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Expecting to teach enhances learning and organization of knowledge in free recall of text passages


Abstract and Figures

The present research assessed the potential effects of expecting to teach on learning. In two experiments, participants studied passages either in preparation for a later test or in preparation for teaching the passage to another student who would then be tested. In reality, all participants were tested, and no one actually engaged in teaching. Participants expecting to teach produced more complete and better organized free recall of the passage (Experiment 1) and, in general, correctly answered more questions about the passage than did participants expecting a test (Experiment 1), particularly questions covering main points (Experiment 2), consistent with their having engaged in more effective learning strategies. Instilling an expectation to teach thus seems to be a simple, inexpensive intervention with the potential to increase learning efficiency at home and in the classroom.
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Expecting to teach enhances learning and organization
of knowledge in free recall of text passages
John F. Nestojko &Dung C. Bui &Nate Kornell &
Elizabeth Ligon Bjork
#Psychonomic Society, Inc. 2014
Abstract The present research assessed the potential effects
of expecting to teach on learning. In two experiments, partic-
ipants studied passages either in preparation for a later test or
in preparation for teaching the passage to another student who
would then be tested. In reality, all participants were tested,
and no one actually engaged in teaching. Participants
expecting to teach produced more complete and better orga-
nized free recall of the passage (Experiment 1) and, in general,
correctly answered more questions about the passage than did
participants expecting a test (Experiment 1), particularly ques-
tions covering main points (Experiment 2), consistent with
their having engaged in more effective learning strategies.
Instilling an expectation to teach thus seems to be a simple,
inexpensive intervention with the potential to increase learn-
ing efficiency at home and in the classroom.
Keywords Memory .Recall .Text processing
When trying to learn new information, we typically do so with
goals or expectations for how we will use that information in
the future. Students, for example, typically have the goal of
maximizing their performance on a later test when learning
new material. In contrast, teachers presumably have the goal of
being able to effectively communicate the new material they
are learning to their students. The present research investigated
whether these different orientationsexpecting a test or
expectingtoteachchanges how new material is learned.
General effects of expectancy on learning and memory
Learnersexpectations about how they will be tested can produce
both quantitative and qualitative effects on their later memory
performance. For example, Szpunar, McDermott, and Roediger
(2007) demonstrated that participants expecting a final cumula-
tive test achieved higher free recall test scores (quantitative
advantage) and greater organization of recall output (qualitative
advantage) on a final cumulative test than did participants not
expecting a final cumulative test. Additionally, in research on so-
called test-expectancy effects, participants expecting a free recall
test typically performed better on both free recall and recognition
tests than did participants expecting a recognition test (e.g.,
Lundeberg & Fox, 1991). Furthermore, while exploring a pro-
cess that he dubbed cognitive tuning, Zajonc (1960)asked
participants to read a letter, telling one group (transmitters)that
they would have to relay the information in the letter to another
person, while telling another group (receivers) that they would
receive additional details from another person about the letter. On
a later test about the contents of the letter, transmitters
outperformed receivers on multiple measures, including one
reflecting organization of information. In sum, learnersexpec-
tations as to how they will later need to utilize to-be-learned
information can apparently affect how they encode and recall that
Is learning affected by expecting to teach versus expecting
to be tested?
Prior research has shown that when a student teaches/tutors
one or more other students, the teaching student often shows
J. F. Nestojko (*):D. C. Bui
Washington University in St. Louis, One Brookings Drive,
Campus Box 1125, St. Louis, MO 63130, USA
E. L. Bjork
University of California, Los Angeles, Los Angeles, CA, USA
N. Kornell
Williams College, Williamstown, MA, USA
Mem Cogn
DOI 10.3758/s13421-014-0416-z
learning gains (e.g., Cohen, Kulik, & Kulik, 1982; Rohrbeck,
Ginsburg-Block, Fantuzzo, & Miller, 2003; Roscoe & Chi,
2007). The research presented herein addressed a different
question: Does having an expectancy to teach, without actually
teaching, enhance learning, as compared with having an ex-
pectancy to be tested? Prior research on this question has
produced inconsistent results. Some studies have reported ad-
vantages of expecting to teach over expecting a test (e.g., Bargh
& Schul, 1980; Benware & Deci, 1984; Coleman, Brown, &
Rivkin, 1997; Ehly, Keith, & Bratton, 1987; Fiorella & Mayer,
2013), whereas others have reported no differences between
these conditions (Renkl, 1995;Ross&DiVesta,1976).
In examining the past literature, we decided upon five
criteria as being necessary for a study to comprise an adequate
assessment of an effect on learning owing to a test expectation
versus a teaching expectation: (1) Instructions manipulating
learning goals must be given prior to exposure to the to-be-
learned material; (2) duration of exposure to the to-be-learned
material must be held constant across conditions; (3) all con-
ditions must be given the same opportunities and suggestions
about how to learn (e.g., if one group is allowed or encouraged
to take notes, the other group must also be allowed or encour-
aged to take notes); (4) the retention interval between studying
and the final criterion test must be held constant; and (5) no
actual teaching can take place after study and prior to mea-
surements of learning. Many of the existing reports involving
a teaching-expectancy condition failed to meet all five criteria,
making it difficult to attribute advantages seen in teaching-
expectancy conditions solely to expectancy (e.g., Benware &
Deci, 1984; Coleman et al., 1997; Ehly et al., 1987;Gregory,
Walker, McLaughlin, & Peets, 2011).
Four reports met our five criteria. In a laboratory-based
experiment by Bargh and Schul (1980), participants were
instructedprior to reading a text passageeither to study
for a test or to prepare to teach another student. On the later
criterion tests, participants instructed to prepare to teach
performed better than did participants instructed to study for
a test. Fiorella and Mayer (2013) found that participants
expecting to teach a lesson on the Doppler effect
outperformed control subjects on an immediate comprehen-
sion test, but not on a delayed comprehension test. This
finding should be interpreted with caution, however, because
participants were allowed to take notes while studying the
passage, and note quantity is positively correlated with recall
even when participants are not allowed to study their notes
afterward (e.g., Aiken, Thomas, & Shennum, 1975; Bui,
Myerson, & Hale, 2013). Fiorella and Mayer did not report
the rate of note taking, but if expecting to teach prompted
more note taking than did expecting to take a test, then it is not
clear whether expecting to teach had a direct benefit on
learning or only an indirect one by increasing note taking.
Ross and DiVesta (1976) manipulated whether participants
expected (1) to deliver an oral explanation or summary of the
to-be-studied material to another student or (2) to take both a
short-answer and multiple-choice test on both general con-
cepts and specific details of the to-be-studied materials. The
participants expecting to give an explanation or summary
(who were never actually required to perform either task but,
instead, were given both types of tests) performed no better on
these tests, as compared with those participants who had
expected to take those specific tests. Finally, in an experiment
by Renkl (1995) in which participants were trained to perform
probability calculus via examination of worked-out problems,
those instructed before training that they would later have to
explain similar problems to another student were no better at
solving similar problems on a later test than were the partic-
ipants instructed that they would later have to solve similar
problems. Thus, the four prior studies that met our criteria
were evenly split: Two showed that expecting to teach en-
hanced learning, as compared with expecting a test, and two
did not.
Predictions with respect to the present research question
As we started the present research, we had several reasons to
predict that expecting a test would produce better later perfor-
mance than would expecting to teach. First, students at uni-
versities (who served as our participants) are arguably more
experienced at preparing to take an exam than they are at
preparing to teach and, most likely, possess minimal devel-
oped skills related to teaching. Second, we thought it possible
that expecting to teach might have a negative impact on
learning because public speaking is a major source of anxiety
for many people (Motley, 1988; Richmond & McCroskey,
1995;Ruscioetal.,2008) and anxiety is also associated with
having to teach (e.g., Ameen, Guffey, & Jackson, 2002;
Gardner & Leak, 1994). Because high levels of anxiety can
be detrimental to performance on many tasks (Yerkes &
Dodson, 1908), the expectation of having to teach could
produce high anxiety in some participants, disrupting their
learning and later performance. Third, violating participants
expectationsas happens when participants are first told that
they will have to teach and then told that, in fact, they are
going to take a test insteadcould lead to a double-cross
effect in which participants lower their effort on a final test in
the condition in which their expectations were violated, al-
though it might also be the case that they would look upon not
having to teach as a relief and, thus, not be so affected.
In contrast to reasons for why expecting a test might
produce better later performance, we also had reasons for
predicting the opposite outcomenamely, that expecting to
teach would produce better performance on a later test than
would expecting to be tested. Teaching typically involves
activities that are known to enhance learning, such as summa-
rizing the critical points of the to-be-learned information,
Mem Cogn
identifying key concepts, seeking relationships among ideas,
and mentally organizing the information (e.g., McKeachie,
Pintrich, Lin, & Smith, 1986). If expecting to teach causes
students to implement these or other effective learning strate-
gies, their learning could be enhanced whether or not they
actually go on to teach the material.
It is, of course, also possible that the two types of expec-
tancy could yield similar outcomes, particularly if students
expecting a test have learned to prepare for tests by focusing
on main points, mentally organizing the to-be-learned materi-
al, and, in general, by engaging in the very encoding strategies
we have suggested are likely to emerge from a teaching-
expectancy mental set.
The present research
In the present research, we sought to answer two main ques-
tions. First, does expecting to teach enhance later test perfor-
mance, as compared with expecting a test? Owing to the
equivocal pattern of results emerging from the literature rele-
vant to our research questions, we first attempted to replicate
the previous observed benefits of expecting to teach (e.g.,
Bargh & Schul, 1980).
Second, does expecting to teach cause individuals to
change the way they process information? We conjectured
that expecting to teach might encourage beneficial encoding
activities such as organizational processing (for similar
arguments, see Bargh & Schul, 1980; Gartner, Kohler, &
Riessman, 1971). Thus, in Experiment 1, we examined the
previously untested prediction that expecting to teach would
lead participants to produce relatively well-organized free
recall responses. We also thought it possible that expecting
to teach would cause participants to remember main points
especially well, on the basis of observations that teachers often
focus on key concepts in learning material (McKeachie et al.,
1986). To assess this potential difference, Experiments 1and 2
included test questions relating to main points, as well as detail
points about the passage. In sum, we used measures of output
organization and comparisons of memory for main and detail
points with the assumption that such measures could provide
insights into whether and, if so, how expecting to teach alters
the study strategies and cognitive processes used by
individuals during learning. The general design we
employed in both Experiment 1and Experiment 2followed
the approach introduced by Bargh and Schul (1980). All
participants were given a passage to read for a specified amount
of time. Prior to reading, participants were told either that they
would later take a test on the contents of the passage or that they
would later teach the contents of the passage to another student.
Participants given the expectancy to teach, however, never
actually did so; instead, they were given the same test as the
participants given the expectancy of taking a test.
Experiment 1
After being told that they would either teach or take a test,
participants read a passage and then took a free recall test. This
test allowed us to quantify the degree to which participants
responses were coherently organized. Additionally, we mea-
sured participantsfocus on key ideas in the passage by using
a passage in which idea units had previously been categorized
as main points, important details, or unimportant details
(Rawson & Kintsch, 2005). After the free recall test, partici-
pants took a short-answer test consisting of questions about
important and unimportant details.
Participants and design
A total of 56 undergraduate students (34 females and 22
males; average age = 20.8 years, SD = 3.27) from the Univer-
sity of California, Los Angeles, served as participants for
course credit. Participants were asked two questions at the
start of the experiment to assess their prior knowledge of the
to-be-learned materials. None reported having seen the movie
The Charge of the Light Brigade. Although 2 participants
(both in the test-expectancy condition) reported having
knowledge of the Crimean War, their performance did not
differ from that of their peers; thus, their data were not ex-
cluded from our analyses.
We employed a 2 × 3 mixed design with expectancy
instructions (teaching-expectancy vs. test-expectancy) manip-
ulated between subjects and information type (main points,
important details, unimportant details) manipulated within
subjects. Multiple measures of performanceproportion cor-
rect recall, organization, efficiency, and retention of different
types of informationwere derived from the free recall and
short-answer tests we administered to all participants.
Materials and measures
We employed a 1,541-word passage (acquired from Rawson
& Kintsch, 2005) comparing the depiction of the Crimean War
in the movie The Charge of the Light Brigade (Curtiz, 1936)
with the actual historical events of that war as a demonstration
of the tendency of movies to portray historical events inaccu-
rately. We chose this passage because Rawson and Kintsch
had identified 125 idea units (IUs) in the passage, including a
subset of 39 that they identified as main points (8 IUs),
important details (15 IUs), and unimportant details (16 IUs;
for additional details, see Rawson & Kintsch, 2005,p.74).We
used the full set of 125 IUs for mostanalyses, and the subset of
39 IUs to analyze levels of knowledge.
We employed two measures to assess our hypothesis re-
garding organization of free recall. First, we measured the
Mem Cogn
organization of our participantsrecall in terms of IUs by
adapting the adjusted ratio of clustering measure (ARC;
Roenker, Thompson, & Brown, 1971) to our purposes. The
ARC was originally intended for use in the context of learning
lists of items belonging to different taxonomic categories (e.g.,
fruits,trees), where it indicates the extent to which learners
cluster their recall output in terms of the categorical member-
ships of the list items. Broadly speaking, the ARC measures
the degree to which a learners recall patterns reflect the
conceptual organization of the studied material. Typically,
the bases for computing ARC measures is to count the number
of repetitions occurring in the recall output, where a repetition
is defined as two items belonging to the same category being
output sequentially (e.g., orange,banana; for a complete
explanation of how to compute ARC scores, see Roenker
et al., 1971). With the present materials, we considered each
IU to correspond to an item(i.e., category member) in the
ARC analysis and each paragraph within the original passage
to correspond to a category.Thus, a repetition occurred
when a participant contiguously recalled two IUs that had
been presented in the same paragraph of the passage. To
illustrate, if a paragraph in the original passage contained six
IUs and a participant output those six units together (even if
not in the same order as in the original passage), the ARC
score for that participant would be higher than the ARC score
for a participant who output the same exact information
scattered throughout his or her recall output. Perfect overlap
between the structure of the original text passage and the
participantsoutput produces ARC = 1.0. Thus, this ARC
measure reflects the degree to which participantsoutput of
IUs corresponds to how those units were originally organized
into paragraphs in the source passage.
Latent semantic analysis (LSA; Landauer, Foltz, & Laham,
1998) was the second measure of organization employed.
LSA measures the sentence-to-sentence coherence of a pas-
sage of text by comparing the relations among terms within a
sentence with a large database of terms, then by comparing
pairs of sentences in sequences from text (see the Sentence
Comparison tool at Perfect overlap
between all sequential pairs of sentences produces LSA = 1.0.
The LSA sentence-to-sentence coherence score for the source
passage used in the present experiment is .22. LSA sentence-
to-sentence coherence scores were computed for each partic-
ipants recall output, and the means across participants were
averaged to obtain the test-expectancy and teaching-
expectancy group means. LSA provides a measure of the
coherence of recall output that is independent of the structure
of the passage participants read.
Our short-answer test (also acquired from Rawson &
Kintsch, 2005) consisted of 8 questions asking about impor-
tant details of the passage and 10 asking about unimportant
details. One question was presented per page in a Microsoft
Word document.
Experiment 1consisted of four phases: study, distractor task,
free recall test, and short-answer test.
Study phase After collecting demographic information, we
told participants that they would have 10 min to read a
passage. They were told that they could not take notes or
highlight or underline items but that they could read the
passage at their own pace and return to previously read parts
of the passage whenever they wanted. Additionally, partici-
pants were told that they would not have access to this passage
once the 10-min reading time expired. Finally, before handing
out the passage, participants in the test-expectancy condition
were told that they would later be given a test on the material
in the passage, whereas participants in the teaching-
expectancy condition were told that they would be teaching
the material in the passage to another participant, who would
then be asked to take a test on the passage.
Distractor task Following the study phase, participants en-
gaged in a distractor task for 25 min (a separate memory
experiment using categorized word lists with no overlap with
the present materials).
Test phase Following the distractor phase, participants in the
test-expectancy condition were toldconsistent with their
expectationthat they were now going to be tested on the
previously read passage. Participants in the teaching-
expectancy condition were asked to take a testinconsistent
with their expectationbecause the student they were sup-
posed to teach had failed to show up.
Free recall test All participants first received a free recall test
for the studied passage. Specifically, they were asked to type
as much information from the passage as they could recall
onto a blank document in Microsoft Word. They were given
unlimited time to recall the information in any format (e.g.,
paragraph form, bullet point, etc.) and were told to inform the
experimenter when they were done. For each participant, the
experimenter recorded the amount of time spent on this test.
Short-answer test Immediately following the free recall test,
the experimenter opened a Microsoft Word document contain-
ing the short-answer test. All participants were instructed to
type their responses directly into the document, to proceed
through the test at their own pace, and to inform the experi-
menter when they were finished.
Effect sizes for comparisons of means are reported as Cohens
dcalculated using the pooled standard deviation of the groups
Mem Cogn
being compared (Olejnik & Algina, 2000, Box 1, Option B).
Effect sizes for analyses of variance (ANOVAs) are reported
as ω
calculated using the formulae provided by Maxwell
and Delaney (2004).
Free recall test
Four aspects of performance were assessed via the free recall
data: amount of correct output (i.e., proportion correct recall),
output efficiency, output organization, and type of information
Two independent raters, blind to conditions, scored the free
recall tests; reliability was high between the two raters
(α= .81). Participants were given a full point for recall of an
entire idea unit, half a point for partial recall of that idea unit,
and zero for no recall. Discrepancies in scoring were resolved
by a third rater who was also blind to the conditions.
Proportion of IUs correctly recalled As is indicated in Fig. 1a,
participants expecting to teach produced a greater proportion
of correct IUs (M= .17, SD = .07) than did participants
expecting a test (M=.13,SD = .08), and this difference was
significant, t(54) = 2.08, p=.043,d=0.56,CI
mean difference
[0.001, 0.079].
Efficiency of free recall Participants initially instructed to
prepare to teach spent slightly less time typing their output
(M= 15.59 min, SD = 7.15) than did participants instructed to
prepare for a test (M= 17.00 min, SD = 10.83), but this
difference did not reach significance, t< 1.00. In addition to
measuring the total time taken by each participant in recalling
the passage, we also measured the efficiency of each partici-
pants recall by dividing the number of idea units recalled by
the total time spent recalling.This measure (# IUs/min), which
is plotted in Fig. 1b, revealed that the teaching-expectancy
group recalled information more efficiently (M=1.50,SD =
0.64) than did the test-expectancy group (M=1.02,SD =.57),
t(54) = 2.94, p=.005,d=0.79,CI
mean difference
[0.151, 0.802].
Organization of free recall As indicated in Fig. 1c, expecting
to teach enhanced the organization of participantsfree recall.
An independent samples t-test indicated a higher ARC score
Fig. 1 Performance (means) on the free recall test (a,b,andc) and the
short-answer test (d) in Experiment 1. Error bars are standard error of the
mean (SEM). (Performance shown in panels a,b,andcis based on the
total set of 125 IUs; performance shown in panel dis based on a subset of
these IUs (8 important and 10 unimportant)
Mem Cogn
for the teaching-expectancy group (M=.81,SD =.18)thanfor
the test-expectancy group (M=.67,SD =.25),t(54) = 2.42,
mean differen ce
[0.024, 0.255], suggesting
that expecting to teach led participants to organize their
encoding and/or their recall in a way that reflected the struc-
ture of the source passage.
In contrast, however, the two expectancy conditions did not
differ in their mean sentence-to-sentence coherence scores. An
independent samples t-test confirmed that the LSA scores for
the teaching-expectancy group (M=.24,SD = .09) and the
test-expectancy group (M=.22,SD = .09) did not differ from
one another, t(54) = 0.89, p=.380,d=0.24,CI
mean differen ce
[0.025, 0.064], suggesting that the sentence-to-sentence co-
herence of recall output was similar for the two groups.
Information type Table 1depicts the relationship between
expectancy instructions and memory for IUs of different types
of information. These means are based on only the 39 idea
units identified as main points (8 IUs), important details
(15 IUs), and unimportant details (16 IUs). The results based
on these 39 IUs appear consistent with the three analyses
already reported, which utilized the full set of 125 IUs, in that
expecting to teach enhanced recall of each type of
We analyzed these data using a 2 (expectancy instruction:
teaching-expectancy vs. test-expectancy) × 3 (information
type: main point vs. important detail vs. unimportant detail)
mixed-design ANOVA. Consistent with the previous analyses
on the full set of125 IUs, the groupexpecting to teach recalled
more idea units (M=.22,SD = .13) than the group expecting a
test (M=.17,SD =.14),F(1, 54) = 3.81, MSE =.035,p=
.056, ω
= .05. There was also a main effect of informa-
tion type, F(1, 54) = 29.2, MSE =.012,p<.001,ω
Post hoc tests revealed that main points (M=.28,SD =.17)
were better recalled than were important details (M=.21,
SD =.13),t(55) = 3.12, p=.003,d=0.47,CI
mean difference
[0.026, 0.116], which, in turn, were better recalled than unim-
portant details (M=.12,SD =.13),t(55) = 4.31, p<.001,d=
0.69, CI
mean difference
[0.047, 0.130]. Post hoc tests also
revealed a teaching-expectancy advantage over test-
expectancy for recall of main points, [t(54) = 2.16, p=
.035, d= 0.56, CI
mean difference
[0.007, 0.181], but not for
important details, t(54) = 1.198, p=.236,d=0.30,CI
[0.028, 0.111] or unimportant details, t(54) = 0.982,
mean difference
[0.035, 0.109]. Important-
ly, however, despite the numerical suggestion of an interac-
tion, the interaction between expectancy instruction and infor-
mation type was not significant, F(1, 54) = 1.22, MSE =.012,
= .09, a point to which we return in the
Discussion section for Experiment 1.
Short-answer test
Average correct recall performance on the short-answer test
for the 8 questions about important details and the 10 ques-
tions about unimportant details is shown in Fig. 1d. The
apparent superior performance for participants in the
teaching-expectancy group was confirmed by the results of a
2 (expectancy instruction: teaching-expectancy vs. test-
expectancy) × 2 (information type: important details vs. un-
important details) mixed-design ANOVA, which revealed a
significant main effect of expectancy instruction, F(1, 54) =
5.04, MSE =.104,p=.03,ω
= .07, indicating better
overall performance for the teaching-expectancy group than
for the test-expectancy group. No effect of information type
emerged, however, F(1, 54) = 0.06, MSE =.023,p=.81,
= .00. Additionally, and consistent with the pattern
observed in the free recall data, a significant interaction be-
tween expectancy instruction and information type was not
obtained, F(1, 54) = 0.88, MSE =.023,p=.35,ω
In Experiment 1, multiple measures of participantsresponses
converged to support the claim that expecting to teach pro-
motes learning in ways that expecting a test does not. First,
expecting to teach enhanced the amount and efficiency of
output in free recall. Additionally, expecting to teach en-
hanced the match of organization of free recall output to the
structure of the source passage (ARC scores), although there
was not a teaching-expectancy advantage in the sentence-to-
sentence coherence measure (LSA scores). Finally, expecting
to teach also produced better performance on short-answer
questions. These findings suggest that participants processed
information differently, and more effectively, when they ex-
pected to teach than when they expected to take a test.
Experiment 1did not provide strong support for the hy-
pothesis that expecting to teach would specifically enhance
recall of important information (as indicated by the lack of a
significant interaction between expectancy instructions and
information type). As can be seen in Table 1,however,the
numerical pattern of our results does appear to indicate that the
Tabl e 1 Mean proportions of free recall idea units correctly recalled at
specific types of information for the different instruction conditions in
Experiment 1
Instructions Level of information
Main (SD) Important
detail (SD)
detail (SD)
Teaching expectancy 0.33 (0.14) 0.23 (0.11) 0.14 (0.14)
Test expectancy 0.24 (0.18) 0.19 (0.15) 0.11 (0.12)
Note. Proportion correct recall is based on 8 main point IUs, 15 important
detail IUs, and 16 unimportant detail IUs. Standard deviations (SDs) are
in parentheses.
Mem Cogn
recall advantage of the teaching-expectancy group over the
test-expectancy group was greater for main points than for the
other two types of information. Specifically, the numerical
advantage for expecting to teach diminished across levels of
importanceof information type (9 %, 4 %, and 3 % in the main
points, important details, and unimportant details, respectively),
and this difference was statistically significant only in the main
points, which had the largest effect size of the three compari-
sons. Thus, in Experiment 2, we further explored the effect of
expecting to teach on how different types of information are
processed and recalled.
Experiment 2
The primary purpose of Experiment 2was to test the hypoth-
esis that teaching-expectancy would selectively enhance
memory for the central ideas of a to-be-learned passage. We
took a different approach to testing this hypothesis in
Experiment 2, however, by choosing a passage that we
considered to contain mostly important information, with the
assumption thatgiven such a passageparticipants
expecting to teach would concentrate even more on finding
and learning the main points of the passage. That is, because
the passage contained a greater number of discrete, important
facts than participants would be able to learn in the time
provided, participants expecting to teach might be motivated
to strategically select main points for conveying to the stu-
dents they expected to teach. Finally, by using a cued-recall
test (fill-in-the-blank), we would be able to assess memory for
information that might be available but not necessarily acces-
sible via free recall testing (Tulving & Pearlstone, 1966),
which could provide additional insight into the effects of
Participants and design
A total of 44 undergraduate students (32 females and 12
males; average age = 19.8, SD = 2.32) from the University
of California, Los Angeles, served as participants for course
credit. Experiment 2employed a 2 × 2 mixed design, with
expectancy instructions (teaching-expectancy vs. test-
expectancy) as a between-subjects variable and information
type (main point vs. detail point) as a within-subjects variable.
At the end of the experiment, participants were asked, Prior
to this experiment, what was your knowledge of the topic of
the passage you read?and were given a 10-point scale on
which to make their ratings (1 = no prior knowledge,10=
did not differ in their background knowledge of the passage,
Along with the 44 students who participated in the exper-
iment proper, an additional 44 undergraduate students (23
females and 21 males; average age = 19.23, SD = 1.10) from
Washington University in St. Louis were used in a pilot study
to categorize test questions as main points and detail points.
Details about the administration of this classification task are
also presented in the Procedure subsection below.
To approximate materials used in educational settings, we
used a 1,300-word scientific passage titled Growth and De-
velopment of the Brain Reflect the Interaction of Intrinsic and
Extrinsic Factors, which was adopted from a university-level
textbook on psychobiology (Rosenzweig, Breedlove, &
Leiman, 2002, pp. 194197).
Experiment 2consisted of several phases: study, distractor,
fill-in-the-blank test, and a test-item judgment task.
Study phase The study phase for Experiment 2including
the wording of the prestudy instructions used to create the
teaching-expectancy and test-expectancy groupswas iden-
tical to that of Experiment 1, with the single exception that
participants in the present experiment were given 9 min, rather
than 10 min, to study the passage.
Distractor phase After study, participants performed an unre-
lated distractor task (a separate memory experiment using
categorized word lists with no overlap with the present mate-
rials) for 30 min before beginning the testing phase.
Test phase Immediately before the test, participants in the
test-expectancy condition were told that they would now take
a test on the passage. Participants in the teaching-expectancy
condition were asked to take a testinconsistent with their
expectationafter being told that the student they were to
teach had failed to show up. Both types of participants then
took a 27-item fill-in-the-blank test, the answers to which
could be found verbatim in the source passage. Each test
question was presented on the computer screen, along with a
text response box. The test was self-paced such that, after
responding, participants could move on to the next question
by hitting the Enter key; if 10 s elapsed with no response, the
computer automatically advanced to the next question.
Test-item-judgment task: defining information type In the sep-
arate pilot study for categorizing test questions as main points
and detail points, participants read the passage and were then
shown the test questions that were used in the experiment
proper. Each question was shown one at a time, but the
Mem Cogn
information that would be missing for experimental partici-
pants was presented in bold, and pilot participants were told
that this information would be missing from the items when
used as actual fill-in-the-blank test questions in a future ex-
periment. Pilot participants were asked to label each test
question as either a main point or a detail point, and they were
told that main points were statements about the overall idea
of the passageand detail points were statements about a
specific detail.For the analysis of memory performance in
the experiment proper, test questions were classified as main
points if more than 50 % of the pilot participants rated them as
main points and as details if more than 50 % rated them as
detail points. Using this majority rulescriterion, one ques-
tion that received an even split of votes was dropped from
analysis. One other question was dropped because only 2
participants answered it correctly. Among the remaining 25
items, 13 were classified as main-point questions (average
rater agreement = 76 %; range of rater agreement = 57 %
91 %) and 12 as detail-point questions (average rater agree-
ment = 86 %; range of rater agreement = 64 % 100 %).
Results and discussion
Correct performance on the fill-in-the-blank test is shown in
Fig. 2, and, as indicated there, orientation instructions and
information type appear to interact, with an expectancy to
teach selectively enhancing performance on questions related
to main points of the passage. To evaluate this observation, a 2
(expectancy instruction: teaching-expectancy vs. test-
expectancy) × 2 (information type: main vs. detail) mixed-
model ANOVA was conducted, with passage background
knowledge included as a covariate. A significant main effect
of information type emerged, F(1, 41) = 8.24, MSE =0.009,
= .02, indicating that main points (M=.51,
SD =.20)werebetterrecalledthandetailpoints(M=.33,
SD = .17). The main effect of expectancy instruction was not
significant, F(1, 41) = 0.76, MSE = 0.054, p= .389, ω
.00, indicating no difference in the average overall performance
of participants expecting to teach (M= .44, SD = .16) and that
of participants expecting a test (M=.39,SD = .17). Critically,
however, a significant interaction between expectancy instruc-
tion and information type was revealed, F(1, 41) = 7.48, MSE =
0.009, p= .009, ω
= .02. Follow-up comparisons (again
using passage background knowledge as a covariate) showed
that the advantage of expecting to teach over expecting a test was
marginally significant for the recall of main points, F(1, 41) =
3.15, MSE = 0.034, p= .083, d=0.45,CI
mean difference
0.211], and that there was no difference between expectancy
groups for the recall of details, F(1, 41) = 0.06, MSE = 0.029, p=
.814, d=0.11,CI
mean difference
[0.116, 0.092]. Thus, the pattern
of results obtained in Experiment 2was consistent with our
hypothesis that an expectancy to teach would induce learners
to focus on the processing of information related to the central
topic of the passage.
General discussion
In two experiments, participants who expected to teach
learned more from a passage than did participants who ex-
pected to take a test. In addition to overall memory perfor-
mance, this difference was reflected by two additional mea-
sures. First, relative to test-expectancy, teaching-expectancy
produced greater organization of output on a later free recall
test (Experiment 1). Second, it selectively enhanced memory
for main points, whereas recall for detail points did not sig-
nificantly differ between the two expectancy conditions
(Experiment 2; a similar but nonsignificant finding was ob-
tained in Experiment 1). Thus, a relatively simple instructional
manipulation led to a meaningful enhancement in how people
processed the information to be learned.
In Experiment 1, organization of recall was measured with
a modified version of the ARC and using LSA. The ARC, in
the present context, measured the degree to which a partici-
pants recall output clustered IUs similarly to how they were
organized into paragraphs in the source passage. Thus, one
explanation for our finding of higher ARC scores for partic-
ipants expecting to teach is that teaching-expectancy led par-
ticipants to pay more attention to the structure of the passage
during study, perhaps because they believed that using the
passages structure would help them better teach their pro-
spective student. Alternatively, a teaching-expectancy may
have enhanced participantsencoding of the passagesorga-
nization in other ways; for example, they may have been
trying to think of ways to improve on the passagesorganiza-
tion. The LSA scores suggest that the latter explanation may
not be the case, however. The sentence-to-sentence coherence
measured by LSA is an index of the coherence of participants
recall output, independent of the structure of the source pas-
sage. The average LSA scores were similar for the teaching-
and test-expectancy groups in Experiment 1,suggestingthat
overall coherence of output was not different between the two
conditions. This result is not particularly surprising, however,
because the LSA scores for both groups were similar to the
LSA score for the source passage, indicating that all of the
participants in this study recalled information in a fairly
In the interest of full disclosure, we note that we also conducted this
analysis using a different criterion for classifying main points and detail
points: Specifically, questions were labeled as main points or detail points
only if 66 % of the pilot participants agreed in their ratings. Using this
criterion, the interaction between expectancy instructions and information
type was not statistically significant, although the pattern of data was
essentially the same as reported using the majority rulescriterion
reported here. The 66 % criterion led to six questions being dropped from
analysis, which reduced the power of the ANOVA to detect differences,
and so we interpret the observed null effect with caution.
Mem Cogn
organized structure. A potentially promising future direction
for research on this topic might be to investigate whether
teaching-expectancy can lead learners to reorganize an initial-
ly poorly structured passage into one with a better structure,
given the expectation of having to teach it to another student.
The evidence in the present research regarding the hypoth-
esis that expecting to teach selectively promotes memory for
important information was less conclusive. In both experi-
ments, expecting to teach versus expecting a test appeared to
convey an advantage for the learning of main points, but not
detail points. Expecting to teach versus expecting to take a test
produced enhanced cued recall of main points, but not of
detail points, in Experiment 2. An analogous pattern for free
recall was observed in Experiment 1, but the interaction
between level of information and expectancy did not obtain
significance. Critically, the effect sizes for the difference
between expectancy conditions was consistently greater
for main points (d= 0.56 and 0.45 in Experiment 1and
Experiment 2, respectively) than for details (d=0.30
and 0.23 for important and unimportant details, respectively,
in Experiment 1, and 0.11 for details in Experiment 2). Con-
sistent with recent arguments that effect sizes are an important
source of information to consider when interpreting results
(Cummings, 2013), we believe that the consistent pattern of
results across the two experiments reported herein supports
the notion that expecting to teach selectively enhances mem-
ory for main points. This issue is not settled, however, and
further research is necessary before strong conclusions can be
made. Another issue not completely resolved in the present
research is whether the observed results reflect enhanced
encoding processes, enhanced retrieval processes, or a com-
bination of the two. One venue of future research, therefore,
during encoding and/or retrieval, which might provide addi-
tional insights into the loci and nature of the processes under-
lying the observed benefits of expecting to teach.
Theoretical implications
Why does expecting to teach enhance organization of output
and encoding of the main points of a passage? The explana-
tion we currently favor is that participants expecting to teach
put themselves into the mindset of a teacher, leading them to
adopt certain effective strategies used by teachers when pre-
paring to teachsuch as organizing and weighing the impor-
tance of different concepts in the to-be-taught material, focus-
ing on main points, and thinking about how information fits
together. These teaching preparation techniques are parallel to
encoding strategies that are known to be powerful learning or
mnemonic processesnamely, relational (organizational) and
item-specific processing strategies.
According to basic research on human memory, relational
and item-specific processing strategies are typically beneficial
to memory (e.g., Hunt & Einstein, 1981). Relational process-
ingprocessing the relationships amongst units of informa-
tionis proposed to enhance recall by increasing the elements
incorporated into memory traces and by allowing for an
effective search strategy at the time of retrieval via generative,
reconstructive means. The idea that relational processing pro-
motes coherent, effective reconstruction fits with our interpre-
tation of the present finding that the higher degree of output
organization displayed by our teaching-expectancy partici-
pants reflects their greater relational processing at encoding.
Item-specific processingthe encoding of single units of
informationis believed to benefit memory by enhancing
the distinctiveness of specific memory traces. Participants in
the present research recalled a larger proportion of main points
than detail points (Experiment 1), and this difference was
exaggerated for participants expecting to teach (Experiment
2), indicating that teaching-expectancy enhances item-specific
processing for specific subtypes of itemsnamely, the main
points of the passage. Hunt and colleagues have claimed that
although tasks often promote one type of processing more
than the other, relational and item-specific processing are not
mutually exclusive but, rather, often interact in ways that
promote overall memory more than does either processing
type alone. Consistent with this notion, the general pattern of
our results indicates that participants expecting to teach may
have benefited from both enhanced relational and item-
specific processing.
Expecting to teach might also have triggered explanatory
questioning, which enhances learning and memory (see, e.g.,
Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013;
Roediger & Pyc, 2012). Explanatory questioning techniques
such as elaborative interrogation (e.g., Pressley, McDaniel,
Turnure, Wood, & Ahmad, 1987) and self-explanation (e.g.,
Berry, 1983; Chi, de Leeuw, Chiu, & LaVancher, 1994)typ-
ically involve prompting learners to generate an explanation
for an explicitly stated fact or solution of a problem. Teachers
often engage in explanatory questioning when preparing to
Fig. 2 Means for fill-in-the-blank test in Experiment 2, displayed as a
function of instruction condition and information type. Using the ma-
jority rulescriterion of rater agreement, 13 questions were classified as
main points, and 12 questions were classified as detail points. Error bars
are standard error of the mean (SEM)
Mem Cogn
teach. Perhaps some of the gains seen in the current studies are
due to participants in the teaching-expectancy conditions ask-
ing themselves explanatory questions while reading.
Finally, an important question to explore in future research
is how prior knowledge might interact with the expectation to
teach. Teachers typically start with some knowledge of the
topic they intend to teach, so they are likely building upon
their current state of understanding when they prepare to teach
new material. Likewise, when participants do, in fact, possess
relevant knowledge, it might be the case that those expecting
to teach would utilize such knowledge more than would those
expecting a test. Thus, the benefit of expecting to teach might
be especially pronounced when learners have prior
Educational implications
Finding effective educational interventions that can be imple-
mented easily and inexpensively is becoming ever more crit-
ical, and it has been argued that one way to make progress
toward this goal is to apply research findings from cognitive
psychology to the classroom (e.g., Roediger & Pyc, 2012).
Over the past century, psychologists have evaluated the effi-
cacy of many study methods and, on the basis of a recent
assessment of the available evidence (Dunlosky et al., 2013),
Roediger and Pyc have suggested that teachers employ the use
of distributed practice (see Cepeda, Pashler, Vul, Wixted, &
Rohrer, 2006), retrieval enhanced learning (see Roediger &
Karpicke, 2006), and explanatory questioning (e.g., Chi et al.,
1994). On the basis of the present findings, we would argue
that instilling students with an expectancy to teach may lead
them to employ some of these techniques and, thus, might be a
vehicle for bringing about learning gains in the classroom.
Furthermore, given the relative ease of instilling such expec-
tations, this technique could be one of the more easily de-
ployed of such interventions. Whereas few students would
know what to do if asked to engage in explanatory
questioningor organizational processing,asking them to
prepare to teachappears to be an instruction they understand
quickly and intuitively.
Admittedly, the deceptive version of teaching-expectancy
employed in the present report would not work in classroom
settings, because students would quickly catch on that they
would not ever be required to teach. Perhaps informing the
classroom that at least one student among them will be re-
quired to teachbut not revealing which studentwould
prompt all students to prepare as if they will have to teach.
This on the hookapproach improves learning following
questions asked by teachers to students in classrooms, when
the question is asked before revealing which student will be
selected to answer it (Harris, Pashler, & Kang, 2014). We are
currently conducting experiments to test whether participants
who believe they might teach show the same learning gains as
those shown for participants expecting to teach in the present
Concluding comments
Teachers learn while they teach (Topping, 1996) and while
they prepare to teach (e.g.,Benware & Deci, 1984). Expecting
to teach appears to encourage effective learning strategies
such as seeking out key points and organizing information
into a coherent structure. Our results suggest that students also
turn to these types of effective learning strategies when they
expect to teach. It is noteworthy, then, that when students
instead expect to be tested, they underutilize these strategies,
although our results clearly indicate that these strategies must
be available to them and, furthermore, would better serve their
presumed goal of achieving good test performance than do the
strategies they instead adopt for this purpose. Students seem to
have a toolbox of effective study strategies that, unless prod-
ded to do so, they do not use.
This pattern of findings is in sync with much recent evi-
dence that students do not necessarily employ activities that
best foster learning, despite their many years of active involve-
ment in both formal and informal learning activities (for a
discussion of this view and the relevant research, see, e.g.,
Bjork & Bjork, 2011; Bjork, Dunlosky, & Kornell, 2013). In
many situations, students appear to need to be guided in how
to discover those strategies that are optimal for learning. The
present research has demonstrated one way of doing so that
promises to be easy to implement in various educational
settings. We hope the present findings encourage future re-
searchers to discover other such potentially easy-to-
implement ways of leading students to adopt more effective
learning strategies.
Acknowledgments This research was supported by a Collaborative
Activity grant from the James S. McDonnell Foundation. We thank
Fredrik Jönsson, Veit Kubik, Victor Sungkhasettee, Katherine Rawson,
and three anonymous reviewers for helpful comments on earlier versions
of the manuscript. Thanks to members of the Bjork Learning and For-
getting Lab for discussion about this project and to Genna Angello,
Lauren Camarillo, and John Walker for scoring the recall data. Finally,
thanks to Jason R. Finley for guidance on how to calculate ω
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... Providing learners with the responsibility to teach a given content after learning it leads them to a greater involvement in the teaching-learning process (Bargh & Schul, 1980;Fiorella & Mayer, 2013, 2014Nestojko et al., 2014). Although some studies did not find learning benefits from expecting to teach (Ehly et al., 1987;Renkl, 1995), most evidence suggests that expecting to teach demonstrated consistent learning benefits when it comes to academic learning (Bargh & Schul, 1980;Fiorella & Mayer, 2013, 2014Nestojko et al., 2014). ...
... Providing learners with the responsibility to teach a given content after learning it leads them to a greater involvement in the teaching-learning process (Bargh & Schul, 1980;Fiorella & Mayer, 2013, 2014Nestojko et al., 2014). Although some studies did not find learning benefits from expecting to teach (Ehly et al., 1987;Renkl, 1995), most evidence suggests that expecting to teach demonstrated consistent learning benefits when it comes to academic learning (Bargh & Schul, 1980;Fiorella & Mayer, 2013, 2014Nestojko et al., 2014). Such effects have been justified by two hypotheses: The first one suggests that learners benefit from an increase in motivation from the perception of autonomy and perception of competence, from the expectancy to teach (Ryan & Deci, 2000). ...
... Previous research has shown that higher motivation scores obtained relate to higher performance in learning tests (Fiorella & Mayer, 2014). The other explanatory hypothesis suggests that expecting to teach leads to an increased information processing, which would lead to a greater cognitive effort when using teaching strategies while preparing to teach (Nestojko et al., 2014). ...
Purpose: Recent evidence suggests learning a motor skill with the expectation of teaching it enhances motor learning. The mechanisms underlying this effect seem to be similar to those of another motor learning condition, the self-control of knowledge of results (KR). Considering the similarities between the mechanisms that underlie these conditions, we aimed to investigate the learning effects obtained through expected teaching and self-controlled conditions, and whether these effects would be additive. Methods: Participants practiced a dart-throwing task under one of the following conditions: a) expecting to teach the skill; b) controlling the KR request; c) combining the two previous conditions; and d) receiving KR in a yoked condition with self-controlled participants. In acquisition phase, motivational aspects, strategies for requesting KR and aspects related to the expectation of teaching were assessed according to each condition. Results: Participants with control over KR and/or with the expectation of teaching the skill showed superior learning of the task compared to the control condition. However, the combination of the experimental conditions did not result in additive learning benefits. Increased perceived competence was found in expecting to teach, self-controlled and combined conditions, compared to the yoked group. Additionally, expecting to teach also affected the way and the frequency learners requested KR. Conclusions: Our findings provide important insights toward understanding the effects of expecting to teach, in addition to demonstrating that expecting to teach affects self-controlled KR scheduling and its use during motor skill acquisition.
... To greater or lesser degree, learning by teaching entails preparatory learning. Research examining the learning effects of preparing to teach has mainly focused on the role of teaching expectancy in preparatory learning and suggested that the effectiveness of learning by teaching is at least partly accounted for by whether students study learning material with or without teaching expectancy (Bargh & Schul, 1980;Benware & Deci, 1984;Fiorella & Mayer, 2013, 2014Kobayashi, 2019a;Nestojko et al., 2014). In contrast, the issue of how effectively students prepare to teach, including whether and how the effectiveness of preparing to teach can be improved, has received little attention by researchers. ...
... Fiorella and Mayer (2013) also argued that learning by preparing for teaching surpasses learning by preparing for test because teaching expectancy promotes deep processing, such as selecting relevant and important information from learning material. Nestojko et al. (2014) suggested that students who expect to teach others are more likely to engage in the selective and organizational processing of to-be-taught information than those who expect to take a test. ...
... However, previous work has yielded mixed results concerning the learning effects of teaching expectancy. Some studies have found that students who studied learning material with the expectation of teaching performed better in memory and comprehension than those who did so without the expectation of teaching (Annis, 1983;Bargh & Schul, 1980;Benware & Deci, 1984;Fiorella & Mayer, 2013, 2014Guerrero & Wiley, 2021;Nestojko et al., 2014), whereas other studies have not (Ehly et al., 1987;Hoogerheide et al., 2016;Renkl, 1995;Rhoads et al., 2019;Wang et al., 2021). By synthesizing these findings metaanalytically, Kobayashi (2019b) revealed that the variance in effect sizes for studying with versus without teaching expectancy was highly heterogeneous (I 2 = 76%), and besides the weighted mean effect size was small to medium in magnitude (Hedges's g = 0.35). ...
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Research on learning by teaching has mostly focused on the learning effects of teaching after preparing individually to teach. This study investigated the impact of preparing collaboratively (versus individually) to teach on learning by teaching. Japanese undergraduate students (n = 96) provided instructional explanations on video or listened to their partners’ instructional explanations after they had studied learning material for teaching in collaboration with their partners or had done so individually in the presence of their partners. Participants who prepared collaboratively to teach provided higher-quality instructional explanations on video and learned better by teaching than those who prepared individually to teach. The quality of the videotaped explanations significantly predicted the outcomes of learning by teaching. Whether after collaborative or individual preparation, there were no significant differences in learning outcomes between those who explained on video and those who listened to their partners’ explanations. These results suggest that although the learning effects of providing and listening to instructional explanations may be comparable, collaborative preparation is more beneficial to learning by teaching than individual preparation.
... Additionally, it has been shown that teaching requires the student to reflect upon their teaching based on the performance of the tutee, which may lead to positive cognitive outcomes [3]. The presence of the Protégé effect has been confirmed in several studies, with positive cognitive [18] and meta-cognitive [19] outcomes. ...
... Teachable agents have been shown to improve learning outcomes over tutoring agents, through the Protégé effect [2], [3], [18]. They have been used in applications where students manipulate interface elements such as concept maps [3], or provide hints to a virtual student [13], rarely using natural language as a teaching modality. ...
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Conversational teachable agents offer a promising platform to support learning, both in the classroom and in remote settings. In this context, the agent takes the role of the novice, while the student takes on the role of teacher. This framing is significant for its ability to elicit the Prot\'eg\'e effect in the student-teacher, a pedagogical phenomenon known to increase engagement in the teaching task, and also improve cognitive outcomes. In prior work, teachable agents often take a passive role in the learning interaction, and there are few studies in which the agent and student engage in natural language dialogue during the teaching task. This work investigates the effect of teaching modality when interacting with a virtual agent, via the web-based teaching platform, the Curiosity Notebook. A method of teaching the agent by selecting sentences from source material is compared to a method paraphrasing the source material and typing text input to teach. A user study has been conducted to measure the effect teaching modality on the learning outcomes and engagement of the participants. The results indicate that teaching via paraphrasing and text input has a positive effect on learning outcomes for the material covered, and also on aspects of affective engagement. Furthermore, increased paraphrasing effort, as measured by the similarity between the source material and the material the teacher conveyed to the robot, improves learning outcomes for participants.
... Learning by teaching: Research has revealed that the learning by teaching method could be seen as a conscious practice of retrieval and knowledge re-organization. Through the aforementioned method, students 're-download' their knowledge which entails information reprocessing on a much deeper level (Koh, Lee & Lim, 2018;Nestojko et al. 2014). ...
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Education in the 21st century is called upon to prepare students with disabilities to enter a high-consciousness society where people can learn, think and react fast. The current review paper aims at investigating the role of fast learning in special education. We trace the essential indicators of speed learning with a special focus on those factors that are most relevant to learning disabilities. Afterward, we present evidence-based training techniques and strategies that rapidly rewire the brain and speed up learning. In addition, we examine the role of ICTs as essential training tools in speed learning. Finally, we discuss the role of metacognition in training fast and conscious learners. The results of this review showed that speed learning training techniques improve all those factors that accelerate learning such as spatial attention, visual span, processing speed, speed reaction, executive functions, metacognition, and consciousness. Most important, fast learning strategies meliorate control processes and spatial intelligence which is extremely fast and powerful. Metacognition provides learners with all those meta-abilites needed to enter a state of peak performance. This study also points to the option of including speed training strategies in schools to create inclusive learning environments and help students with or without disabilities to transcend their limitations and become conscious and high-capacity learners.
... Although this issue received quite some attention in non-interactive teaching research (e.g., Fiorella & Mayer, 2013;Hoogerheide et al., 2014) and in the early interactive teaching literature (e.g., Benware & Deci, 1984;Renkl, 1995;Ross & Di Vesta, 1976), research has yielded mixed findings. Several studies found that studying learning materials with a teaching expectancy improved learning outcomes relative to studying for a test (e.g., Fiorella & Mayer, 2013;Nestojko et al., 2014), while others found no differences (e.g., Hoogerheide et al., 2016a;Rhoads et al., 2019). ...
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Teaching the contents of study materials by providing explanations to fellow students can be a beneficial instructional activity. A learning-by-teaching effect can also occur when students provide explanations to a real, remote, or even fictitious audience that cannot be interacted with. It is unclear, however, which underlying mechanisms drive learning by non-interactive teaching effects and why several recent studies did not replicate this effect. This literature review aims to shed light on when and why learning by non-interactive teaching works. First, we review the empirical literature to comment on the different mechanisms that have been proposed to explain why learning by non-interactive teaching may be effective. Second, we discuss the available evidence regarding potential boundary conditions of the non-interactive teaching effect. We then synthesize the available empirical evidence on processes and boundary conditions to provide a preliminary theoretical model of when and why non-interactive teaching is effective. Finally, based on our model of learning by non-interactive teaching, we outline several promising directions for future research and recommendations for educational practice.
... ch material as part of an actual course curriculum. These laboratory studies included specific controlled conditions that do not necessarily suggest they would translate to success in a classroom. In many of these studies, participants studied unfamiliar material for a brief time in a controlled setting (Hiller et al., 1973;Hoogerheide et al., 2016, Nestojko et. al, 2014. For example, in Hoogerheide et al.'s (2016) study participants watched a 13-minute video on unfamiliar material and then received 6 minutes to prepare to teach the content. Participants also typically deliver very short lectures (e.g., Fiorella & Kuhlmann, 2020;Fiorella & Mayer, 2013). Participants in Fiorella and Mayer's (2013) study ...
Students report poor learning goals and study strategies. Educators may encourage better learning by requiring students to complete assessments that promote generative learning. The benefits of engaging in generative processes suggest encouraging them through teaching-to-learn assignments may be helpful. There is little research examining the benefits of teaching-to-learn conducted as part of a classroom curriculum with appropriate control conditions. The current study examines the benefits of teaching-to-learn on conceptual knowledge learning by requiring 53 students to prepare and deliver a lecture in one unit and write a paper in another unit. Students then answered questions covering their lecture and paper topics on both a unit and surprise final exam. Analyses on exams revealed students answered a greater percentage of the questions about their lecture topic correctly (84.91% and 76.23%) than their paper topic (76.98% and 67.92%) on both the unit and final exam respectively.
One big challenge for undergraduate research students is gaining independence in the laboratory. In this curricular project, undergraduate students transformed research protocols developed for experienced scientists into protocols understandable to someone new to a laboratory. This process enabled themselves and other students to more quickly learn and master new techniques and advance to independent projects. Typically, students started with an original research protocol that assumed basic knowledge, such as instructions that came with a kit (i.e. plasmid purification kit instructions). Students created notes that explained the purpose of each step and reagent and provided example calculations. Then students illustrated the protocols with photos of materials needed, equipment used, action shots of difficult steps and screenshots of software programs. This approach has been used by students in laboratory courses and by new independent research students learning laboratory techniques. In the laboratory courses where students contributed to this project as part of a writing assignment, additional professional experience was gained by presenting a talk about their completed Illustrated Protocols to their classmates and by creating group posters that were presented at an undergraduate research symposium. After completion of this activity, undergraduate students gained confidence by applying their new knowledge to create user-friendly protocols. Students reported increased understanding of what is happening in each step, while instructors reported increased student independence and confidence that the protocol was being applied correctly and consistently. Thus, designing Illustrated Protocols enhanced learning and independence for the students creating the protocol and provided valuable help for future students.
Bringing metacognition onto college campuses is a transformational experience for students and faculty as well as their institutions. In this chapter, we share a collection of metacognitive activities and describe their value in and outside of the classroom based upon our experiences in the community college setting. These activities are easily implemented and help students take ownership of their learning.
The ability to teach is recognized as a core skill for many professionals, including veterinarians, but undergraduate opportunities to develop this skill are not always available. A complementary teaching certificate offered during the clinical years of an undergraduate veterinary program was evaluated to investigate student experiences of the program and the perceived benefits and challenges of participating. The study used a mixed methods approach with questionnaires to provide an overview of the participant experience and semi-structured interviews to gain a deeper insight into students’ experiences of the program. Two cohorts completed questionnaires comprising Likert-style and open-ended questions on the 3-year teaching certificate, the first cohort after 1 year of the program and the second cohort at completion. Interviews with participants from both cohorts were thematically analyzed to identify recurring themes. An average of 27% of students per academic year enrolled in the certificate program, most of whom completed it. Additionally, four to six students per cohort applied for Associate Fellow of the Higher Education Academy (AFHEA), and 19 students have achieved this recognition. Key themes from the data included that students felt the certificate built their confidence, increased their veterinary knowledge, and helped them become better teachers, with time management and reflection the biggest challenges. The Undergraduate Certificate of Veterinary Medical Education was seen as a good teaching foundation, while working toward the AFHEA provided some insight into higher education and academic careers. A structured teaching program offers students the opportunity to develop their learning and reflection both as students and future educators.
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Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: General description of the technique and why it should work How general are the effects of this technique? 2a. Learning conditions 2b. Student characteristics 2c. Materials 2d. Criterion tasks Effects in representative educational contexts Issues for implementation Overall assessment The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques. To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students’ performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self-explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique. Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness. The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students’ performance, so other techniques should be used in their place (e.g., practice testing instead of rereading). Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research.
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Three experiments examined note-taking strategies and their relation to recall. In Experiment 1, participants were instructed either to take organized lecture notes or to try and transcribe the lecture, and they either took their notes by hand or typed them into a computer. Those instructed to transcribe the lecture using a computer showed the best recall on immediate tests, and the subsequent experiments focused on note-taking using computers. Experiment 2 showed that taking organized notes produced the best recall on delayed tests. In Experiment 3, however, when participants were given the opportunity to study their notes, those who had tried to transcribe the lecture showed better recall on delayed tests than those who had taken organized notes. Correlational analyses of data from all 3 experiments revealed that for those who took organized notes, working memory predicted note-quantity, which predicted recall on both immediate and delayed tests. For those who tried to transcribe the lecture, in contrast, only note-quantity was a consistent predictor of recall. These results suggest that individuals who have poor working memory (an ability traditionally thought to be important for note-taking) can still take effective notes if they use a note-taking strategy (transcribing using a computer) that can help level the playing field for students of diverse cognitive abilities. (PsycINFO Database Record (c) 2013 APA, all rights reserved)
We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.
We conducted a meta-analysis of both classroom and laboratory studies of the effects of expecting a recall, recognition, essay, multiple-choice or true-false test on students’ subsequent achievement. In laboratory studies, studying with a recall set produced strong positive effect sizes for both discrete and prose materials. However, studying with a recognition set produced no effects with discrete materials and small negative effects with prose materials. In contrast, results from classroom studies indicated that students achieved most when preparing for the type of test they received. These results run counter to standard wisdom in the college study skills area and lead us to challenge the assumption that laboratory studies on expecting tests of recall and recognition provide a useful analog to test expectancy effects involving essay and multiple-choice tests in the classroom.