The Effectiveness of Second Language Strategy Instruction

Language Learning
ISSN 0023-8333
SYSTEMATIC REVIEW ARTICLE
The Effectiveness of Second Language
Strategy Instruction: A Meta-analysis
Luke Plonsky
Northern Arizona University
Research on the effects of second language strategy instruction (SI) has been extensive
yet inconclusive. This meta-analysis, therefore, aims to provide a reliable, quantitative
measure of the effect of SI as well as a description of the relationship between SI and the
variables that moderate its effectiveness (i.e., different learning contexts, treatments, and
outcome variables). A comprehensive search was conducted to collect the population
of SI studies. Effect sizes were calculated for 61 primary studies, contributing a total
of 95 unique samples, all of which were coded for potential moderators. The findings
indicate a small to medium overall effect of SI (d = 0.49). Variables found to moderate
its effectiveness include type and number of strategies, learning context (second vs.
foreign language), and length of intervention. Following a contextualized interpretation
of the results, the article concludes with a discussion of theoretical, practical, and
methodological implications.
Keywords strategy instruction; language learning strategies; second language acquisition; meta-analysis
Introduction
Second language (L2) learning strategies have been the object of a large
body of research over the last 30 years. Dozens of reports have examined
This study was carried out during my graduate studies at Michigan State University, and I am
grateful to several individuals there and elsewhere for their guidance and suggestions. First, I thank
Fred Oswald, who was integral to all stages of this project. For their time, expertise, and insight in
providing input on the study and comments on earlier drafts of this article, my sincere appreciation
also goes to Paula Winke, Shawn Loewen, Susan Gass, Patti Spinner, Rebecca Foote, Mark Shea,
Maren Schierloh, and Shaofeng Li. Finally, a previous version of this study was presented at the
2008 Second Language Research Forum at the University of Hawaii, and I am very thankful for
the helpful comments of those in attendance.
Correspondence concerning this article should be addressed to Luke Plonsky, Department of
English, Northern Arizona University, PO Box 6032, Flagstaff, AZ 86011. Internet: luke.plonsky@
nau.edu
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C 2011 Language Learning Research Club, University of Michigan
DOI: 10.1111/j.1467-9922.2011.00663.x
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the types of strategies learners use as well as how they are used in conjunction with other variables such as gender, proficiency, style, motivation, autonomy, and epistemological beliefs (Green & Oxford, 1995; Ikeda & Takeuchi,
2006; Jiménez Catalán, 2003; Mogagwe & Oliver, 2007; O’Malley, Chamot,
Stewner-Manzanares, Russo, & Küpper, 1985; Oxford & Nyikos, 1989; Yamamorio, Isoda, Hiromori, & Oxford, 2003). Flanking this line of predominantly descriptive and correlational inquiry, experimental research on L2 strategy instruction (SI) has been recognized as a source of significant theoretical
and practical potential (Chamot, 2001, 2005; Cohen, Weaver, & Li, 1998;
Dörnyei, 1995; R. Ellis, 2005; Grenfell, 2007; McDonough, 1995). Defined as
explicit instruction on specific practices or techniques that can be employed
autonomously to improve one’s L2 learning and/or use (Y. Chen, 2007; Ellis &
Sinclair, 1989; Taylor, Stevens, & Asher, 2006; Tudor, 1996), SI (also known
as learner training [Reiss, 1981; Tudor, 1996] and strategies-based instruction
[Cohen, 1998]) ultimately aims to “empower students by allowing them to take
control of the language learning process” (Cohen, 1998, p. 70).
In addition to intuitive appeal and popularity among L2 teachers, empirical
support for SI has been found across many learning contexts (e.g., foreign
and second language), treatments (e.g., number and type of strategies, length
of intervention), and outcome variables, such as reading and writing (e.g.,
El-Koumy, 1999; Lawson & Hogben, 1998). The overall results, however, are
hardly conclusive. Studies of SI have also produced negative and mixed results across many of the same contexts, treatments, and outcome variables
(e.g., Bimmel, van den Bergh, & Oostdam 2001; Maxim, 2002). Moreover,
doubts about the effectiveness of SI have been raised due to methodological
flaws in previous research (e.g., small sample sizes, nonrandom group assignment, exclusion of comparison groups), the complexity of variables that affect
L2 strategy use, uncertainty of long-term effects, cost/benefit ratio concerns,
nonempirically justified strategies, a lack of valid and reliable instruments, and
the absence of a comprehensive theory (Bimmel et al., 2001; Chamot, 2005;
Dörnyei, 1995; Green & Oxford, 1995; Gu, 1996; Hassan et al., 2005; Kimura,
Masuhara, Fukada, & Takeuchi, 1993; Macaro, 2006; Macaro & Erler, 2007;
Manchón, Roca de Larios, & Murphy, 2007; McDonough, 1995; Nakatani,
2005; Nakatani & Goh, 2007; Oxford, 1993a; Purpura, 1998; Rees-Miller,
1993; Thompson & Rubin, 1996; Walters, 2006). Furthermore, although many
articles and book chapters have provided suggestions for and critical accounts
of SI (Chen, 2007; Cohen et al., 1998; Dörnyei, 1995; Hassan et al., 2005;
McDonough, 1995; Rubin, Chamot, Harris, & Anderson, 2007; Thompson &
Rubin, 1996), no comprehensive meta-analysis of the research in this area has
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been carried out to date. The first goal of this article is, therefore, to obtain
a reliable, quantitative measure of the effectiveness of SI. Second, this study
seeks to determine the relationship between the effectiveness of SI and different
L2 contexts, treatment characteristics, and outcome variables.
Literature Review
Learning strategies research began with the study of “good” language learners.
Researchers in this area (Naiman, Fröhlich, Stern, & Todesco, 1996; Rubin,
1975; Rubin & Thompson, 1994; Stern, 1975), initially concerned with explaining the variability in success among L2 learners, sought to describe the
characteristics and practices of successful language learners in the hopes of
understanding them and passing them on to less successful learners. However,
the qualities of “good” language learners were found to vary, which prevented
the formulation of a precise prescription for how to learn a language successfully (Gan, Humphreys, & Hamp-Lyons, 2004; Politzer & McGroarty, 1985;
Stevick, 1989).
The focus shifted, then, from “good” language learning to L2 strategies.
Defined as the steps taken on the part of language learners to improve their
L2 learning and/or use1 (Oxford, 1990), strategies proved a more worthwhile
pursuit because of the complexity involved in L2 learning and the broad applicability of strategies to different learners, tasks, and contexts (Chamot, 1993;
Gillette, 1987; Griffiths & Parr, 2001; Hong-Nam & Leavell, 2006). Research
on L2 strategies, including SI, took on an individualized conceptualization of
language learning, which was welcomed as part of a movement in education
toward learner-centeredness (see Nunan, 1988; Nyikos, 1996; Tudor, 1996;
Wenden, 2002). In addition to connecting to education through a common
concern with learners on an individual level, strategies received increased attention in second language acquisition (SLA) as models of communicative
competence began to include a component of strategic competence (Bachman,
1990; Bachman & Palmer, 1996; Canale, 1983; Canale & Swain, 1980; Dörnyei
& Thurrell, 1991). Additionally, although strategic competence originally referred only to communication (i.e., listening and speaking) strategies employed
to compensate for linguistic deficiencies (Canale & Swain, 1980), proponents of
SI perceived this line of inquiry as theoretical backing for their more pragmatic
agendas relating to many different types, uses, and applications of strategies.
With support from these and other areas (e.g., self-regulatory learning in educational psychology) (cf. Tseng, Dörnyei, & Schmitt, 2006; Wenden, 1998),
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teachers and researchers alike have used SI to both practical and theoretical
ends.
As stated earlier, the effects of SI have been tested widely with different
learning contexts, treatments, and outcome variables. However, secondary research on SI has presented accounts that lack comprehensiveness, only describe
SI across a single dimension (e.g., outcome variables), and/or are systematic
but somewhat coarsely grained compared to meta-analytic methods. Despite
the over 100 articles and books chapters providing reviews and methodological
recommendations for this type of intervention, no solid models have emerged,
and the accumulated findings have been largely mixed. Therefore, in line with
this study’s dual purpose of describing the overall effectiveness of SI research
as well as determining the relationships between SI and other variables that may
moderate its effectiveness, the remainder of this literature review will present
the breadth of SI research across the following dimensions: learning contexts,
treatments, and outcomes (i.e., dependent variables).
Contexts
As Carrell (1998) pointed out, successful strategy use and, consequently, strategy instruction are context-dependent. In other words, effective SI may depend
on the context in which strategies are taught and used. With this in mind, this
subsection describes several contextual variables hypothesized to moderate the
effectiveness of SI.
Proficiency
In order to understand how proficiency might affect strategy instruction, it is
also helpful to consider how it affects strategy use. A significant amount of
research has studied the two, and the results have generally described their
relationship as positive and linear (Cohen, 1998; Green & Oxford, 1995; Park,
1997; Wharton, 2000). However, some studies have found a curvilinear relationship between the two, characterized by more frequent use of strategies by
intermediate learners than low- or high-proficiency learners (Corrales, 1989;
Hong-Nam & Leavell, 2006; Phillips, 1991). Oxford (1999) explained this
finding by suggesting that after attaining a certain level of competence in the
L2, more advanced learners’ use of strategies becomes increasingly automatic
and therefore less available to conscious inspection. Mixed results such as
these underscore the difficulty in predicting the effectiveness of SI at different
levels.2 A related challenge is the lack of research that has controlled for proficiency. Most studies have investigated the effects of SI with beginner (e.g.,
Morin, 2003), intermediate (e.g., Cohen, Weaver, & Li, 1996), or, to a lesser extent, advanced and mixed-proficiency groups (e.g., Linan-Thompson, Vaughn,
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Hickman-Davis, & Kouzekanani, 2003; Walters, 2006). Ikeda and Takeuchi
(2003) and Moore and Surber (1992) are among the few who have tested the
relative effects of SI at different levels, as recommended by Ikeda (2002). In
both studies, the higher proficiency group was able to benefit more from the
treatment than the lower proficiency group. Ikeda and Takeuchi explained that
this finding may be due to lower and higher level processing. Chularut and
DeBacker (2004), who also compared learners at different proficiencies, however, did not find an advantage for SI in more advanced learners (see also
Maxim, 2002; Vandergrift & Tafaghodtari, 2010). The synthetic nature of the
present study allows for comparisons to be made between different proficiency
levels despite the lack of control for this variable in primary studies.
Second Versus Foreign Language
In addition to various proficiency levels, SI has also been tested in both secondlanguage (e.g., Fraser, 1999; O’Malley et al., 1985) and foreign language
environments (e.g., Barnett 1988a, 1988b; El-Koumy, 1999). Due to logistical
constraints, primary research has paid only minimal attention to differences
between second and foreign language strategy use and the comparative effects
of SI in both contexts. Riley and Harsch (1999), in a study unique in its
consideration of L2 students in both learning environments, found no effect
for their treatment, although pretreatment differences did exist between the two
groups.
Age, Level of Education, and Classroom Versus Laboratory
To round out this discussion of context, it should be mentioned that learners’
age, level of education, and experimental context (i.e., classroom vs. laboratory)
may also influence the effectiveness of SI. It may be of interest, for example,
to determine whether greater (meta)cognitive capacity of adults offers an advantage over children or whether the control afforded by conducting research
in a lab enhances the effects of SI. Again, the primary research to date has
produced results across all educational levels with children (e.g., Macaro &
Erler, 2007), adolescents (e.g., Rodriguez & Sadoski, 2000), and adults (e.g.,
Song, 1997) in both classroom (Maleki, 2007) and laboratory settings (Liu,
Zhang, Wang, Deng, & Niu, 2007). With the exception of Feyten, Flaitz, and
LaRocca (1999), however, very few individual studies have controlled for any
of these variables.
Treatments
Although there is widespread agreement among language researchers and practitioners on the benefits of L2 strategies, there is much less agreement on how
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to foster a student’s ability to employ strategies for language learning and use.
To be sure, there are many treatment-related variables to consider, yet very few
studies have investigated the relative effects of different methods of SI (Walters,
2004, 2006). Unfortunately, the lack of theory in this area has left researchers
and practitioners to design studies of SI based largely on convenience, intuition,
and/or some level of idiosyncrasy.
Whether the objective is empirical or pedagogical, one important step in
designing a program of SI is deciding which and how many strategies to teach.
The variety of strategies and strategy classifications available presents one
of the greatest opportunities to researchers interested in SI. That same variety,
however, also presents one of the greatest challenges to theoretical and synthetic
accounts of this area, as there is no consensus on a particular classification
scheme for sensibly and appropriately summarizing results (Chamot, 1993;
Chaudron, 2006; Oxford, 1994; Wong, 2005).
Common to several classification schemes, however, is a distinction among
cognitive, metacognitive, and social/socioaffective strategies (e.g., Gan et al.,
2004; Purpura, 1997). Cognitive strategies, employed while engaging directly
with the L2, include activities such as rehearsal, organization, and elaboration
(O’Malley & Chamot, 1990; Oxford, 1990). Some examples of cognitive strategies taught in studies of SI are the keyword method (Avila & Sadoski, 1996)
and developing questions while reading (Bimmel et al., 2001). Metacognitive
strategies, which involve preparation prior to or reflection following L2 contact
or use, have been suggested in previous reviews as an essential component
to improving L2 performance through SI (Carrell, 1998; Macaro, Graham, &
Vanderplank, 2007; Vandergrift, 2003). Two of the metacognitive strategies
taught in Dreyer and Nel (2003), for example, include setting goals for reading
and planning how to read. Social strategies generally relate to interaction with
others. Clarification requests and cooperation with peers are two examples, the
latter of which was selected as part of O’Malley et al.’s (1985) early study
of SI.
Stemming from educational psychology, the delineation of strategies described above has been used widely in L2 strategies research (e.g., O’Malley
& Chamot, 1990). Additional schemes have been developed, however, with
more finely and coarsely grained distinctions. Bialystok (1990) and Dörnyei
(1995), for example, grouped strategies based on whether their purpose is to
assist in L2 learning or use. The skills that certain strategies are used with (e.g.,
reading, speaking) have also served to sort L2 strategies as taught in studies of
SI (Chamot, 2005; Cohen, 1990; Cohen & Macaro, 2007; McDonough, 1995;
Oxford, 1994); such a system is convenient for comparisons across studies of
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SI with skill-specific outcome variables. Finally, regardless of which strategy
classification is adopted, many researchers in this area suggest that only strategies with empirical support for their effectiveness should be taught and that
the rationale for choosing them should be communicated to the learners (e.g.,
Chamot & Rubin, 1994; Rivera-Mills & Plonsky, 2007).
In addition to different types of strategies, the effectiveness of SI may also
be moderated by the length of the treatment (Carrell, 1998; Manchón, 2007;
Nyikos & Fan, 2007). This issue has raised concerns about the cost/benefit
ratio of SI. Bialystok (1990) argued that “what one must teach students of a
language is not strategy, but language” (p. 147) (see also Kellerman, 1991).
Most researchers take a more moderate position, suggesting that class time
given to SI must be justified as a worthwhile departure away from other L2
tasks that focus more on the language itself (Feyten et al., 1999; Oxford, 1993a).
Meanwhile, for SI to be effective, it must be thorough, allowing time for students
to fully understand how and when the strategies can be used, with plenty of
opportunities for guided practice (Chamot, Barnhardt, El-Dinary, & Robbins,
1999; Chamot & O’Malley, 1996; Hosenfeld, Arnold, Kirchofer, Laciura, &
Wilson, 1981; Nyikos & Fan, 2007; Oxford & Leaver, 1996). Wide variability in
this dimension exists across studies. Interventions range from shorter, intensive
programs of 5 hours in 2 weeks (Raymond, 1993), for example, to treatment
periods that are spread out over longer periods, as in Macaro and Erler’s (2007)
14-month longitudinal study. However long or short, Macaro (2001) suggested
that materials be developed with appropriate time allocated to different activities
such as awareness-raising, practice, scaffolding, and evaluation (see Carrell,
1998).
Several other judgments must be made relating to how SI is conducted.
One concern is whether the strategies are taught as an embedded or separate
component of the curriculum, with most experts recommending the former
(Chamot & Küpper, 1989; O’Malley & Chamot, 1990; Oxford, 1993b; Oxford
et al., 1990; Walters, 2006). Additional considerations include the language of
instruction, particularly for foreign language contexts (e.g., Chamot, 2005) and
among beginner-level learners (Chamot, 1994), as well as whether the teacher
or researcher should impart the intervention. Although researcher-led SI may
improve the balance of instruction among experimental groups, familiarity of
strategies, and full disclosure of treatment procedures in the written report, the
more common and convenient practice of teacher- and teacher/researcher-led
interventions (e.g., Y. Chen, 2007; Hsu, 2003; Rodriguez & Sadoski, 2000) may
benefit from an established rapport with students and a greater understanding
of their backgrounds, needs, and preferences.
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Outcome Variables
The discussion so far has been centered on the independent variables of the
population of studies in question. In this subsection, the review turns to the
dependent/outcome variables that have been used to measure the effects of SI.
The skill most frequently measured as an outcome or dependent variable
in studies of SI is reading. Much of the work in this area draws on evidence
of successful reading SI from first-language (L1) contexts (see Dymock, 2007;
Walters, 2004). Taylor et al.’s (2006) meta-analysis of reading SI, replicated by
a subset of the data in the present study, yielded overall positive evidence in
favor of interventions involving L2 reading SI as well (Hedges’s g = 0.54).
Second language writing has also been the object of interventions aimed
at improving students’ strategies and abilities. Sengupta (2000), for example,
trained foreign language high school students on revision strategies. Although
somewhat scarce (Manchón et al., 2007), studies that have focused on SI for
writing include Bishop (2001) and Ching (2002).
Several studies have also sought to improve learners’ verbal communication
by teaching them strategies for speaking and listening.3 It has been suggested
that the real-time constraints inherent to these skills may limit the teachability of
strategies for this mode (Chamot, 2005; Farrell & Mallard, 2006). Furthermore,
the social/interactive nature of tasks that involve more than one interlocutor can
obscure the outcomes of SI for speaking and listening (Nakatani & Goh, 2007).
Despite these challenges, an active line of research has attempted, with varying
degrees of success, to train students on strategies that can be employed with
these skills (e.g., Y. Chen, 2005; Lam, 2006; Rubin, 1990; Scullen & Jourdain,
2000; Thompson & Rubin, 1996).
A number of studies have also sought to develop learners’ ability to acquire
new words. Some of these strategies are meant to help learners resist behaviors
such as rote memorization that stem from beliefs that reduce language learning
to habit formation (Abraham & Vann, 1987; Fan, 2003; Graham, 1997; Griffiths,
2007; Horwitz, 1987, 1988; Kern, 1995; Mori, 1999; Schulz, 2001). Drawing on
current models of vocabulary acquisition (Pavičić, 2008; Schmitt, 2000), many
studies have aimed to improve students’ efforts in this area through strategies
that promote deeper processing (e.g., Avila & Sadoski, 1996; Brown & Perry,
1991; Fraser, 1999).
In addition to the skill-specific outcomes discussed above, many studies
have included measures of other dependent variables, such as (a) students’ beliefs, attitudes, and awareness of strategies themselves or of language learning
more generally (e.g., Bull & Ma, 2001; Chamot, 1993; Laoire, 2007; Sengupta,
2000); (b) autonomy (Y. Chen, 2007); (c) frequency and variety of strategy use
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(Chamot, 1993; Cohen et al., 1996; Flaitz & Feyten, 1996; Ikeda & Takeuchi,
2003; Macaro & Erler, 2007); (d) overall proficiency (Feyten et al., 1999);
(e) grammatical accuracy (Ayaduray & Jacobs, 1997; Cadierno-Lopez, 1992);
and (f) pronunciation (Hazan, Sennema, Iba, & Faulkner, 2005; Maleki,
2007).
Research Questions
The studies cataloged above portray the multidimensional nature of instruction
on L2 strategies. Although advances have been made toward understanding the
complexity of this type of intervention, no secondary studies have employed
the methodological and theoretical rigor needed to locate reliable patterns and
suggest a model of SI (e.g., Chamot, 2005). Furthermore, although literature
reviews can be helpful in determining trends and gaps in the research, they
are often unable to provide answers to empirical questions relating to complex
constructs and processes (Lipsey & Wilson, 2001). It is beneficial for the field,
therefore, to assess the cumulative findings of mature domains such as SI—an
active area of SLA research since the early 1980s and the focus of over 400
empirical, theoretical, and review articles (see Plonsky, 2010)—by means of
quantitative, secondary analyses (Norris & Ortega, 2006).
A synthetic investigation of SI by means of meta-analysis can supply the
empirical motivation for a working model that explains the extent to which
strategies have been taught successfully and the strength of the relationship
between SI and different contexts, treatments, outcome variables, and research
methods. Taking into consideration these goals as well as recommendations for
synthetic research within the field as a whole, the present meta-analysis seeks
to answer the following two research questions:
1. How effective is L2 strategy instruction?
2. What is the relationship between the effectiveness of SI and different
learning contexts, treatments, outcome variables, and research methods?
Methods
Data Collection and Coding
Prior to collecting data for meta-analysis, a population of studies must be
defined and representatively, if not exhaustively, sampled (Lipsey & Wilson,
2001). The criteria that define the body of research to be meta-analyzed must be
broad enough to produce robust findings across a number of studies worthy of
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meta-analysis, yet conceptually (if not operationally) narrow enough to avoid
inappropriate aggregation of findings (i.e., the apples and oranges problem;
see Cooper, 2009; Plonsky & Oswald, in press). Taking these principles into
consideration, this meta-analysis included all studies that met the following
eligibility criteria: (a) participants studying an L2, (b) treatment involving
instruction on one or more L2 strategies, (c) data collected and compared in a
control-experimental (between groups) design, (d) a quantitative measure of the
effect of SI as a dependent variable, and (e) a reported effect size (or sufficient
data reported to extract an effect size).
The search for studies that met these criteria began with keyword searches.
Four academic databases—Linguistics and Language Behavior Abstracts
(LLBA), the Educational Information Resource Center (ERIC), PsycInfo, and
Google Scholar—were searched using combinations of the following words:
(a) learner training, (b) strategies-based instruction, (c) learning to learn,
(d) strategies instruction, (e) strategy instruction, (f) second language, and
(g) strategies. In addition, forward citations were retrieved for seminal papers
(e.g., O’Malley et al., 1985) using Web of Science. These searches produced
thousands of studies, reviews, books, and Web pages of researchers, and references from relevant sources were then examined for additional studies. The
cycle of locating studies, reference consultation, and study retrieval continued
until the search was believed to be exhaustive (i.e., only producing duplicate
studies) within the parameters of the inclusion criteria.
Once collected, the studies were organized into a database and coded to
determine which moderators and study effects would be numerous enough to
allow for meta-analysis. The coding scheme (see Table 1), designed to reflect
the dimensions of SI discussed in the review of the literature, was developed through an iterative process that considered previous reviews of SI (e.g.,
Hassan et al., 2005; McDonough, 1999), recommendations for best practices
of SI (e.g., Harris et al., 2001; Oxford et al., 1990), methodological and theoretical commonalities and idiosyncrasies within studies of SI, and other related
but previously understudied or unstudied phenomena. To ensure that data were
obtained reliably, a subset of 10 studies (approximately 16% of the total 61)
was coded by a second rater, who was trained on the conventions and operationalizations of the coding scheme. The overall agreement between the
two raters was 96%. Item-by-item agreement was also calculated to determine whether greater definitional clarity was needed in any particular area
(Orwin & Vevea, 2009), and agreement was found to be at or above 90% on
all items.
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1002
1003
Identification
Author
Title
Year of publication
Type of publication
Context
Second or foreign language
Participants’ age (average)
Educational institution
Classroom or laboratory
Country
Participants’ L1
Participants’ L2
Proficiency level
Method
Pretest?
Control group?
Random assignment?
Delayed posttest?
Groups equal before treatment?
Variablea
High school
Lab
Intermediate
No
No
No
No
No
Beginner
Yes
Yes
Yes
Yes
Yes
Foreign
Second
Elementary
Class
Conference
Article
Table 1 Data coded from primary studies
Advanced
University
Report
Values
Dissertation
Unpubublished
(Continued)
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b
a
Metacognitive
No
No
Researcher
No
L2
No
W
Cognitive
Yes
Yes
Teacher
Yes
L1
Yes
R
L
Both
Both
Socioaffective
Values
S
V
P
O
U
Variables without labeled values are continuous, noncategorical, or open-ended.
R = reading, W = writing, L = listening, S = speaking, V = vocabulary, P = pronunciation; O = overall L2 ability, U = strategy use, A = attitude.
Treatment
Strategies taught (general)
Strategies taught (specific)
Number of strategies taught
Empirical justification of strategies?
Rationale explained to Ss
Length of treatment (days/weeks)
Length of treatment (hours)
Treatment provider
Experience/training of teacher
Embedded into curriculum
Language of treatment
Opportunities for practice
Outcome(s)
N-size control group
N-size treatment group(s)
Dependent variableb
Measure/instrument
Reliability
Statistical tests used
Means
Standard deviations
Effect sizes
Variablea
Table 1 Continued
A
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Analysis
As is customary in meta-analysis, effect sizes (Cohen’s d) served as the basis
for all quantitative analyses. Although eight studies in the final sample reported
an effect size statistic, none reported a d-value, and effect sizes used in the
analysis were calculated from the reported means, standard deviations, and
sample sizes. Thirty-six studies lacked one or more of these statistics, and
requests were sent to 15 authors, only 5 of whom responded with the missing
data. The remaining studies were excluded from the analysis. In total, 218
studies of SI were identified, 157 of which were excluded due to one or more of
the following reasons: missing data (e.g., Carrell, Pharis, & Liberto, 1989;
Nunan, 1997b; Paige, Cohen, & Shively, 2004; Sasaki, 2000; Seo, 2005),
lack of control/comparison group (e.g., Hagaman & Reid, 2008; Salataci &
Akyel, 2002; Shen & Huang, 2007; Wyra, Lawson, & Hungi, 2007), qualitative
outcomes (e.g., Cotterall, 1990; Coyle, 2007; Lam & Wong, 2000; Nunan, 1996,
2002), strategies taught for purposes other than L2 learning or use (e.g., Amer,
1993; Min, 2005), treatment conditions that focused on language learning (as
opposed to the learning of strategies) (e.g., Cheung, 1997; Sagarra & Alba,
2006), L2 learner data combined with L1 data (Gaskill & Murphy, 2004;
Lovett et al., 2008; Muñiz-Swicegood, 1994), strategies used to teach the L2
but not explicitly taught to participants (e.g., Bahr & Dansereau, 2005; Barcroft,
2002; Boers, Eyckmans, Kappel, Stengers, & Demecheleer, 2006; Desrochers,
Gelinas, & Wieland, 1989; Desrochers, Wieland, & Coté, 1991; Sanchez, 2004),
and duplicate data reported elsewhere (Cohen, Weaver, & Li, 1995, 1998;
Flaitz & Feyten, 1996; Graham & Macaro, 2008; Nunan, 1997a). Thirty-two
dissertations including SI were also identified but excluded because they were
not freely accessible (e.g., Aninao, 1993; Holunga, 1994; Kohler, 2002; Ozeki,
2000). A total of 61 individual reports were included in the final sample.
Multiple Effect Sizes
From a meta-analytic perspective, it is straightforward when each primary study
produces a single effect size. After collecting and coding the data, however, it
was apparent that many of the studies to be aggregated had produced more than
one measure of the treatment effect. Referred to as stochastic dependency, this
condition is present in meta-analysis when studies include multiple outcome
measures taken by the same participants (see Gleser & Olkin, 2009). Several
well-defined procedures are available for dealing with stochastically dependent
data. One approach randomly selects a single effect size per study but, in doing
so, systematically discards valuable data. A second approach involves calculating an average of the effect sizes for each study (Lipsey & Wilson, 2001); this
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technique was applied when multiple effect sizes came from the same group
of participants. Thus, the average effect from each sample/experimental group
was treated as an independent data point, separate from effect sizes from different groups of participants within an individual study (see Lipsey & Wilson,
2001, pp. 112–113). In total, effect sizes were obtained from 95 unique samples
and 6,791 participants.
Weighting of Effect Sizes
As is often the case in meta-analytic research, the coding process brought to
light several limitations of SI research (e.g., unreported reliability of measures).
Whereas some meta-analysts advocate exclusion of studies that fail to meet
certain methodological standards (e.g., Slavin, 1986; see also Valentine, 2009),
all studies that met the criteria were included, thus avoiding a necessarily
subjective determination of quality while gaining access to a greater portion
of the accumulated research. That is not to say, however, that the issue of
quality has been ignored. Absorbing into the analysis different methods and
weaknesses that are inherent to primary studies presents a problem that can be
addressed empirically and/or mitigated by the volume of the sample (Lipsey &
Wilson, 2001; Norris & Ortega, 2006; Oswald & Plonsky, 2010; Plonsky, 2011;
Valentine, 2009). Hunter and Schmidt (2004), for instance, devised a set of techniques for weighting effect sizes based on minimally subjective assessments of
quality, such as measurement reliability and range restriction. Accounting for
these and other statistical artifacts would likely show that the effects of SI have
been somewhat misrepresented by understated and inconsistent results (Oswald
& McCloy, 2003). Due to the lack of reporting on reliability in primary studies
(25 of the 61 studies), however, weighting of the effect sizes in this study was
based solely on individual sample sizes. By employing this straightforward adjustment, the analysis presents an appropriate reflection of the statistical power
of larger sample sizes while preserving interpretability and clarity of findings.
Averages
Once effect sizes were coded, combined, and weighted, the research questions
were addressed. The first step was to find the weighted mean effect size and
confidence intervals for the population of studies included in the meta-analysis;
this simple calculation was used to answer research question 1, which pertained
to the overall effectiveness of SI. Research question 2 sought to describe any
systematic variability among studies of SI. Average effect sizes and confidence
intervals were calculated again for subgroups formed on the basis of moderating
variables identified a priori. In order to group and compare average effects,
four continuous variables were collapsed into dichotomous categories: (a) age
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Figure 1 Funnel plot of unweighted effect sizes (M = 0.58; k = 95) and sample sizes.
(<12, ≥12; this break was chosen to reflect assumptions about differences
in how children and adults learn languages); (b) proficiency level (beginner,
intermediate/advanced); (c) number of strategies taught (≤8, >8; most studies
taught either a small number of strategies up to about 8 or much larger set
of strategies); and (d) length of treatment (≤2 weeks, >2 weeks; this cutoff
reflects and tests the difference between approximately half of the studies that
were carried out over a few days and those that were carried out over several
weeks or months).
Publication Bias
As described earlier, this study intended to include a representative if not exhaustive population of studies. Several factors, however, may have contributed
to a bias in observed values. The symmetry of a funnel plot provides a general indication of whether the sampled studies reflect publication bias resulting from suppression of nonstatistically significant findings on the part of
journals and/or individual researchers (Rosenthal, 1979; Rothstein, Sutton, &
Borenstein, 2005; Vevea & Woods, 2005). Further motivation for an analysis of
the file-drawer problem stems from the positive orientation toward SI expressed
either implicitly or explicitly by many researchers.4
The funnel plot in Figure 1 displays a slightly higher number of effect sizes
to the right of the aggregated (unweighted) average (d = 0.58). This distribution
indicates some evidence for publication bias in the population of primary
studies. If no bias were present, the plots would be more equally weighted
on both sides of the mean. Nevertheless, it is important to remember that
publication bias is only one of the variables that may contribute irregularities
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Table 2 Overall effects of SI (d)
95% Confidence
interval
Control vs. Experimental groupb
a
b
M
Ka
SE
Lower
Upper
0.49
95
0.02
0.44
0.53
Unique samples.
Sixty-one unique studies; 6,791 individual participants.
(e.g., asymmetry) in a funnel plot. Others include heterogeneity in the sample
(i.e., the presence of moderators) and issues associated with the validity and
reliability of outcome measures (Sterne, Becker, & Egger, 2005). In addition
to assessing the presence of publication bias, the funnel plot also provides an
indication of the proximity of the observed mean to the true population mean.
As sample size increases, standard error is expected to decrease, thus yielding a
more accurate measure of the treatment effect. Likewise, as we see in this plot,
studies with smaller samples exhibit greater variance in effect sizes, whereas
larger sample sizes (i.e., those higher up on the plot) cluster closer to the mean.
Results
The first research question addresses the effectiveness of SI by aggregating
effects from the population of primary studies. The overall weighted mean
(d = 0.49; see Table 2) represents a medium effect on Cohen’s (1988) scale
but would be characterized as small to medium with respect to Oswald and
Plonsky’s (2010) proposed standards for interpreting effect sizes in SLA (see
also Wolf, 1986).5 Benchmarks aside, the overall weighted mean tells us that,
on average, the experimental participants scored approximately 0.5 standard
deviations above control group participants on the outcome measures. (See the
appendix for mean effect sizes from all 95 contributing samples.6 )
Hall, Tickle-Degnen, Rosenthal, and Mosteller (1994) told us that the purpose of meta-analysis is to “summarize and add new knowledge” (p. 24–25,
emphasis added). Therefore, in addition to obtaining a measure of the overall
effect, this study was concerned with locating systematic differences in the
effectiveness of SI across certain substantive and methodological features. To
address this question, average effect sizes were calculated for subgroups formed
by the study characteristics that had been coded (see Table 1). Moderator analyses were carried out for those items discussed in previous research to potentially
affect the outcome of SI. Table 3 presents the weighted moderator analyses.
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Table 3 Effectiveness of SI by subgroups
95% Confidence
interval
Group
M (d)
K
Na
SE
Lower
Upper
L2
FL
<12
≥12
Elementary
High school
University
Class
Lab
Low
Intermediate/
advanced
0.84
0.46
1.29
0.47
0.78
0.23
0.55
0.43
0.79
0.35
0.66
13
82
5
90
9
26
58
65
28
40
49
290
3365
79
3576
414
1277
1924
2980
523
2147
1357
0.06
0.03
0.10
0.02
0.06
0.06
0.03
0.03
0.05
0.04
0.03
0.71
0.41
1.10
0.42
0.67
0.12
0.49
0.38
0.70
0.28
0.60
0.96
0.51
1.49
0.52
0.89
0.35
0.61
0.49
0.89
0.42
0.73
Cognitive
Metacognitive
≤8
>8
≤2 weeks
>2 weeks
0.48
0.24
0.69
0.28
0.42
0.53
82
26
64
17
44
47
3108
1601
2116
828
1524
1999
0.03
0.05
0.03
0.07
0.04
0.03
0.43
0.14
0.64
0.15
0.34
0.47
0.53
0.34
0.74
0.41
0.50
0.59
Reading
Writing
Listening
Speaking
Vocabulary
Pronunciation
Grammar
General
Strategy use
Attitude
0.74
0.42
0.06
0.97
0.64
0.70
0.01
−0.14
1.11
0.27
33 1116 0.03
0.67
6 214 0.11
0.21
10 680 0.16 −0.26
10 259 0.06
0.85
32 967 0.04
0.57
1
30 —
—
1
30 —
—
4 382 0.14 −0.41
9 426 0.05
1.01
4 156 0.15 −0.02
0.81
0.63
0.37
1.10
0.72
—
—
0.13
1.20
0.57
62
33
53
33
35
60
0.59
0.48
0.71
0.51
0.73
0.48
Subgroup
Value
Context
Setting
Age
Institution
Class
Proficiency
Treatment
Strategy type
No. of strategies
Treatment length
Outcomes
Target skills
Method
Pretest
Yes
No
Random groups
Yes
No
Reliability reported Yes
No
a
0.54
0.39
0.65
0.42
0.65
0.42
2353
1302
2062
1062
1069
2586
0.03
0.04
0.03
0.05
0.04
0.03
0.48
0.31
0.60
0.33
0.58
0.36
Experimental participants only.
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Figure 2 Effect sizes for subgroups based on contextual variables.
Five contextual variables were assessed with respect to their relationship
with the effectiveness of SI (see Figure 2). Larger effects were obtained in
second language settings and with younger learners over foreign language settings and older learners, respectively. These data, however, must be interpreted
with caution due to an outlier (d = 3.92) among a relatively small sample of
studies in the L2 context.7 The effect of studies conducted in postsecondary
institutions (d = 0.55) is also more than twice as large as in high school studies
(d = 0.23). Finally, SI in laboratories (d = 0.79) produced much larger effects
than classroom studies (d = 0.43).
With respect to treatment-related variables (see Figure 3), there is a clear
difference in outcomes following instruction on cognitive (d = 0.48) versus
metacognitive (d = 0.24) strategies. There is also an advantage for longer
interventions as well as those that focus on only a few strategies.
Turning to the effects of SI with different outcome variables, the data indicate that SI is much more effective for certain skills than for others (Figure 4).
Overall, medium to large effects were obtained for treatment groups over comparison groups in reading, speaking, vocabulary, pronunciation, and strategy
use; more modest effects were found for writing and attitude toward language
learning; negligible effects resulted from studies with listening, grammar, and
general language ability as dependent variables.
Finally, as mentioned earlier, meta-analysis enables an empirical assessment
of the relationship between study quality and obtained effects (see Plonsky,
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Figure 3 Effect sizes for subgroups based on treatment variables.
Figure 4 Effect sizes for subgroups based on outcome variables.
2011; Plonsky & Gass, 2011). To that end, average effect sizes were calculated for subgroups based on certain aspects of research design and reporting
practices in the population of studies (see Figure 5). Studies that pretested for
pretreatment differences between treatment and comparison groups produced
a larger effect, on average, than studies that did not pretest (d = 0.54 and
d = 0.40, respectively). Random group assignment,8 another measure of
methodological quality taken to ensure equality between groups, was also
found to relate to larger effects. Finally, studies that reported reliability yielded
much larger effects (d = 0.65) than those that failed to do so (d = 0.42).
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Figure 5 Effect sizes for subgroups based on methodological variables.
Discussion
The previous section presented results for the two research questions addressed
in this study. The first research question was answered with an estimate of the
overall effect of SI (weighted d = 0.49; unweighted d = 0.58; K = 95; N =
6,791). As mentioned earlier, the magnitude of this result could generally be
described as a small to medium effect. Cohen (1988) and others (e.g., Hedges,
2008), however, cautioned that effect sizes are most accurately understood in
relation to other effect sizes. To that end, it is worth noting the strength of
this finding compared to the results of Hattie’s (1987, 1992) summary of 304
meta-analyses of a wide variety of topics in educational research, which found
an overall average effect of d = 0.40. Lipsey and Wilson (1993) also combined
the effect sizes from 80 meta-analyses of diverse areas within the domain of
educational research in which the comparison groups, similar to most SI studies,
received an alternate or traditional treatment. The overall mean from that body
of research, d = 0.34, was actually somewhat smaller than from L2 SI. Moving
closer to the focus of this study, it is interesting to note that Hattie, Biggs, and
Purdie’s (1996) meta-analysis of SI in L1 education yielded an overall d-value
that is nearly identical to that of this study: 0.45.
As a relatively young field, the comparisons that can be made between
meta-analytic findings from L2 research are few but growing. Overall effects
of meta-analyses in SLA have generally been larger than the effects of SI
(see Oswald & Plonsky, 2010). For example, in one of several meta-analyses
concerned with corrective feedback, Russell and Spada (2006) found an overall
effect of d = 1.16 (cf. Li, 2010, which found an overall d-value of 0.63). Larger
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effects were also found in recent meta-analyses of research on task-based
interaction by Keck, Iberri-Shea, Tracy-Ventura, and Wa-Mbaleka (2006) and
on CALL glosses by Abraham (2008) (d = 0.92 and d = 0.73, respectively).
However, not all effects from SLA have been so large. The results of Spada
and Tomita’s (2010) four separate meta-analyses of the relationship between
types of instruction and types of L2 features found d-values ranging from 0.33
to 0.88, and Lee and Huang’s (2008) synthesis of input enhancement found
an overall effect of d = 0.22. To summarize, although the overall finding of
the present study appears consistent with moderate effects found from other,
related disciplines within the social sciences, it may be viewed as modest
when compared to other bodies of L2 research that have undergone metaanalysis. As the field of SLA continues to accumulate data and additional effects
and relationships are meta-analyzed, a broader base of aggregated findings
will be available to compare and interpret results. (See also Plonsky, 2011,
for data and discussion on the magnitude of effects found across the field
of SLA.)
In addition to assessing the magnitude of its effects, the main finding of this
study can also be understood with respect to its relevance for language learning
and teaching from both theoretical and practical standpoints. Although some
theories of L2 acquisition consider the importance of strategies (e.g., R. Ellis,
1995; McLaughlin, 1987; Young & Perkins, 1995), there has been much less
concern with the role of SI beyond general assertions that cognitive processes
cannot be taught (e.g., Bialystok, 1990; Poulisse, 1993). Future models of
language learning, particularly within instructed SLA, should therefore account
for an accelerated rate of acquisition when learners are taught to self-regulate
using strategies. This study also carries important implications for language
practitioners, such as an empirical justification for integrating learner training
programs into L2 curricula. Scholars in this area, however, emphasize the
importance of context-specific teacher training for SI (Chamot & Küpper,
1989; Chamot & Rubin, 1994). Concomitantly, caution must be exercised in
designing SI programs due to the range of variables found to relate to L2
strategy use and the effectiveness of SI (e.g., Lujan-Ortega & Clark-Carter,
2000; Rossiter, 2005; Yamamorio et al., 2003).
Results from the subgroup analyses indicate a surprisingly clear picture of
how different contexts, treatments, outcomes, and aspects of study quality relate
to the effectiveness of SI. With respect to increased effects of SI for students
of intermediate/advanced over beginner levels of proficiency, it should first be
acknowledged that L2 proficiency is a highly complex construct (Bachman &
Palmer, 1996), and identifying levels for the purposes of subgroup analyses is
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somewhat arbitrary. Furthermore, removing two outliers from the intermediate/advanced subgroup approximates the means and confidence intervals of the
two groups. That is not to say that differences in the effects of SI do not exist
at different levels (cf. Taylor et al., 2006; Vandergrift & Tafaghodtari, 2010).
As discussed in the literature review, several studies have found intermediate
and advanced language learners to use strategies more often and more effectively than novices (Wharton, 2000; cf. Shmais, 2003). Similarly, SI appears to
yield outcomes for language learning and use in which more advanced students
are enabled to more accurately incorporate novel strategies into their strategic
repertoire (i.e., the rich get richer). On the other hand, although not addressed
specifically by this study, ceiling effects may come into play when strategies are
taught to learners who use strategies effectively prior to any formal intervention
(S.-Q. Chen, 1990; Yoshida-Morise, 1998). Taken together, these findings highlight the importance of selecting strategies for SI that are both level appropriate
and that are based on pretreatment measures of strategy use (Chamot & Rubin,
1994; Grenfell & Harris, 1999; Harris, 2003).
The advantage found for SI in second language over foreign language
contexts is perhaps not surprising (see Taylor et al., 2006). In addition to a
different set of motivations, second language learners are aided by increased
exposure to the target language and greater opportunities to practice their
strategies and skills (Dörnyei, 2005). It is also worth noting the substantial
difference in the effects of SI obtained in secondary (d = 0.23) compared to
postsecondary (d = 0.55) institutions. This result may be partially explained by
range restriction; that is, college students are likely a more homogenous group,
thus producing less group variance, which yields larger effect sizes. The large
difference between effects from laboratory and classroom studies—the last of
the contextual variables to be discussed—can be interpreted in a couple of ways.
The data could be viewed in opposition to the claim that SI is most effective
when integrated as part of the L2 curriculum (e.g., O’Malley & Chamot, 1990).
However, the advantage of laboratory settings is more likely due to inherently
greater control over the treatment and other potentially intervening variables.
Similar findings have also been observed in previous meta-analyses of L2
research (e.g., Li, 2010; Mackey & Goo, 2007).
Results from aggregated group effects across different treatment-related
variables emphasize once again the importance of strategy selection in SI. Although instruction on both cognitive and metacognitive strategies is beneficial,
the general finding is that SI is most effective when a less-is-more approach
is adopted (Chamot, 1994). Moderator analyses also revealed larger effects for
longer treatments (see Hattie et al., 1996, for a discussion of the effects of
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length for SI in L1 contexts). From a pedagogical standpoint, these findings
may prompt some to question whether the benefits of SI outweigh the costs.
Several scholars have leveled such critiques, claiming that time in the classroom
is better spent learning and using the language rather than learning how to learn
and use the language (Kellerman, 1991; Rees-Miller, 1993). Alternately, this
finding reinforces the notion that the greatest gains result from SI when learners
are allowed to develop their use of strategies over time (see, e.g., Chamot &
O’Malley, 1994).
With medium to large effect sizes for 7 out of 10 dependent variables, the
results for subgroups based on outcome variables are mixed but mostly in favor
of SI. Although further research may be needed to increase the consistency of
results for some outcome variables (e.g., writing, grammar, pronunciation), the
lack of instruments tested for reliability may be a greater cause for concern
(Dörnyei, 2005; Dörnyei & Skehan, 2003; for examples of how more reliable
measures for strategies research can be developed, see Gao, 2005; Pavičić,
2008, ch. 4; Tseng et al., 2006). In addition to the relatively small number
of studies reporting estimates of measurement reliability, there is evidence
for a relationship between (the reporting of) reliability and the effectiveness
of SI (i.e., d-values of 0.65 and 0.42 were found for studies that did and
did not report reliability of outcome measures, respectively). Interestingly, an
identical pattern of effects was also found in subgroups of studies that employed
(d = 0.65) and did not employ (d = 0.42) random group assignment. It is
not unreasonable to attribute a portion of these differences in effects to the
researchers who carried out and reported on these studies. In other words, it
may be that researchers who insist on adhering to superior research practices are
more likely to produce studies that are based more accurately on relevant theory
and that use more rigorous methods, both of which may contribute to larger
effects. Along with concerns about instrument reliability and other indications
of quality in primary research, this study has also revealed a number of measures
of questionable validity. Maleki (2007), for example, employed a written test to
assess the effect of SI for oral communication. Finally, although opportunities
to practice strategies were present in nearly all studies, the similarity between
practice exercises and dependent measures ranged from identical to starkly
different. Unfortunately, it was not possible to code for the degree to which the
intervention prepared participants for the specific skill or task measured as the
outcome variable. Future reviews might consider training-task similarity as an
additional moderator of the effects of SI (see Perkins & Salomon, 1989, for a
discussion of this issue as it relates to L1 SI).
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Suggestions for Future Research
This article has attempted to provide an indication of the theoretical and practical potential of SI. Like many secondary reviews, however, the completeness
of the present study is contingent on a population of primary studies that has
yet to provide answers to many questions relating to SI. The burden to fill those
gaps rests largely on the community of L2 researchers. However, progress in
this area—and in the field of SLA as a whole—will be obstructed unless more
researchers choose to adopt a synthetically minded approach to their work (see
Norris & Ortega, 2006). Although some decisions about study design in classroom research are necessarily based on convenience, other aspects of carrying
out and reporting on studies are entirely within the control of individual researchers. With these sentiments in mind, this article will conclude by offering
the following suggestions for SI research, many of which echo previous calls
for more general reform of L2 research practices (see, e.g., N. C. Ellis; 2006;
Norris & Ortega, 2000, 2006; Plonsky & Gass, 2011):
1. The value of SI depends greatly on whether and to what extent its effects
last over time, yet only eight of the studies contributing to this metaanalysis included delayed posttests. Therefore, additional measurements
of the persisting effects of SI are needed.
2. Our cumulative knowledge of the effects of SI would also benefit from more
thorough reporting of data, including basic descriptive statistics needed to
aggregate and compare findings across studies, such as standard deviations,
sample sizes, means, confidence intervals, effect sizes, and measurement
reliability. In addition to aiding future reviews of SI, improved reporting
practices will also enable researchers and consumers of primary studies to
contextualize and interpret findings more accurately.
3. More detailed explanations of treatment procedures are needed for findings
to be replicated and compared to other studies (see Carrell, 1998). For
example, 57 of the 61 studies reported providing opportunities for students
to practice the strategies they were taught, yet many of the qualitative and
quantitative aspects of that practice were unclear or omitted.
4. In addition to more complete reporting of procedures and results, the entire
community of language professionals has much to gain from an increased
availability of published and unpublished data. Indeed, the community
of L2 researchers might consider taking steps toward dealing with this
issue from both the top-down (e.g., by implementing stricter standards for
publication) and the bottom-up (e.g., by greater willingness of individuals
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to grant access to synthetically minded researchers for the purposes of
additional or secondary analyses).
5. Although the body of research on SI is extensive, several gaps in the empirical literature remain. In terms of learner populations, additional studies
are needed with second (as opposed to foreign) language, preadolescent,
and advanced learners. This study has also revealed an imbalance among
outcome variables/skills, with further research needed on the effects of SI
on writing (see Plonsky, 2009b), listening, pronunciation, grammar, and
attitudes. Although the effects of instruction on certain strategies have been
studied extensively (e.g., the keyword method), scores of strategies remain
untested (e.g., very few socioaffective strategies have been taught in studies
of SI; for an example of socioaffective SI in the L1 context, see Pintrich &
de Groot, 1990). Additional studies, particularly in these underresearched
areas, will not only clarify the role of potential moderators, but they will
also allow us to examine interactions between moderators. Although not
yet predicted by theory in this area, it is not unreasonable to expect the
effectiveness of SI to be influenced by multiple variables simultaneously.
Consider, for example, the difference in literacy skills between child and
adult L2 learners and how it might relate to unique effects of SI for reading
and writing versus listening and speaking. The larger issue here, however,
is one of intentionality. In order to reliably determine relationships (via
meta-analysis) between the effectiveness of SI and those variables hypothesized to moderate its effectiveness, decisions about learners, outcomes,
strategies, and so forth need to be made deliberately, with due consideration
given not only to the predictions of relevant models but also to the extent
to which the variables of interest have been studied or perhaps overlooked.
6. Finally, reliance on p-values for determining the significance of an effect
presents a major barrier to progress in SI research. This practice, the object
of over five decades of debate and controversy in other social sciences
(e.g., Abelson, 1997; Balluerka, Gómez, & Hidalgo, 2005; J. Cohen, 1994;
Lykken, 1968), has been characterized as an uninformative, unreliable,
and arbitrary means of interpreting data (e.g., Norris & Ortega, 2000;
Oswald & Plonsky, 2010; Plonsky, 2009a; Schmidt, 1996). Null hypothesis
significance testing has surely done the domain of SI research a disservice
by ignoring the magnitude of effects and distilling continuous data into
a crude yes/no dichotomy. For example, a vote-count of studies of SI—a
type of synthesis that tallies and compares the number of studies finding
statistically significant results to those that did not (e.g., Cooper, 1998)—
would grossly overestimate its effectiveness. Moreover, several studies
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contributed practically meaningful d-values from comparisons that failed
to reach statistical significance, whereas others that found a statistically
significant difference (usually due to larger sample sizes) contributed very
modest effect sizes (e.g., Kusiak, 2001).
Conclusion
This article presents a meta-analysis of 95 samples from 61 studies of the
effectiveness of L2 SI. Several variables were found to moderate that effect, including context, age, proficiency, educational level, setting, type and number of
strategies taught, outcome variable, and duration of SI. The study also produced
evidence to support claims of a relationship between certain methodological
characteristics of primary studies (pretesting, random group assignment, reporting of reliability) and the effects of SI they produce.
In closing, I would like to mention that the intent behind this article is not to
provide what would surely be a premature closure on a lasting and productive
line of L2 inquiry. On the contrary, the retrospective value of the current study
is perhaps no greater than its prospective value. In addition to summary results
and interpretations of hypotheses both tested and untested in primary studies,
this article has shown the interplay of variables that affect SI to be complex and
in need of further study in order to fully understand the many implications of
SI for both theory and practice.
Revised version accepted 5 May 2010
Notes
1 The definition of a strategy has been the object of much debate. Disagreements
exist over (a) whether strategies should be characterized as internal or external
mechanisms (Wenden, 1987), (b) whether a strategy is a plan of action or the action
itself (Phakiti, 2003), and (c) the definitional equivalency of terms often used to
describe or define strategies (e.g., tactic, process, action, technique) (see Oxford,
1990). See Macaro (2006) for a thorough discussion of this issue.
2 This topic also highlights another challenge to secondary accounts of SI—the
subjectivity with which individual researchers define the proficiency of their
participants.
3 Some claim that communication strategies need not be taught, assuming transfer
from the L1 (Kellerman, 1991). Others (e.g., Wolfersberger, 2003) have argued that
although transfer of strategies is possible, students need assistance identifying their
L1 strategies and applying them to L2 tasks. An anonymous reviewer pointed out,
however, that listening strategies may represent an exception to this tendency
because they are rarely taught in the L1 or L2.
Language Learning 61:4, December 2011, pp. 993–1038
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4 It should be noted that unpublished studies were not excluded; on the contrary, I
was active in seeking out relevant, unpublished research. Nevertheless, citations of
fugitive literature are relatively uncommon, and all but a few (two or three) of the
studies were peer-reviewed and/or published.
5 Valentine and Cooper (2003) cautioned that these rules of thumb are meant for the
social sciences in general and that J. Cohen (1988) himself recommended that
effect sizes are most useful when understood in relation to other, field-specific
effects. For example, it would not be appropriate or particularly useful to interpret
the magnitude of effects from education compared to economics or physiological
psychology. Oswald and Plonsky (2010), likewise, insisted that the same principle
can be applied to findings from different domains of SLA research, which must
develop their own standards for practical significance.
6 In order to facilitate replication and/or reanalysis, the dataset used in this study will
be made available upon request.
7 When the outlier is removed, the average effect size for second language and
foreign language settings is roughly equal (d = 0.61 and 0.46, respectively), with
overlapping confidence intervals.
8 For practical reasons, most SLA research involves the use of intact classes.
Although convenient for classroom-based research, intact classes can restrict the
research design by impeding random assignment to experimental conditions (Hatch
& Lazaraton, 1990). One of the aims of this article was to measure the relationship
between the effectiveness of SI and certain aspects of quality. Therefore, for the
purposes of this study, random group assignment was designated to any study that
randomly assigned participants, whether individually or as a class, to a treatment or
control condition.
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Appendix
Effects and n sizes from All 95 Contributing Samples
Sample
n
d
Sample
n
d
Atay & Ozbulgan
(2007)
Avila & Sadoski
(1996)
Ayaduray & Jacobs
(1997)
Barnett (1988a)
Barnett (1988b)
Bejarano et al. (1997)
Bimmel et al. (2001)
Bouvet & Close (2006)
Campos et al. (2004)
25
0.97
Macaro & Erler (2007)
62
0.62
18
0.72
Maleki (2007)
30
1.22
16
3.56
Moore & Surber
(1992)
25
0.78
108
51
15
12
5
91
91
91
85
0.17
0.20
0.38
0.26
1.33
0.57
0.76
0.66
0.45
13
9
28
11
7
15
14
18
31
0.80
−0.54
0.84
−0.17
0.03
0.02
0.24
0.43
0.29
85
85
0.39
0.60
135
28
1.08
1.09
Morin (2003)
Morin (2006)
Morin & Goebel
(2001)
Najar (1999)
Nakatani (2005)
Continued
Language Learning 61:4, December 2011, pp. 993–1038
1036
Plonsky
Meta-analysis of L2 Strategy Instruction
Appendix
Continued
Sample
n
d
Sample
n
d
Carrell (1985)
Chularut & DeBacker
(2004)
14
20
1.54
1.33
Naughton (2006)
O’Malley et al. (1985)
24
27
1.56
0.66
20
7
53
89
62
84
42
126
3.92
−0.76
0.36
0.78
1.74
−1.05
−1.61
0.22
0.27
0.27
0.52
2.47
1.33
0.64
1.96
0.98
130
8
0.58
−0.20
Rasekh & Ranjbary
(2003)
Raymond (1993)
Rao (2007)
26
30
29
12
12
22
15
27
21
39
0.55
0.52
8
8
32
40
29
−0.03
−0.23
0.02
0.10
0.52
Rico (2008)
Rubin (1990)
Saif (2008)
Schueller (2009)
39
10
294
36
30
0.46
0.75
−0.25
1.18
1.47
39
8
0.08
0.36
24
14
0.75
0.89
11
0.96
14
−1.47
18
22
65
0.67
−0.05
0.65
Sengupta (2000)
35
35
48
−0.27
0.43
0.76
21
0.47
Thomas & Wang
(1996)
13
−0.07
Cross (2009)
Dornyei (1995)
Dreyer & Nel (2003)
El-Koumy (1999)
Feyten et al. (1999)
Flaitz et al. (1995)
Gladwin &
Stepp-Greany
(2008)
Graham (2007)
Graham & Macaro
(2008)
Hamp-Lyons & Proulx
(1982)
Hogben & Lawson
(1997)
Huang & Ma (2007)
Ikeda & Takeuchi
(2003)
Pappa et al. (2003)
Pressley et al. (1980)
Soleimani (2008)
Continued
1037
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Plonsky
Meta-analysis of L2 Strategy Instruction
Appendix
Continued
Sample
n
d
Jin (2002)
Kern (1989)
Kern (2005)
Kimura et al. (1993)
Kitajima (1997)
Kusiak (2001)
23
26
26
29
13
78
−0.25
0.56
0.65
0.010
1.00
0.18
Lam (2006)
20
−0.02
Lam (2009)
Lawson & Hogben
(1998)
Liu et al. (2007)
20
14
0.55
1.39
30
0.80
Sample
Walters (2006)
Vandergrift &
Tafaghodtari (2010)
Viswat & Jackson
(1994)
Zhang (2008)
Zhicheng (1992)
n
d
13
14
14
14
11
59
1.27
0.41
0.39
−0.79
−0.24
0.57
150
0.22
50
15
2.71
0.93
Note. Each row represents a unique sample.
Language Learning 61:4, December 2011, pp. 993–1038
1038