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Effectiveness of Interventions for English Learners with Word Reading Difficulties: A Research Synthesis

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Title: Effectiveness of Interventions for English Learners with Word Reading Difficulties: A Research Synthesis
Language: English
Authors: Solari, Emily J. (ORCID 0000-0002-8498-9834), Kehoe, Karen F., Cho, Eunsoo, Hall, Colby, Vargas, Isabel, Dahl-Leonard, Katlynn, Richmond, Cassidi L., Henry, Alyssa R., Cook, Lysandra, Hayes, Latisha, Conner, Carlin
Source: Learning Disabilities Research & Practice. Aug 2022 37(3):158-174.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 17
Publication Date: 2022
Document Type: Journal Articles
Information Analyses
Reports - Research
Education Level: Elementary Education
Descriptors: Instructional Effectiveness, Intervention, English Language Learners, English (Second Language), Second Language Learning, Word Recognition, Reading Difficulties, Meta Analysis, Elementary School Students, Reading Instruction
DOI: 10.1111/ldrp.12286
ISSN: 0938-8982
1540-5826
Abstract: This study meta-analyzed the last four decades (1980-2020) of reading intervention research focused on improving reading outcomes for English language (EL) students in Grades K-5 with or at risk for word reading difficulties. Experimental and quasi-experimental group design and single-case experimental design (SCED) studies were included; 10 group design and 7 SCED studies met inclusion criteria (m = 61; total student N = 2,270). Visual inspection of the effect size distribution revealed that the assumption of between-study heterogeneity was not supported; therefore, the findings were synthesized for SCED studies separately from those reported in group design studies. Implications for practice, policy, and future research are discussed.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1351536
Database: ERIC
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  Value: <anid>AN0159630099;7mj01aug.22;2022Oct14.06:20;v2.2.500</anid> <title id="AN0159630099-1">Effectiveness of Interventions for English Learners with Word Reading Difficulties: A Research Synthesis </title> <p>This study meta‐analyzed the last four decades (1980–2020) of reading intervention research focused on improving reading outcomes for English language (EL) students in Grades K–5 with or at risk for word reading difficulties. Experimental and quasi‐experimental group design and single‐case experimental design (SCED) studies were included; 10 group design and 7 SCED studies met inclusion criteria (m = 61; total student N = 2,270). Visual inspection of the effect size distribution revealed that the assumption of between‐study heterogeneity was not supported; therefore, the findings were synthesized for SCED studies separately from those reported in group design studies. Implications for practice, policy, and future research are discussed.</p> <p>In public schools across the United States, English learners (ELs) constitute a large and fast‐growing student demographic (National Center for Educational Statistics [NCES], 2020). Specifically, in the fall of 2018, more than 10% of the nation's K–12 students were classified as ELs, representing approximately 5 million students (NCES, 2020). Nationwide, the number of ELs in school grew by 28% from 2000 to 2016, with 17 states seeing a greater than 100% increase (Office of English Language Acquisition, 2020).</p> <p>National data demonstrate that many children who are ELs are not able to read proficiently; for example, the majority of fourth‐grade EL students are not reading at a proficient level (10%; National Association of Education Progress, 2019). Complex and interrelated factors explain disparities in elementary‐grade reading performance between EL and English monolingual (EM) students. Demographic data suggest that Hispanics experience systemic inequities in access to education, healthcare, and economic opportunities, and communities with large proportions of ELs experience higher poverty rates (National Center for Education Statistics, 2019). As a result, more ELs attend schools of poorer quality and with fewer resources and experienced teachers than do EM students (Cosentino de Cohen et al., 2005; Gándara et al., 2003; Office for Civil Rights, 2016). Income disparities also factor into fewer out‐of‐school‐time (OST) opportunities available to ELs, both before and after the instructional day and over the summer months (Cosentino de Cohen et al., 2005), which may further exacerbate any disparities in early literacy performance that are present at school entry.</p> <p>Given the growth of this population of students nationwide, it is imperative that we develop a better understanding of the instructional practices that efficiently and effectively improve their early literacy skills. In contrast to recent research syntheses and meta‐analyses that have investigated mean effects of interventions on literacy development for diverse populations of EL students, this study concentrated on analyzing the effects of reading interventions on reading outcomes for ELs who have severe word reading difficulties (EL students with dyslexia).</p> <p>The Simple View of Reading (SVR; Gough & Tunmer, 1986), an empirically validated framework for understanding reading comprehension development for both English monolingual (EM) students (Catts et al., 2005; Cutting & Scarborough, 2006; García & Cain, 2014; Kim, 2017, 2020; Oakhill & Cain, 2012; Solari et al., 2021) and ELs (Grimm et al., 2018; Joshi et al., 2015; Lervåg et al., 2018), postulates that both decoding and linguistic comprehension are necessary for reading comprehension. When a child exhibits weaknesses across both domains (i.e., both decoding and linguistic comprehension), they are at greater risk for developing reading difficulties (RD) than a child with either decoding or linguistic deficits.</p> <p>Linguistic comprehension in English has been found to explain more variance in reading comprehension for elementary‐grade EL students with reading comprehension difficulties than for elementary‐grade EM students (Cho et al., 2019). However, not all Els have difficulties with reading comprehension solely as a result of poor English linguistic comprehension. Word‐level reading difficulties (WLRD) remain an important source of poor performance on reading comprehension for EL students (Cho et al., 2019). Although ELs with WLRD are likely to benefit from interventions focusing on language comprehension, because such interventions support their developing proficiency in spoken English, they are unlikely to improve reading outcomes for this population unless they also attend to the foundational reading skills that contribute to decoding, encoding and reading fluency.</p> <p>It can be difficult to discern whether an EL student's WLRD stem primarily from (a) still‐developing English language skills, (b) lack of exposure to instruction, or (c) underlying weaknesses in phonological processing and other foundational skills that support decoding and encoding. When educators attribute ELs' WLRD to gaps in language knowledge alone, they may fail to implement reading interventions that seek to improve foundational skills or knowledge (e.g., phonemic awareness, grapheme‐phoneme correspondences) that research has shown to be critical for reading success (e.g., Gersten et al., 2020; Wanzek et al., 2016, 2018). Better understanding the effectiveness of early literacy instruction of students who are both EL and have WLRD is important to inform instruction for this population of students.</p> <hd id="AN0159630099-2">EVIDENCE‐BASED EARLY LITERACY INSTRUCTION FOR STUDENTS WITH READING DIFFICULTIES</hd> <p>A considerable research base has identified features of effective instructional interventions for EM students with RD (e.g., Slavin et al., 2011; Wanzek et al., 2016, 2018). Gersten et al. (2020) systematically synthesized 33 rigorous experimental and quasi‐experimental reading intervention studies with students with RD in Grades 1–3. The authors reported a mean effect of reading interventions on combined reading outcomes of.39 (<emph>p</emph> <.001), with interventions yielding the largest effects on word and pseudoword reading outcomes (<emph>g</emph> = 0.41), followed by reading comprehension (<emph>g</emph> = 0.32) and passage reading <emph>(g</emph> = 0.31).</p> <p>Other research reviews have identified instructional practices (e.g., Baker et al., 2012; Gersten et al., 2007) and interventions (Ludwig et al., 2019; Richards‐Tutor et al., 2016; Roberts et al., 2021) that support reading development for elementary‐aged ELs. However, because ELs included in meta‐analyzed studies may have demonstrated RD stemming from linguistic comprehension, decoding difficulties, or both, it is unclear whether the practices or interventions described in these reviews demonstrate efficacy in improving reading outcomes for the subset of ELs who struggle with reading because of poor decoding skills or have profiles of dyslexia. For EL students with dyslexia or specific word reading difficulties (SWRD), it is likely that interventions will be most effective when they integrate foundational skills training with efforts to promote language development (Roberts et al., 2021; Snyder et al., 2017). Yet the extant research examining intervention effects on literacy outcomes for this population has yet to be synthesized.</p> <hd id="AN0159630099-3">PRIOR SYNTHESES OF INTERVENTION RESEARCH FOR ENGLISH LEARNERS IN THE United States</hd> <p>In the last 20 years, several research syntheses and meta‐analyses have investigated mean effects of interventions for elementary‐grade ELs (Cheung & Slavin, 2012; Hall et al., 2017; Ludwig et al., 2019; Richards‐Tutor et al., 2016; Roberts et al., 2021; Snyder et al., 2017) and identified factors that are associated with positive effects on early reading outcomes.</p> <p>For example, Cheung and Slavin (2012) conducted a best‐evidence synthesis examining the effects of whole‐class and/or small‐group reading instruction on reading outcomes of Spanish‐dominant ELs. Study outcomes included both standardized and researcher‐created assessment of reading. Included studies had to measure comprehension but may also have measured other subcomponent skills, such as decoding (Cheung & Slavin, 2012). Overall effects of reading instruction on reading outcomes were small (effect size [ES] =.23), but the researchers noted considerable variation across studies. Programs that produced the largest effects incorporated explicit instruction, a small‐group or one‐on‐one instructional format, and opportunities for collaborative learning (Cheung & Slavin, 2012). It is worth noting that, although this review was published in 2012, all but two included studies were published prior to 2000, with the majority of studies (10 of 13) published in the 1970s.</p> <p>More recently, Richards‐Tutor et al. (2016) and Ludwig et al. (2019) conducted meta‐analyses examining the effects of reading interventions on reading outcomes for ELs in Grades K–12. Studies analyzed by Richards‐Tutor et al. (2016) were published in peer‐reviewed journals between 2000 and 2012 whereas Ludwig et al. (2019) included both published and unpublished studies conducted between 1990 and 2018.</p> <p>Richards‐Tutor et al. (2016) found generally positive intervention effects on reading outcomes, including comprehension (reading cloze, reading passages, listening comprehension), phonological awareness (PA), phonics/word reading, and fluency, for elementary‐grade ELs with or at risk for learning disabilities (LDs). Among the nine studies in which all participants were enrolled in Grades K–5 and in which interventions included instruction in word‐ or passage‐reading fluency or foundational skills, effect sizes ranged from <emph>g</emph> = –0.62 to 1.24 (median, <emph>g</emph> = 0.30) on standardized measures of reading skills and from <emph>g</emph> = –0.74 to 0.90 (median, <emph>g</emph> = 0.57) on researcher‐developed measures. Effects were mostly positive for studies in which all participants were primary‐grade students (i.e., K–2), ES ranging in size from <emph>g</emph> = –0.39 to 1.24, with a median of <emph>g</emph> = 0.47. Studies that included students in third through fifth grade tended to report smaller effect sizes, ranging from <emph>g</emph> = –0.62 to 1.09, with a median ES of <emph>g =</emph> 0.12. Ludwig et al. (2019) reported larger effects (range, <emph>d = –</emph>0.00 to 3.41) across 26 studies on reading accuracy, fluency, and comprehension outcomes. Although intervention effects tended to be larger in studies in which EL participants were in kindergarten and first grade (<emph>N</emph> = 17) than in studies with ELs in Grades 2–12 (<emph>n</emph> = 5), grade level was not a significant moderator. Both studies reported effects by outcome domain (i.e., PA, real/nonword reading, reading fluency, comprehension). Richards‐Tutor et al. (2016) reported largest effect sizes on PA outcomes (median, <emph>g</emph> = 0.91). Ludwig et al. (2019) reported the largest effect sizes for reading accuracy (mean, <emph>d</emph> = 1.22). Both syntheses found smallest effects for reading comprehension outcomes: Richards‐Tutor et al. (2016) reported median ES of <emph>g</emph> = –0.01; Ludwig et al. (2019) reported mean ES of <emph>d</emph> = 0.50.</p> <p>A meta‐analysis by Roberts et al. (2021) included studies published in peer‐reviewed journals from 2000 to 2020 that investigated the effects of interventions on reading outcomes for ELs in Grades K–2 yielding 76 effect sizes on standardized and unstandardized foundational skills, reading fluency, oral language, and reading comprehension outcomes with an overall mean ES on combined reading outcomes of <emph>g</emph> = 0.23. Hall et al. (2017) synthesized peer‐reviewed intervention research published from 1995 to 2015 with samples of ELs in Grades 4–8. Across 11 studies, authors reported an overall ES of <emph>g</emph> = 0.35 on standardized and unstandardized measures of reading comprehension, vocabulary, content knowledge, and writing. When considering standardized measures alone, however, effects reported in both reviews were considerably smaller: <emph>g</emph> = 0.01 in Hall et al. (2017) and <emph>g</emph> = –0.09 to 0.33 in Roberts et al. (2021). In contrast to Richards‐Tutor et al. (2016), who found largest effect sizes for PA (range, <emph>g</emph> = 0.38 to 1.24, median, <emph>g</emph> = 0.91) and few significant effects on fluency and comprehension, Roberts et al. (2021) reported larger mean effects for fluency (<emph>g</emph> = 0.30) than for both foundational skills (i.e., word reading and PA; <emph>g</emph> = 0.27) and comprehension (<emph>g</emph> = 0.27). Of the studies in Hall et al. (2017), all but four included EL participants in Grades 6–8. Of the four studies, in which all students were enrolled in fourth or fifth grade, only two reported effects of interventions on standardized measures of comprehension: <emph>g</emph> = 0.13 and <emph>g</emph> = 0.14.</p> <p>Taken together, the findings of these reviews suggest that interventions designed to improve literacy outcomes for ELs in the early elementary grades (i.e., K–2) may be more effective, on average, than those implemented with ELs in upper‐elementary school (i.e., Grades 3–5). Findings also suggest that it may not be useful to compare the effects on standardized outcomes with those on unstandardized or combined outcomes, as effects on the former tend to be significantly smaller than those on unstandardized outcomes (Slavin & Madden, 2011; Swanson, 1999).</p> <hd id="AN0159630099-4">Inclusion of Single‐Case Experimental Designs in Research Synthesis</hd> <p>Given the challenge of recruiting large samples of ELs with WLRD to sufficiently power experimental and quasi‐experimental group design studies, there is a need for a systematic review of studies that have used diverse methodological approaches. Single‐case experimental designs (SCED; e.g., multiple‐baseline, alternating‐treatment designs) enable inferences about the causal effects of interventions using smaller sample sizes. Two recent reviews of intervention research with ELs (Baker et al., 2018; Snyder et al., 2017) included SCED studies. Baker et al. (2018) estimated mean intervention effects for the same population of ELs (i.e., those at risk for academic difficulties or with diagnosed LD), including exclusively SCED research published from 2000 to 2015. Of the reading intervention studies included in Baker et al.'s (2018) review, one (Viel‐Ruma et al., 2010) included older ELs (i.e., in Grades 9–11) and three studies (Rahn et al., 2015; Ross & Begeny, 2011; Santoro et al., 2006) included elementary students but did not meet the requirements for the current synthesis for a variety of reasons: They (a) compared two treatment conditions with no acceptable comparison condition (Ross & Begeny, 2011); (b) had too small a sample size to calculate an effect size (Rahn et al., 2015); or (c) included EL participants with broad RD who did not necessarily show evidence of WLRD (Santoro et al., 2006). Snyder et al. (2017) included both group design and SCED studies from a similar time period (2003–2015). Like in Baker et al. (2018), the studies in Snyder et al. (2017) also focused on K–12 ELs; however, Snyder et al. (2017) did not require study participants to have or be at risk for academic difficulties.</p> <p>Comparing the findings among these prior syntheses is complicated by sample differences. Specifically, Baker et al. (2018) and Richards‐Tutor et al. (2016) examined intervention effects for ELs with or at risk for (broadly defined) RD only, while other studies (Hall et al., 2017; Ludwig et al., 2019; Roberts et al., 2021; Snyder et al., 2017) included studies with samples of ELs who were and were not at risk for RD. No reviews to date have specifically addressed empirical questions about the efficacy or reading instruction for elementary‐aged EL students who have WLRD.</p> <hd id="AN0159630099-5">Variation in Study Participants' First Language</hd> <p>In addition to differences in risk status, first language of study participants also varied among these prior syntheses of EL intervention research. Specifically, Cheung and Slavin (2012) restricted their review to studies with samples of Spanish‐speaking ELs only. Even though first language was not an inclusion criterion in any of the other syntheses reviewed above, a large majority of participants in the studies included by Hall et al. (2017), Ludwig et al. (2019), Richards‐Tutor et al. (2016), and Snyder et al. (2017) spoke Spanish as their first language. Thus, findings reported by these researchers most accurately reflect the effects of reading interventions on predominantly Spanish‐speaking ELs, which constitute approximately three‐fourths of the EL student population in the United States (NCES, 2020).</p> <p>Only Baker et al. (2018), who included studies published between 2000 and 2015, explicitly reported the inclusion of three studies with non‐Spanish‐speaking ELs. Yet, the first languages of ELs in several recent studies (e.g., Dussling, 2016; Jozwik & Mustian, 2020; Wise et al., 2016) have included Arabic, Bosnian, Cantonese, and Turkish. With the increasing linguistic diversity of ELs (NCES, 2020), there is a need to synthesize previous findings with current reading intervention research on samples of ELs for whom Spanish is not the only native language.</p> <hd id="AN0159630099-6">THE PRESENT STUDY</hd> <p>In order to extend recent research, this study concentrated on intervention effects for elementary‐aged (i.e., Grades K–5) EL students from multiple language backgrounds who demonstrate WLRD (i.e., dyslexia) rather than any type of broadly defined reading or language difficulties. Further, we meta‐analyzed effects reported in both experimental and quasi‐experimental group design and SCED studies conducted between 1980 and 2020. We classified as SCD studies that adhered to these principles and included sufficient opportunities to demonstrate a functional relation between a researcher‐manipulated variable—in this case, a reading intervention with a foundational skills component, implemented with ELs who have or at risk for WLRD (Ledford & Gast, 2018)—and reading outcomes.</p> <p>The methods we used to search for and calculate effect sizes for the SCED studies were novel. First, we included SCED studies that appeared in the gray literature (e.g., unpublished dissertations) as well as those that were published in peer‐reviewed journals, provided the studies met our inclusion and quality criteria. This allowed us to include a larger body of SCED studies focused on reading interventions for elementary ELs (<emph>N</emph> = 7) than either Baker et al. (2018; i.e., <emph>N</emph> = 5) or Snyder et al. (2017; i.e., <emph>N</emph> = 1). Second, we used the same between‐case standardized mean difference (BC‐SMD; Hedges et al., 2012, 2013) effect size metric across both SCED and group‐design studies, which allowed us to draw comparisons between ES obtained from studies that used different methodologies to investigate mean effects of interventions for ELs with or at risk for WLRD. This has not been done previously in systematic reviews on this topic. That is, Baker et al. (2018) included only SCED studies and used <emph>d</emph>‐statistics (Marso & Shadish, 2015) that assumed no trend effect and a treatment effect that was constant between cases, assumptions that were not tenable in the included studies. Moreover, Baker et al. (2018) calculated effect sizes for individual studies and did not meta‐analyze to calculate the mean effects. Further, Snyder et al. (2017) included both SCED studies and group‐design studies but they did not meta‐analyze the effects. In the present review, while we conducted separate meta‐analyses for each study type (i.e., group design vs. SCED), we used the same BC‐SMD (Hedges et al., 2013) effect size metric across both groups of studies. To our knowledge, ours is the first synthesis on reading interventions for EL to take this approach.</p> <p>The purpose of the review is twofold: (a) to describe the reading interventions that have been studied as a means of improving reading outcomes for ELs with or at risk for WLRD and (b) to estimate the mean effects of reading interventions and explore whether effects vary for different outcome types. We address the following research questions.</p> <p></p> <ulist> <item> What are the characteristics (e.g., methodology, participant sample, intervention components, outcomes measured) of included studies that examine the effects of reading interventions on reading outcomes for ELs with or at risk for WLRD?</item> <p></p> <item> For group design studies, what is the mean effect of reading interventions that have a foundational reading skills component on combined reading outcomes for ELs with or at risk for WLRD in Grades K–5? Do effects vary for different outcome types (i.e., PA, word reading/decoding, spelling, oral reading fluency, comprehension)?</item> <p></p> <item> For SCED studies, what is the mean effect of reading interventions that have a foundational reading skills component on combined reading outcomes for ELs with or at risk for WLRD in Grades K–5? What are the mean effects on nonword reading fluency and oral reading fluency of connected text?</item> </ulist> <hd id="AN0159630099-7">METHOD</hd> <p></p> <hd id="AN0159630099-8">Search Procedures</hd> <p>The review began with a search of peer‐reviewed articles and gray literature published in English between January 1, 1980, and January 1, 2020. Studies were identified through a four‐step process: (a) identification of studies through searches of electronic databases; (b) screening of abstracts; (c) identification of studies through other sources; and (d) full‐text review.</p> <hd id="AN0159630099-9">Identification of Studies through Electronic Databases and Registers</hd> <p>First, a search of electronic databases ERIC and PsycINFO was conducted, applying the following Boolean search terms to identify study samples: dyslex*, reading difficult*, reading disabilit*, risk for reading difficult*, risk for reading disabilit*, reading delay*, reading disorder*, learning disab* the types of reading interventions under study: reading interven*, reading instruction, phon*, correspondence*, reading fluency. This step identified 16,395 studies, 2,643 of which were duplicates and were removed prior to screening.</p> <hd id="AN0159630099-10">Screening</hd> <p>Second, a team of nine reviewers screened the abstracts of the remaining 13,752 studies, with each abstract independently screened by two reviewers. Studies were recommended for full‐text review if (a) participants were in Grades K–5; (b) instruction took place over more than one session; (c) the intervention included instruction in word reading accuracy/fluency, passage reading accuracy/fluency, or skills foundational to word reading (i.e., PA, knowledge of grapheme‐phoneme correspondences); (d) the primary language of instruction was English; (e) the intervention was school‐based; (f) the study included both pre‐ and postintervention measures of at least one code‐related skill; (g) the study design was an experimental or quasi‐experimental group design or SCED; and (h) the study included sufficient information to enable the calculation of at least one effect size on an outcome of interest (i.e., PA, word/nonword reading, oral reading fluency, reading comprehension). If this information could not be ascertained from the abstract, a study was recommended for full‐text review. All disagreements were resolved by the fourth author. Abstract screening resulted in 550 texts that were reviewed in full for study eligibility.</p> <hd id="AN0159630099-11">Identification of Studies through Other Sources</hd> <p>We also conducted ancestral searches of 11 prior meta‐analyses and syntheses: Baker et al. (2018), Cheung & Slavin (2012), Gersten et al. (2020), Hall et al. (2017), Ludwig et al. (2019), Richards‐Tutor et al. (2016), Roberts et al. (2021), Snyder et al. (2017), Suggate (2016), and Wanzek et al. (2016, 2018). These searches yielded a combined total of 182 studies, 73 of which were duplicates either across studies or because they had already been identified through our database search. The remaining 109 studies were reviewed in full for study eligibility.</p> <hd id="AN0159630099-12">Full‐Text Review</hd> <p>Together, our search of electronic databases and ancestral search yielded a total of 659 studies that required full‐text review. The first step in the full‐text review process was to determine if sample participants were ELs in K–5 (or, if grade levels not provided, if the participant sample had a mean age between 5 and 11 years old) and demonstrated dysfluent text reading, difficulty reading words/nonwords in isolation, or weakness in one or more skills that are foundational to word reading (e.g., PA, grapheme‐phoneme correspondence knowledge). At least 50% of the analytic sample in group design studies and at least three participants in SCED studies had to meet these criteria in order to be included. Three hundred ninety‐five studies did not meet this criterion and, therefore, were excluded without further review. The remaining 264 studies were determined to include a suitable sample and were reviewed in full by two independent coders against predetermined inclusion criteria, described below. The second and fourth authors resolved all disagreements. Figure 1 depicts a PRISMA (preferred reporting items for systematic reviews and meta‐analyses; Moher et al., 2009) diagram displaying the number of studies screened and reviewed at each step in the search process.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/7MJ/01aug22/ldrp12286-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ldrp12286-fig-0001.jpg" title="1 PRISMA diagram. [Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <hd id="AN0159630099-14">INCLUSION AND EXCLUSION CRITERIA</hd> <p>To investigate mean intervention effects on the reading outcomes of K–5 ELs with or at risk for WLRD, studies were included if they met all of the following criteria:</p> <p></p> <ulist> <item> Participants were in Grades K–5 or the mean age of participants was 5.0–11.0 years old.</item> <p></p> <item> At least 50% of study participants were ELs who demonstrated evidence of WLRD or weakness in one or more foundational skills underlying word reading and/or spelling (see the <emph>Word‐Level Reading Difficulties</emph> section below for further details) <emph>or</emph> results were disaggregated for a subset of students meeting this description.</item> <p></p> <item> Instruction occurred over more than one session.</item> <p></p> <item> The intervention included instruction in at least one of the following components: PA; phonics; decoding; encoding (spelling); word reading accuracy or fluency; passage reading accuracy; or fluency.</item> <p></p> <item> Instruction was delivered in English.</item> <p></p> <item> The setting was participants' schools (including after‐school and summer interventions).</item> <p></p> <item> The study reported intervention effects on at least one of the following outcomes in English: PA; decoding (of either real words or pseudowords that follow permitted English spelling patterns); spelling; oral passage reading fluency; or comprehension.</item> <p></p> <item> The study had an experimental or quasi‐experimental group design or SCED that investigated the effects of a researcher‐manipulated intervention on the outcomes of ELs with or at risk for WLRD.</item> <p></p> <item> There was an acceptable treatment‐comparison contrast, including business‐as‐usual (BAU) classroom reading instruction; BAU exposure to nonreading instruction or no instruction; a researcher‐manipulated nonreading intervention; or a research‐manipulated intervention that did not include training in word reading, fluency, or foundational skills. A group design study must have contrasted the outcomes of treated ELs to a similar group of ELs in an acceptable comparison condition. An SCED study must have contrasted outcomes of a case during intervention to that case's outcomes during baseline if the case was exposed to acceptable comparison condition at baseline.</item> <p></p> <item> The study provided enough information to enable independent calculation of effect size. Eighteen studies (10 group design, 8 SCED) met the above inclusion criteria. One SCED study (Bliss et al., 2006) was later excluded because it contained only one case, which did not permit calculation of BC‐SMD. This left a total of 17 studies that were included in the current synthesis.</item> </ulist> <hd id="AN0159630099-15">WORD‐LEVEL READING DIFFICULTIES (WLRD)</hd> <p>To determine whether study participants met the second criterion, it was necessary to establish parameters around acceptable evidence that ELs had or were at risk for WLRD. Some researchers use performance below a given cut point (e.g., the 25th or 16th percentile) on normed measures of reading achievement as evidence of dyslexia (Fletcher et al., 2018). In contrast, although an IQ‐achievement discrepancy model is no longer the gold standard for diagnosing learning disabilities, including dyslexia (Miciak & Fletcher, 2020), this model continues to predominate in some schools and districts and was used to identify participants' WLRD in studies included in our review. Other included studies relied on a more evidence‐based approach to identifying WLRD, incorporating information about how well a child responded to evidence‐based reading instruction within a response to intervention (RTI) or multitiered systems of support (MTSS) framework (Miciak & Fletcher, 2020).</p> <p>Given the dual challenges of inconsistent criteria applied to the diagnosis of dyslexia and the under‐ and/or late identification of this disorder within the EL population (O'Connor et al., 2013; Samson & Lesaux, 2009), we intentionally elected to accept as evidence any one of multiple possible indicators of current or potential WLRD among EL study participants rather than applying a strict cut‐point. In eight studies (Apichatabutra, 2009; Arriaza de Allen, 2010; Baker et al., 2016; Gunn et al., 2005; Gyovai et al., 2009; Landa & Barbetta, 2017; Vaughn et al., 2006a, 2006b), students' performance fell into the "at risk" or "some risk" (i.e., intensive or strategic) categories on the Dynamic Indicators of Basic Early Literacy Skills (DIBELS; Good and Kaminski, 2002). In four studies (Jozwik & Mustian, 2020; Vaughn, 2006a, 2006b; Wise et al., 2016), EL participants performed at or below the 30th percentile on norm‐referenced assessments of word reading/foundational skills. In two studies (Vadasy & Sanders, 2010, 2011), students scored below 1.5 <emph>SD</emph>s below the mean on the Comprehensive Test of Phonological Processing (CTOPP; Wagner et al., 1999). Jacob et al. (2016) restricted their sample to students in Grades 2–5 who were half a year to 2.5 years behind grade level, as measured by the Sight Word Efficiency subtest of the Test of Word Reading Efficiency (TOWRE; Torgesen et al., 1999) and the AimsWeb (Shinn et al., 2004) oral reading fluency task. In one SCED study (McCarty, 2012), all EL participants had a diagnosis of LD, with documented difficulties with decoding and/or reading fluency. In two studies (Denton et al., 2004; Dussling, 2016), ELs' performance on the Word Attack and Word Identification subtests of the Woodcock Reading Mastery Test—Revised (WRMT‐R; Eaves, 1990) provided evidence of WLRD. Finally, in Ehri et al. (2007), first‐grade ELs were eligible for intervention if they could name at least 17 letters but struggled with oral reading a preprimer‐level passage on an informal reading inventory.</p> <p>We excluded studies in which ELs were identified as struggling with reading comprehension, vocabulary knowledge, English oral language proficiency, or overall reading when this identification was not also supported by evidence of WLRD or risk factors associated with WLRD. For example, in Denton et al. (2004), ELs were referred to intervention due to low reading performance and then separated into two groups based on pretreatment assessments. In the current review, analyses included <emph>only</emph> EL students assigned to the Read Well intervention (and comparable students assigned to control) based on their low WRMT‐R Word Attack (<emph>M</emph> = 86.8–88.9) and Word ID standard scores (<emph>M</emph> = 84.7–87.9). ELs assigned to the Read Naturally intervention, who had more typical word reading scores (<emph>M =</emph> 93.4–97.6) but struggled with passage comprehension (<emph>M</emph> = 87.6–89.3), were excluded from our analyses.</p> <hd id="AN0159630099-16">CODING PROCEDURES</hd> <p></p> <hd id="AN0159630099-17">Coder Training</hd> <p>Each of the 16 studies was coded by at least two members of a 10‐person coding team that included 7 graduate students, 1 postdoctoral fellow, and 2 associate professors from the curriculum, instruction, and special education and educational psychology departments of their university's school of education. Prior to coding, each coder underwent a two‐hour training and was required to achieve 90% reliability with a gold standard on a practice study.</p> <hd id="AN0159630099-18">Study Coding</hd> <p>We coded for participant, intervention/comparison, outcomes, and methodological characteristics of each study. Participant characteristics included grade level(s), native language, and eligibility for free or reduced‐price lunch (FRPL). We also tracked the name of each intervention; whether instruction was delivered in a one‐on‐one or small‐group format; the position of the individual(s) delivering instruction (i.e., classroom, special education, or bilingual teacher; reading specialist; paraprofessional; member of a research team; computer‐delivered intervention; other); and intended dosage, or the total amount of instructional time in minutes, the intervention was designed to provide. Additionally, we coded for incorporation of specific instructional components: PA; decoding, morphology; encoding; text reading fluency; vocabulary; and comprehension. We also coded study outcomes for domain (i.e., PA, real‐word reading, nonword reading, encoding, oral reading fluency, reading comprehension) and type (i.e., standardized, unstandardized/researcher‐developed). Finally, we coded for methodological characteristics of each study: sample size, study type (i.e., group design or SCED), and design.</p> <hd id="AN0159630099-19">EFFECT‐SIZE CALCULATION</hd> <p>For SCED studies, we used BC‐SMD to estimate the size of treatment effects. BC‐SMD estimates average treatment effects at the study level, rather than at the individual student level. It is based on a hierarchical linear model to accommodate the nested data structure (i.e., repeated assessments within individual) and capture variance at the within‐case and between‐case level (Pustejovsky et al., 2014). We used a flexible modeling approach developed by Pustejovsky et al. (2014) that incorporates time trends to be included as fixed or random effects. The advantages of BC‐SMDs are that they are design‐comparable, allowing BC‐SMD effect sizes to be compared with those of group design studies and combined in a single meta‐analysis.</p> <p>Calculation of BC‐SMD followed three steps. First, research assistants extracted raw data from the 364 graphs used in each of the 7 included SCED studies using WebPlotDigitizer (Rohatgi, 2014). Three studies provided intervention in a small‐group setting, one of which provided only small‐group average data (Arriaza de Allen, 2010). For the two small‐group intervention studies (Dussling, 2016; Gyovai et al., 2009), we used small groups' average scores calculated from the extracted individual student data. Second, we loaded individual studies to the web application of the <emph>scdhlm</emph> package (Pustejovsky et al., 2022). We only considered the baseline and intervention phase (i.e., removing data from the maintenance phase), given that only a few studies measured maintenance effects (e.g., Arriaza de Allen, 2010; Landa & Barbetta, 2017; McCarty, 2012). In addition, in one study, the intervention was interrupted by a school break and, therefore, we only included data that were collected before the school break (Arriaza de Allen, 2010).</p> <p>With visual inspection, we determined the best growth parameters for each study. The purpose was to estimate BC‐SMD from the models as similarly as possible across the SCED studies so that we could later calculate pooled ES across studies. While four studies showed changes only in level during the treatment phase, the rest of the studies demonstrated an increasing linear trend. Moreover, linear trends seemed to vary across participants within a study, making it necessary in most cases to include time trends as random effects. Thus, we fit random intercepts (levels) during the baseline phase and random intercepts and linear slopes (liner time trends) during the treatment phase. The only exception was Jozwik and Mustian (2020), where linear slope was modeled as fixed effects because of a nonconvergence issue. Finally, we constrained the number of baseline sessions to five and the follow‐up time‐points to 18 across the studies, because this was the common time parameter across studies. While we used BC‐SMD from this model in the meta‐analysis, we report BC‐SMD estimated from each individual study's initial and follow‐up time in Table 2. For all models, we used the restricted maximum likelihood estimation method.</p> <p>For group design studies, we calculated standardized group mean difference (SMD) effect size with small‐sample bias correction (Hedges' <emph>g</emph>) using the reported posttest <emph>M</emph>s and <emph>SD</emph>s by groups if the descriptive statistics were provided; otherwise, we used the reported statistical tests (e.g., <emph>p</emph>‐value for independent <emph>t</emph>‐tests).</p> <hd id="AN0159630099-20">META‐ANALYSES</hd> <p>The intent was to combine effect sizes from both SCED and group‐design studies within a single meta‐analysis; however, visual inspection of the effect‐size distribution indicated that the assumption of between‐study heterogeneity for these two groups was not supported. Additionally, there were considerable differences in sample characteristics and intervention design across study groups (i.e., group design vs. SCED; see Table 1) that led us to believe effects were not entirely comparable. Thus, separate meta‐analyses were conducted for SCED and group‐design studies. Publication bias was examined with contour‐enhanced funnel plots and a robust Eggers' regression test. All analyses were conducted with the <emph>metafor</emph> package (Viechtbauer, 2010) and the <emph>clubSandwich</emph> package (Pustejovsky, 2016) for the <emph>R</emph> statistical computing environment (R Core Team, 2018).</p> <p>1 TABLEStudy Features</p> <p> <ephtml> <table><thead><tr><th>Authors (Year); <italic>Sample Size</italic></th><th>Study Design</th><th>Grade(s)</th><th>L10002</th><th>FRPL0003</th><th>Dosage0004</th><th>Implementer</th><th>Group Size</th><th>Program</th><th>Instructional Components0005</th><th>Outcomes Measured0006</th></tr></thead><tbody><tr><td>Apichatabutra (2009); N = 5</td><td>SCED:MBP</td><td>3, 4</td><td>Bangladeshi, Korean, Thai</td><td>NR</td><td>1,000—1,400</td><td>ESL‐certified teachers</td><td>SG</td><td>Phonics for Reading</td><td>PA, D, E, F, V, C</td><td>ORF</td></tr><tr><td>Arriaza de Allen et al. (2010); N = 10</td><td>SCED:MBG</td><td>2</td><td>Spanish</td><td>Y</td><td>1,800</td><td>Researcher</td><td>SG</td><td>UFLI</td><td>PA, D, E, F, V, C</td><td>NWR, WR</td></tr><tr><td>Baker (2016) N = 78</td><td>GD:E</td><td>1</td><td>Spanish</td><td>NR</td><td>1,800</td><td>Teachers; IA</td><td>SG</td><td>Transitions Lessons</td><td>PA, D, E, F, V, C</td><td>NWR, WR, E, ORF, C</td></tr><tr><td>Denton et al. (2004); N = 99</td><td>GD:E</td><td>2, 3, 4, 5</td><td>Spanish</td><td>NR</td><td>1,200</td><td>University undergraduates</td><td>SG</td><td>Read Well</td><td>PA, D, F, V, C</td><td>NWR, WR, C</td></tr><tr><td>Dussling (2016); N = 13</td><td>SCED:MBP</td><td>1</td><td>Bosnian, Chinese, French, Lingala, Russian, Tamil, Tshiluba, Turkish</td><td>NR</td><td>900</td><td>Researcher</td><td>SG</td><td>Road to Reading</td><td>PA, D, E, F, C</td><td>NWR</td></tr><tr><td>Ehri et al. (2007); N = 128</td><td>GD:QE</td><td>1</td><td>Spanish</td><td>Y</td><td>NR</td><td>IA</td><td>1:1</td><td>Reading Rescue</td><td>PA, D, E, F, V, C</td><td>NWR, WR, C</td></tr><tr><td>Gunn et al. (2005); N = 256</td><td>GD:E</td><td>K, 1, 2, 3</td><td>Spanish</td><td>NR</td><td>7,200</td><td>Researcher‐hired IA</td><td>SG</td><td>Reading Mastery, Corrective Reading</td><td>PA, D, M, F, C</td><td>NWR, WR, ORF, C</td></tr><tr><td>Gyovai et al. (2009); N = 12</td><td>SCED</td><td>K, 1</td><td>Somali, Spanish, Vietnamese</td><td>Y</td><td>280—1,200</td><td>Researcher</td><td>SG</td><td>Early Reading Intervention</td><td>PA, D, E</td><td>PA, NWR</td></tr><tr><td>Jacob et al. (2016); N = 1,166</td><td>GD:E</td><td>2, 3, 4, 5</td><td>NR</td><td>Y</td><td>1,890</td><td>Community volunteers</td><td>1:1</td><td>Reading Partners</td><td>D, F, V, C</td><td>WR, ORF, C</td></tr><tr><td>Jozwik and Mustian (2020); N = 3</td><td>SCED:MPP</td><td>3</td><td>Arabic</td><td>N</td><td>1,320</td><td>ESL‐certified SPED teacher</td><td>1:1</td><td>LEA with assistive technology</td><td>F, V, C</td><td>ORF</td></tr><tr><td>Landa and Barbetta (2017); N = 4</td><td>SCED:MPP</td><td>1</td><td>Spanish</td><td>NR</td><td>600—1,200</td><td>Researcher</td><td>1:1</td><td>Repeated Reading</td><td>F, V, C</td><td>ORF, C</td></tr><tr><td>McCarty (2012); N = 5</td><td>SCED:MBP</td><td>2, 4, 5</td><td>Spanish</td><td>NR</td><td>60–180</td><td>Researcher; SPED teachers</td><td>1:1</td><td>Timed Reading Practice</td><td>F</td><td>ORF</td></tr><tr><td>Vadasy and Sanders (2010); N = 148</td><td>GD:E</td><td>K</td><td>Multiple (≥28); Most common: Spanish, Vietnamese, Chinese, Somali, Tagalog</td><td>NR</td><td>2,160</td><td>IA</td><td>1:1</td><td>Sound Partners</td><td>PA, D, E, F</td><td>PA, NWR, WR, E, ORF, C</td></tr><tr><td>Vadasy and Sanders (2011); N = 187</td><td>GD:E</td><td>1</td><td /><td>Y</td><td>2,400</td><td>IA</td><td>1:1</td><td>Sound Partners</td><td>PA, D, E, F</td><td>PA, NWR, WR, E, ORF, C</td></tr><tr><td>Vaughn et al. (2006a); N = 48</td><td>GD:E</td><td>1</td><td>Spanish</td><td>NR</td><td>8,000</td><td>Bilingual SPED teachers</td><td>SG</td><td>Proactive Reading</td><td>PA, D, E, F, V, C</td><td>PA, NWR, WR, E, ORF, C</td></tr><tr><td>Vaughn et al. (2006b); N = 96</td><td>GD:E</td><td>1</td><td>Spanish</td><td>NR</td><td>8,000</td><td>Bilingual SPED teachers</td><td>SG</td><td>Proactive Reading</td><td>PA, D, E, F, V, C</td><td>PA, NWR, WR, E, ORF, C</td></tr><tr><td>Wise et al. (2016); N = 12</td><td>GD:E</td><td>1</td><td>Cantonese, Hebrew, Russian, Turkish</td><td>NR</td><td>900</td><td>Researcher</td><td>SG</td><td>PA + letter‐sound instruction</td><td>PA, D</td><td>PA, WR</td></tr></tbody></table> </ephtml> </p> <p>1 1:1 = intervention delivered in 1:1 student:teacher ratio; ABAB = treatment‐withdrawal (i.e., treatment‐reversal) design; ES L = English as a second language; ERI = early reading intervention; GD:E = group design: experimental; GD:QE = group design: quasi‐experimental; IA = instructional assistants; LEA = language experience approach; MBP = multiple baseline across participants; MBG = multiple baseline across groups; MPP = multiple probe across participants; N = no; NR = not reported; PA = phonological awareness; RES = reading rescue; SCED = single‐case experimental design; SG = small group; SPED = special education or intervention teacher; UFLI = University of Florida Literacy Initiative; Y = yes.</p> <ulist> <item>2 a Participants' first language(s).</item> <item>3 b FRPL: 50% or more of participants were eligible for free or reduced‐price lunch; code applied to the analytic sample, not students in the population (e.g., school, district) from which the sample was drawn.</item> <item>4 c Total intervention time in minutes.</item> <item>5 d Instructional components: PA = phonological awareness; D = decoding; E = encoding; M = morphology; F = fluency; V = vocabulary; C = comprehension.</item> <item>6 e PA = phonological awareness; NWR = nonword reading; WR = word reading; E = encoding; ORF = oral reading fluency; C = comprehension.</item> </ulist> <hd id="AN0159630099-21">Group‐Design Studies</hd> <p>We first inspected outliers using a value below the first quartile minus three times the interquartile range or above the third quartile plus three times the interquartile range (Tukey, 1997). Many included studies reported intervention effects on multiple reading outcomes and several studies included multiple comparisons as a result of having more than one intervention or control condition. Therefore, we used three‐level, multivariate random‐effects analyses, assuming a correlation of.80. We applied robust variance estimation (RVE) adjustments to standard errors, hypothesis tests, and confidence intervals using study as the clustering unit. The model decomposes the variance of each effect size into three parts. The Level 1 variance represents the sampling variance estimated via the traditional variance calculation. The Level 2 variance represents within‐study variance. Finally, the Level 3 variance is the variance between studies.</p> <p>To represent the heterogeneity between studies and within each study, we report the restricted maximum likelihood estimate of the between‐study variance (τ<sups>2</sups>) and within‐study variance (ω<sups>2</sups>). We also report the <emph>Q</emph> statistics and <emph>I</emph><sups>2</sups> statistic partitioned to each level, which is a relative measure of the extent to which heterogeneity among true effect sizes contributes to the observed variation in the effect size estimates. We used <emph>I</emph><sups>2</sups> of 50% as the criterion for indicating sufficient heterogeneity to warrant moderator analysis. The only moderator we considered was reading outcome type, which was examined as dummy‐coded factor variables with PA outcome as a reference.</p> <hd id="AN0159630099-22">SCED Studies</hd> <p>Eleven BC‐SMDs were estimated from seven studies, because four studies reported multiple outcome data. Therefore, we fit multivariate random‐effects models assuming a correlation of.80 and further applied the RVE method to standard errors, hypothesis tests, and confidence intervals to make small‐sample corrections (Tipton & Pustejovsky, 2015), using study as a clustering unit. In addition, we fit separate univariate random effects models for oral reading fluency and nonword reading fluency outcomes.</p> <hd id="AN0159630099-23">RESULTS</hd> <p></p> <hd id="AN0159630099-24">Descriptive</hd> <p>The final corpus of studies consisted of 17 studies: 10 experimental or quasi‐experimental group‐design studies and 7 SCED studies. Table 1 summarizes methodological characteristics (i.e., number of participants, study type and design), participant characteristics (i.e., grade level[s], first language, FRPL eligibility), and intervention characteristics (i.e., dosage, intervention implementer, group size, program name, and instructional components).</p> <hd id="AN0159630099-25">ANALYTIC</hd> <p></p> <hd id="AN0159630099-26">Group Design Studies</hd> <p>The meta‐analysis included 10 studies encompassing 50 effect sizes and a combined sample of 2,218 students. No outliers were detected. The weighted average effect on combined reading outcomes was estimated as <emph>g</emph> = 0.31, <emph>p</emph> <.01. There was substantial heterogeneity across effect sizes (<emph>Q</emph> = 168.47, <emph>df</emph> = 49, <emph>p</emph> <.01), with between‐study variance of τ<sups>2</sups> =.02 and within‐study variance of ω<sups>2</sups> =.03 with a total <emph>I</emph><sups>2</sups> of = 64.31% (Level 2 = 35.19% and Level 3 = 29.13%). Results from the moderator analysis indicated that the mean effect was <emph>g</emph> = 0.28 (<emph>p</emph> =.10) for PA outcomes; <emph>g</emph> = 0.36 (<emph>p</emph> =.01) for word/nonword reading outcomes; <emph>g</emph> = 0.27 (<emph>p</emph> =.05) for spelling outcomes; <emph>g</emph> = 0.28 (<emph>p</emph> =.03) for oral reading fluency outcomes; and <emph>g</emph> = 0.26 (<emph>p</emph> =.01) for reading comprehension outcomes. However, the omnibus <emph>F</emph>‐test indicated that outcome type was not a statistically significant moderator (<emph>F</emph> [<reflink idref="bib4" id="ref1">4</reflink>, 3.07] = 2, <emph>p</emph> =.29) of intervention effects.</p> <hd id="AN0159630099-27">SCED Studies</hd> <p>The weighted mean effect on combined reading outcomes was estimated as <emph>g</emph> = 0.48 (<emph>df</emph> = 4.38; <emph>p</emph> =.18). There was substantial heterogeneity across effect sizes (<emph>Q</emph> = 62.73, <emph>df</emph> = 10, <emph>p</emph> <.01), with between‐study variance of τ<sups>2</sups> =.00 and within‐study variance of ω<sups>2</sups> =.70. (Individual ES estimated from SCED studies are presented in in Table 2, weighted mean across study types can be found in Table 3) Effect sizes ranged from <emph>g</emph> = –0.41 to 5.25. Of the five outcome domains examined across SCED studies (PA, nonword reading, real‐word reading, oral reading fluency, comprehension), we could calculate mean effects for oral reading fluency and nonword reading only, because these two outcomes were examined in more than one study. The weighted mean effect on oral reading fluency outcomes was <emph>g</emph> = 0.02 (<emph>df</emph> = 3; <emph>p</emph> =.90). The weighted mean effect on nonword reading fluency outcomes was 2.03 (<emph>df</emph> = 2; <emph>p</emph> =.02), which was the only weighted mean ES to reach statistical significance for SCED studies.</p> <p>2 TABLEEffect Sizes by Outcome Type for SCED Studies</p> <p> <ephtml> <table><thead><tr><th>Outcome Domain</th><th>Study</th><th>Measure</th><th><italic>g</italic> (SE)</th><th>95% CI</th></tr></thead><tbody><tr><td>Phonological Awareness</td><td>Gyovai et al. (2009)</td><td>DIBELS PSF</td><td>1.16 (1.27)</td><td>–3.13</td><td>5.44</td></tr><tr><td>Nonword Reading</td><td>Arriaza de Allen et al. (2010)</td><td>Researcher‐created (CPPM)</td><td>5.25 (4.67)</td><td>–9.08</td><td>19.58</td></tr><tr><td /><td>Dussling (2016)</td><td>DIBELS NWF (CLS)</td><td>3.32 (2.57)</td><td>–4.79</td><td>11.42</td></tr><tr><td /><td>Dussling (2016)</td><td>DIBELS NWF (WRC)</td><td>2.26<sup>*</sup> (.76)</td><td>0.57</td><td>3.94</td></tr><tr><td /><td>Gyovai et al. (2009)</td><td>DIBELS NWF (CLS)</td><td>1.483 (2.02)</td><td>–5.95</td><td>8.91</td></tr><tr><td>Real‐Word Reading</td><td>Arriaza de Allen et al. (2010)</td><td>Researcher‐created (CSPM)</td><td>0.37 (2.97)</td><td>–12.35</td><td>13.09</td></tr><tr><td>Oral Reading Fluency</td><td>Apichatabutra (2009)</td><td>DIBELS ORF (WCPM)</td><td>–0.07 (.63)</td><td>–1.49</td><td>1.35</td></tr><tr><td /><td>Jozwik and Mustian (2020)</td><td>AimsWeb (WCPM)</td><td>0.37 (1.05)</td><td>–3.97</td><td>4.71</td></tr><tr><td /><td>Landa and Barbetta (2017)</td><td>Grade‐level passages (WCPM)</td><td>1.22 (.92)</td><td>–1.42</td><td>3.85</td></tr><tr><td /><td>McCarty et al. (2012)</td><td>DIBELS ORF (WCPM)</td><td>–0.17 (.22)</td><td>–0.71</td><td>0.38</td></tr><tr><td>Comprehension</td><td>Landa and Barbetta (2017)</td><td>Grade‐level passages (LC questions)</td><td>–0.41 (.64)</td><td>–1.74</td><td>0.92</td></tr></tbody></table> </ephtml> </p> <ulist> <item>7 CI = confidence interval; CLS = correct letter sounds; CPPM = correct pseudowords per minute; CSPM = correct sight words per minute; DIBELS = Dynamic Indicators of Basic Early Literacy Skills; LC = literal comprehension; NWF = nonsense word fluency; PSF = phonemic segmentation fluency; WCPM = words correct per minute; WRC = words read correctly.</item> <item>8 <sups>*</sups><emph>p</emph> <.05.</item> <item>9 <sups>**</sups><emph>p</emph> <.01.</item> <item>10 <sups>***</sups><emph>p</emph> ⩽.00.</item> <item>3 TABLEWeighted Mean Effect Sizes by Outcome Type and Study Design</item> </ulist> <p> <ephtml> <table><thead><tr><th>Outcome Domain</th><th>Study Design</th><th><italic>g</italic></th><th><italic>p</italic> Value</th><th>95% CI</th></tr></thead><tbody><tr><td>Combined Reading Outcomes</td><td>Group Design</td><td>0.31<sup>*</sup></td><td>.01</td><td>0.11</td><td>0.50</td></tr><tr><td /><td>SCED</td><td>0.48</td><td>.18</td><td>–0.33</td><td>1.28</td></tr><tr><td>Phonological Awareness</td><td>Group Design</td><td>0.28</td><td>.10</td><td>–0.07</td><td>0.63</td></tr><tr><td /><td>SCED</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Nonword Reading</td><td>Group Design</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td /><td>SCED</td><td>2.03<sup>*</sup></td><td>.02</td><td>0.82</td><td>3.25</td></tr><tr><td>Real/Nonword Reading</td><td>Group Design</td><td>0.36<sup>*</sup></td><td>.01</td><td>0.12</td><td>0.60</td></tr><tr><td /><td>SCED</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Spelling</td><td>Group Design</td><td>0.27</td><td>.05</td><td>0.01</td><td>0.54</td></tr><tr><td /><td>SCED</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Oral Reading Fluency</td><td>Group Design</td><td>0.28<sup>*</sup></td><td>.03</td><td>0.03</td><td>0.54</td></tr><tr><td /><td>SCED</td><td>0.02</td><td>.90</td><td>–0.51</td><td>0.56</td></tr><tr><td>Comprehension</td><td>Group Design</td><td>0.26<sup>*</sup></td><td>.01</td><td>0.06</td><td>0.47</td></tr><tr><td /><td>SCED</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table> </ephtml> </p> <ulist> <item>11 CI = confidence interval; NA = not available; SCED = single‐case experimental design.</item> <item>12 <sups>*</sups><emph>p</emph> <.05.</item> <item>13 <sups>**</sups><emph>p</emph> <.01.</item> <item>14 <sups>***</sups><emph>p</emph> ⩽.00.</item> </ulist> <hd id="AN0159630099-28">Small‐Study Bias</hd> <p>Although Eggers' regression tests did not reveal a statistically significant intercept for either group‐design (β = –.18, <emph>t</emph> = 1.26, <emph>df</emph> = 2.12; <emph>p</emph> =.33) or SCED studies (β = –.78, <emph>t</emph> =.71, <emph>df</emph> = 4.02; <emph>p</emph> =.52), perhaps due to the small number of studies, visual inspection of the funnel plots indicated the possibility of small‐study bias, such that lower standard errors were associated with smaller effect sizes, with an asymmetrical pattern.</p> <hd id="AN0159630099-29">DISCUSSION</hd> <p>The purpose of this meta‐analysis was to identify reading instructional practices that have efficacy in improving reading outcomes for ELs with dyslexia or WLRD. This population of students was of particular interest because of its increasing size and unique characteristics—during the elementary school years, these students are simultaneously developing language skills and learning how to read in English. ELs who have WLRD have been understudied, a better understanding of which instructional practices are effective has the potential to inform classroom practice. In order to understand effective practices in authentic classroom settings, included studies were classroom‐based with K–5 students; included studies were experimental, quasi‐experimental, and SCED designs.</p> <p>First, the study investigated the characteristics of the 17 included studies, which examined effects of reading interventions on reading outcomes of K–5 ELs with or at risk for WLRD. Next, the mean effect of interventions containing a foundational skills component across the 10 included experimental and quasi‐experimental group‐design studies and whether effects varied by outcome domain was estimated. Separately, the effects of the seven SCED studies on the reading outcomes of ELs with or at risk for WLRD across outcome domains were investigated. Results support the notion that explicit and systematic instruction in subcomponent skills is effective for students with EL who also have WLRD, similar to findings from papers that have investigated the impact of reading intervention on EL students without WRD and EM students. This suggests that interventions with similar characteristics to those implemented for EL students without WLRD or dyslexia can have an impact on EL students with WLRD or dyslexia.</p> <hd id="AN0159630099-30">INTERVENTION CHARACTERISTICS</hd> <p>Across all methodologies, the included studies represented varied intervention time dedicated to essential literacy skill development. Seven literacy focus areas were identified (PA, decoding, encoding, morphology, fluency, vocabulary, comprehension); fluency was addressed in the greatest number of interventions (<reflink idref="bib15" id="ref2">15</reflink>), followed by decoding (<reflink idref="bib14" id="ref3">14</reflink>) and PA (<reflink idref="bib13" id="ref4">13</reflink>). Twelve studies reported that interventions included a focus on comprehension; encoding and vocabulary instruction were addressed in 10 studies. Only one study (Gunn et al., 2005) reported providing instruction in morphology. Finally, seven studies (41%) investigated the effects of interventions that addressed all five instructional areas recommended in the National Reading Panel (NRP)'s 2000 report: PA, phonics [decoding], fluency, vocabulary, and comprehension (NRP et al., 2000; see Table 1, Column 11.) Trends in intervention components were detected, such that intervention components varied with participant grade level. As an example, in the 11 studies in which all study participants were enrolled in the primary grades (K–2), PA and decoding were components of interventions in all but one study (Landa & Barbetta, 2017). Fluency instruction was included in all interventions implemented with students in Grade 3 and older.</p> <hd id="AN0159630099-31">MEAN EFFECTS ACROSS GROUP‐DESIGN STUDIES</hd> <p>Results of the current meta‐analysis of group‐design studies revealed a statistically significant overall mean weighted effect on combined reading outcomes (<emph>g</emph> = 0.31, <emph>p</emph> <.01). This ES is larger than that reported by Cheung and Slavin (2012) and Roberts et al. (2021), both of whom reported an overall mean effect of reading interventions on elementary‐aged ELs of <emph>g</emph> = 0.23. One possible explanation for the larger ES in the present study is that the two prior syntheses included studies with both at‐risk and not‐at‐risk ELs, whereas the current study focused strictly on ELs with or at risk for word reading difficulty. Findings from the current study are similar to those that only included EMs—larger effects are generally found for students with RD compared to typically developing readers. Two recent meta‐analyses of research examining the effects of extensive reading interventions on reading outcomes for K–3 EMs with RD (Wanzek et al., 2018) and at‐risk EM readers in Grades 1–3 (Gersten et al., 2020) found statically significant weighted mean ES on combined reading outcomes of <emph>g</emph> = 0.39. The larger ES found by Gersten et al. (2020) and Wanzek et al. (2018) may be partly attributable to the narrower grade band in those studies (i.e., K–3) than in the current synthesis (i.e., K–5), as prior work with EMs (Ehri et al., 2007; Suggate, 2010; Wanzek & Vaughn, 2007) and ELs (Ludwig et al., 2019) suggests interventions serving younger readers may have larger effects than those that serve older students. Similar to the current study, one meta‐analysis (Slavin et al., 2011) examined mean effects of reading interventions on outcomes for EMs with RD and found an identical ES to what we report (<emph>g</emph> = 0.31). Together with Wanzek et al. (2018) and Gersten et al. (2020), these results suggest that reading interventions may be similarly effective in improving outcomes for K–5 EL students with or at risk for RD as they are in improving outcomes for EM peers with or at risk for RD.</p> <hd id="AN0159630099-32">Effects by Outcome Domain</hd> <p>Column 11 of Table 1 provides a summary of outcome domains measured by each study. As illustrated, across group‐design studies, reading outcome type was not a significant moderator of intervention effects (<emph>F</emph> [4,3.07] = 2, <emph>p</emph> =.29). Yet, analysis of ES by outcome domain revealed three significant ES: real/nonword reading fluency, <emph>g</emph> = 0.36; oral reading fluency, <emph>g</emph> = 0.28; and comprehension, <emph>g</emph> = 0.26. The word reading and fluency weighted average ES are similar in magnitude to the median ES reported by Richards‐Tutor et al. (2016) (i.e., word reading, <emph>g</emph> = 0.33; fluency, <emph>g</emph> = 0.24), who focused on intervention effects for ELs at risk for RD. The ES we obtained, however, were somewhat smaller than the weighted mean ES reported by Gersten et al. (2020), who focused on intervention effects for EMs with or at risk for RD (i.e., word reading, <emph>g</emph> = 0.41; fluency, <emph>g</emph> = 0.31). Again, participants' age or grade level is one possible explanation for the larger ES reported by Gersten et al. (2020). Whereas that meta‐analysis included studies with Grades 1–3 participants only, studies in both Richards‐Tutor et al. (2016) and the current synthesis included K–5 participants, with larger ES found for younger participants (i.e., Grades K–2).</p> <p>The finding that there are differences in intervention effects on word reading, fluency, and reading comprehension outcomes within and across systematic reviews may be attributable to differences in instructional components in the studies. In both the current review and in Gersten et al. (2020), the majority of studies featured interventions that emphasized both code‐ and meaning‐focused skills. This dual focus may explain why mean comprehension effects in the current study, similar to Gersten et al. (2020; <emph>g</emph> = 0.26 and <emph>g</emph> = 0.32, respectively) were larger than the median effect on comprehension outcomes reported by Richards‐Tutor et al. (2016; <emph>g</emph> = 0.01). Richards‐Tutor et al. (2016) review considered only about half of all included studies to be "comprehensive," or covering at least four of the five content areas emphasized by the NRP's report in 2000 (NRP et al., 2000; i.e., PA, phonics, fluency, vocabulary, comprehension). The remaining studies tended to focus on <emph>either</emph> code‐related skills, such as PA and phonics, when interventions were implemented with primary‐grade students <emph>or</emph> on fluency and comprehension, without a foundational skills component, when interventions were implemented at the upper elementary levels (Richards‐Tutor et al., 2016, p. 153). By contrast, studies in Gersten et al. (2020) consistently featured interventions that emphasized multiple instructional components, with the majority focusing on decoding and fluency <emph>plus</emph> at least one meaning‐focused area, such as vocabulary or comprehension. Similarly, per the inclusion criteria, for the current study, all interventions were required to feature a foundational skills component, and we found that all but one (Wise et al., 2016) also included a focus on vocabulary or comprehension. Research suggests that interventions that emphasize both code‐ and meaning‐related skills are more effective for improving reading comprehension outcomes than those that focus on one set of skills in isolation (e.g., Al Otaiba et al., 2022; Baker et al., 2012). Considered together with other systematic reviews (e.g., Gersten et al., 2020; Richards‐Tutor et al., 2016), our findings corroborate evidence that this is true for interventions implemented with EL students with WLRD (Baker et al., 2012), who are simultaneously developing proficiency in both linguistic comprehension and code‐focused skills.</p> <p>Surprisingly, the current study did not reveal significant effects of reading interventions on PA outcomes for K–5 ELs (<emph>g</emph> = 0.28, <emph>p</emph> =.10). This finding stands in contrast to the large and significant median effect on PA outcomes reported by Richards‐Tutor et al. (2016; i.e., <emph>g</emph> = 0.86), but the effect was similar in size (<emph>g</emph> = 0.27) to the mean effect on foundational skills, including PA, reported by Roberts et al. (2021). It is noteworthy that the current review included just five studies that reported PA outcomes, decreasing the likelihood of detecting a significant effect.</p> <hd id="AN0159630099-33">EFFECTS ACROSS SCED STUDIES</hd> <p>Although practically significant, the overall mean weighted effect did not reach statistical significance for SCED studies, perhaps due to the low power (<emph>g</emph> = 0.48, <emph>p</emph> =.18). One other systematic review (Baker et al., 2018) focused specifically on SCED research investigating the effects of reading interventions for ELs at risk for RD but did not meta‐analyze effects. Therefore, the estimated mean weighted effect of <emph>g</emph> = 0.48 is the first known estimate of an average ES on combined reading outcomes resulting from a synthesis of SCED research on interventions serving K–5 ELs who have or are at risk for WLRD.</p> <p>Substantial heterogeneity was found within SCDE studies (ESs ranged between –0.17 and 3.37). Although we did not conduct moderator analysis due to the small sample size, ESs seem to vary depending on the outcome type such that SCED studies tend to have stronger effect on nonword reading outcomes (1.28–3.37) than on other outcomes. In addition, studies with students in Grades 3–5 (–0.17 to 0.56) tend to have smaller effects than studies with students in Grades K–2 (0.20–3.37). However, grade level is conflated with type of outcome because studies with older students tend to have oral reading fluency and comprehension outcomes whereas studies with younger students tend to have nonword reading outcome.</p> <p>We found notable differences between SCED and group‐design studies. In contrast to the significant mean ES on oral reading fluency outcomes for group‐design studies (<emph>g</emph> = 0.28), the mean ES on fluency was small and nonsignificant for SCED studies (<emph>g</emph> = 0.02). The mean ES on nonword reading did reach statistical significance (<emph>g</emph> = 2.03); however, it is difficult to compare this with the ES for group‐design studies (<emph>g</emph> = 0.36), which included both real‐word and nonword reading fluency outcomes.</p> <hd id="AN0159630099-34">LIMITATIONS</hd> <p>Several limitations are worth noting and considering within the context of the findings from this systematic review. First, given the wide publication date range allowed within our inclusion criteria, we expected that more studies would be eligible for inclusion in this study; particularly, for SCED studies. Yet, although it yielded practically significant effect size estimates, analysis of the SCED studies did not find statistically significant effect sizes, except in the domain of nonword reading. There are many reasons why this could be the case; in particular, there was tremendous variability in effect sizes both within and across the individual studies, and few studies met the criteria for inclusion in this systematic review.</p> <p>Additionally, the small number of studies that met inclusion criteria limited our ability to examine participant characteristics (e.g., age, first language), intervention features (e.g., dosage, implementer), or methodological considerations (e.g., study design) that may have moderated treatment effects. Similarly, although we restricted our search to studies (both group design and SCED) with experimental designs, we had too few studies to either examine study quality variables as moderators (e.g., Hall et al., 2017) or to exclude studies that did not meet What Works Clearinghouse (WWC) quality standards (Gersten et al., 2020). Having larger number of studies would have enabled us to conduct moderator analyses that could have shed light on interventions or intervention features that are most effective for particular subsets of ELs with or at risk for WLRD.</p> <p>In an effort to include experimental research from multiple sources, the search was not limited to studies published only in peer‐reviewed journals, as in other analyses (e.g., Baker et al., 2018; Richards‐Tutor et al., 2016; Snyder et al., 2017). Our search strategy yielded five unpublished dissertations that were included in our final corpus of 17 studies (one group design and four SCED studies), or about 29% of all included studies. We acknowledge that additional steps might have been taken to further increase the amount of gray literature we screened for inclusion. For example, we did not contact prominent authors in the field or searched the ProQuest database to identify unpublished reports or other types of gray literature. Adding these elements to our search strategy may have led to the inclusion of a larger number of studies that could have contributed to our knowledge about intervention effects on literacy outcomes for EL students with WLRD.</p> <hd id="AN0159630099-35">IMPLICATIONS FOR FUTURE RESEARCH, PRACTICE, AND POLICY</hd> <p>The findings from this systematic review raise concerns about the lack of studies investigating intervention effects for ELs, especially given the tremendous growth the nation has seen in this population of students over recent decades. Future research is needed to investigate effects of early reading interventions with sufficiently large samples and report on treatment fidelity and implementation to better inform the field about how to best teach ELs with or at risk for WLRD. It is important that future research describe intervention implementation and business‐as‐usual conditions in detail, to allow for a more complete understanding of treatment impacts. Policymakers, administrators, and teachers are all eager to learn how to best instruct EL students who have WLRD. Findings from this study suggest that explicit and systematic early literacy instruction that includes a foundational skills component but also addresses meaning‐related skills is effective for ELs with or at risk for WLRD.</p> <hd id="AN0159630099-36">CONCLUSIONS</hd> <p>Meta‐analyses of group design studies indicate that, overall, early reading interventions have a positive effect on the reading performance of ELs with WLRD, including word/nonword reading, passage reading, and reading comprehension. Further, meta‐analyses of SCED studies reveal few significant mean effects but wide variability across studies. In some ways, the most important finding of this systematic review was that there is a dearth of research on the topic of EL students with specific WLRD. We, therefore, echo the call issued by Roberts et al. (2021) for more rigorous evaluations of reading interventions serving diverse ELs, especially those who have or are at risk for WLRD.</p> <ref id="AN0159630099-37"> <title> Footnotes </title> <blist> <bibl id="bib1" type="bt">1</bibl> <bibtext> In addition to these nine studies, Richards‐Tutor et al. ([55]) included two additional studies—Lovett et al. ([40]) and Vaughn et al. ([77])—in which some or all EL participants were enrolled in Grades 6–8. In the current review, we were interested only in effects on elementary English learners in Grades K–5. Therefore, in our discussion of Richards‐Tutor et al. ([55]), we omit effect sizes obtained from these two studies.</bibtext> </blist> <blist> <bibl id="bib2" type="bt">2</bibl> <bibtext> Denton et al. ([13]) reported WRMT‐R standard scores: <emph>M<subs>wordattack</subs></emph> = 86.8–88.9; <emph>M<subs>wordID</subs></emph> = 84.7–87.9. 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A school‐based phonological awareness intervention for struggling readers in early French immersion. Reading and Writing, 29 (2), 183 – 205. https://doi.org/10.1007/s11145‐015‐9585‐9</bibtext> </blist> </ref> <aug> <p>By Emily J. Solari; Karen F. Kehoe; Eunsoo Cho; Colby Hall; Isabel Vargas; Katlynn Dahl‐Leonard; Cassidi L. Richmond; Alyssa R. Henry; Lysandra Cook; Latisha Hayes and Carlin Conner</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author</p> <p></p> <p>Emily Solari  is the Edmund H. Henderson Professor of Education at the School of Education and Human Development in the Curriculum, Instruction, and Special Education department at University of Virginia. Dr. Solari's research focuses on better understanding reading development in subgroups of learners who are at‐risk for reading difficulties and those identified with reading disabilities, with the goal of developing and implementing evidence‐based language and reading instruction and interventions.</p> <p>Karen Kehoe  is a post‐doctoral research associate in the Curriculum, Instruction, and Special Education Department at the University of Virginia School of Education and Human Development. She received her Ph.D. in educational psychology from UVA in 2022. Her research interests include understanding the child, instructional, and environmental factors that promote or hinder the early reading success of students at risk for reading difficulties.</p> <p>Eunsoo Cho  is an Associate Professor in the Special Education and Educational Psychology/Educational Technology programs at Michigan State University and she received her PhD from Vanderbilt University. Her research aims to understand cognitive and motivational predictors of reading development, and she leverages such knowledge to research evidence‐based practices in reading assessment and intervention for students with learning difficulties.</p> <p>Colby Hall  is an assistant professor in the Department of Curriculum, Instruction, and Special Education at the University of Virginia's School of Education and Human Development. Her research primarily focuses on identifying features of effective literacy instruction for students with or at risk for literacy‐learning difficulties; inference instruction as a means of improving reading comprehension; and reading instruction for bilingual/multilingual students.</p> <p>Isabel Vargas  is a doctoral student in the Curriculum, Instruction, and Special Education Department at the University of Virginia School of Education and Human Development. Her research interests revolve around multilingual learners, including (a) evidence‐based literacy instruction for linguistically diverse students, (b) misidentification of multilingual students in special education, and (c) bilingual education.</p> <p>Katlynn Dahl‐Leonard  is a doctoral student in the Reading Education program in the School of Education and Human Development at the University of Virginia. Her research interests revolve around effective literacy instruction for early‐elementary students with or at risk for reading difficulties, including teacher understanding and implementation of evidence‐based literacy practices.</p> <p>Cassidi Richmond  is a doctoral student in the Special Education Program at the University of Virginia School of Education and Human Development. Her scholarly interests include reading comprehension supports for adolescents with or at risk for reading comprehension difficulties. She also works with pre‐ and in‐service teachers to support their use of evidence‐based literacy practices in the classroom.</p> <p>Alyssa Henry  is a National Center for Special Education Research Postdoctoral Fellow at the University of Virginia. She received her Ph.D. from University of California, Davis in 2019. She studies language and literacy development, with a particular focus on children with autism. Her research is centered on the design and testing of evidence‐based intervention strategies to improve language and reading outcomes for children with disabilities.</p> <p>Lysandra Cook  is an associate professor in the Special Education Program at the University of Virginia School of Education and Human Development, and received her PhD from Kent State University. Her scholarly interests revolve around translating research to practice in special education, including (a) identifying and implementing evidence‐based practices, (b) supporting pre‐ and in‐service teachers to be critical consumers of research, and (c) researching high‐quality teacher preparation.</p> <p>Latisha Hayes  is an associate professor in the Reading Education Program and director of McGuffey Reading Services at the University of Virginia School of Education and Human Development. Her scholarly interests include translating research to practice for preservice and in‐service teachers focused on 1) data‐based decision making, 2) evidence‐based practices in core and intervention settings, and 3) word‐based reading difficulties.</p> <p>Carlin Conner  is a Senior Research Scientist at the University of Virginia in the School of Education. She received her PhD from Southern Methodist University. Her research interests include literacy assessment and interventions, with a focus on language learners and children with reading difficulty. Carlin has also spent time as both a special education teacher and education instructional coach.</p> </aug> <nolink nlid="nl1" bibid="bib15" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib14" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib13" firstref="ref4"></nolink>
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  Group: ISSN
  Data: 0938-8982<br />1540-5826
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study meta-analyzed the last four decades (1980-2020) of reading intervention research focused on improving reading outcomes for English language (EL) students in Grades K-5 with or at risk for word reading difficulties. Experimental and quasi-experimental group design and single-case experimental design (SCED) studies were included; 10 group design and 7 SCED studies met inclusion criteria (m = 61; total student N = 2,270). Visual inspection of the effect size distribution revealed that the assumption of between-study heterogeneity was not supported; therefore, the findings were synthesized for SCED studies separately from those reported in group design studies. Implications for practice, policy, and future research are discussed.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2022
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1351536
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1351536
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/ldrp.12286
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 158
    Subjects:
      – SubjectFull: Instructional Effectiveness
        Type: general
      – SubjectFull: Intervention
        Type: general
      – SubjectFull: English Language Learners
        Type: general
      – SubjectFull: English (Second Language)
        Type: general
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: Word Recognition
        Type: general
      – SubjectFull: Reading Difficulties
        Type: general
      – SubjectFull: Meta Analysis
        Type: general
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Reading Instruction
        Type: general
    Titles:
      – TitleFull: Effectiveness of Interventions for English Learners with Word Reading Difficulties: A Research Synthesis
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Solari, Emily J.
      – PersonEntity:
          Name:
            NameFull: Kehoe, Karen F.
      – PersonEntity:
          Name:
            NameFull: Cho, Eunsoo
      – PersonEntity:
          Name:
            NameFull: Hall, Colby
      – PersonEntity:
          Name:
            NameFull: Vargas, Isabel
      – PersonEntity:
          Name:
            NameFull: Dahl-Leonard, Katlynn
      – PersonEntity:
          Name:
            NameFull: Richmond, Cassidi L.
      – PersonEntity:
          Name:
            NameFull: Henry, Alyssa R.
      – PersonEntity:
          Name:
            NameFull: Cook, Lysandra
      – PersonEntity:
          Name:
            NameFull: Hayes, Latisha
      – PersonEntity:
          Name:
            NameFull: Conner, Carlin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 0938-8982
            – Type: issn-electronic
              Value: 1540-5826
          Numbering:
            – Type: volume
              Value: 37
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Learning Disabilities Research & Practice
              Type: main
ResultId 1