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Abstract

This article provides a conceptual review of the principles of input spacing as they might relate specifically to oral task repetition research and presents some of the common methodological considerations from the broader input spacing literature. The specific considerations discussed include the interaction between intersession intervals and retention intervals, the manipulation of posttests as a between‐participants variable, the number of task repetitions, absolute versus relative spacing, the criterion of learning, task type versus exact task repetition, and blocked versus interleaved practice. Each of these considerations is discussed with links, as appropriate, to the relevant empirical input spacing and task repetition literature. The purpose of this review is to highlight how, in many cases, these methodological considerations have been overlooked by task repetition researchers, including in studies where input spacing has and has not been a direct focus, and to suggest ways of addressing these methodological shortcomings in future research. A one‐page Accessible Summary of this article in non‐technical language is freely available in the Supporting Information online and at https://oasis‐database.org
Language Learning ISSN 0023-8333
CONCEPTUAL REVIEW ARTICLE
Spacing Effects in Task Repetition Research
John Rogers
The Hong Kong Polytechnic University
Abstract: This article provides a conceptual review of the principles of input spacing
as they might relate specifically to oral task repetition research and presents some of the
common methodological considerations from the broader input spacing literature. The
specific considerations discussed include the interaction between intersession intervals
and retention intervals, the manipulation of posttests as a between-participants variable,
the number of task repetitions, absolute versus relative spacing, the criterion of learning,
task type versus exact task repetition, and blocked versus interleaved practice. Each of
these considerations is discussed with links, as appropriate, to the relevant empirical
input spacing and task repetition literature. The purpose of this review is to highlight
how, in many cases, these methodological considerations have been overlooked by task
repetition researchers, including in studies where input spacing has and has not been a
direct focus, and to suggest ways of addressing these methodological shortcomings in
future research.
Keywords task repetition; input spacing; research methods; task-based language
teaching; second language; instruction
Introduction
Over the past 30 years, a substantial body of literature within the field of second
language acquisition (SLA) has examined the use of tasks in second language
A one-page Accessible Summary of this article in non-technical language is freely available in the
Supporting Information online and at https://oasis-database.org
I would like to thank Gavin Bui, Peter Skehan, the editors of Language Learning, and the anony-
mous reviewers for their helpful comments and suggestions on previous versions of this article. I
would also like to extend a particular note of gratitude to the handling editor, Judit Kormos, for
her insightful comments and feedback throughout the revision process. Any remaining errors are,
of course, my own.
Correspondence should be addressed to John Rogers, Department of English and Communi-
cation, Faculty of Humanities, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Email: John.Rogers@polyu.edu.hk
The handling editor for this article was Judit Kormos.
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Rogers Input Spacing and Task Repetition
(L2) learning, particularly how oral tasks might best be designed and imple-
mented to optimize their benefits for L2 development (R. Ellis et al., 2019;
Long, 2015; Skehan, 2018). Within this strand of research, task repetition, or
whether learners are given the opportunity to repeat a task after having com-
pleted it, has garnered considerable attention due to its potential for facilitating
both task performance and L2 development (Bygate, 2018). Despite a consid-
erable amount of empirical research on task repetition, the precise influence of
repeating a task remains unclear, both with regard to task performance, as mea-
sured by various indices of complexity, accuracy, and fluency, and with regard
to L2 development. Input spacing, or the amount of time between task repeti-
tions, has been identified as one variable that might account for the inconsistent
findings across the literature (DeKeyser, 2018). Researchers studying task rep-
etition have only recently begun to control for this variable in order to examine
how manipulations of the time between task repetitions influence L2 perfor-
mance and/or development (Bui et al., 2019; Y. Suzuki, 2021; Y. Suzuki &
Hanzawa, 2022).
Given this recent interest, it would appear to be an opportune time to review
the principles of input spacing as they might relate specifically to oral task
repetition and to present some of the common methodological considerations
from the broader input spacing literature. The purpose here is to highlight how,
in many cases, these methodological considerations have been overlooked in
task repetition research, including studies in which spacing has and has not
been a direct focus, and to suggest ways of addressing these methodological
shortcomings in future research.
This article has two broad aims. The first is to aid future task rep-
etition researchers in examining the effects of spacing in a systematic,
methodologically robust manner that will contribute to both the literature
on task repetition within the field of SLA and the literature on input spac-
ing within the broader psychological literature. The second aim is to ben-
efit SLA researchers for whom spacing is not a direct focus, by show-
ing that the same methodological considerations also have implications for
their research. In the following sections, I first provide a concise back-
ground to relevant input spacing research in the fields of psychology and
SLA. I then briefly describe why input spacing is theoretically relevant to
task repetition and review empirical task repetition studies that have directly
manipulated spacing as an independent variable of interest. I then high-
light a number of methodological considerations from the literature on in-
put spacing; where appropriate, I link these to empirical research on oral task
repetition.
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Rogers Input Spacing and Task Repetition
Background: Input Spacing
Input spacing is rooted in optimizing the benefits of repetition in the learning
of new content and/or skills. Specifically, this area examines how the different
amounts of time that elapse between repetitions influence performance, learn-
ing, and/or retention. There are a number of key terms related to this. Massed,
as in “massed practice,” refers to experimental manipulations in which repeti-
tions occur in immediate succession, for example, where a learner completes a
task and is then asked to repeat the same task immediately without a break. In
contrast, distributed, as in “distributed practice,” with spaced as an alternative,
refers to conditions in which there is a gap between the repetitions (Cepeda
et al., 2006; Rogers, 2017), for example, where learners complete a task and
repeat the same task in the next class a day or two later. The term spacing ef-
fect refers to the superiority of distributed conditions over massed conditions
in terms of learning or retention. The term lag effect refers to the differential
effects of two different spacing conditions, such as a shorter lag versus a longer
lag. The term distribution of practice effects has been suggested as a blanket
term for both lag and spacing effects (Cepeda et al., 2006).
Spacing effects are generally considered one of the most robust effects in
all of the psychological literature (Dempster, 1988) and have been demon-
strated in a remarkably wide array of learning tasks across modalities and
across various physical and mental skills. Furthermore, they have been demon-
strated across all ages from infants to older adults, including samples of older
adults suffering from neurological disorders such as dementia (Balota et al.,
2007; see Wiseheart et al., 2019, for a review). In addition to presenting con-
sistent and ubiquitous findings, the literature on spacing effects is vast; as an
illustration, a review of spacing effects on verbal learning included over 300
primary studies (Cepeda et al., 2006).
Meta-analyses of the spacing literature have generally reported medium-to-
large-sized effects for distributed practice when compared to massed practice,
although the magnitude appears to be influenced by a range of variables, in-
cluding the target skill and/or material to be learned (Wiseheart et al., 2019).
Lee and Genovese’s (1988) meta-analysis of the literature on motor learning re-
vealed a large effect for distributed practice over massed practice (g=0.96).1
Donovan and Radosevich (1999) reported a modest overall effect of d=0.46
across a broad range of task types, although the magnitude of the effect was
moderated by the complexity of the target material (from d=0.07 for complex
tasks, such as air-traffic control, to d=0.97 for simple motor tasks, such as
climbing a ladder). Wiseheart et al. (2019), who drew on the meta-analytic data
in Cepeda et al.’s (2006) work, reported an overall effect size of d=0.85 for
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Rogers Input Spacing and Task Repetition
distributed conditions over massed conditions in verbal learning tasks. Sim-
ilarly, Latimier et al. (2021) reported a large effect size for spaced retrieval
practice (g=0.74), and S. K. Kim and Webb (2022) reported a large effect
size for spaced L2 learning, with larger effects for delayed posttests (g=0.58
and 0.80 for immediate and delayed testing, respectively).
Main Theoretical Accounts of Spacing Effects in Learning
Three theoretical accounts have generally been included in most reviews of the
spacing literature, namely, deficient processing, encoding (or contextual) vari-
ability, and study-phase retrieval (see Delaney et al., 2010, for a more compre-
hensive discussion of unitary and hybrid theories of spacing effects in learn-
ing). Although none of these theories fully account for the available data re-
garding the distribution of practice effects (Wiseheart et al., 2019), all three
proposed mechanisms have been shown to influence the magnitude and/or
presence of distribution of practice effects in various ways; thus, they have
important implications for researchers in cognitive psychology and SLA.
Deficient processing posits that the effects of input spacing are a function
of the total amount of attentional processing of the target stimuli, with greater
amounts of attention resulting in superior learning and retention (Delaney
et al., 2010; Dempster, 1989; Toppino & Gerbier, 2014). Deficient process-
ing proposes that the second presentation of an item receives greater amounts
of processing in distributed conditions compared to massed conditions; that is,
massed presentations are processed at a deficient level relative to distributed
repetitions (Koval, 2019). Deficient processing accounts thus predict that ex-
perimental manipulations would be able to nullify the spacing effect if they
were successful in directing participants’ attention to spaced and massed items
equally, or may even result in a reverse spacing effect if attention could be
directed more toward massed items. Some research has suggested that this is
the case, both in cognitive psychology (e.g., Toppino et al., 2009; see Delaney
et al., 2010, for a review) and SLA (e.g., Uchihara et al., 2019, discussed be-
low). This finding has important theoretical and methodological implications
for the understanding of spacing effects in learning, which I will return to later
in this article.
Encoding, or contextual, variability posits that spacing increases the vari-
ability in memory representations, which provides more cues that can be used
for retrieval (Greene, 2008). Studies that have attempted to test the theory of
encoding variability, specifically the link between higher levels of contextual
cues and greater amounts of learning, have yielded mixed results, as changes
in contextual variability between repetitions sometimes result in poorer recall
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Rogers Input Spacing and Task Repetition
than when the contexts remain constant (for reviews, see Delaney et al., 2010;
Dempster, 1989; Toppino & Gerbier, 2014). In summary, similarly to the defi-
cient processing account, existing evidence suggests that encoding variability
may influence spacing either positively or negatively depending on the experi-
mental manipulation.
Study-phase retrieval posits that the act of retrieving information stored in
memory strengthens the representation and facilitates subsequent access to it
(Toppino & Bloom, 2002). As information stored in memory deteriorates over
time, the difficulty of retrieval increases, so that more effort is required to re-
trieve the information (Delaney et al., 2010). Effortful retrieval (as might be
seen with longer periods between retrieval attempts) is more beneficial than is
less effortful retrieval (such as when there is a relatively short period between
retrieval attempts). In this regard, longer spacing gaps can be more beneficial
because they result in desirable difficulties (Bjork et al., 2013); that is, they re-
sult in effortful cognitive processing, which facilitates learning. Because short
spacing might result in relatively small degrees of retrieval difficulty, it is not
optimal for retention. By contrast, long spacing may lead to information being
forgotten (retrieval failure), which does not support long-term memory (e.g.,
Toppino & Bloom, 2002). Hence, optimal spacing that is neither too short nor
too long may, in theory, yield desirably difficult retrieval that can lead to en-
hanced learning outcomes (see Y. Suzuki et al., 2019, for a L2-specific frame-
work for inducing desirable difficulties; see also Lightbown, 2008; Rogers &
Leow, 2020; Serfaty & Serrano, 2022; Y. Suzuki et al., 2020, for further dis-
cussions of desirable difficulties in L2 learning).
Methodology of Distributed Practice Research
Spacing research can be broadly categorized into two main groups in terms
of methodological and conceptual emphasis: the within-session and between-
sessions paradigms.
Within-Session Paradigm
The within-session paradigm is characterized by manipulations of spacing
across a short period, typically within a single training session. Multiple per-
mutations of this paradigm can be found within the literature. Some research
has examined the effects of the speed of presentation within a list, such as
when individual items on a list to be learned are presented at varying rates
(e.g., 1 s between items versus 2 s between items; e.g., Hovland, 1949),
thus varying the amount of time between items on a particular list on which
each item appears only once. Another permutation is having a single list that
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Rogers Input Spacing and Task Repetition
contains multiple repetitions of individual items and altering the distance of
repeat presentations of individual items. This can be achieved by having some
items repeated in close proximity and others repeated after a greater num-
ber of intervening items (see Delaney et al., 2010, for a review of spacing
studies utilizing a within-session design). The distribution of items is real-
ized via the presentation order of items within a list, that is, the number of
items between each presentation. Massed items are presented in immediate
succession with no intervening items between repetitions of individual target
items (e.g., AAAXXBBBXXCCC, where Xindicates a filler item), whereas
distributed items will have a number of intervening items, including filler
items, between each repetition of a target item (e.g., ABCXXABCXXABC,
where there are four intervening items between repetitions of each target
item).
Such an approach is advantageous because it allows for the entire study
to be completed in a single experimental session; however, it does not permit
the investigation of spacing effects over longer time scales. A variant of the
within-session paradigm examines blocked versus interleaved presentations.
Interleaving represents a form of spaced practice in which the emphasis is on
the sequence of items rather than on the amount of spacing between each pre-
sentation and the next. Blocked items are presented in a massed fashion, with
one complete set of items being followed by a complete set of another group of
items (such as AAABBBCCC), whereas an interleaved presentation alternates
between the items (such as ABCABCABC; Rohrer, 2012).
Between-Sessions Paradigm
In contrast to the within-session paradigm, the between-sessions paradigm is
better suited to examining the effects of spacing over longer periods of time.
In the between-sessions paradigm, material is studied and is then restudied on
one or more further occasions, with varying gaps between the different study
sessions. The simplest version consists of two learning sessions, whereas more
complex designs can include multiple learning sessions of identical or different
lengths, together with multiple rest intervals of identical or changing lengths
(e.g., Bahrick et al., 1993).
The amount of time between training sessions is referred to as the in-
tersession interval (ISI). In between-sessions designs, the ISI can be very
short, as would be the case in massed conditions, or could be longer, such
as 1 day or 1 week, as would be the case in distributed conditions. In stud-
ies that are concerned with the efficacy of learning, learning is assessed in
terms of the number of training sessions required to reach a certain criterion
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Rogers Input Spacing and Task Repetition
of learning (such as error-free recall), or the total number of errors produced
during the process of reaching this criterion (e.g., Ebbinghaus, 1885/1964).
In studies focusing on recall or retention, a testing session is conducted fol-
lowing the final training session (e.g., Cepeda et al., 2008). The amount of
time between the final training session and the testing session is referred
to as the retention interval (RI). The RI can be very short, with the test-
ing session taking place almost immediately following the final training ses-
sion, or longer, with testing occurring days, weeks, or months later, for in-
stance. By manipulating the RI, researchers are able to assess the degree to
which spacing influences the retention of the learned material over longer
periods, as well as possible interactions between the ISI and the RI, that is,
the degree to which the length of the ISI influences retention (for a review
of spacing studies utilizing a between-sessions paradigm, see Küpper-Tetzel,
2014).
Absolute Versus Relative Spacing
Several studies have utilized either the within-session or the between-sessions
paradigm to examine the effects of relative spacing, or how multiple ISIs are
distributed relative to each other, on learning and retention (see Balota et al.,
2007, for an early scoping review). Relative spacing schedules can be equal,
expanding,orcontracting. For example, the equal (or uniform) 3–3–3 schedule
has three units (trials or units of time) separating each presentation of a target
item. In an expanding 1–3–5 schedule, the first, second, and third ISIs are 1,
3, and 5 days (or other units), respectively. By contrast, a contracting 5–3–1
schedule decreases ISIs from 5 days to 3 days to 1 day. Comparisons of dif-
ferent relative spacing schedules must control for the total amount of ISIs in
a training phase, which is described as absolute spacing (Balota et al., 2007;
Nakata, 2015). For example, a 3–3–3 schedule and a 1–3–5 schedule both re-
sult in a total of nine units (3 +3+3=1+3+5=9), unlike a 5–5–5
schedule versus a 1–3–5 schedule (5 +5+5=15 >1+3+5=9). Studies
with two or more sessions differ in terms of focus (absolute or relative spac-
ing) and complexity, with additional experimental sessions involving multiple
additional variables (such as the number of training sessions) that may interact
with absolute or relative spacing to influence learning outcomes. Despite these
differences, fundamental theoretical accounts of absolute and relative spacing
effects are the same and draw upon the same theoretical models (i.e., deficient
processing, contextual variability, and/or study-phase retrieval) to explain their
benefits toward learning and retention (Gerbier & Toppino, 2015).
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Rogers Input Spacing and Task Repetition
One Spacing Effect or Many Effects?
Although the distributed practice effect is generally discussed as a unitary con-
struct, Toppino and Gerbier (2014) noted that the spacing effect (that is, a com-
parison between a massed and a spaced condition) is undeniably general and
robust, whereas lag effects (that is, comparisons between two spaced condi-
tions) are more difficult to define and demonstrate. In studies that have inves-
tigated both spacing effects and lag effects, a common finding is that spaced-
condition groups outperform the massed-condition group with relatively large
effect sizes (d1.0), but there is little to no difference in performance be-
tween those in the different distributed conditions (e.g., Cepeda et al., 2008,
2009). This finding also received some support in a recent meta-analysis of L2
learning studies in which no lag effects were found in studies examining ISIs
greater than 1 day in length (S. K. Kim & Webb, 2022). This finding, as well as
the spacing literature in the broader field of SLA, is the focus of the following
section.
Spacing Effects in Second Language Acquisition
Spacing studies in SLA have grown organically out of interest in the effec-
tiveness of various L2 curricula, and it is only recently that SLA researchers
have begun to draw more closely on the psychological literature. Early interest
in spacing effects in SLA can be traced back to work by Lightbown (1998),
as well as to well-known curricular-level investigations into the effectiveness
of intensive language programs (e.g., Lightbown & Spada, 1994; see Serrano,
2012, for a review). Following Bird’s (2010) study on spacing effects on L2
grammar learning, there has been a dramatic increase in the number of studies
examining the effects of spacing on the learning of grammar (e.g., Kasprow-
icz et al., 2019; Rogers, 2015; Serfaty & Serrano, 2022; Y. Suzuki, 2017; Y.
Suzuki & DeKeyser, 2017), and vocabulary (e.g., Koval, 2022; Nakata, 2015;
Nakata & Elgort, 2021; Rogers & Cheung, 2020, 2021; Serrano & Huang,
2018, 2021). More recently, research has also begun to examine how spacing
interacts with task repetition to influence task performance and L2 develop-
ment (Bui et al., 2019; Kobayashi, 2022; Y. Suzuki, 2021; Y. Suzuki et al.,
2022; Y. Suzuki & Hanzawa, 2022).
Taking this body of literature as a whole, the findings of spacing studies
in SLA have not provided clear support for the superiority of longer spaced
conditions over shorter spaced conditions. For example, curricular-level
studies have provided support for more intensive teaching/learning schedules
over more distributed conditions (e.g., Collins & White, 2011; Serrano, 2011;
Serrano & Muñoz, 2007). Experimental SLA studies have returned mixed
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Rogers Input Spacing and Task Repetition
results, with some studies finding support for distributed learning conditions
(e.g., Bird, 2010; Rogers, 2015) and other studies finding advantages for
more intensive conditions (e.g., Rogers & Cheung, 2020; Y. Suzuki, 2017).
Explanations for these equivocal findings have included theoretical ones (Y.
Suzuki, 2021) and methodological ones (Rogers, 2021; see the discussion of
testing effects below).
Two recent meta-analyses of SLA research (S. K. Kim & Webb, 2022;
Uchihara et al., 2019) have also highlighted the complicated nature of spac-
ing effects in L2 learning. Uchihara et al. (2019) conducted a meta-analysis
of correlational studies that examined repetition effects in incidental vocab-
ulary learning from the reading of texts. This study revealed a stronger re-
lationship between the frequency with which target L2 items occur in texts
and the subsequent learning of the items when repeated readings occurred in a
massed fashion (r=.38, a medium-sized effect) rather than in a spaced fashion
(r=.23, a small-sized effect). The authors hypothesized that this finding was
due to salience; specifically, that the massed encounters led to increased famil-
iarity with the text content, thus allowing learners to shift their attention away
from content toward the unknown vocabulary items. Considered in the light of
theoretical explanations of spacing effects in learning, this finding is intrigu-
ing because it appears that incidental learning conditions might result in the
deficient processing of target lexical items under spaced conditions—a finding
that runs in the opposite direction to predictions in the broader literature on
spacing.
A more recent meta-analysis by S. K. Kim and Webb (2022) directly exam-
ined the effects of spacing on L2 learning. Similarly to previous meta-analyses
of the broader literature on spacing (e.g., Cepeda et al., 2006; Donovan & Ra-
dosevich, 1999; Wiseheart et al., 2019), this study indicated a medium-to-large
effect of spacing (massed vs. spaced practice) on L2 learning in both immedi-
ate posttests (g=0.58, 95% CI [0.16, 1.00]) and delayed posttests (g=0.80,
95% CI [0.44, 1.17]). This result supports previous claims in cognitive psy-
chology regarding the general benefits of spacing for learning and retention,
most notably that the benefits of spacing are most apparent in delayed testing
(Cepeda et al., 2006; Wiseheart et al., 2019). This finding compared spaced
versus massed practice. Lag effects (comparing shorter vs. longer spacing con-
ditions) were not found in the immediate tests (g=−0.15, 95% CI [0.37,
0.06]), and only a small effect was observed at delayed testing (g=0.40, 95%
CI [0.16, 0.64]).
I will now highlight more findings by S. K. Kim and Webb (2022) that
are pertinent to this review. With respect to studies using between-sessions
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Rogers Input Spacing and Task Repetition
methodologies (i.e., multiple sessions), further moderator analyses did not re-
veal significant differences for different spacing conditions in immediate or
delayed testing. This finding indicates that longer spaced conditions did not
result in greater learning or retention compared to shorter spacing conditions
when ISIsexceeded 1 day (e.g., 1-day ISI vs. 7-day ISI). This finding cor-
roborates previous observations concerning the robustness of spacing effects
(advantages for spaced conditions over massed conditions) but not lag effects
(advantages for longer lags over shorter lags; Gerbier & Toppino, 2015). How-
ever, as noted in the sections above, ISIs interact with RIs to influence learning
and retention. S. K. Kim and Webb’s (2022) analyses did not examine this in-
teraction, nor did they code primary studies for optimal ISI–RI ratios. Thus,
this may be one potential explanation for this finding.2
Furthermore, the moderator analysis of frequency effects revealed that,
when comparing shorter and longer spaced conditions, increased frequency
of practice (e.g., the number of trials within a single learning session) was as-
sociated with increased effects for shorter spacing. This finding points toward
potential benefits of intensive learning conditions for L2 learning, particularly
in the initial stages of learning. With regard to SLA, this finding might be re-
lated to the results of the curricular-level studies outlined above, and might
indicate that the criterion of learning—that is, the quantity and/or quality of
the knowledge established during the initial learning session—may also inter-
act with spacing intervals (Rawson & Dunlosky, 2011; Toppino et al., 2018;
discussed further below). In the next section, I shift focus to review the is-
sue of task repetition before discussing theoretical rationales regarding why
input spacing might moderate the effects of task repetition. This is followed
by a discussion of existing studies that have examined spacing effects in task
repetition.
Task Repetition
A task “is an activity which requires learners to use language, with emphasis
on meaning, to attain an objective” (Bygate et al., 2001, p. 11). Task repeti-
tion refers to “repetitions of the same or slightly altered tasks—whether whole
tasks, or parts of a task” (Bygate & Samuda, 2005, p. 43), and is often divided
into exact task repetition and procedural (or task type) repetition. Exact task
repetition involves performing the same task more than once. In other words,
there is repetition of the same task type with the same content. Procedural rep-
etition involves repeated performance of the same task type, but with different
content. An example of exact task repetition would entail learners perform-
ing the same task on multiple occasions, such as an information-exchange task
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Rogers Input Spacing and Task Repetition
about hosting an American friend. A contrasting example of procedural repe-
tition would entail learners performing three information-exchange tasks with
different content, such as (a) hosting an American friend, (b) describing school
events or activities, and (c) discussing mayoral candidates (Y. Kim & Tracy-
Ventura, 2013). A growing body of research has now provided solid evidence
indicating the positive effects of both exact and procedural oral task repeti-
tion across a range of task types and learner populations (e.g., Lambert et al.,
2017; Mackey et al., 2007; see Li & Rogers, 2021, for a bibliography of task
repetition research in SLA).
Theoretical rationales for the benefits of oral task repetition are typically
linked to learners’ conscious or unconscious attentional processes and to ar-
ticulating how these attentional processes may shift as a result of each subse-
quent task repetition. First, with regard to oral task performance, researchers
often draw on Levelt’s (1989) and Kormos’s (2006) speech production mod-
els, which include the three main stages of conceptualization, formulation,
and articulation. Conceptualization involves the creation of a preverbal mes-
sage; formulation involves the encoding of the preverbal message in linguistic
form; and articulation involves the production of the speech utterance. When
learners perform tasks for the first time, they devote considerable attentional
resources to the conceptualization of the intended message. Subsequent task
performances will be likely to require fewer attentional resources at the con-
ceptualization stage, as learners will be more familiar with the task content
and the intended message, thus allowing for additional attentional resources to
be shifted to the formulation and articulation stages. Following this model, ex-
act task repetition is predicted to be more beneficial than procedural repetition
because exact task repetition will require fewer attentional resources at the con-
ceptualization stage (Bygate, 1996; Fukuta, 2016; Skehan, 2018). These bene-
fits may depend, however, on how researchers operationalize task performance.
Research in cognitive psychology has indicated that variability in practice has
benefits for the generalizability of knowledge (DeKeyser, 2020). In this sense,
given adequate practice, procedural repetition might be predicted to lead to
better performance than exact task repetition on a novel test task of same task
type.
A second theoretical rationale for the benefits of task repetition is related
to L2 development; specifically, it is proposed that task repetition may facili-
tate implicit learning via structural and lexical priming mechanisms. Priming
refers to the phenomenon of prior exposure to, or production of, specific lin-
guistic forms that results in the activation of these forms, which affects subse-
quent comprehension and/or production (Trofimovich & McDonough, 2011).
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Rogers Input Spacing and Task Repetition
Priming results in a tendency for the learners to use similar structures in sub-
sequent utterances (Chang et al., 2000); when a task is repeated, priming ef-
fects might influence task performance because learners have the opportunity
to draw on activated lemmas and their associated elements, such as syntac-
tic structures and phonological information, thus decreasing the demands on
formulation processes (Skehan, 2018). Priming may facilitate not only L2 per-
formance in subsequent tasks, but also L2 development in a number of ways.
First, both task repetition and priming are associated with the reuse of con-
structions, which has been linked theoretically (N. C. Ellis & Wulff, 2020) and
empirically (De Jong & Perfetti, 2011; De Jong & Tillman, 2018; Y. Suzuki
et al., 2022) to L2 fluency development. Second, it has been argued that prim-
ing may involve additional mechanisms in addition to simple activation, and
that it may also represent a form of implicit learning because it may represent
long-term changes in the L2 interlanguage system. In other words, each reuse
of a lexical or syntactic unit across task repetitions can raise the activation level
of these units and can constitute an implicit learning opportunity (e.g., Chang
et al., 2000).
Although there is growing consensus that task repetition is beneficial for L2
performance and (potentially) for L2 development (Bygate, 2018), a number of
conceptual and methodological issues obscure the interpretations of previous
empirical findings (DeKeyser, 2018). One such issue is the spacing between
task repetitions. A range of temporal gaps between task performances has been
used in the literature, from immediate task repetition (e.g., Sun & Révész,
2021) to days (e.g., Gass et al., 1999) or weeks (e.g., Bygate, 2001). These
gaps often appear to have been chosen arbitrarily (DeKeyser, 2018).3Across
the task repetition literature, the spacing between task repetitions has not been
adequately controlled for, and studies have only recently begun to examine
the effects of spacing on L2 task performance and L2 development systemati-
cally. In the following sections, I first review theoretical explanations for how
spacing between task repetitions might influence task performance and L2 de-
velopment. I then discuss the extant research on task repetition that has directly
manipulated input spacing as an independent variable.
How Might Input Spacing Influence the Effects of Task Repetition on
Task Performance?
There are theoretical rationales, as well as some incipient empirical evidence,
indicating that input spacing influences task performance and, potentially, L2
development. If a relatively short gap exists between task repetitions, there
will be a decrease in the attentional demands with regard to the processes of
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Rogers Input Spacing and Task Repetition
conceptualization and formulation during subsequent performances (Bygate &
Samuda, 2005; Lambert et al., 2017; Skehan, 2018). When the gaps between
repetitions are longer, we may expect the memory of the first performance to
fade as time passes, and the benefits of task repetition to decrease as longer
stretches of time pass.
With regard to L2 development, let us consider the underlying knowledge
and skills that may be used and developed during oral task practice. Task repe-
tition may facilitate fluency development via lexical access and automatization
processes. Numerous empirical studies of task repetition have shown that L2
learners were able to increase the fluency of their performances when repeat-
ing oral tasks (De Jong & Perfetti, 2011; De Jong & Tillman, 2018; Y. Suzuki,
2021; Y. Suzuki et al., 2022; Y. Suzuki & Hanzawa, 2022). In addition, as the
sections below will demonstrate, spacing between task repetitions appears to
further influence learners’ performance on indices of oral task fluency. These
benefits of practice may not simply reflect L2 performance but may also be
beneficial for L2 development. For example, studies investigating the relation-
ship between cognitive and utterance fluency have found a relationship be-
tween lexical retrieval speed and/or the efficiency of grammatical encoding,
on the one hand, and fluent spoken performances, on the other (De Jong et al.,
2012; Kahng, 2020; S. Suzuki & Kormos, 2022). Thus, the demonstrated ben-
efits of task repetition regarding utterance fluency may also enhance the speed
and ease of lexical access and contribute to the automatization of grammatical
encoding processes, though additional research is needed to establish whether
these are necessarily causal relationships.
Empirical Studies
I will now discuss the empirical studies that have examined spacing in task
repetition, namely, the works of Bui et al. (2019), Y. Suzuki (2021), Y. Suzuki
et al. (2022), Kobayashi (2022), and Y. Suzuki and Hanzawa (2022). In these
studies, a general trend is that massed practice, or spaced practice with short
ISIs, appears to lead to increased fluency when compared to distributed prac-
tice.
Bui et al.’s (2019) study was the first to manipulate spacing in task repe-
tition directly; the authors investigated spacing effects in exact task repetition
on adult learners’ task performances in a picture-description task. The partici-
pants (university students) completed the task twice, and the spacing between
the repetitions was manipulated between participants according to five differ-
ent conditions: immediate, a 1-day interval, a 3-day interval, a 1-week interval,
and a 2-week interval. Performance was measured across a range of measures
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Rogers Input Spacing and Task Repetition
of complexity, accuracy, and fluency. The results of this study were intriguing
because they indicated different benefits accruing from spacing length depend-
ing on the measure used. Specifically, the results indicated that massed task
repetition might be more beneficial in terms of speed fluency, whereas longer
gaps, specifically 1-week intervals, might be more beneficial in terms of com-
plexity and repair fluency, although it should be noted that the magnitude of
spacing effects was relatively small (both η2=.05).
Y. Suzuki (2021) examined the effect of blocked versus interleaved pro-
cedural task repetition practice on the development of L2 fluency. In this
study, the participants—university students with Japanese as their first lan-
guage (L1)—engaged in task repetition practice for three different narrative
tasks (A, B, and C). The repetitions were performed over a period of 3 days
under either blocked conditions (Day 1: A–A–A; Day 2: B–B–B; Day 3: C–C–
C) or interleaved conditions (Day 1: A–B–C; Day 2: A–B–C; Day 3: A–B–C).
The participants also completed pretests and 1-day delayed posttests, which
examined transfer via novel tasks that were not included in the training phase.
Performance was measured via a battery of fluency measures. The results of
this study indicated that blocked practice led to more fluent performance dur-
ing the training stage of the study, with medium-to-large-sized effects (ηp2=
.06–.37), and that the improvements in some measures, notably the articulation
rate and the midclause pause duration, were also transferred to novel tasks, also
with medium-sized effects (ηp2=.08–.10).
Y. Suzuki et al. (2022) reanalyzed Y. Suzuki’s (2021) data to investigate
how spacing schedules might influence the reuse of lexical constructions, and
found that blocked practice led to more instances of the reuse of lexical con-
structions, with large-sized effects (d=.85–1.24). They also found positive
relationships between the reuse of constructions and fluency changes during
training and between the reuse of constructions and fluency changes from
pretest to posttest. With regard to the interleaved condition, the reuse of con-
structions was also associated with improvements in some aspects of fluency,
particularly speed and breakdown fluency, but was associated with increased
effort in the form of increases in midclause and clause-final pauses. This study
provides additional evidence highlighting the mechanism whereby more inten-
sive learning conditions might benefit L2 fluency development because they
influence the reuse of constructions and this reuse is associated with increased
proceduralization processes.
Kobayashi (2022) examined the effects of input spacing on narrative
task performance. In this study, learners (N=38, L1 Japanese) engaged in
procedural task repetition of oral narratives (cartoon prompts) under either
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Rogers Input Spacing and Task Repetition
massed (immediate repetition) or spaced (1-week ISI) conditions. Spacing was
manipulated as a between-participant variable. The effects of spacing were
gauged by first comparing learners’ performances in the two training tasks, and
then comparing performance changes in the pretest and posttests, which con-
sisted of novel tasks. The results did not reveal significant differences between
the massed and spaced conditions in measures of accuracy, complexity, flu-
ency, or lexical variety in the first and second task performances. The analyses
of pretest and posttest data indicated a significant improvement for the spaced
group over the massed group with regard to lexical variety, with a small-sized
effect (η2=.064). No other significant differences were found.
Finally, Y. Suzuki and Hanzawa (2022) examined the effects of input spac-
ing on the development of L2 fluency. In this study, 79 learners of English
(university students, L1 Japanese) were assigned to massed, short-spaced, long-
spaced, or control conditions. The participants in the massed condition com-
pleted an oral narrative task (an ordered picture story) six times in a single
classroom session. The participants in the short-spaced condition completed
the narrative task three times, followed by a 45-min break, then three more
times. The participants in the long-spaced condition completed the task three
times, followed by a 1-week ISI, before completing the task three more times.
The students also participated in pretests, immediate posttests, and 1-week de-
layed posttests with novel oral narrative prompts. The results of this study in-
dicated that the massed conditions led to greater fluency across the training
stage, as well as in the immediate posttests with medium-to-large effect sizes
(ηp2=.08–.14), but no significant differences were observed across the differ-
ent spacing schedules in the delayed posttests.
Taken together, the results of these studies reveal that the spacing be-
tween task repetitions influences task performance and, potentially, L2 devel-
opment. This finding indicates that input spacing, as a variable, needs to be
taken into account in task repetition research. Given acceptance of the above
argument that input spacing affects task repetition, there is also a need to con-
sider other important methodological variables that can moderate the effect
of input spacing on task repetition. The following section reviews a number
of these methodological variables, specifically the interaction of spacing in-
tervals, the experimental manipulation of posttest(s), the number of repeti-
tions, absolute versus relative spacing, the criterion of learning, and blocked
versus interleaved practice. These variables will be illustrated with examples
from the task repetition literature where possible and as appropriate. Because
relatively few empirical task repetition studies have directly examined the ef-
fects of spacing, and to illustrate a previous point that spacing may serve as a
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Rogers Input Spacing and Task Repetition
confounding variable to account for the inconsistent findings across the task
repetition literature, this section will also include examples of task repetition
studies that have not directly manipulated spacing as part of their experimental
design.
Methodological Considerations
Interaction Between Intersession Intervals and Retention Intervals
Research on input spacing in the field of cognitive psychology has long sought
to identify the optimal gap between training sessions (ISI) with regard to how
well the studied material is retained after various periods of delay (RIs). An
important finding in this strand of research is that the optimal length of the ISI
interacts with the length of the RI (Delaney et al., 2010), with some studies
indicating that the optimal ISI was approximately 10 to 40% of the RI (Cepeda
et al., 2008). In other words, if the posttest is administered 30 days after the last
training session, an ISI of 3 days (30 days ×10%) to 12 days (30 days ×40%)
may yield the highest retention. Cepeda et al.’s (2008) study also suggested
that the optimal ISI:RI ratio decreased as the RI increased. Specifically, for a
1-week RI, the optimal ISI:RI ratio was 20 to 40%, whereas for a 350-day RI,
the optimal ISI:RI ratio was 5 to 10%, although the results also depended on
the nature of the assessment task (e.g., recall vs. multiple choice).
To the best of my knowledge, no task repetition study has directly ex-
amined whether the optimum ISI:RI ratios apply to oral task repetition. The
oral task repetition studies that have utilized delayed posttests (e.g., De Jong
& Perfetti, 2011; Y. Suzuki, 2021) have not explicitly justified their timing
in view of optimal ISI:RI ratios, and often have what appear to be nonopti-
mal ISI:RI ratios based on Cepeda et al.’s (2008) model. For example, Khezr-
lou’s (2021) task repetition study utilized a 1-day ISI with a 21-day RI. In this
case, the ISI:RI ratio (<5%) falls outside of the optimum range for shorter
lags.4
SLA research to date that has set out to test the optimal ISI:RI ratios has
had decidedly mixed results (Rogers, 2021), and so it is unclear if the optimal
ISI:RI ratio identified by Cepeda et al. (2008) is necessarily optimal for SLA.
If testing ISI:RI ratios is not adopted as a research question in future research
on task repetition, researchers might still consider the ISI:RI ratio as part of
their experimental design in order to prevent their posttests from falling far
outside of the optimal ratio. For example, if using massed task repetition, then
a posttest following a long delay may be suboptimal.
A final point regards reporting practices. As the available evidence sug-
gests that the spacing between task repetitions influences task performance
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Rogers Input Spacing and Task Repetition
and that the gap between training and testing may also influence final test
performance, task repetition researchers should strive to report the exact
amount of time between task repetitions (e.g., 10 min, 1 day, 2 days) as well as
the exact time between the final task repetition and test, if applicable. Doing
so will facilitate future efforts at replication and synthesis of data across the
task repetition literature (Plonsky, 2013).
Manipulation of Posttests or Transfer Tasks
Another methodological issue concerns the use of multiple posttests. In numer-
ous task repetition studies, learning transfer has been measured via novel tasks
that were not included in the training phase of the experiment (e.g., Y. Kim
& Tracy-Ventura, 2013; Y. Suzuki, 2021). For example, Y. Kim and Tracy-
Ventura (2013) counterbalanced three novel tasks as a pretest, a 1-day delayed
Posttest 1, and a 2-week delayed Posttest 2. As the posttests comprised novel
tasks involving the same procedure but different content from the training
phase, and from each other, they can be classified as procedural task repetition.
Numerous studies have shown that procedural task repetition influences per-
formance and language development (e.g., Skehan, 2018). Thus, it is intuitive
that, in a design with multiple posttests (e.g., a standard pretest–immediate
posttest–delayed posttest design), the act of performing the first posttest may
influence the performance in subsequent posttests via a testing effect.
Input spacing studies in cognitive psychology typically manipulate their
posttest sessions as a between-participant variable; that is, each participant
(or group of participants) only participates in a single posttest (e.g., Cepeda
et al., 2008; for discussions, see Rogers, 2021; Y. Suzuki, 2017). This is in
contrast to the typical practice in the field of SLA, in which posttest sessions
are manipulated as a within-participant variable; that is, all the participants
take part in all the testing sessions. The theoretical rationale for manipulat-
ing posttests between participants is that retrieval mechanisms influence learn-
ing via a testing effect (Roediger & Karpicke, 2006; Yanagisawa & Webb,
2021). This means that successful retrieval during the initial testing (e.g., in an
immediate posttest) would influence the performance in subsequent posttests,
thus obscuring whether any differences in performance in delayed tests were
confounded by a testing effect. As studies using models of L2 speech per-
formance also suggest that some retrieval mechanisms and/or priming effects
might be involved via formulation processes in L2 speech production (e.g.,
Fukuta, 2016; S. Suzuki & Kormos, 2022), future researchers might take note
of potential testing effects in their experimental designs to control for this po-
tential confound more robustly.
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Rogers Input Spacing and Task Repetition
Number of Repetitions
As noted, the vast majority of empirical spacing studies have utilized two train-
ing sessions as part of their experimental designs (with some exceptions; see
below). In the broader literature, very little research has explored the interac-
tion between the number of training sessions and the magnitude of the distri-
bution of practice effects. This is curious given that, from its origin, the advan-
tages of spacing have been hypothesized to be associated with a considerable
number of repetitions” (Ebbinghaus, 1885/1964, p. 89; emphasis mine). How-
ever, this does not mean that the spacing effect is only present when learners
engage in a task “a considerable number of times,” as the vast spacing liter-
ature can attest. What research there is on increasing the number of training
sessions suggests that there is a positive relationship between the number of
training sessions and the magnitude of spacing effects on long-term retention
(Bahrick et al., 1993). This effect can theoretically be linked to the repeated
retrieval opportunities associated with additional training episodes (Toppino
& Gerbier, 2014).
Task repetition studies have typically included two, three, or four task rep-
etitions as part of their experimental designs, although some studies have in-
cluded up to 11 (e.g., Ahmadian, 2011). In the literature on task repetition, it is
clear that the number of repetitions can influence performance. For example,
in Sample and Michel’s (2014) study involving young children, task repetition
improved task performance, although the quality of the performance fluctu-
ated between the second and third repetitions. It is of interest that the trade-off
effects between structural complexity and accuracy/fluency were observed dur-
ing the first and second task iterations but disappeared during the third itera-
tion. This finding suggests that the attentional demands of the task decreased in
the third performance; specifically, the third task performance led to the faster
and smoother retrieval of primed linguistic information. Lambert et al. (2017),
who asked learners to repeat tasks six times within a single lesson, observed a
sharp increase in speech rate across the first three performances, followed by
gradual increases in performances four to five and, finally, a leveling off in per-
formances five to six, suggesting diminishing returns across multiple instances
of task repetition.
What remains an open empirical question, however, is how spacing might
interact with the number of repetitions to influence task performance and L2
development. One avenue for future researchers to explore this possible inter-
action would be an approximate or conceptual replication of an existing task
repetition study with additional spacing conditions. For example, in Lambert
et al.’s (2017) original study design, the participants performed the task six
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Rogers Input Spacing and Task Repetition
times in a single “massed” session. A conceptual replication of this study might
also include additional “spaced” experimental groups: for example, massed
session versus 1-day ISI versus 7-day ISI. By carrying out a conceptual repli-
cation in this manner, researchers would be able to examine the interaction
between spacing and number of task repetitions, as well as provide evidence
toward the generalizability of the original findings.
Absolute Versus Relative Spacing
Relative spacing refers to experimental designs that include multiple ISIs and
examine the effects of how these intervals are distributed in relation to each
other, whether in equal, expanding, or contracting schedules. According to
theoretical models of spacing, such as study-phase retrieval and the desirable
difficulty framework, expanding spacing conditions should result in superior
learning outcomes, although conclusive evidence is currently lacking (Latimier
et al., 2021). The initial short ISIs that are characteristic of expanding sched-
ules (e.g., 1–3–5 instead of 3–3–3 or 5–3–1) might aid successful retrieval
on the second presentation of an item and strengthen retrieval in subsequent
presentations after longer ISIs, which pose greater retrieval difficulties (Bjork
et al., 2013). By contrast, the longer initial ISIs in contracting or equal spac-
ing schedules (such as 5–3–1 or 3–3–3 opposed to 1–3–5) decrease the odds
of successful retrieval during the second presentation. It should also be noted
that expanding schedules containing tasks that are too easy may not induce
desirable difficulties. In this case, it could be expected that equal or contract-
ing schedules may result in greater learning advantages (Gerbier & Toppino,
2015). Relative spacing schedules might also be particularly important for L2
language development because they may facilitate retrieval via lexical and syn-
tactic priming (Bygate, 2001; Bygate & Samuda, 2005; De Jong & Perfetti,
2011).
No task repetition study has systematically examined absolute versus rela-
tive spacing. This is an important point that requires empirical testing, as the
combined size of all ISIs (i.e., absolute spacing) within an expanding schedule
may mediate any observed benefits of the expanding schedule. From a cur-
ricular perspective, research might explore the question of how tasks might be
best utilized and sequenced according to equal, contracting, or expanding spac-
ing schedules. Such research might explore how absolute and relative spacing
schedules might be integrated into existing theoretical frameworks of task se-
quencing (e.g., Robinson, 2022).
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Rogers Input Spacing and Task Repetition
Criterion of Learning
Research in the field of cognitive psychology has identified the criterion of
learning in the initial training session as an important variable in the effects
of spacing. As a practical illustration, students studying via flashcards do not
typically study for a fixed number of trials, but until a desired level of per-
formance has been reached (Kornell & Bjork, 2007). In other words, students
do not cycle through the flashcard deck twice, but cycle through it until they
are able to recall each item successfully, that is, until they have attained the
learning criterion.
In a seminal series of experiments, Rawson and Dunlosky (2011) manip-
ulated the criterion of learning in the initial study session (one to four correct
retrievals of each target item), as well as the number of subsequent relearning
sessions (one to five). Learning was measured via a range of posttests up to
4 months later. The results indicated that increases in the criterion level (from
one to four correct retrievals in the initial study) and increases in the number
of restudy sessions were associated with greater learning outcomes. An inter-
esting further finding was that the effects of meeting the initial criterion level
diminished as the number of relearning sessions increased. This makes sense
intuitively, because learners who do not take part in relearning will benefit
from a criterion for learning that is initially higher. Based on the overall data,
the authors concluded that the most cost-efficient and effective method was to
study to a criterion of three correct recalls in the initial study session and to
follow this with three learning sessions at widely spaced intervals.
In a different study, Toppino et al. (2018) examined whether the degree of
initial training moderated spacing effects across expanding, equal, and con-
tracting schedules. As part of this study, university students studied 72 L1
word–pseudoword word pairs (e.g., jacketproome). The initial training level
was manipulated between participants, and contracting (11–1), expanding (1–
11), and equal (6–6) practice schedules were manipulated within participants.
Participants in the low-training condition engaged in two study trials of the
target items, whereas participants in the high-training condition engaged in
one round of study followed by five rounds of practice testing with correc-
tive feedback. A final cued-recall test was administered following a 2-week
RI. The results of this study indicated a significant advantage of the expanding
schedule over the uniform and contracting schedules at low levels of train-
ing; however, there were no significant differences at higher levels of train-
ing. The results provided a possible methodological explanation for the in-
consistency in previous findings of studies examining equal versus relative
spacing.
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Rogers Input Spacing and Task Repetition
Overall, these studies point toward the influence of the level of learning
and/or performance in the initial learning session on the effectiveness of sub-
sequent learning sessions and later retention. However, the question that then
arises is how to operationalize the criterion of learning in studies of task repe-
tition: henceforth, criterion of task performance. One potential avenue would
be to examine task completion as a variable. For example, Sample and Michel
(2014) used a spot-the-differences task; over three iterations, each repetition
led to the participants identifying more differences. This is clearly of pedagog-
ical interest because it indicates that task repetition will benefit students who
are not able to complete a task successfully on the initial attempt. Researchers
might also consider other criteria of task performance such as a defined thresh-
old of complexity, fluency, or accuracy.
Another avenue for researchers to investigate might be the strategic ma-
nipulation of task difficulty and/or task complexity. For example, in a task
repetition study with child learners, Pinter (2007) allowed the learners to
complete the initial task performance in their L1 before performing the task
three times in the L2. From a pedagogical perspective, the performance of
the first task served as a form of scaffolding for younger learners, to accli-
matize them to the procedure and content of the task to be completed. From
a theoretical perspective, the performance in the first task might be viewed
as a form of pretask planning because the initial task repetition, in which
the task demands were arguably lower, was then followed by repetitions of
more complex tasks (Skehan, 2018). Although a number of task-based studies
have examined the impact of cognitive task demands, for instance, by com-
paring task performances across tasks with different levels of difficulty (e.g.,
Michel et al., 2019) in order to test the predictions of Skehan’s (2009) trade-
off hypothesis and/or the cognition hypothesis (e.g., Robinson, 2022), to my
knowledge, no study has examined how spacing might influence the crite-
rion of task performance or the interaction between task complexity and these
variables.
Blocked Versus Interleaved Practice
In the literature on task repetition, one study (Y. Suzuki, 2021) aimed to di-
rectly examine the effects of blocked versus interleaved task repetition on task
performance. Y. Suzuki (2021) examined the repetition of procedural tasks; in
this study, the participants performed three different oral narrative tasks over 3
days under blocked conditions (Day 1: A–A–A; Day 2: B–B–B; Day 3: C–C–
C) or interleaved conditions (Day 1: A–B–C; Day 2: A–B–C; Day 3: A–B–C).
Future research might utilize other combinations; for example, a longitudinal
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Rogers Input Spacing and Task Repetition
study might compare six iterations of exact task repetition (A–A–A–A–A–A)
versus procedural repetition (A–B–C–D–E–F) versus a hybrid condition (A–
A–A–B–C–D), or blocked (A–A–A–B–B–B–C–C–C) versus interleaved (A–
B–C–A–B–C–A–B–C) versus hybrid (A–A–B–B–A–B–C) conditions (see
Nakata & Suzuki, 2019, for similar approaches to examining the learning of
L2 grammar). Such research might prove valuable for practitioners and cur-
riculum designers in considering how tasks might be optimally arranged in a
single lesson, across a syllabus, or across a broader curriculum.
As to the relevance of interleaving for task repetition research methodol-
ogy, there are numerous examples of task repetition studies that have included
interleaved manipulations of tasks as part of their experimental designs. For
example, Lambert et al. (2017) used three different one-way task types (in-
struction, narrative, and opinion) and a two-way dialogue task. The partici-
pants performed all four tasks in pairs before changing partners and repeating
all four tasks until six repetitions were complete. To control for task-order ef-
fects, Lambert et al. counterbalanced the order of the tasks across participants
(e.g., A–B–C–D, B–C–D–A, C–D–B–A, or D–A–B–C).
Like Lambert et al. (2017), task repetition researchers whose experiments
include multiple tasks should control for task-order effects by counterbalanc-
ing the order of the tasks across the participants and/or experimental groups,
for instance by using a Latin-square design. In addition, research on interleav-
ing suggests that it is not only the sequence of the tasks that should be taken
into consideration, but also the fact that there is a sequence. Thus, depending
on the research questions, researchers may wish to include a comparison or
blocked task condition, in order to disentangle the effects of task repetition
from the effects of interleaving.
Conclusion
The use of tasks remains a central issue in L2 research and pedagogy
(R. Ellis et al., 2019). The use of task repetition as a way of promoting L2 de-
velopment has received increased attention and research focus over the past 20
years, including some preliminary interest in applying the findings from cog-
nitive psychology, particularly those concerning the effects of input spacing
on task performance and L2 development. The purpose of this article was to
promote research on this topic, and to promote more robust research designs
by highlighting a number of methodological variables that have been shown
to influence the magnitude of spacing effects in the wider psychological lit-
erature. It is hoped that the discussion in this article will help researchers of
task repetition to make more informed methodological decisions as part of the
Language Learning 00:0, xxxx 2022, pp. 1–30 22
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Rogers Input Spacing and Task Repetition
process of planning their experimental designs, and will raise additional re-
search questions that future research might investigate.
Final revised version accepted 22 July 2022
Notes
1 Hedges’s gand Cohen’s dbelong the same family of effect sizes. Hedges’s ghas
been corrected for small samples (n<20), whereas Cohen’s dhas not. The
functional difference between Hedges’s gand Cohen’s dis very small, and they may
be interpreted according to the same benchmarks (e.g., 0.2 =a small-sized effect,
etc.). See Lakens (2013) for a more in-depth discussion of the calculations for and
differences and similarities between Hedges’s g, Cohen’s d, and other effect sizes.
2 I would like to thank an anonymous reviewer for highlighting this point.
3 Imprecise or incomplete reporting practices further obscure this issue in some
cases.
4 The calculation here is the most conservative based on the information available in
the article.
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... The advantage of spaced learning over massed learning is referred to as the spacing effect. A separate but related phenomenon to the spacing effect is the lag effect (Rogers, 2017(Rogers, , 2022. The lag effect is concerned with the question of whether varying interval lengths between exposures have differential effects on retention (e.g., whether longer spacing between repetitions facilitates learning more than does shorter spacing). ...
Article
Research has suggested that long spacing (i.e., temporal intervals) within a training session facilitates second language vocabulary learning. Studies, however, have been limited to treatment that involved sessions for only initial learning but not subsequent relearning. Furthermore, most studies have investigated only the benefits of spacing without considering its potential costs (i.e., increased duration of the treatment). In our study, we examined the benefits and costs of within‐session spacing for both initial learning and relearning. In this study, 170 Japanese‐speaking university students learned 20 English–Japanese word pairs using one of the following four combinations of initial and relearning spacing: long–long, long–short, short–long, and short–short spacing. The results showed that introducing long spacing for both initial learning and relearning (long–long) led to better long‐term retention and higher efficiency scores (i.e., number of words learned per trial) despite the increased duration of the treatment. These findings suggest that the benefits of long spacing outweigh its costs. A one‐page Accessible Summary of this article in non‐technical language is freely available in the Supporting Information online and at https://oasis‐database.org
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This study examined the distributed practice effects on L2 speaking task repetition (TR). Previous research indicated that speaking TR has beneficial effects on learners’ later speech. To examine the distributed practice effects on learners’ second repetition and post-repetition speech, 38 university students were engaged in cartoon narrations. The procedure included: a pre-test, two repetition tasks with a 1-week intersession interval (spaced practice group) or without an interval (massed practice group), and a post-test. The grammatical complexity, grammatical accuracy, fluency, and lexical variety of learners’ performances were examined using ANCOVA. The lexical variety scores increased more in the spaced practice group than the massed practice group in the pre- and post-test comparisons, indicating that spaced TR may induce lexical variety in post-repetition speech. 本研究は第二言語によるスピーキング反復練習における分散学習の効果を調査した。スピーキング反復練習は2回目以降の発話を向上させると報告されている。分散練習が2回目の反復練習及び新しいタスクにおける効果を検証するため、38人の大学生は漫画の内容を描写した。プレテスト、2回の反復練習、ポストテストを1週間間隔(分散学習群)、又は間隔を空けずに(集中学習群)実施した。発話の文法的複雑性、文法的正確性、流暢性、語彙の豊かさを共分散分析を用いて検証した。プレテストとポストテストの結果において分散学習群の語彙の豊かさが向上し、分散学習は語彙の豊かさを向上させることが示唆された。 キーワード フォーカス・オン・フォーム 反復間隔 口頭産出 分散効果 分散学習
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This file serves to provide a comprehensive (self-archived) bibliography of references related to task repetition and language learning. We are posting this in hopes of fostering further research in this area. We will update this file periodically. Please send any suggested references to rjrogers@eduhk.hk
Article
Research has produced mixed findings regarding the effects of spacing L2 study. In order to know how this potentially very powerful learning tool can be useful, it is important to understand the cognitive mechanisms that drive the effects in L2 learning and how the operation of these mechanisms may be affected by variables relevant for SLA contexts. In this study, I examine the contribution of the dual mechanism of successful effortful retrieval during study to the lag effect in foreign vocabulary learning from L2-L1 retrieval practice. I additionally investigate the effects of feedback study time on the operation of the two cognitive mechanisms under investigation. Native speakers of English studied Finnish vocabulary during L2-L1 retrieval practice in paired-associate learning while their response latencies and accuracy were recorded. Results suggest that: (a) successful effortful retrieval underlies benefits of spacing L2-L1 retrieval practice: even with immediate feedback study, the benefits of effort are conditional on retrieval success; (b) successful retrieval is more beneficial than unsuccessful retrieval, contrary to proposals where this was not directly tested; and (c) imposing longer study time externally has little benefit, unlike what has been previously found with learner-regulated longer study time. Implications for L2 learning and teaching are discussed.