handful of potential potent com-
binations are being investigated (retrieval practice and
the keyword mnemonic: Miyatsu & McDaniel, 2018;
retrieval practice and rereading: Nguyen & McDaniel,
2016; marking and massing/spacing: Yue etal., 2015; see
also Intelligent Tutoring of the Structure Strategy3: for
applied research taking this approach, see Meyer etal.,
2002; Meyer & Wijekumar, 2017; Meyer, Wijekumar, &
Lin, 2011; Wijekumar etal., 2014). More research is
needed to provide insights into powerful and practical
combinations of study strategies.
Another exciting avenue for future research is exam-
ining how technology might interact with these differ-
ent study strategies. Because much of the research on
these popular study strategies was conducted before
the advent of technological advances such as laptops,
iPads, and smartphones, it is unclear how technology
might influence the benefits of these study strategies
(but for exceptions, see Bui etal., 2013; Mueller &
Oppenheimer, 2014). For example, the use of comput-
ers makes it easier for learners to take and edit notes
402 Miyatsu et al.
and outlines. As mentioned in the note-taking section,
an emergent question is whether students should take
notes by writing and engage in generative strategies or
by typing and transcribing as many notes as possible.
Does the editing allowed by computers make outlining
on computers better than hand-written outlines? There
are various smartphone applications that allow students
to easily copy and paste information to create flash
cards or even websites that provide premade flash
cards. Is studying with these new types of flash cards
as effective as studying with flash cards that are made
by learners themselves?
Finally, a venerable idea that merits revisiting is the
concept of generative learning and how it contributes
to the observed benefit of each strategy. As touched
upon in the previous sections, many study strategies
have generation and utilization components (e.g., the
encoding and storage function of note-taking). Isolating
the benefits of these components will provide impor-
tant insights on optimal implementation. For instance,
the existing research on flash cards has focused primar-
ily on the use component. Thus, it is unknown whether
generation of flash cards (by students) has any unique
benefits to learning aside from simply using the flash
cards. Likewise, would instructor-provided notes be
more beneficial than student-generated notes?
Concluding comments
It is clear that students have strong study-strategy pref-
erences that do not align with the strategies being
advocated by cognitive psychologists (Dunlosky etal.,
2013; Hartwig & Dunlosky, 2012; Karpicke etal., 2009;
Kornell & Bjork, 2007; Wissman etal., 2012; Yan etal.,
2014). Accordingly, a productive approach might be to
assist students with optimizing the strategies that they
prefer. In addition, we suggest using these popular
study strategies as vehicles to introduce the strategies
endorsed by cognitive psychologists into students’
study routines. Our review serves as a starting point
for the approach of identifying and augmenting strate-
gies that students already use. It is our hope that stu-
dents use these assessments of their favorite study
strategies to help them study more effectively, that
instructors become aware of opportunities to actively
assist students’ optimal implementation of these strate-
gies, and that psychologists become interested in con-
ducting research on exploring effective augmentations
to these strategies.
Declaration of Conflicting Interests
The authors declared that there were no conflicts of interest with
respect to the authorship or the publication of this article.
Notes
1. Aside from spaced study, another way to augment reread-
ing might be to encourage high-level processing (i.e., situation-
model processing; Johnson-Laird, 1983; Kintsch, 1988; see also
Bos, De Koning, Wassenburg, & van der Schoot, 2016) during
the second reading. One way to stimulate such processing is to
encourage the reader to explicitly consider the state of his or
her situation model by making judgments of inferencing (JOIs;
i.e., learners judge how likely they would be able to apply
the core knowledge they just learned in the future) before the
second reading, which can enhance inference and problem-
solving performance relative to a second reading with no JOIs
(e.g., Nguyen & McDaniel, 2016, in which JOIs followed a
retrieval practice phase before a second reading).
2. It bears mention that outlining can be used for writing rather
than for improving comprehension of text or lecture material.
Learner-generated outlining without prior training facilitates
essay writing by improving the structure of the written product
and it appears to reduce mental effort (De Smet, Brand-Gruwel,
Leijten, & Kirschner, 2014; De Smet, Broekkamp, Brand-Gruwel,
& Kirschner, 2011; Kellogg, 1990; but see De Smet, Brand-
Gruwel, Broekkamp, & Kirschner, 2012).
3. Intelligent Tutoring of the Structure Strategy is a reading
comprehension training that incorporates several study strat-
egies, such as identifying text structure, monitoring compre-
hension through summarizing main points according to the
identified test structure, and retrieval using the text structure
as the guide (see also Bartlett, 1978; Meyer & Poon, 2001;
Meyer & Ray, 2011; Meyer etal., 1989/2014; Ray & Meyer, 2011;
Williams etal., 2005).
References
Amer, A. A. (1994). The effect of knowledge-map and under-
ling training on the reading comprehension of scientific
texts. English for Specific Purposes, 13, 35–45.
Amlund, J. T., Kardash, C. A. M., & Kulhavy, R. W. (1986).
Re petitive reading and recall of expository text. Reading
Research Quarterly, 21, 49-58.
Annis, L., & Davis, J. K. (1978). Study techniques and cog-
nitive style: Their effect on recall and recognition. The
Journal of Educational Research, 71, 175–178.
Austin, J. L., Lee, M. G., Thibeault, M. D., Carr, J. E., &
Bailey, J. S. (2002). Effects of guided notes on university
students’ responding and recall of information. Journal
of Behavioral Education, 11, 243–254.
Ausubel, D. P. (1960). The use of advance organizers in
the learning and retention of meaningful verbal material.
Journal of Educational Psychology, 51, 267–272.
Ausubel, D. P. (2000). The acquisition and retention of knowl-
edge: A cognitive view. Boston, MA: Kluwer Academic
Publishers, 2000.
Barbetta, P. M., & Skaruppa, C. L. (1995). Looking for a way
to improve your behavior analysis lectures? Try guided
notes. The Behavior Analyst, 18, 155–160.
Barnett, J. E., & Seefeldt, R. W. (1989). Read something once,
why read it again? Repetitive reading and recall. Journal
of Reading Behavior, 21, 351-360.
Five Popular Study Strategies 403
Bartlett, B. J. (1978). Top-level structure as an organizational
strategy for recall of classroom text (Unpublished doctoral
dissertation). Arizona State University, Tempe.
Barton, W., Jr. (1930). Outlining as a study procedure. The
Teachers College Record, 32, 473–474.
Bell, K. E., & Limber, J. E. (1938). Reading skill, textbook
marking, and course performance. Literacy Research and
Instruction, 49, 56–67.
Berkowitz, S. J. (1986). Effects of instruction in text organi-
zation on sixth-grade students’ memory for expository
reading. Reading Research Quarterly, 21, 161–178.
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated
learning: Beliefs, techniques, and illusions. Annual
Review of Psychology, 64, 417–444.
Blalick, S. M., Blalick, D. H., & Wark, D. M. (1977). The effects
of experimenter-underlining and expertise on the reten-
tion of text material. In P. D. Pearson & J. Hansen (Eds.),
Reading: Theory, research, and practice. Twenty-sixth
yearbook of the National Reading Conference (pp. 26–31).
Clemson, SC: The National Reading Conference, Inc.
Blanchard, J., & Mikkelson, V. (1987). Underlining perfor-
mance outcomes in expository text. The Journal of Edu-
cational Research, 80, 197–201.
Bos, L. T., De Koning, B. B., Wassenburg, S. I., & van der
Schoot, M. (2016). Training inference making skills using
a situation model approach improves reading comprehen-
sion. Frontiers in Psychology, 7, Article 116. doi:10.3389/
fpsyg.2016.00116
Bretzing, B. H., & Kulhavy, R. W. (1979). Notetaking and depth
of processing. Contemporary Educational Psychology, 4,
145–153.
Brown, A. L., & Smiley, S. S. (1978). The development of
strategies for studying texts. Child Development, 49,
1076–1088.
Bui, D. C., & McDaniel, M. A. (2015). Enhancing learning dur-
ing note-taking using outlines and illustrative diagrams.
Journal of Applied Research in Memory and Cognition,
4, 129–135.
Bui, D. C., & Myerson, J. (2014). The role of working memory
abilities in lecture note-taking. Learning and Individual
Differences, 33, 12–22.
Bui, D. C., Myerson, J., & Hale, S. (2013). Note-taking with
computers: Exploring alternative strategies for improved
recall. Journal of Educational Psychology, 105, 299–309.
Callender, A. A., & McDaniel, M. A. (2009). The limited ben-
efits of rereading educational texts. Contemporary Journal
of Educational Psychology, 34, 30–41.
Carrier, M. (2003). College students’ choice of study strategies.
Perceptual and Motor Skills, 96, 54–56.
Carrier, M., & Pashler, H. (1992). The influence of retrieval
on retention. Memory & Cognition, 20, 633–642.
Cashen, V. M., & Leicht, K. L. (1970). Role of the isolation effect
in a formal educational setting. Journal of Educational
Psy chology, 61, 484–486.
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.
Christensen, C. M., & Stordahl, K. E. (1955). The effect of
organizational aids on comprehension and retention.
Journal of Educational Psychology, 46, 65–74.
Cioffi, G. (1986). Relationships among comprehension strate-
gies reported by college students. Literacy Research and
Instruction, 25, 220–232.
Collingwood, V., & Hughes, D. C. (1978). Effects of three
types of university lecture notes on student achievement.
Journal of Educational Psychology, 70, 175–179.
Corkill, A. J. (1992). Advance organizers: Facilitators of recall.
Educational Psychology Review, 4, 33–67.
Cornelius, T. L., & Owen-DeSchryver, J. (2008). Differential
effects of full and partial notes on learning outcomes and
attendance. Teaching of Psychology, 35, 6–12.
Crouse, J. H., & Idstein, P. (1972). Effects of encoding cues
on prose learning. Journal of Educational Psychology,
63, 309–313.
Davis, J. K., & Annis, L. (1978). The effect of study tech-
niques, study preferences, and familiarity on later recall.
The Journal of Experimental Education, 47, 92–96.
De Smet, M. J. R., Brand-Gruwel, S., Broekkamp, H., &
Kirschner, P. A. (2012). Write between the lines: Electronic
outlining and the organization of text ideas. Computers
in Human Behavior, 28, 2107–2116.
De Smet, M. J. R., Brand-Gruwel, S., Leijten, M., & Kirschner,
P. A. (2014). Electronic outlining as a writing strategy:
Effects on students’ writing products mental effort and
writing process. Computers & Education, 78, 352–366.
De Smet, M. J. R., Broekkamp, H., Brand-Gruwel, S., &
Kirschner, P. A. (2011). Effects of electronic outlining on
students’ argumentative writing performance. Journal of
Computer Assisted Learning, 27, 557–574.
Di Vesta, F. J., & Gray, G. S. (1972). Listening and note taking.
Journal of Educational Psychology, 63, 8–14.
Dumke, D., & Schäfer, G. (1986). Verbesserung des lernens
aus texten durch trainiertes unterstrichen [Improving text
learning by trained underlining]. Psychologie in Erziehung
und Unterricht, 33, 210–219.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., &
Willingham, D. T. (2013). Improving students’ learning
with effective learning techniques: Promising directions
from cognitive and educational psychology. Psychological
Science in the Public Interest, 14, 4–58.
Eggen, P. D., Kauchak, D. P., & Kirk, S. (1978). The effect
of hierarchical cues on the learning of concepts from
prose materials. The Journal of Experimental Education,
46, 7–11.
Einstein, G. O., Morris, J., & Smith, S. (1985). Note-taking,
individual differences, and memory for lecture informa-
tion. Journal of Educational Psychology, 77, 522–532.
Eylon, B., & Reif, F. (1984). Effects of knowledge organization
on task performance. Cognition and Instruction, 1, 5–44.
Fass, W., & Schumacher, G. M. (1978). Effects of motivation,
subject activity, and readability on the retention of prose
materials. Journal of Educational Psychology, 70, 803–807.
Fiorella, L., & Mayer, R. E. (2015). Learning as a generative
activity: Eight learning strategies that promote understand-
ing. Cambridge, England: Cambridge University Press.
404 Miyatsu et al.
Fowler, R. L., & Barker, A. S. (1974). Effectiveness of high-
lighting for retention of text material. Journal of Applied
Psychology, 59, 358–364.
Gernsbacher, M. A., Varner, K. R., & Faust, M. E. (1990).
Investigating differences in general comprehension skill.
Journal of Experimental Psychology: Learning, Memory, and
Cognition, 16, 430–445. doi:10.1037/0278-7393.16.3.430
Glover, J. A., & Corkill, A. J. (1987). Influence of paraphrased
repetitions on the spacing effect. Journal of Educational
Psychology, 79, 198–199.
Glynn, S. M., & Di Vesta, F. J. (1977). Outline and hierarchi-
cal organization as aids for study and retrieval. Journal
of Educational Psychology, 69, 89–95.
Golding, J. M., Wasarhaley, N. E., & Fletcher, B. (2012). The
use of flashcards in an introduction to psychology class.
Teaching of Psychology, 39, 199–202.
Gurung, R. A. R., Weidert, J., & Jeske, A. (2010). Focusing on
how students study. Journal of the Scholarship of Teaching
and Learning, 10, 28–35.
Haenggi, D., & Perfetti, C. A. (1992). Individual differ-
ences in reprocessing of text. Journal of Educational
Psychology, 84, 182–192.
Hartley, J. (1976). Lecture handouts and student note-taking.
Programmed Learning and Educational Technology, 13,
58–64.
Hartley, J., Bartlett, S., & Branthwaite, A. (1980). Underlining
can make a difference: Sometimes. The Journal of
Educational Research, 73, 218–224.
Hartwig, M. K., & Dunlosky, J. (2012). Study strategies of
college students: Are self-testing and scheduling related
to achievement. Psychonomic Bulletin & Review, 19,
126–134.
Hausman, H., & Kornell, N. (2014). Mixing topics while
studying does not enhance learning. Journal of Applied
Research in Memory and Cognition, 3, 153–160.
Hoon, P. W. (1974). Efficacy of three common study methods.
Psychological Reports, 35, 1057–1058.
Howe, M. J. A. (1970). Repeated presentation and recall of
meaningful prose. Journal of Educational Psychology, 61,
214–219.
Idstein, P., & Jenkins, J. R. (1972). Underlining versus repeti-
tive reading. The Journal of Educational Research, 65,
321–323.
Johnson-Laird, P. N. (1983). Mental models: Towards a cog-
nitive science of language, inference, and consciousness.
Cambridge, MA: Harvard University Press.
Kang, S. H., & Pashler, H. (2012). Learning painting styles:
Spacing is advantageous when it promotes discriminative
contrast. Applied Cognitive Psychology, 26, 97-103.
Karpicke, J. D. (2009). Metacognitive control and strategy
selection: Deciding to practice retrieval during learn-
ing. Journal of Experimental Psychology: General, 138,
469–486.
Karpicke, J. D., & Blunt, J. R. (2011). Retrieval practice pro-
duces more learning than elaborative studying with con-
cept mapping. Science, 331, 772–775.
Karpicke, J. D., Butler, A. C., & Roediger, H. L. (2009).
Metacognitive strategies in student learning: Do students
practice retrieval when they study on their own? Memory,
17, 471–479.
Karpicke, J. D., & Roediger, H. L., III. (2007). Repeated
retrieval during learning is the key to long-term retention.
Journal of Memory and Language, 57, 151–162.
Karpicke, J. D., & Roediger, H. L., III. (2008). The critical
importance of retrieval for learning. Science, 319, 966–968.
Kellogg, R. T. (1990). Effectiveness of prewriting strategies
as a function of task demands. The American Journal of
Psychology, 103, 327–342.
Kiewra, K. A. (1985). Investigating notetaking and review: A
depth of processing alternative. Educational Psychologist,
20, 23–32.
Kiewra, K. A., Benton, S. L., Kim, S., Risch, N., & Christensen, M.
(1995). Effects of note-taking format and study technique
on recall and relational performance. Contemporary
Educational Psychology, 20, 172–187.
Kiewra, K. A., Dubois, N. F., Christensen, M., Kim, S., &
Lindberg, N. (1989). A more equitable account of the
note-taking functions in learning from lecture and from
text. Instructional Science, 18, 217–232.
Kiewra, K. A., DuBois, N. F., Christian, D., & McShane, A.
(1988). Providing study notes: Comparison of three types
of notes for review. Journal of Educational Psychology,
80, 595–597.
Kiewra, K. A., & Frank, B. M. (1988). Encoding and exter-
nal-storage effects of personal lecture notes, skeletal
notes, and detailed notes for field-independent and field-
dependent learners. The Journal of Educational Research,
81, 143–148.
Kintsch, W. (1988). The role of knowledge in discourse
comprehension: A construction integration model.
Psychological Review, 94, 163–182.
Klare, G. R., Mabry, J. E., & Gustafson, L. M. (1955). The rela-
tionship of patterning (underlining) to immediate reten-
tion and to acceptability of technical material. Journal of
Applied Psychology, 39, 40–42.
Klemm, W. R. (1976). Efficiency of handout “skeleton” notes
in student learning. Improving College and University
Teaching, 24, 10–12.
Kobayashi, K. (2005). What limits the encoding effect of
note-taking? A meta-analytic examination. Contemporary
Educational Psychology, 30, 242–262.
Kobayashi, K. (2006). Combined effects of note-taking/-
reviewing on learning and the enhancement through
interventions: A meta-analytic review. Educational
Psychology, 26, 459–477.
Kornell, N. (2009). Optimising learning using flashcards:
Spacing is more effective than cramming. Applied
Cognitive Psychology, 23, 1297–1317.
Kornell, N., & Bjork, R. A. (2007). The promise and perils
of self-regulated study. Psychonomic Bulletin & Review,
14, 219–224.
Kornell, N., & Bjork, R. A. (2008). Optimizing self-regulated
study: The benefits-and costs-of dropping flashcards.
Memory, 16, 125–136.
Kornell, N., & Son, L. K. (2009). Learners’ choices and beliefs
about self-testing. Memory, 17, 493–501.
Five Popular Study Strategies 405
Krug, D., Davis, T. B., & Glover, J. A. (1990). Massed versus
distributed repeated reading: A case of forgetting helping
recall? Journal of Educational Psychology, 82, 366–371.
Kulhavy, R. W., Dyer, J. W., & Silver, L. (1975). The effects of
notetaking and test expectancy on the learning of text
material. The Journal of Educational Research, 68, 363–365.
Larson, R. B. (2009). Enhancing the recall of presented mate-
rial. Computers & Education, 53, 1278–1284.
Leicht, K. L., & Cashen, V. M. (1972). Type of highlighted
material and examination performance. The Journal of
Educational Research, 65, 315–316.
Leutner, D., Leopold, C., & Den Elzen-Rump, V. (2007). Self-
regulated learning with a text-highlighting strategy: A
training experiment. Zeitschrift für Psychologie/Journal
of Psychology, 215, 174–182.
Little, J. L., & McDaniel, M. A. (2015). Metamemory monitoring
and control following retrieval practice for text. Memory
& Cognition, 43, 85–98.
Lorch, R. F., Lorch, E. P., & Klusewitz, M. A. (1995). Effects
of typographical cues on reading and recall of text.
Contemporary Educational Psychology, 20, 51–64.
Lutes, L. D., Daiss, S. R., Barger, S. D., Read, M., Steinbaugh,
E., & Winett, R. A. (2012). Small changes approach pro-
motes initial and continued weight loss with a phone-
based follow-up: Nine-month outcomes from ASPIRES
II. American Journal of Health Promotion, 26, 235–238.
Martin, N. D., Nguyen, K., & McDaniel, M. A. (2016). Structure
building differences impact learning from educational
text: Effects on encoding, retention, and metacognitive
control. Contemporary Educational Psychology, 46, 52–60.
Mathews, C. O. (1938). Comparison of methods of study for
immediate and delayed recall. Journal of Educational
Psychology, 29, 101–106.
Mayer, R. E. (1975). Information processing variables in learn-
ing to solve problems. Review of Educational Research,
45, 525–541.
Mayer, R. E. (1980). Elaboration techniques that increase the
meaningfulness of technical text: An experimental test of
the learning strategy hypothesis. Journal of Educational
Psychology, 72, 770–784.
Mayer, R. E. (1983). Can you repeat that? Qualitative effects
of repetition and advance organizers on learning from sci-
ence prose. Journal of Educational Psychology, 75, 40–49.
Mayer, R. E. (2008). Learning and instruction (2nd ed.).
Upper Saddle River, NJ: Pearson.
McCabe, J. (2011). Metacognitive awareness of learning strate-
gies in undergraduates. Memory & Cognition, 39, 462–476.
McDaniel, M. A., Bugg, J. M., Liu, Y., & Brick, J. (2015). When
does the test-study-test sequence optimize learning and
retention? Journal of Experimental Psychology: Applied,
21, 370–382.
McDaniel, M. A., Howard, D. C., & Einstein, G. O. (2009). The
read-recite-review study strategy: Effective and portable.
Psychological Science, 20, 516–522.
McNamara, D. S., O’Reilly, T. P., Best, R. M., & Ozuru, Y.
(2006). Improving adolescent students’ reading compre-
hension with ISTART. Journal of Educational Computing
Research, 34, 147–171.
Meyer, B. J. F., Middlemiss, W., Theodorou, E., Brezinski, K. L.,
McDougall, J., & Bartlett, B. J. (2002). Effects of structure
strategy instruction delivered to fifth-grade children using
the Internet with and without the aid of older adult tutors.
Journal of Educational Psychology, 94, 486–519.
Meyer, B. J. F., & Poon, L. W. (2001). Effects of structure
strategy training and signaling on recall of text. Journal
of Educational Psychology, 93, 141–159.
Meyer, B. J. F., & Ray, M. N. (2011). Structure strategy inter-
ventions: Increasing reading comprehension of exposi-
tory text. International Electronic Journal of Elementary
Education, 4, 127–152.
Meyer, B. J. F., & Rice, G. E. (1989). Prose processing in adult-
hood: The text, the reader, and the task. In L. W. Poon,
D. C. Rubin, & B. A. Wilson (Eds.), Everyday cognition
in adulthood and late life (pp. 157–194). New York, NY:
Cambridge University Press.
Meyer, B. J. F., & Wijekumar, K. K. (2017). Intelligent tutoring
of the structure strategy: A reading strategy tutor. In S. A.
Crossley & D. S. McNamara (Eds.), Adaptive educational
technologies for literacy instruction (pp. 82–103). New
York, NY: Routledge.
Meyer, B. J. F., Wijekumar, K. K., & Lin, Y. C. (2011).
In dividualizing a web-based structure strategy inter-
vention for fifth graders’ comprehension of nonfiction.
Journal of Educational Psychology, 103, 140–168.
Meyer, B. J. F., Young, C. J., & Bartlett, B. J. (2014). Memory
improved: Enhanced reading comprehension and memory
across the life span through strategic text structure. New
York, NY: Psychology Press. (Original work published
1989)
Miyatsu, T., & McDaniel, M. A. (2018). Adding the keyword
mnemonic to retrieval practice: A potent combination for
foreign language vocabulary learning? Manuscript submit-
ted for publication.
Monroe, W. S. (1921). Types of learning required of pupils
in the seventh and eighth grades and in the high school
(Bureau of Educational Research Bulletin No. 7). Urbana:
University of Illinois.
Montis, K. K. (2007). Guided notes: An interactive method for
success in secondary and college mathematics classrooms.
Focus on Learning Problems in Mathematics, 29, 55–68.
Mueller, P. A., & Oppenheimer, D. M. (2014). The pen is
mightier than the keyboard: Advantages of longhand over
laptop note taking. Psychological Science, 25, 1159–1168.
Mulligan, N. W., & Peterson, D. J. (2015). Negative and posi-
tive testing effects in terms of item-specific and rela-
tional information. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 41, 859–871.
Nguyen, K., & McDaniel, M. A. (2016). The JOIs of text
comprehension: Supplementing retrieval practice to
enhance inference performance. Journal of Experimental
Psychology: Applied, 22, 59–71.
Nist, S. L., & Hogrebe, M. C. (1987). The role of underlin-
ing and annotating in remembering textual information.
Reading Research and Instruction, 27, 12–25.
Nist, S. L., & Kirby, K. (1989). The text marking patterns of
college students. Reading Psychology, 10, 321–338.
406 Miyatsu et al.
Peper, R. J., & Mayer, R. E. (1978). Note taking as a generative
activity. Journal of Educational Psychology, 70, 514–522.
Peterson, S. E. (1992). The cognitive functions of underlining
as a study technique. Reading Research and Instruction,
31, 49–56.
Preiss, R. W., & Gayle, B. M. (2006). A meta-analysis of the
educational benefits of employing advanced organizers.
In B. M., Gayle, R. W., Preiss, N., Burrell, & M. Allen (Eds.),
Classroom communication and instructional processes:
Advances through meta-analysis (pp. 329–344). Mahwah,
NJ: Erlbaum.
Pyc, M. A., & Rawson, K. A. (2007). Examining the efficiency
of schedules of distributed retrieval practice. Memory &
Cognition, 35, 1917–1927.
Pyc, M. A., & Rawson, K. A. (2011). Costs and benefits of drop-
out schedules of test-restudy practice: Implications for
student learning. Applied Cognitive Psychology, 25, 87–95.
Rawson, K. A. (2012). Why do rereading lag effects depend on
test delay? Journal of Memory and Language, 66, 870–884.
Rawson, K. A., & Dunlosky, J. (2002). Are performance pre-
dictions for text based on ease of processing? Journal of
Experimental Psychology: Learning, Memory, and Cog-
nition, 28, 69–80.
Rawson, K. A., & Dunlosky, J. (2011). Optimizing schedules of
retrieval practice for durable and efficient learning: How
much is enough? Journal of Experimental Psychology:
General, 140, 283–302.
Rawson, K. A., Dunlosky, J., & Thiede, K. W. (2000). The
rereading effect: Metacomprehension accuracy improves
across reading trials. Memory & Cognition, 28, 1004–1010.
Rawson, K. A., & Kintsch, W. (2005). Rereading effects depend
on time of test. Journal of Educational Psychology, 97,
70–80.
Ray, M. N., & Meyer, B. J. F. (2011). Individual differences
in children’s knowledge of expository text structures: A
review of literature. International Electronic Journal of
Elementary Education, 4, 67–82.
Rickards, J. P., & August, G. J. (1975). Generative under-
lining strategies in prose recall. Journal of Educational
Psychology, 67, 860–865.
Rickards, J. P., & Denner, P. R. (1979). Depressive effects of
underlining and adjunct questions on children’s recall of
text. Instructional Science, 8, 81–90.
Roediger, H. L., III, & Karpicke, J. D. (2006a). The power of
testing memory: Basic research and implications for edu-
cational practice. Perspectives on Psychological Science,
1, 181–210.
Roediger, H. L., III, & Karpicke, J. D. (2006b). Test-enhanced
learning: Taking tests improves long-term retention.
Psychological Science, 17, 249–255.
Roediger, H. L., III, & Pyc, M. A. (2012). Inexpensive tech-
niques to improve education: Applying cognitive psychol-
ogy to enhance educational practice. Journal of Applied
Research in Memory and Cognition, 1, 242–248.
Russell, I. J., Caris, T. N., Harris, G. D., & Hendricson, W. D.
(1983). Effects of three types of lecture notes on medi-
cal student achievement. Journal of Medical Education,
58, 627–636.
Schellings, G. L., Van Hout-Wolters, B. H., & Vermunt, J. D.
(1996). Individual differences in adapting to three different
tasks of selecting information from texts. Contemporary
Educational Psychology, 21, 423–446.
Schmidmaier, R., Ebersbach, R., Schiller, M., Hege, I., Holzer,
M., & Fischer, M. R. (2011). Using electronic flashcards
to promote learning in medical students: Retesting versus
restudying. Medical Education, 45, 1101–1110.
Schnell, T. R., & Rocchio, D. J. (1978). A comparison of under-
lying strategies for improving reading comprehension and
retention. Reading Horizons, 18, Article 4. Retrieved from
http://scholarworks.wmich.edu/reading_horizons/vol18/
iss2/4
Segal, J., Chipman, S., & Glaser, R. (1985). Relating instruction
to basic research. Hillsdale, NJ: Erlbaum.
Senzaki, S., Hackathorn, J., Appleby, D. C., & Gurung, R. A.
(2017). Reinventing flashcards to increase student learn-
ing. Psychology Learning & Teaching, 16, 353–368.
Silvers, V. L., & Kreiner, D. S. (1997). The effects of pre-
existing inappropriate highlighting on reading compre-
hension. Literacy Research and Instruction, 36, 217–223.
Soderstrom, N. C., & Bjork, R. A. (2015). Learning versus
performance: An integrative review. Perspectives on
Psychological Science, 10, 176–199.
Soderstrom, N. C., Kerr, T. K., & Bjork, R. A. (2016). The
critical importance of retrieval and spacing for learning.
Psychological Science, 27, 223–230.
Stine-Morrow, E. A. L., Gagne, D. D., Morrow, D. G., &
DeWall, B. H. (2004). Age differences in rereading.
Memory & Cognition, 32, 696–710.
Stordahl, K. E., & Christensen, C. M. (1956). The effect of
study techniques on comprehension and retention. The
Journal of Educational Research, 49, 561–570.
Taylor, B. M. (1982). Text structure and children’s compre-
hension and memory for expository material. Journal of
Educational Psychology, 74, 323–340.
Taylor, B. M., & Beach, R. W. (1984). The effects of text struc-
ture instruction on middle-grade students’ comprehen-
sion and production of expository text. Reading Research
Quarterly, 19, 134–146.
Taylor, K., & Rohrer, D. (2010). The effects of interleaved
practice. Applied Cognitive Psychology, 24, 837–848.
Thiede, K. W., & Dunlosky, J. (1999). Toward a general model
of self-regulated study: An analysis of selection of items for
study and self-paced study time. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 25, 1024–
1037.
Todd, W. B., & Kessler, C. C. (1971). Influence of response
mode, sex, reading ability, and level of difficulty on four
measures of recall of meaningful written material. Journal
of Educational Psychology, 62, 229–234.
Trumbo, M. C., Leiting, K. A., McDaniel, M. A., & Hodge, G. K.
(2016). Effects of reinforcement on test-enhanced learning
in a large, diverse introductory college psychology course.
Journal of Experimental Psychology: Applied, 22, 148–160.
Tuckman, B. W. (1993). The coded elaborative outline as a
strategy to help students learn from text. The Journal of
Experimental Education, 62, 5–13.
Five Popular Study Strategies 407
Tulving, E. (1966). Subjective organization and effects of rep-
etition in multi-trial free-recall learning. Journal of Verbal
Learning and Verbal Behavior, 5, 193–197.
Vaughn, K. E., & Rawson, K. A. (2011). Diagnosing criterion-
level effects on memory: What aspects of memory are
enhanced by repeated retrieval? Psychological Science,
22, 1127–1131.
Verkoeijen, P. P. J. L., Rikers, R. M. J. P., & Ozsoy, B. (2008).
Distributed rereading can hurt the spacing effect in text
memory. Applied Cognitive Psychology, 22, 685–695.
Wahlheim, C. N., Dunlosky, J., & Jacoby, L. L. (2011). Spacing
enhances the learning of natural concepts: An investiga-
tion of mechanisms, metacognition, and aging. Memory
& Cognition, 39, 750–763.
Walvoord, B. E., Anderson, V. J., Breihan, J. R., McCarthy, L. P.,
Robinson, S. M., & Sherman, A. K. (1995). Functions of
outlining among college students in four disciplines.
Research in the Teaching of English, 29, 390–421.
Weinstein, C. E., Goetz, E. T., & Alexander, P. A. (1988).
Learning and study strategies: Issues in assessment, instruc-
tion, and evaluation. New York, NY: Academic Press.
Wijekumar, K. K., Meyer, B. J. F., & Lei, P. (2012). Large-scale
randomized controlled trial with 4th graders using intel-
ligent tutoring of the structure strategy to improve non-
fiction reading comprehension. Educational Technology
Research and Development, 60, 987–1013.
Wijekumar, K. K., Meyer, B. J. F., & Lei, P. (2013). High-
fidelity implementation of web-based intelligent tutoring
system improves fourth and fifth graders content area
reading comprehension. Computers & Education, 68,
366–379.
Wijekumar, K. K., Meyer, B. J. F., Lei, P., Lin, Y. C., Johnson,
L. A., Spielvogel, J. A., . . . Cook, M. (2014). Multisite
randomized controlled trial examining intelligent tutoring
of structure strategy for fifth-grade. Journal of Research
on Educational Effectiveness, 7, 331–357.
Williams, J. P., Hall, K. M., Lauer, K. D., Stafford, K. B.,
DeSisto, L. A., & deCani, J. S. (2005). Expository text
comprehension in the primary grade classroom. Journal
of Educational Psychology, 97, 538–550.
Willmore, D. J. (1966). A comparison of four methods of study-
ing a college textbook. (Doctoral dissertation, University of
Minnesota). Dissertation Abstracts, 27, 2413a. University
Microfilms No. 67888.
Wissman, K. T., Rawson, K. A., & Pyc, M. A. (2012). How and
when do students use flashcards? Memory, 20, 568–579.
Wittrock, M. C. (1974). Learning as a generative process.
Educational Psychologist, 11, 87-95.
Yan, V. X., Thai, K. P., & Bjork, R. A. (2014). Habits and
beliefs that guide self-regulated learning: Do they vary
with mindset? Journal of Applied Research in Memory and
Cognition, 3, 140–152.
Yue, C. L., Storm, B. C., Kornell, N., & Bjork, E. L. (2015).
Highlighting and its relation to distributed study and
students’ metacognitive beliefs. Educational Psychology
Review, 27, 69–78.
... Our rationale for choosing to focus on highlighting is that it is a commonly used and easy to use learning strategy (Miyatsu et al. 2018) or instructional design feature (Mayer. 2021). ...
... 20). However, in a more recent review, Miyatsu et al. (2018) argue that although the evidence seems to indicate that highlighting and rereading may not be effective in educational contexts, such argument overlooks the possibility that these strategies may be effective in specific circumstances. The reason is that another way to scrutinize these strategies is to compare their effectiveness relative to no study strategies, such as when students only read the text. ...
... The literature search started with a snowball approach by recollecting the studies referred to by Miyatsu et al. (2018) and Dunlosky et al. (2013) in those sections dedicated to reviewing the highlighting strategy. Additionally, we examined the meta-analysis on signaling with media conducted by Schneider et al. (2018), which included an analysis of organizational highlighting as a mode of text signaling. ...
Article
Full-text available
The present study examines the existing published research about the effectiveness of learner-generated highlighting and instructor-provided highlighting on learning from text. A meta-analysis was conducted of scientifically rigorous experiments comparing the learning outcomes (i.e., performance on memory and/or comprehension tests) of students (i.e., college students and/or K-12 students) who read an academic text with or without being asked to highlight important material (i.e., with or without learner-generated highlighting) or who read an academic text with or without the important material already being highlighted (i.e., with or without instructor-provided highlighting). We found 36 published articles that met these criteria ranging from the years 1938 to 2019, which generated 85 effect sizes. The results showed that learner-generated highlighting improved memory but not comprehension, with average effect sizes of 0.36 and 0.20, respectively; and instructor-provided highlighting improved both memory and comprehension, both with an average effect size of 0.44. Learner-generated highlighting improved learning for college students but not for school students, with average effect sizes of 0.39 and 0.24, respectively; and instructor-provided highlighting improved learning for both college and school students, with average effect sizes of 0.41 and 0.48, respectively. We discuss the theoretical and practical implications of these findings.
... Experiment 1 tested the derring effect in learners' knowledge retention and higher-order application performance by comparing deliberate erring against traditional errorless learning techniques. Whereas previous error research has often pitted active errorful learning against passive errorless learning methods such as reading only (Huelser & Metcalfe, 2012;Kornell et al., 2009), we used two active errorless learning controls that are popular among students and educational researchers to dissociate the effects of deliberate erring from those of active learning (Freeman et al., 2014): copying with underlining, and elaborative studying with concept mapping. 1 Underlining is a popular study strategy that students frequently report adopting, alongside the functionally similar technique of highlighting (Dunlosky et al., 2013), with a meta-analyzed selfreported frequency of use of 53% (Miyatsu et al., 2018). The technique of underlining is relatively easy to use and has even been spontaneously adopted by learners as young as those in the fifth grade (Brown & Smiley, 1978). ...
... The technique of underlining is relatively easy to use and has even been spontaneously adopted by learners as young as those in the fifth grade (Brown & Smiley, 1978). Learner-generated underlining has been shown to benefit memory, particularly for marked compared to unmarked information, across a variety of assessments such as free-recall, short-answer, and fill-in-the-blank tests (Miyatsu et al., 2018), although its efficacy for higher-order learning outcomes such as inferencing remains questionable (Peterson, 1991). Presumably, underlining aids learning by encouraging elaborative processing through the active selection of important text content 1 Other popular study techniques that students have routinely reported adopting include rereading and flash cards (i.e., self-testing; retrieval practice), with meta-analyzed frequencies of use of 78% and 55%, respectively (Miyatsu et al., 2018). ...
... Learner-generated underlining has been shown to benefit memory, particularly for marked compared to unmarked information, across a variety of assessments such as free-recall, short-answer, and fill-in-the-blank tests (Miyatsu et al., 2018), although its efficacy for higher-order learning outcomes such as inferencing remains questionable (Peterson, 1991). Presumably, underlining aids learning by encouraging elaborative processing through the active selection of important text content 1 Other popular study techniques that students have routinely reported adopting include rereading and flash cards (i.e., self-testing; retrieval practice), with meta-analyzed frequencies of use of 78% and 55%, respectively (Miyatsu et al., 2018). However, both of these techniques were less suited as active errorless comparison methods in our study-rereading is largely passive in nature despite being error-free, whereas the use of flash cards inherently introduces naturalistic errors when learners inadvertently recall incorrect information or fail to recall it during study, although retrieval practice has been established as an effective learning technique for retention (Dunlosky et al., 2013). ...
Article
Our civilization recognizes that errors can be valuable learning opportunities, but for decades, they have widely been avoided or, at best, allowed to occur as serendipitous accidents. The present research tested whether greater learning success could paradoxically be achieved through making errors by intentional design, relative to traditional errorless learning methods. We show that deliberately committing and correcting errors even when one knows the correct answers enhances learning—a counterintuitive phenomenon that we termed the derring effect. Across two experiments (N = 160), learners engaged in open-book study of scientific expository texts and were then tested on their retention and higher-order application of the material to analyze a novel news event. Deliberate error commission and correction during study produced not only better recall performance, but also superior knowledge application compared to two errorless study techniques that are popular among students and educational researchers: copying with underlining, and elaborative studying with concept mapping (Experiment 1). These learning benefits persisted even over generating alternative conceptually correct answers, revealing that the derring effect is not merely attributable to generation or elaboration alone, but is unique to producing incorrect responses (Experiment 2). Yet, learners were largely unaware of these advantages even after experiencing them. Our results suggest that avoiding errors in learning may not always be most optimal. Rather, deliberate erring is a powerful strategy to enhance meaningful learning. We discuss implications for educational practice in redesigning conventional approaches to errors: To err is human; to deliberately err is divine.
... Dabei verwenden sie häufig verschiedene Techniken, um ihr Lernen zu optimieren und ihre Leistungen in Prüfungen erfolgreich abzurufen (Eysenck, 2020). Eine Metaanalyse von Miyatsu et al. (2018) zeigte die fünf häufigsten Strategien auf, die von den Studierenden präferiert wurden. 78 % der Studierenden gaben an, den Stoff wiederholt zu lesen, 55 % verwendeten Karteikarten, um das gelernte Material selbst abzufragen und 53 % markierten oder unterstrichen Teile des Textes. ...
... 78 % der Studierenden gaben an, den Stoff wiederholt zu lesen, 55 % verwendeten Karteikarten, um das gelernte Material selbst abzufragen und 53 % markierten oder unterstrichen Teile des Textes. Darüber hinaus machten sich 30 % der Befragten während der Lehrveranstaltungen und während des Lesens der Texte Notizen, wohingegen 23 % Stichpunkte zu den wichtigsten zu lernenden Inhalten formulierten (Miyatsu et al., 2018). Das wiederholte Lernen oder Lesen des Materials ist demnach die häufigste von Studierenden angewandte Strategie (Anderson, 2020). ...
... Eine weitere Erklärung könnte sein, dass erneutes Lesen weniger anstrengend und fordernd ist als eine Abrufübung und aus diesem Grund von Studierenden als attraktiver wahrgenommen wird (Eysenck, 2020). Miyatsu et al. (2018) betonten, dass es bei allen Lernstrategien, aber vor allem beim erneuten Lesen wichtig sei, die Vorgehensweisen optimal einzusetzen, um ihre Effektivität zu steigern. Vor diesem Hintergrund gaben sie einige Empfehlungen für den Bildungskontext. ...
Thesis
Das erneute Lernen ist eine häufig von Studierenden angewandte Technik zur Vertiefung des Lernstoffs und zur Vorbereitung auf Prüfungen. Wenn nicht in jedem Durchgang das gesamte Material wiederholt wird, sondern nur eine Teilmenge, spricht man von selektivem erneutem Lernen. Durch eine selektive Wiederholung kann nicht nur das Erinnern des wiederholten Materials erleichtert werden, sondern auch das Erinnern der nicht wiederholten Items. Ziel der vorliegenden Studie war es, zu untersuchen, wie sich der Zeitpunkt der selektiven Wiederholung auf die relativen Beiträge der wiederhol-ten und nicht wiederholten Items an der gesamten Erinnerungsleistung auswirkt. Zu Be-ginn wurden den Probanden alle Wörter zweimal in randomisierter Reihenfolge präsen-tiert. Bei der selektiven Wiederholung wurde die Hälfte der Items zweimal in randomi-sierter Reihenfolge dargeboten. Die selektive Wiederholung fand entweder nach zwei Minuten oder 24 Stunden statt, oder die Items wurden nicht wiederholt. In der Testphase sollten die Teilnehmenden alle Wörter abrufen, wobei die Reihenfolge durch den jewei-ligen Anfangsbuchstaben des Wortes vorgegeben wurde. Die Ergebnisse zeigen, dass das selektive erneute Lernen nach 24 Stunden und nach zwei Minuten zu einer signifikant besseren Erinnerungsleistung der wiederholten Items im finalen Test führte, als wenn keine Wiederholung stattfand. Dass kein signifikanter Vorteil der selektiven Wiederho-lung nach 24 Stunden gegenüber zwei Minuten gefunden werden konnte, widerspricht den bisherigen Befunden zum verteilten Lernen. Die Befunde zur Abrufspezifität des hemmenden Effekts der selektiven Wiederholung konnten bestätigt werden. Der Abruf der nicht wiederholten Items wurde durch selektives erneutes Lernen nicht beeinträchtigt. Obwohl sich keine signifikanten Unterschiede in der Erinnerungsleistung der nicht wie-derholten Items zwischen den drei Bedingungen zeigten, wurden die meisten Items er-folgreich erinnert, wenn die selektive Wiederholung nach zwei Minuten stattfand. Dadurch lässt sich vermuten, dass ein förderlicher Effekt des selektiven erneuten Lernens nach zwei Minuten auftreten könnte. Die Studie bietet das Potenzial, um unter Einbezug von unterschiedlichen Zeitpunkten, Stimuli und Hinweisreizen, weiter zu untersuchen, wie die Erinnerungsleistung durch erneutes selektives Lernen optimiert werden kann.
... Teaching summarization improves both the quality of written summaries and students' overall text comprehension (Duke, Pearson, Strachan, & Billman, 2011;Taylor & Beach, 1984). The defining characteristic of successful summarization instruction involves main idea identification and text structure recognition (Miyatsu, Nguyen, & McDaniel, 2018). Text structure can scaffold students' summarization skills, as it provides tools and heuristics to distinguish main ideas from unimportant information. ...
... Text structure can scaffold students' summarization skills, as it provides tools and heuristics to distinguish main ideas from unimportant information. In addition, it helps students understand how these main ideas are organized, which helps them write coherent summaries (Miyatsu et al., 2018). ...
... For instance, laboratory research with educationally relevant expository text has demonstrated that when mental representations are disorganized -as is likely during early stages of learningthen generative learning techniques may be optimal by virtue of providing structure and cohesion; conversely, if mental representations are well-integrated and organized-as is likely during later stages of learning-then retrieval practice may prove superior as a method of consolidating this highly coherent information (Roelle & Nückles, 2019). When comparative studies are conducted, it is critical to consider the optimal implementations of each method (Miyatsu et al., 2018); comparing an optimal version of one strategy against a poor implementation of another makes findings difficult to interpret. Furthermore, comparative studies should include combinations of learning techniques in order to fully represent the options available to learners, who are not bound to adhere to a single technique. ...
... In the last decades, various generative learning strategies have been shown to be highly effective in enhancing long-term learning and comprehension across various student characteristics (e.g., working memory or fluid intelligence) and academic fields (e.g., Dunlosky et al., 2013;Fiorella & Mayer, 2016). The application of effective learning strategies has also been shown to be correlated with future academic success (Geller et al., 2018;Hartwig & Dunlosky, 2012), yet there is evidence suggesting that students often rely on rather ineffective learning strategies in everyday life (for reviews, see McDaniel & Einstein, 2020;Miyatsu et al., 2018). One prominent example of a generative but seldom spontaneously used learning strategy is to generate self-explanations (see e.g., Wylie & Chi, 2014). ...
Article
Full-text available
Self-regulated learning is the capacity to monitor and regulate your learning activities and is vital in an increasingly complex and digitalized world with unlimited amounts of information at your fingertips. The current Special Issue highlights five articles and one report, which provide different approaches for teachers to promote effectively self-regulated learning in various educational contexts: training, feedback, and addressing teachers' misconceptions. This editorial serves as a succinct review article and an introduction to the content of this issue. Training programs frequently teach information about effective learning strategies. Accordingly, Benick et al. (2021) found that students reported using more learning strategies when their teachers provided direct-strategy instruction combined with a learning diary, as compared to when these supports were not implemented. Yet, in this study, no transfer effect on academic performance was observed. Note that it is important that students are motivated to engage with these training courses and the learning strategies that are taught. Accordingly, van der Beek et al. (2021) investigated high school students in their last year before graduation and demonstrated that "motivated" students more often participated in a voluntary, self-regulated-learning training. However, a utility-value and implementation-intention intervention did not increase the likelihood of participation. McDaniel et al. (2021) reported a theoretical training framework addressing multiple components of self-regulated learning. The authors then tested a pilot college course based on this framework: knowledge of and belief in the effectiveness of learning strategies are targeted combined with efforts to promote students' commitment and planning to apply these strategies (Knowledge-Belief-Commitment-Planning framework; McDaniel & Einstein, 2020). Another approach to promote self-regulated learning is to provide feedback and opportunities to effectively process and utilize it. Bürgermeister et al. (2021) developed an effective online tool supporting preservice teachers to assess and provide feedback on peer learners' self-regulated use of effective learning strategies. Kuepper-Tetzel and Gardner (2021) demonstrated how to enhance self-regulated processing of feedback by temporarily withholding university students' grades in favor of accessing and engaging with the feedback first. Finally, teachers' misconceptions about learning can affect the degree to which teachers can scaffold students' learning how to learn. As a first step, to address these misconceptions , Eitel et al. (2021) developed and psychometrically evaluated the Misconceptions about Multimedia Learning Questionnaire (MMLQ). Using the MMLQ, the authors showed that (preser-vice) teachers endorsed three out of four common misconceptions of self-regulated multimedia learning, with the potential to design instructional devices to refute them and thereby to promote/home/plj rather than hinder self-regulated learning in students. Taken together, the contributions of the current Special Issue highlight self-regulated learning as a critical skill at all levels of education, which can be promoted through structured training programs, various uses of feedback, and addressing misconceptions about self-regulated learning from (pre-service) teachers.
... Testen führt zwar langfristig zu besseren Lernleistungen als Notizenmachen, allerdings kann es kurzfristig (man denke an eine sehr kurzfristige Klausurvorbereitung) durchaus zweckmäßiger sein, sich mittels Notizenmachen vorzubereiten.Ein Blick in die Literatur zum Notizenmachen (z. B.Miyatsu, Nguyen & McDaniel, 2018) zeigt zudem, dass Notizenmachen besonders dann lernförderlich ist, wenn es ein Review der Notizen zu einem späteren Zeitpunkt bzw. vor der finalen Testung gibt. ...
Article
Zusammenfassung. Da der Testungseffekt in der evidenzbasierten Lehr- / Lernforschung als einer der am besten gesicherten Befunde gilt (z. B. Dunlosky, Rawson, Marsh, Nathan & Willingham, 2013 ), ist er in besonderer Weise dazu geeignet, grundlegende Probleme bei der Nutzbarmachung von in der experimentellen Forschung gut gesicherten Befunden zu verdeutlichen. Im Zentrum dieses Beitrags stehen das Verhältnis zwischen Lernresultaten und metakognitiven Erwartungen, die Komplexität von Lernmaterialien als Moderator für den Testungseffekt sowie Unterschiede zwischen Feld- und Laborexperimenten.
Chapter
Flashcards are a popular study tool, however learner decisions can lower their effectiveness. One such decision is whether or not to drop a concept from study. Using objective mastery criteria that adaptively determine when to add or drop an item from study based on performance may improve learning outcomes in flashcard-based tasks. The effectiveness of adaptive flashcard-based learning may also vary based on the cognitive ability of the learner. The current study examined the impact of adaptive mastery instructional strategies on learning butterfly species and whether or not the impact of adaptive mastery varies by cognitive ability. Three learning conditions were compared: a No Add/Drop group (all items remain in the deck throughout study), a Mastery Drop group (start with all items, then drop after an item is mastered), and a Mastery Add group (start with three items, add items once mastered). A pre-post-transfer test design was used both immediately after training and one week later. Participants also completed the symmetry span task and a change detection task to evaluate cognitive ability. Results show the worst overall immediate pre-post learning gains in the Mastery Drop condition compared to the Mastery Add and No Add/Drop conditions which showed similar learning gains. This pattern went away when looking at delayed pre-post learning gains. Cognitive ability did not have any impact on learning performance, suggesting that similar strategies work equally well across all levels of cognitive ability. These results suggest adaptively adding cards is better than dropping them, though if there are no time constraints, leaving all concepts in the deck leads to the best overall learning in the short term.
Article
Many science, technology, engineering, and math (STEM) community college students do not complete their degree, and these students are more likely to be women or in historically excluded racial or ethnic groups. In introductory courses, low grades can trigger this exodus. Implementation of high-impact study strategies could lead to increased academic performance and retention. The examination of study strategies rarely occurs at the community college level, even though community colleges educate approximately half of all STEM students in the United States who earn a bachelor's degree. To fill this research gap, we studied students in two biology courses at a Hispanic-serving community college. Students were asked their most commonly used study strategies at the start and end of the semester. They were given a presentation on study skills toward the beginning of the semester and asked to self-assess their study strategies for each exam. We observed a significantly higher course grade for students who reported spacing their studying and creating drawings when controlling for demographic factors, and usage of these strategies increased by the end of the semester. We conclude that high-impact study strategies can be taught to students in community college biology courses and result in higher course performance.
Article
In this article, we highlight an underappreciated individual difference: structure building. Structure building is integral to many everyday activities and involves creating coherent mental representations of conversations, texts, pictorial stories, and other events. People vary in this ability in a way not generally captured by other better known concepts and individual difference measures. Individuals with lower structure-building ability consistently perform worse on a range of comprehension and learning measures than do individuals with higher structure-building ability, both in the laboratory and in the classroom. Problems include a range of comprehension processes, including encoding factual content, inhibiting irrelevant information, and constructing a cohesive situation model of a text or conversation. Despite these problems, recent research is encouraging in that techniques to improve the learning outcomes for low-ability structure builders have been identified. We argue that the accumulated research warrants the recognition of structure building as an important individual difference in cognitive functioning and that additional theoretical work is needed to understand the underpinnings of structure-building deficits.
Article
Full-text available
The keyword mnemonic and retrieval practice are two cognitive techniques that have each been identified to enhance foreign language vocabulary learning. However, little is known about the use of these techniques in combination. Previous demonstrations of retrieval-practice effects in foreign language vocabulary learning have tended to use several rounds of retrieval practice. In contrast, we focused on a situation in which retrieval practice was limited to twice per item. For this situation, it is unclear whether retrieval practice will be effective relative to restudying. We advance the view that the keyword mnemonic catalyzes the effectiveness of retrieval practice in this learning context. Experiment 1 (48-h delay) partially supported this view, such that there was no testing effect with retrieval practice alone, but the keyword-retrieval combination did not promote better retention than keyword alone. Experiments 2 and 3 (1-week delay) supported the catalytic view by showing that the keyword-retrieval combination was better than keyword alone, but in the absence of keyword encoding there was no retrieval practice effect (replicating Experiment 1). However, with four rounds of retrieval practice, a marginally significant testing effect emerged (Experiment 3). Moreover, the routes through which participants reached each answer were identified by asking retrieval-route questions in Experiments 2 and 3. Keyword-mediated retrieval, which was observed sometimes even in no-keyword instructed conditions, was shown to be more effective than unmediated retrieval. Our findings suggest that incorporating effective encoding techniques prior to retrieval practice could augment the effectiveness of retrieval practice, at least for vocabulary learning.
Article
Full-text available
This study aimed to enhance third and fourth graders’ text comprehension at the situation model level. Therefore, we tested a reading strategy training developed to target inference making skills, which are widely considered to be pivotal to situation model construction. The training was grounded in contemporary literature on situation model-based inference making and addressed the source (text-based versus knowledge-based), type (necessary versus unnecessary for (re-)establishing coherence), and depth of an inference (making single lexical inferences versus combining multiple lexical inferences), as well as the type of searching strategy (forward versus backward). Results indicated that, compared to a control group (n = 51), children who followed the experimental training (n = 67) improved their inference making skills supportive to situation model construction. Importantly, our training also resulted in increased levels of general reading comprehension and motivation. In sum, this study showed that a ‘level of text representation’-approach can provide a useful framework to teach inference making skills to third and fourth graders.
Article
Full-text available
We examined the impact of repeated testing and repeated studying on long-term learning. In Experiment 1, we replicated Karpicke and Roediger's (2008) influential results showing that once information can be recalled, repeated testing on that information enhances learning, whereas restudying that information does not. We then examined whether the apparent ineffectiveness of restudying might be attributable to the spacing differences between items that were inherent in the between-subjects design employed by Karpicke and Roediger. When we controlled for these spacing differences by manipulating the various learning conditions within subjects in Experiment 2, we found that both repeated testing and restudying improved learning, and that learners' awareness of the relative mnemonic benefits of these strategies was enhanced. These findings contribute to understanding how two important factors in learning-test-induced retrieval processes and spacing-can interact, and they illustrate that such interactions can play out differently in between-subjects and within-subjects experimental designs.
Article
Two studies examined the effectiveness of a flashcard-based study strategy, Flashcards-Plus, in an ecologically valid context. The strategy requires students to create flashcards designed to increase their ability to retain, comprehend, and apply textbook material to exams. In Studies 1a (n ¼ 73) and 1b (n ¼ 62), we introduced all students to the Flashcards-Plus method and compared their exam scores. Students who used this strategy scored significantly higher than those who did not. In Study 2 (n ¼ 434), we randomly assigned six introductory psychology courses to either receive a classroom lecture with the Flashcards-Plus strategy (i.e., three experimental courses) or no lecture (i.e., three control courses). Students in the experimental courses scored significantly higher than those in the control courses after the lecture. The results from all three studies demonstrate that students who were introduced to the Flashcards-Plus study strategy scored significantly higher on exams following the lecture than students who were not. These findings suggest that this easily implemented teaching strategy can help students achieve deeper levels of processing (i.e., comprehension and application) in a self-directed manner, which benefit students' performance.
Article
A robust finding within laboratory research is that structuring information as a test confers benefit on long-term retention-referred to as the testing effect. Although well characterized in laboratory environments, the testing effect has been explored infrequently within ecologically valid contexts. We conducted a series of 3 experiments within a very large introductory college-level course. Experiment 1 examined the impact of required versus optional frequent low-stakes testing (quizzes) on student grades, revealing students were much more likely to take advantage of quizzing if it was a required course component. Experiment 2 implemented a method of evaluating pedagogical intervention within a single course (thereby controlling for instructor bias and student self-selection), which revealed a testing effect. Experiment 3 ruled out additional exposure to information as an explanation for the findings of Experiment 2 and suggested that students at the college level, enrolled in very large sections, accept frequent quizzing well. (PsycINFO Database Record
Article
Structure building describes the process by which people mentally organize information while reading to comprehend and later recall text. We investigated how individual differences in structure building ability affect students' learning of complex, educational texts. College students studied a complex text with a mechanical theme, engaged in retrieval practice, made metamemory judgments, and then were given another study opportunity. Following a short delay, memory and comprehension of the text were assessed with a range of dependent measures, including performance on free recall, multiple-choice questions, and problem-solving questions. Participants with high structure building ability outperformed those with low structure building ability on information recalled, factual and inference multiple-choice questions, and problem solving questions. Although high and low structure builders demonstrated equivalent metamemory accuracy, high structure builders appear to better regulate their restudy time according to a discrepancy reduction strategy, which allowed them to acquire more new information that they were unable to retrieve initially during recitation. Our findings suggest that low structure builders may suffer from deficiencies at many levels of text representations as well as deficiencies in metacognitive control during restudy. Our study highlights structure building ability as an important individual difference for learning educational texts and furthers our understanding of exactly what aspects of learning are related to these differences.
Article
30 college males in liberal arts curricula were assigned to study passages by 3 different methods: reading, reading with underlining, and reading with note taking. When verbal intelligence (Scholastic Aptitude Test-Verbal) was held constant, the 3 groups did not differ on questions about comprehension of passages. The findings question the assumed value of underlining and note-taking during study and imply that future research might better be aimed at improving verbal rather than study skills in undergraduate populations.
Article
The present study asks whether laboratory findings, that isolation of an item facilitates its recall, extend to formal educational settings. Four groups of students from a General Psychology class were differentiated in terms of the type of material which was isolated in assigned readings. Principles, examples of principles, or trivial statements were selected for isolation by underlining; readings of the remaining group did not contain underlinings. Class examinations included questions directed at knowledge of each type of material. Slight support was obtained for the prediction that exam performance on a particular type of material depended upon whether the material was isolated. There was no evidence for an overall facilitation by isolation. That is, underlining of one type of material did not appear to aid exam performance on non-isolated materials.