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Effects of Data-Based Individualization for Students with Intensive Learning Needs: A Meta-Analysis

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Title: Effects of Data-Based Individualization for Students with Intensive Learning Needs: A Meta-Analysis
Language: English
Authors: Jung, Pyung-Gang (ORCID 0000-0002-7076-6031), McMaster, Kristen L., Kunkel, Amy K., Shin, Jaehyun, Stecker, Pamela M.
Source: Learning Disabilities Research & Practice. Aug 2018 33(3):144-155.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Peer Reviewed: Y
Page Count: 12
Publication Date: 2018
Document Type: Journal Articles
Reports - Research
Information Analyses
Descriptors: Meta Analysis, Academic Achievement, Effect Size, Comparative Analysis, Individualized Instruction, Teacher Student Relationship
DOI: 10.1111/ldrp.12172
ISSN: 0938-8982
Abstract: We examined the mean effect of teachers' use of data-based individualization (DBI) on the performance of students with intensive learning needs across academic areas and factors influencing the effects of DBI on student achievement. A total of 57 effect sizes from 14 studies were categorized into two comparisons: DBI Only (comparisons between DBI and a business-as-usual control) and DBI Plus (comparisons in which DBI implementers had access to additional information on student performance while they implemented DBI, compared to a control). The mean effect of DBI Only on student performance was g = 0.37; the mean effect of DBI Plus was g = 0.38. Differential effects of DBI were found depending on the nature of CBM tasks, frequency of CBM administration, and type and frequency of supports provided to teachers. Findings support the use of DBI to enhance student outcomes across academic areas.
Abstractor: As Provided
Entry Date: 2018
Accession Number: EJ1187555
Database: ERIC
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  Value: <anid>AN0131205642;7mj01aug.18;2018Aug14.09:19;v2.2.500</anid> <title id="AN0131205642-1">Effects of Data‐Based Individualization for Students with Intensive Learning Needs: A Meta‐Analysis </title> <p>Abstract: We examined the mean effect of teachers’ use of data‐based individualization (DBI) on the performance of students with intensive learning needs across academic areas and factors influencing the effects of DBI on student achievement. A total of 57 effect sizes from 14 studies were categorized into two comparisons: DBI Only (comparisons between DBI and a business‐as‐usual control) and DBI Plus (comparisons in which DBI implementers had access to additional information on student performance while they implemented DBI, compared to a control). The mean effect of DBI Only on student performance was g = 0.37; the mean effect of DBI Plus was g = 0.38. Differential effects of DBI were found depending on the nature of CBM tasks, frequency of CBM administration, and type and frequency of supports provided to teachers. Findings support the use of DBI to enhance student outcomes across academic areas.</p> <p>Educational researchers and practitioners have made great strides in developing, testing, and implementing specialized instructional approaches to improve academic outcomes for children at risk for or with learning disabilities. Evidence indicates that many children benefit when teachers implement research‐based standard treatment protocols with fidelity (Fuchs, Mock, Morgan, & Young, [<reflink idref="bib17" id="ref1">17</reflink>] ). Yet approximately 3 to 5 percent of school‐aged students do not benefit sufficiently from standard treatment protocols and require more intensive, individualized instruction (Wanzek & Vaughn, [<reflink idref="bib54" id="ref2">54</reflink>] ). Such instruction is likely most appropriate for children identified as needing “tertiary prevention” within multitiered systems of support (Fuchs, Fuchs, & Compton, [<reflink idref="bib14" id="ref3">14</reflink>] ), or as part of special education services provided to children with disabilities.</p> <p>In the field of special education, a long‐standing approach to intensifying instruction entails monitoring a student's responsiveness to instruction and using the resulting data to determine when instructional changes are needed. This general approach has been referred to as Data‐Based Program Modification (DBPM; Deno & Mirkin, [<reflink idref="bib12" id="ref4">12</reflink>] ), experimental teaching (Casey, Deno, Marston, & Skiba, [<reflink idref="bib8" id="ref5">8</reflink>] ), data‐based instruction (Fuchs, Fuchs, & Stecker, [<reflink idref="bib15" id="ref6">15</reflink>] ), and data‐based individualization (National Center on Intensive Intervention [NCII], n.d.). In this article, we refer to this approach as data‐based individualization (DBI) to signify the systematic data‐based process of evaluating and modifying instructional programs for students who require the most intensive, individualized instruction as a part of their educational programs.</p> <hd id="AN0131205642-2">Overview of DBI and Curriculum‐Based Measurement (CBM)</hd> <p>Whereas researchers and practitioners have conceptualized DBI in somewhat different ways, it generally entails the following steps: (a) establish the student's current level of performance; (b) set a long‐range academic goal; (c) implement high‐quality, research‐based instruction with fidelity, while monitoring the student's progress on a frequent (e.g., weekly) basis; (d) use data‐based decision rules to determine when instructional changes are needed; (e) develop hypotheses about the student's needs and implement instructional changes based on those hypotheses; (f) use progress monitoring data to evaluate the effectiveness of those changes; and (g) continue this process until the student reaches the instructional goal.</p> <p>Ongoing progress monitoring is essential to this data‐based decision‐making process. Although many types of progress‐monitoring tools are available, DBI relies on reliable and valid assessments that are sensitive to growth over relatively short time periods to support ongoing decision‐making. Curriculum‐based measurement (CBM; Deno, [<reflink idref="bib11" id="ref7">11</reflink>] ) is well‐suited for this purpose: It is a research‐validated assessment practice for judging the adequacy of student academic growth over time (Fuchs, [<reflink idref="bib19" id="ref8">19</reflink>] ). CBM was developed by Stanley Deno and colleagues at the University of Minnesota, with the primary purpose of providing special education teachers with reliable, valid assessment data to inform ongoing instructional decision‐making (Deno, [<reflink idref="bib11" id="ref9">11</reflink>] ). CBM was intended to be used as a general outcome measure, meaning that it reflects overall proficiency in a given academic domain, rather than mastery of discrete skills (Fuchs & Deno, [<reflink idref="bib20" id="ref10">20</reflink>] ). It typically requires repeated measurement using multiple forms of equivalent difficulty, yielding time‐series data that reflect student progress within that domain. CBM is simple and efficient: Brief samples of behavior (e.g., the number of words read correctly in 1 min) correlate strongly with critical academic outcomes, such as overall reading proficiency (Wayman, Wallace, Wiley, Tichá, & Espin, [<reflink idref="bib55" id="ref11">55</reflink>] ).</p> <p>A seminal study by Fuchs, Deno, and Mirkin ([<reflink idref="bib13" id="ref12">13</reflink>] ) examined reading achievement among students whose teachers did or did not collect CBM data for instructional decision‐ making. Results indicated that students with mild disabilities whose teachers used CBM performed significantly better on passage‐reading fluency and reading subtests of a standardized, norm‐referenced achievement test than did contrast students whose teachers had not used CBM. Since then, researchers have continued to examine and demonstrate effects of teachers’ use of CBM on both teachers’ instructional practices and students’ academic outcomes.</p> <p>Another essential component of DBI is the consistent application of data‐based decision rules. In the most recent published synthesis of research on teachers’ use of CBM for instructional decision‐making, Stecker, Fuchs, & Fuchs ([<reflink idref="bib50" id="ref13">50</reflink>] ) found that students made significantly greater growth than control counterparts when their teachers modified instruction based on CBM data. Teachers were more likely to be responsive to students’ needs when they (a) used data‐based decision rules, (b) received diagnostic feedback in the form of skills analysis, and (c) received recommendations for making instructional changes. Computer applications prompted teachers’ use of decision rules and contributed to their overall satisfaction with CBM. Further, when computer applications incorporated data collection features, they reduced teacher time and increased the usability and feasibility of CBM. However, Stecker et al. ([<reflink idref="bib50" id="ref14">50</reflink>] ) noted that modifying programs for individual students was challenging for teachers, and recommended that researchers continue to expand ways in which diagnostic feedback could be provided and how technology could be used to facilitate teachers’ use of CBM.</p> <hd id="AN0131205642-3">Rationale for Current Meta‐Analysis</hd> <p>Despite evidence that teachers’ use of CBM within a DBI framework can improve student outcomes, DBI is not widely used in practice (Fuchs et al., [<reflink idref="bib15" id="ref15">15</reflink>] ; Fuchs, McMaster, & Al Otaiba, [<reflink idref="bib16" id="ref16">16</reflink>] ). Practitioners often are uncertain as to how to individualize instruction for students with intensive academic needs (cf. Roehrig, Duggar, Moats, Glover, & Mincey, [<reflink idref="bib45" id="ref17">45</reflink>] ). DBI is a promising solution to this problem, yet it has been more than a decade since the DBI literature was synthesized (Stecker et al., [<reflink idref="bib50" id="ref18">50</reflink>] ). To provide guidance for educators about how best to intensify and individualize instruction, it seems critical to update the current state of the evidence of effects of DBI on student outcomes.</p> <p>In addition to providing an overall estimate of DBI's effects on student outcomes, we examined potential moderators of the effects to illuminate elements essential to DBI's success. Specifically, we identified variables associated with positive student outcomes from Stecker et al. ([<reflink idref="bib50" id="ref19">50</reflink>] ), as well as those associated with components of DBI, including (a) progress monitoring characteristics, (b) data management characteristics, (c) decision rules, and (d) teacher training and support. We focused our review on DBI's effects on outcomes for students with disabilities, assuming these students represent those with the most intensive academic needs.</p> <hd id="AN0131205642-4">Purpose and Research Questions</hd> <p>The purpose of this meta‐analysis was to examine the effects of teachers’ use of DBI to improve academic performance for K‐12 students with intensive learning needs, including those with disabilities. Specifically, we asked: (a) What is the mean effect of teachers’ use of DBI on student achievement across academic areas (reading, mathematics, and spelling/writing)? and (b) What factors influence the strength of the effects of DBI on student outcomes?</p> <hd id="AN0131205642-5">METHOD</hd> <hd id="AN0131205642-6">Literature Search</hd> <p>We searched existing literature examining the effects of teachers’ use of DBI on students’ academic outcomes via three steps. First, we identified relevant literature from previous reviews on teachers’ use of CBM for instructional decision‐making (Fuchs & Fuchs, [<reflink idref="bib21" id="ref20">21</reflink>] ; Stecker et al., [<reflink idref="bib50" id="ref21">50</reflink>] ). Second, we conducted an electronic database search using Academic Search Premier, ERIC, PsychINFO, Education Full Text, and ProQuest Dissertations and Theses. Keywords were grouped into three categories: (<reflink idref="bib1" id="ref22">1</reflink>) CBM, DBI, DBPM, and data‐based decision‐ making (curriculum‐based measurement, curriculum‐based measures, CBM, use of curriculum‐based measurement, using curriculum‐based measurement, implementation of curriculum‐based measurement, data‐based instruction, data‐based individualization, data‐based program modification, data‐based decision‐making), (<reflink idref="bib2" id="ref23">2</reflink>) student outcomes (student achievement, academic achievement, academic outcome, student learning), and (<reflink idref="bib3" id="ref24">3</reflink>) teacher supports (consultation, skills analysis, teacher feedback, expert system). Keywords from Category 1 were searched in titles, and keywords in Categories 2 and 3 were searched anywhere in the text; Keywords in Category 1 were combined with keywords in Categories 2 and 3 separately. Third, we conducted an ancestral search by examining the reference lists of all included studies.</p> <hd id="AN0131205642-7">Inclusion Criteria</hd> <p>Each study selected for this synthesis met nine specific criteria. Each study had to (a) use CBM as a progress monitoring tool (defined as administering CBM frequently during the study period); (b) include at least one student achievement measure at post‐test in a basic academic area such as reading, mathematics, spelling, or writing; (c) include intervention and progress monitoring across at least seven weeks (the minimum time period within which an instructional modification can be made and evaluated when standard decision rules are applied; Stecker et al., [<reflink idref="bib50" id="ref25">50</reflink>] ); and (d) include teachers’ use of CBM data to make individualized instructional decisions. Each study also had to (e) include student participants with disabilities receiving special education services, to ensure that studies focused on students with intensive academic needs. If a study included students with disabilities in addition to other study participants (e.g., students identified as at‐risk), we computed effect sizes only for students with disabilities. Each study also had to (f) employ an experimental or quasi‐experimental design; (g) include a business‐as‐usual control group, in order to examine effects of DBI in comparison to typical conditions in schools; (h) include sufficient data to compute an effect size; and (i) be published in English.</p> <p>First, we identified 16 studies from two previous reviews (Fuchs & Fuchs, [<reflink idref="bib21" id="ref26">21</reflink>] ; Stecker et al., [<reflink idref="bib50" id="ref27">50</reflink>] ). Of those studies, we excluded eight, in which teachers made decisions based on class‐wide CBM data rather than for individuals (Fuchs, Fuchs, Hamlett, Phillips, & Bentz, [<reflink idref="bib29" id="ref28">29</reflink>] ; Fuchs, Fuchs, Phillips, Hamlett, & Karns, [<reflink idref="bib34" id="ref29">34</reflink>] ; Fuchs et al., [<reflink idref="bib30" id="ref30">30</reflink>] ), not enough information was included to compute effect sizes (e.g., Skiba, Wesson, & Deno, [<reflink idref="bib46" id="ref31">46</reflink>] ), or there was no business‐as‐usual control group (Fuchs, Fuchs, & Hamlett, [<reflink idref="bib24" id="ref32">24</reflink>] ; Stecker & Fuchs, [<reflink idref="bib49" id="ref33">49</reflink>] ; Tindal, Fuchs, Christensen, Mirkin, & Deno, [<reflink idref="bib53" id="ref34">53</reflink>] ; Wesson, [<reflink idref="bib56" id="ref35">56</reflink>] ).</p> <p>Electronic searches yielded 1,171 articles, which were screened in two phases. In Phase 1, the first author reviewed titles, abstracts, and methods, and removed duplicates and studies that clearly did not meet inclusion criteria, yielding 14 studies. In Phase 2, we examined these studies in depth. Eight were excluded because they did not include student outcomes (Capizzi & Fuchs, [<reflink idref="bib7" id="ref36">7</reflink>] ), did not apply decision rules (Snyder, [<reflink idref="bib47" id="ref37">47</reflink>] ), were duplicates of published articles (Jung, [<reflink idref="bib39" id="ref38">39</reflink>] ; Stecker, [<reflink idref="bib48" id="ref39">48</reflink>] ), or did not include a control condition (Allinder & BeckBest, [<reflink idref="bib2" id="ref40">2</reflink>] ; Allinder & Oats, [<reflink idref="bib4" id="ref41">4</reflink>] ; Fuchs, [<reflink idref="bib18" id="ref42">18</reflink>] ; Tichá, [<reflink idref="bib52" id="ref43">52</reflink>] ). Adding the six eligible studies from this search to the eight studies from previous reviews yielded 14 total studies. Two authors checked 15 percent of studies identified in the initial search, and one author checked 50 percent of studies identified as eligible. Interrater agreement was calculated as number of agreements divided by the total number of agreements plus disagreements, with the result multiplied by 100. Disagreements were discussed and resolved. Interrater agreement was 97 percent for the search and 98 percent for screening.</p> <hd id="AN0131205642-8">Coding</hd> <p>Each study was coded for descriptive characteristics related to (a) study information, (b) participants, (c) student outcomes, (d) progress monitoring, (e) data management, (f) decision rules, and (g) teacher support. We developed a coding manual to define variables and updated it during the coding process as needed. The final code book can be obtained from the first author.</p> <hd id="AN0131205642-9">Study Information</hd> <p>Coded study information included year of publication, publication type (peer‐reviewed journal, dissertation, or research report), and study design (experimental or quasi‐experimental). We coded academic area based on the primary outcomes that were measured (reading, mathematics, or spelling/writing).</p> <hd id="AN0131205642-10">Participants</hd> <p>Participant codes included sample size, grade range, and disability category (e.g., learning disabilities, emotional and behavioral disorders, or intellectual disabilities).</p> <hd id="AN0131205642-11">Student Outcomes</hd> <p>Student outcomes included measures of academic performance using CBM or CBM‐like tasks, commercial norm‐referenced tests, or researcher‐developed tests. CBM‐like measures mimicked the tasks used for progress monitoring but were designated specifically for pre‐ and posttesting and were not the same assessments used for progress monitoring. Researcher‐developed tests used standardized administration procedures and usually addressed more generalized performance, or included broader skills that spanned grade levels.</p> <hd id="AN0131205642-12">Progress Monitoring</hd> <p>We coded who administered and scored CBM (DBI implementers, computer software, or mixed), who developed the CBM task (researcher‐ or teacher‐generated), administration frequency, and type of CBM task (e.g., oral reading, maze, computation). The progress monitoring period was coded as the total number of weeks of DBI implementation.</p> <hd id="AN0131205642-13">Data Management</hd> <p>Data management codes included type of data management (whether CBM results were stored and graphed by teachers, or by computer software); whether decision rules were applied by the teacher, or automatically generated by computer software; and whether feedback for instructional decisions, diagnostic information on student performance, and instructional recommendations were provided to teachers. If recommendations were provided, the specific method also was coded. For example, some researchers used a software program called an “expert system” that provided instructional strategies with specific directions based on students’ CBM performance and class work (e.g., Fuchs, Fuchs, Hamlett, & Allinder, [<reflink idref="bib26" id="ref44">26</reflink>] , [<reflink idref="bib27" id="ref45">27</reflink>] ).</p> <hd id="AN0131205642-14">Decision Rules</hd> <p>Decision rule codes included the type of rule used (point‐based rule, trend‐line rule, or combination of point‐based and trend‐line). Point‐based rules used consecutive points on a graph, and trend‐based rules used a trend line reflecting student progress (e.g., using linear regression). Consecutive points and/or the trend line were compared to a goal line (representing desired growth from baseline to a long‐term goal) to make an instructional decision. If a certain number of consecutive points and/or the trend line fell below the goal line, the teacher changed instruction. In some studies, if a certain number of consecutive points, or the trend line, rose above the goal line, the teacher increased the long‐term goal.</p> <hd id="AN0131205642-15">Teacher Support</hd> <p>In all studies, DBI implementers received ongoing support during the study period. Support codes included type of support (individual consultation, self‐monitoring, or small‐group collaborative problem solving) and frequency of support provided per week.</p> <hd id="AN0131205642-16">Interrater Agreement</hd> <p>One author coded all studies; another coded 20 percent independently using the coding manual. Disagreements between coders were discussed and resolved. Interrater agreement was 88 percent. The disagreements occurred most often on average years of teaching experience in treatment and control conditions followed by type of decision rules and total number of support occasions.</p> <hd id="AN0131205642-17">Calculation of Effect Sizes</hd> <p>To indicate the magnitude and direction of DBI's effects (Lipsey & Wilson, [<reflink idref="bib43" id="ref46">43</reflink>] ), Cohen's d was calculated as the mean difference between treatment and control conditions divided by the pooled SD. Because Cohen's d tends to overestimate the effect size in studies with small samples (Cooper, Hedges, & Valentine, [<reflink idref="bib10" id="ref47">10</reflink>] ), a correction factor, J, was used:</p> <p>To get an unbiased estimate of Hedge's g, the correction factor was multiplied Cohen's d: g = J x d. All effect sizes were computed using Comprehensive Meta‐Analysis (CMA) software (Borenstein, Hedges, Higgins, & Rothstein, [<reflink idref="bib6" id="ref48">6</reflink>] ).</p> <hd id="AN0131205642-18">Statistical Analysis of Effect Sizes</hd> <p>During coding, multiple effect sizes were calculated from each study if there were more than one treatment condition. The effect sizes were categorized into two types of comparisons. Within each study, DBI was compared to a business‐as‐usual control, called a “DBI Only” comparison; however, some studies also included an enhanced version of DBI, which we called “DBI Plus,” as a second comparison with the control group. In DBI Plus, DBI implementers were given diagnostic information on student performance and/or recommendations for instructional modifications while they implemented DBI. Studies yielded 14 comparisons for DBI Only and 6 for DBI Plus.</p> <p>We used a weighted random‐effects model for DBI Only comparisons because they were heterogeneous in terms of DBI characteristics (i.e., they included varying ways of monitoring student progress, managing data, and applying data‐based decision rules). We used a weighted fixed‐effects model for DBI Plus comparisons because they were homogeneous in terms of these characteristics. For example, they used similar computer software to administer, score, and store CBM data and apply decision rules using forms developed by researchers, and provided diagnostic information and/or instructional recommendations. In addition, the Q‐value indicated significant heterogeneity among effects in DBI Only (Q[<reflink idref="bib13" id="ref49">13</reflink>] = 31.05, p = .003), and little evidence of heterogeneity among effects in DBI Plus (Q[<reflink idref="bib5" id="ref50">5</reflink>] = 2.20, p = .82).</p> <p>Overall mean DBI effects were examined using study as the unit of analysis for the two comparison groups (DBI Only and DBI Plus) separately. To identify possible factors influencing the effects of DBI on student performance, a categorical moderator analysis was conducted using effect size as the unit of analysis. Variables in this analysis included (a) progress monitoring characteristics, (b) data management characteristics, (c) decision rules, and (d) teacher support. Differential effects of DBI were also examined by academic area and type of student outcome.</p> <hd id="AN0131205642-19">Assumptions of Independence</hd> <p>If a single study uses multiple outcome measures or includes multiple comparison groups, effect sizes computed are not independent of each other (Borenstein, Hedges, Higgins, & Rothstein, [<reflink idref="bib5" id="ref51">5</reflink>] ). To avoid violating the assumption of independence of effects, we adopted a “shifting unit of analysis” approach proposed by Cooper ([<reflink idref="bib9" id="ref52">9</reflink>] ), which minimizes the dependence of effects while retaining as many effect sizes as possible. Specifically, we used each study as the unit of analysis to examine the overall mean effects for DBI Only and DBI Plus. For moderator analyses, however, we used individual effect sizes as the unit of analysis. We selected effect sizes that contained unique and independent information. For example, when several effect sizes were drawn from different subtests that measured different constructs (e.g., decoding and comprehension in reading), all effect sizes were selected. If effect sizes were drawn from the same subtests but aligned with different comparisons (DBI Only and DBI Plus), all effect sizes were selected. Thus, a total of 57 effect sizes from the 14 studies were computed (k = 43 for DBI Only, k = 14 for DBI Plus).</p> <hd id="AN0131205642-20">RESULTS</hd> <p>To answer the first research question, overall effects of DBI across academic areas were analyzed for two comparisons: DBI Only and DBI Plus. To answer the second question regarding factors influencing effects of DBI, we used categorical moderator analyses.</p> <hd id="AN0131205642-21">Descriptive Features of the Studies</hd> <p>Table summarizes basic study information, participants, student outcomes, and effect sizes. Tables and provide the number of studies and percentages for each coded variable. Most studies (86 percent; n = 12) were conducted in the 1980s and 1990s. The academic area of focus was split relatively evenly among reading, mathematics, and spelling/writing.</p> <p>Descriptions of Individual Studies</p> <p> <ephtml> <table border="1" cellpadding="12"><tr><th /><th>Study Information</th><th>Student Participants</th><th>Student Outcomes</th><th>Hedge's g</th><th>95% CI</th></tr><tr><th>Study</th><th>Publication type</th><th>Academic area</th><th>Study design</th><th>N</th><th>Grade level</th><th>Disability categories</th><th>Type of measures</th><th>DBI Only</th><th>DBI Plus</th><th>DBI Only</th><th>DBI Plus</th></tr><tr><td>King, Deno, Mirkin, & Wesson ()</td><td>RR</td><td>R</td><td>Exp</td><td>38</td><td>1–6</td><td>NS</td><td>CBM‐like, Norm‐referenced</td><td>−0.09</td><td /><td>[−0.32, 0.15]</td><td /></tr><tr><td>Fuchs, Deno, & Mirkin ()</td><td>J</td><td>R</td><td>Exp</td><td>141</td><td>NS</td><td>LD, EBD, ID, PI, others</td><td>CBM‐like, Norm‐referenced</td><td>0.74</td><td /><td>[0.42, 1.05]</td><td /></tr><tr><td>Jones & Krouse ()</td><td>J</td><td>R, M</td><td>Exp</td><td>NS</td><td>3–6</td><td>NS</td><td>CBM‐like, Researcher‐ developed</td><td>0.43</td><td /><td>[−0.06, 0.92]</td><td /></tr><tr><td>Fuchs, Fuchs, & Hamlett ()</td><td>J</td><td>M</td><td>Exp</td><td>60</td><td>2–9</td><td>LD, EBD, ID</td><td>Researcher‐developed, Norm‐referenced</td><td>0.43</td><td /><td>[0.001, 0.85]</td><td /></tr><tr><td>Fuchs, Fuchs, & Hamlett ()</td><td>J</td><td>R</td><td>Exp</td><td>53</td><td>2–9</td><td>LD, EBD</td><td>Norm‐referenced</td><td>1.23</td><td /><td>[0.28, 2.17]</td><td /></tr><tr><td>Fuchs, Fuchs, & Hamlett ()</td><td>J</td><td>S</td><td>Exp</td><td>54</td><td>NS</td><td>LD, EBD, ID</td><td>Researcher‐developed</td><td>−0.11</td><td>−0.10</td><td>[−0.99, 0.77]</td><td>[−0.98, 0.78]</td></tr><tr><td>Fuchs, Fuchs, Hamlett & Stecker ()</td><td>J</td><td>M</td><td>Exp</td><td>91</td><td>3–9</td><td>LD, EBD</td><td>Researcher‐developed</td><td>0.22</td><td>0.35</td><td>[−0.38, 0.81]</td><td>[−0.25, 0.95]</td></tr><tr><td>Fuchs, Fuchs, Hamlett, & Allinder ()</td><td>J</td><td>S</td><td>Exp</td><td>59</td><td>2–8</td><td>LD, EBD</td><td>Researcher‐developed</td><td>0.30</td><td>0.22</td><td>[−0.55, 1.14]</td><td>[−0.62, 1.06]</td></tr><tr><td>Fuchs, Fuchs, Hamlett, & Allinder ()</td><td>J</td><td>S</td><td>Exp</td><td>92</td><td>3–9</td><td>LD, EBD</td><td>Researcher‐developed</td><td>−0.02</td><td>0.36</td><td>[−0.61, 0.58]</td><td>[−0.24, 0.96]</td></tr><tr><td>Fuchs, Fuchs, Hamlett & Stecker ()</td><td>J</td><td>M</td><td>Exp</td><td>63</td><td>2–8</td><td>LD, EBD</td><td>Researcher‐developed</td><td>0.11</td><td>0.67</td><td>[−0.46, 0.68]</td><td>[0.08, 1.25]</td></tr><tr><td>Fuchs, Fuchs, Hamlett, & Ferguson ()</td><td>J</td><td>R</td><td>Exp</td><td>63</td><td>2–8</td><td>LD, EBD</td><td>Researcher‐developed</td><td>0.21</td><td>0.35</td><td>[−0.12, 0.55]</td><td>[0.02, 0.68]</td></tr><tr><td>Allinder ()</td><td>J</td><td>M</td><td>Quasi‐exp</td><td>58</td><td>3–6</td><td>LD, EBD, ID</td><td>Researcher‐developed</td><td>0.64</td><td /><td>[0.01, 1.28]</td><td /></tr><tr><td>Allinder, Bolling, Oats, & Gagnon ()</td><td>J</td><td>M</td><td>Exp</td><td>54</td><td>NS</td><td>LD, ID</td><td>Researcher‐developed</td><td>0.58</td><td /><td>[−0.27, 1.43]</td><td /></tr><tr><td>Jung, McMaster, & delMas ()</td><td>J</td><td>W</td><td>Exp</td><td>46</td><td>1–3</td><td>LD, EBD, ID, DCD, ASD, PI, others</td><td>CBM, Norm‐referenced</td><td>0.63</td><td /><td>[0.35, 0.92]</td><td /></tr></table> </ephtml> </p> <p>1 Note. J = peer‐reviewed journal; RR = research report; R = reading; M = mathematics; S = spelling; W = writing; NS = not specified; LD = learning disability; EBD = emotional and behavioral disorder; ID = intellectual disability; DCD = developmental cognitive disorder; ASD = autism spectrum disorder; PI = physical impairment; CBM = curriculum‐based measures.</p> <p>Progress Monitoring Characteristics</p> <p> <ephtml> <table border="1" cellpadding="3"><tr><th>Variables</th><th>n</th><th>percent</th></tr><tr><td>Administrators</td><td /><td /></tr><tr><td>DBI implementers</td><td>4</td><td>29</td></tr><tr><td>Computer software</td><td>8</td><td>57</td></tr><tr><td>Mixed</td><td>2</td><td>14</td></tr><tr><td>Scorers</td><td /><td /></tr><tr><td>DBI implementers</td><td>4</td><td>29</td></tr><tr><td>Computer software</td><td>8</td><td>57</td></tr><tr><td>Mixed</td><td>2</td><td>14</td></tr><tr><td>Nature of CBM task</td><td /><td /></tr><tr><td>Researcher provided</td><td>11</td><td>79</td></tr><tr><td>Teacher generated</td><td>2</td><td>14</td></tr><tr><td>Not specified</td><td>1</td><td>7</td></tr><tr><td>Frequency</td><td /><td /></tr><tr><td>Daily</td><td>1</td><td>7</td></tr><tr><td>Twice per week</td><td>12</td><td>86</td></tr><tr><td>Three times per week</td><td>1</td><td>7</td></tr><tr><td>Type of CBM</td><td /><td /></tr><tr><td>Oral Reading</td><td>3</td><td>21</td></tr><tr><td>Maze</td><td>1</td><td>7</td></tr><tr><td>Recall and cloze</td><td>1</td><td>7</td></tr><tr><td>Math computation</td><td>3</td><td>21</td></tr><tr><td>Spelling</td><td>3</td><td>21</td></tr><tr><td>Picture Word and Word Dictation</td><td>1</td><td>7</td></tr><tr><td>Not specified</td><td>2</td><td>14</td></tr><tr><td>Progress Monitoring Period</td><td /><td /></tr><tr><td>5 to 10 weeks</td><td>1</td><td>7</td></tr><tr><td>11 to 15 weeks</td><td>6</td><td>43</td></tr><tr><td>16 to 20 weeks</td><td>6</td><td>43</td></tr><tr><td>over 21 weeks</td><td>1</td><td>7</td></tr></table> </ephtml> </p> <p>Data Management, Decision Rules, and Teacher Support Characteristics</p> <p> <ephtml> <table border="1" cellpadding="4"><tr><th /><th>Variables</th><th>n</th><th>percent</th></tr><tr><td>Data Management</td><td>Type of Data Management</td><td /><td /></tr><tr><td /><td>DBI implementers</td><td>4</td><td>29</td></tr><tr><td /><td>Computer software</td><td>10</td><td>71</td></tr><tr><td /><td>Applying Rules</td><td /><td /></tr><tr><td /><td>DBI implementers</td><td>3</td><td>22</td></tr><tr><td /><td>Computer software</td><td>9</td><td>64</td></tr><tr><td /><td>Mixed</td><td>1</td><td>7</td></tr><tr><td /><td>Not specified</td><td>1</td><td>7</td></tr><tr><td /><td>Feedback on Decisions</td><td /><td /></tr><tr><td /><td>Yes</td><td>7</td><td>50</td></tr><tr><td /><td>No</td><td>3</td><td>21</td></tr><tr><td /><td>Not specified</td><td>4</td><td>29</td></tr><tr><td /><td>Diagnostic Feedback</td><td /><td /></tr><tr><td /><td>Yes</td><td>5</td><td>36</td></tr><tr><td /><td>No</td><td>9</td><td>64</td></tr><tr><td /><td>Instructional Recommendations</td><td /><td /></tr><tr><td /><td>Yes</td><td>3</td><td>21</td></tr><tr><td /><td>No</td><td>11</td><td>79</td></tr><tr><td>Decision Rules</td><td>Decision‐Making Rules</td><td /><td /></tr><tr><td /><td>Point‐based rule</td><td>0</td><td>0</td></tr><tr><td /><td>Trend‐line rule</td><td>8</td><td>57</td></tr><tr><td /><td>Combination of point‐based and trend</td><td>5</td><td>36</td></tr><tr><td /><td>Not specified</td><td>1</td><td>7</td></tr><tr><td /><td>Minimum Number for Instructional Decision</td><td /><td /></tr><tr><td /><td>4 points plus trend line</td><td>5</td><td>36</td></tr><tr><td /><td>7 to 10 points for trend line</td><td>5</td><td>36</td></tr><tr><td /><td>8 points for trend line</td><td>3</td><td>21</td></tr><tr><td /><td>Not specified</td><td>1</td><td>7</td></tr><tr><td>Teacher Support</td><td>Type of Support</td><td /><td /></tr><tr><td /><td>Individual consultation</td><td>11</td><td>79</td></tr><tr><td /><td>Self‐monitoring</td><td>1</td><td>7</td></tr><tr><td /><td>Small‐group collaborative problem solving+ individual consultation</td><td>1</td><td>7</td></tr><tr><td /><td>Not specified</td><td>1</td><td>7</td></tr><tr><td /><td>Frequency of Support</td><td /><td /></tr><tr><td /><td>Weekly</td><td>3</td><td>21</td></tr><tr><td /><td>Every 1–2 weeks</td><td>3</td><td>21</td></tr><tr><td /><td>Every 2 weeks</td><td>4</td><td>28</td></tr><tr><td /><td>Every 1–3 weeks</td><td>1</td><td>7</td></tr><tr><td /><td>Every 2–3 weeks</td><td>1</td><td>7</td></tr><tr><td /><td>Not specified</td><td>2</td><td>14</td></tr></table> </ephtml> </p> <hd id="AN0131205642-22">Overall Effects of DBI on Student Performance</hd> <p>The mean effect of DBI Only on student performance across academic areas was g = 0.37 (k = 14, CI<subs>95</subs>= 0.17, 0.58), indicating statistically significant effects of DBI Only for enhancing student performance. The Q test results indicated statistically significant heterogeneity among studies contributing to the overall effect (Q[<reflink idref="bib13" id="ref53">13</reflink>] = 31.05, p = .003). The I<sups>2</sups> value indicated that 58.1 percent of variability in the effect sizes was due to between‐studies variance. The overall mean effect of DBI Plus on student performance was similar: g = 0.38 (k = 6; CI<subs>95</subs>= 0.15, 0.61), indicating statistically significant effects on student performance. However, the Q test did not indicate heterogeneity among effects (Q[<reflink idref="bib5" id="ref54">5</reflink>] = 2.20, p = .82), and I<sups>2</sups> was zero.</p> <hd id="AN0131205642-23">Differential Effects of DBI Characteristics on Student Performance</hd> <p>To examine possible factors influencing effects of DBI on student performance, moderator analyses were conducted across progress monitoring, data management, decision rules, and support characteristics. Effects by academic area and type of student outcome measures also were examined. Table provides a summary of results from moderator analyses for the DBI Only comparisons. Because the Q test results showed little heterogeneity between groups for DBI Plus, a moderator analysis was not conducted for this comparison.</p> <p>Summary Statistics of Subgroup Analysis for DBI Only</p> <p> <ephtml> <table border="1" cellpadding="8"><tr><th /><th>Variables</th><th>k</th><th>g</th><th>S.E.</th><th>95% CI</th><th>Q‐value</th><th>p‐Value</th></tr><tr><td>Study Information</td><td>Academic Area</td><td /><td /><td /><td /><td>Q(2) = 1.56</td><td>0.46</td></tr><tr><td /><td>Reading</td><td>20</td><td>0.28</td><td>0.11</td><td>[0.08, 0.49]</td><td>30.24</td><td>0.05</td></tr><tr><td /><td>Mathematics</td><td>12</td><td>0.37</td><td>0.13</td><td>[0.11, 0.62]</td><td>4.81</td><td>0.94</td></tr><tr><td /><td>Spelling/Writing</td><td>11</td><td>0.47</td><td>0.13</td><td>[0.22, 0.71]</td><td>9.09</td><td>0.52</td></tr><tr><td>Progress monitoring</td><td>CBM Administrator</td><td /><td /><td /><td /><td>Q(2) = 2.24</td><td>0.33</td></tr><tr><td /><td>DBI implementers</td><td>21</td><td>0.37</td><td>0.10</td><td>[0.17, 0.57]</td><td>31.95</td><td>0.04</td></tr><tr><td /><td>Computer software</td><td>17</td><td>0.24</td><td>0.11</td><td>[0.02, 0.45]</td><td>8.57</td><td>0.93</td></tr><tr><td /><td>Mixed</td><td>5</td><td>0.59</td><td>0.21</td><td>[0.18, 0.99]</td><td>3.09</td><td>0.54</td></tr><tr><td /><td>Nature of CBM tasks</td><td /><td /><td /><td /><td>Q(2) = 21.18</td><td><0.001</td></tr><tr><td /><td>Teacher‐generated</td><td>5</td><td>0.80</td><td>0.16</td><td>[0.49, 1.11]</td><td>1.25</td><td>0.87</td></tr><tr><td /><td>Researcher‐developed</td><td>31</td><td>0.39</td><td>0.08</td><td>[0.24, 0.54]</td><td>19.30</td><td>0.93</td></tr><tr><td /><td>Not specified</td><td>7</td><td>−0.09</td><td>0.02</td><td>[−0.33, 0.15]</td><td>4.25</td><td>0.64</td></tr><tr><td /><td>CBM frequency</td><td /><td /><td /><td /><td>Q(2) = 15.72</td><td><0.001</td></tr><tr><td /><td>Daily</td><td>7</td><td>−0.09</td><td>0.12</td><td>[−0.33, 0.15]</td><td>4.25</td><td>0.64</td></tr><tr><td /><td>Two times per week</td><td>33</td><td>0.47</td><td>0.07</td><td>[0.33, 0.62]</td><td>24.30</td><td>0.83</td></tr><tr><td /><td>Three times per week</td><td>3</td><td>0.45</td><td>0.26</td><td>[−0.06, 0.96]</td><td>1.71</td><td>0.43</td></tr><tr><td>Data management</td><td>Data management type</td><td /><td /><td /><td /><td>Q(1) = 0.12</td><td>0.73</td></tr><tr><td /><td>DBI implementers</td><td>21</td><td>0.37</td><td>0.10</td><td>[0.17, 0.57]</td><td>31.95</td><td>0.04</td></tr><tr><td /><td>Computer software</td><td>22</td><td>0.31</td><td>0.10</td><td>[0.12, 0.50]</td><td>13.92</td><td>0.87</td></tr><tr><td /><td>Decision Rules</td><td /><td /><td /><td /><td>Q(3) = 1.03</td><td>0.79</td></tr><tr><td /><td>DBI implementers</td><td>18</td><td>0.36</td><td>0.11</td><td>[0.15, 0.58]</td><td>30.08</td><td>0.03</td></tr><tr><td /><td>Computer software</td><td>21</td><td>0.33</td><td>0.10</td><td>[0.14, 0.52]</td><td>13.07</td><td>0.88</td></tr><tr><td /><td>Mixed</td><td>1</td><td>−0.12</td><td>0.47</td><td>[−1.04, 0.81]</td><td>0.00</td><td>1.00</td></tr><tr><td /><td>Not specified</td><td>3</td><td>0.45</td><td>0.26</td><td>[−0.06, 0.96]</td><td>1.71</td><td>0.43</td></tr><tr><td /><td>Feedback on decisions</td><td /><td /><td /><td /><td>Q(2) = 4.07</td><td>0.13</td></tr><tr><td /><td>Provided</td><td>18</td><td>0.48</td><td>0.10</td><td>[0.28, 0.68]</td><td>11.58</td><td>0.83</td></tr><tr><td /><td>Not provided</td><td>17</td><td>0.22</td><td>0.11</td><td>[0.01, 0.42]</td><td>23.64</td><td>0.10</td></tr><tr><td /><td>Not specified</td><td>8</td><td>0.40</td><td>0.16</td><td>[0.08, 0.72]</td><td>6.48</td><td>0.49</td></tr><tr><td>Decision rules</td><td>Decision rules type</td><td /><td /><td /><td /><td>Q(2) = 0.23</td><td>0.89</td></tr><tr><td /><td>Trend line‐based</td><td>28</td><td>0.34</td><td>0.08</td><td>[0.18, 0.50]</td><td>37.41</td><td>0.09</td></tr><tr><td /><td>Points and trend line‐based</td><td>12</td><td>0.29</td><td>0.12</td><td>[0.05, 0.53]</td><td>6.50</td><td>0.84</td></tr><tr><td /><td>Not specified</td><td>3</td><td>0.43</td><td>0.25</td><td>[−0.06, 0.92]</td><td>1.71</td><td>0.43</td></tr><tr><td /><td>Name of decision rules</td><td /><td /><td /><td /><td>Q(3) = 1.91</td><td>0.59</td></tr><tr><td /><td>4 points and trend line</td><td>12</td><td>0.30</td><td>0.13</td><td>[0.06, 0.55]</td><td>6.53</td><td>0.84</td></tr><tr><td /><td>7 to 10 points</td><td>17</td><td>0.28</td><td>0.12</td><td>[0.05, 0.51]</td><td>27.46</td><td>0.04</td></tr><tr><td /><td>8 points</td><td>11</td><td>0.48</td><td>0.13</td><td>[0.24, 0.73]</td><td>8.01</td><td>0.63</td></tr><tr><td /><td>Not specified</td><td>3</td><td>0.45</td><td>0.26</td><td>[−0.06, 0.96]</td><td>1.71</td><td>0.43</td></tr><tr><td>Teacher support</td><td>Type of support</td><td /><td /><td /><td /><td>Q(3) = 16.75</td><td><0.001</td></tr><tr><td /><td>Individual consultation</td><td>34</td><td>0.46</td><td>0.07</td><td>[0.32, 0.60]</td><td>24.98</td><td>0.84</td></tr><tr><td /><td>Self‐monitoring</td><td>1</td><td>0.60</td><td>0.45</td><td>[−0.28, 1.49]</td><td>0.00</td><td>1.00</td></tr><tr><td /><td>Small‐group collaborative support + individual consultation</td><td>1</td><td>0.86</td><td>0.40</td><td>[0.07, 1.64]</td><td>0.00</td><td>1.00</td></tr><tr><td /><td>Not specified</td><td>7</td><td>−0.09</td><td>0.12</td><td>[−0.33, 0.15]</td><td>4.25</td><td>0.64</td></tr><tr><td /><td>Frequency of support</td><td /><td /><td /><td /><td>Q(5) = 21.26</td><td><0.001</td></tr><tr><td /><td>Weekly</td><td>14</td><td>0.66</td><td>0.10</td><td>[0.46, 0.89]</td><td>5.97</td><td>0.95</td></tr><tr><td /><td>Every 1–2 weeks</td><td>9</td><td>0.21</td><td>0.14</td><td>[−0.08, 0.49]</td><td>2.85</td><td>0.94</td></tr><tr><td /><td>Every 2 weeks</td><td>7</td><td>0.49</td><td>0.18</td><td>[0.15, 0.84]</td><td>5.03</td><td>0.54</td></tr><tr><td /><td>Every 1–3 weeks</td><td>2</td><td>0.23</td><td>0.32</td><td>[‐0.40, 0.85]</td><td>0.001</td><td>0.97</td></tr><tr><td /><td>Every 2–3 weeks</td><td>2</td><td>−0.02</td><td>0.32</td><td>[−0.63, 0.61]</td><td>0.56</td><td>0.45</td></tr><tr><td /><td>Not specified</td><td>9</td><td>0.00</td><td>0.12</td><td>[−0.25, 0.27]</td><td>10.31</td><td>0.24</td></tr><tr><td>Student achievement outcomes</td><td>Type of outcome measure</td><td /><td /><td /><td /><td>Q(2) = 4.80</td><td>0.09</td></tr><tr><td /><td>CBM/CBM‐like</td><td>10</td><td>0.55</td><td>0.12</td><td>[0.33, 0.76]</td><td>7.57</td><td>0.58</td></tr><tr><td /><td>Norm‐referenced measure</td><td>12</td><td>0.27</td><td>0.15</td><td>[−0.03, 0.57]</td><td>22.16</td><td>0.02</td></tr><tr><td /><td>Researcher‐developed measure</td><td>21</td><td>0.27</td><td>0.10</td><td>[0.08, 0.46]</td><td>11.29</td><td>0.94</td></tr></table> </ephtml> </p> <p>2 *p < .05, **p < .001</p> <hd id="AN0131205642-24">Academic Area</hd> <p>The effects of DBI Only on student performance were not statistically different across reading, mathematics, and spelling/writing, though there was little remaining heterogeneity between group effects (Q[<reflink idref="bib2" id="ref55">2</reflink>] = 1.56, p = .46). Most effects (k = 20) came from studies of DBI in reading. Effects from studies of spelling/writing were the largest (g = 0.47), followed by mathematics (g = 0.37) and reading (g = 0.28). Confidence intervals in this category did not include zero, indicating statistical significance of all three effects.</p> <hd id="AN0131205642-25">Progress Monitoring</hd> <p>When categorized by the CBM administrator, there were no statistically significant differences between group effects, and little remaining heterogeneity (Q[<reflink idref="bib2" id="ref56">2</reflink>] = 2.24, p = .33, I<sups>2</sups> = 8.6 percent). Mean effects of DBI implementers, computer software, and a combination of the two were all positive and statistically significant for student performance (g = 0.37, 0.24, and 0.59, respectively). When categorized by the nature of CBM tasks, the Q test indicated statistically significant differences between effects (Q[<reflink idref="bib2" id="ref57">2</reflink>] = 21.28, p < .001). CBM forms made by teachers or provided by researchers contributed to statistically significant positive effects on student performance (g = 0.80 and 0.39, respectively). When grouped by frequency of CBM administration, there was significant heterogeneity between groups (Q[<reflink idref="bib2" id="ref58">2</reflink>] = 15.72, p < .001). Studies administering CBM two or three times weekly contributed to similar effect sizes (g = 0.47 and 0.45, respectively); however, only the mean effect of the twice‐weekly monitoring was statistically significant and positive. The mean effect on student performance for daily CBM administration was negative and not statistically significant.</p> <hd id="AN0131205642-26">Data Management</hd> <p>There were no differences between mean effects based on who managed data and implemented decision rules, indicating little heterogeneity among effects in each category. Mean effects from DBI implementers and computer software for managing data and applying decision rules were all positive (range: g = 0.31–0.36) and statistically significant. Mean effects were almost twice as large when feedback on decisions was provided (g = 0.48) than when feedback was not provided (g = 0.24), though the effect size for studies not specifying whether feedback was provided was similar and also statistically significant (g = 0.40).</p> <hd id="AN0131205642-27">Decision Rules</hd> <p>When categorized by decision‐rule characteristics, the Q test did not indicate significant heterogeneity between effects (Q[<reflink idref="bib2" id="ref59">2</reflink>] = 0.23, p = .89 for type, Q[<reflink idref="bib3" id="ref60">3</reflink>] = 1.91, p = .59 for minimum number of points needed). Most effects came from studies using trend‐line rules (k = 28), with a statistically significant mean effect (g = 0.34). The mean effect for studies using an 8‐point trend‐line rule was the largest (g = 0.48), and studies using 4‐point and trend‐line rules in tandem, or 7‐ to 10‐point trend‐line rules, contributed to positive, reliable effects.</p> <hd id="AN0131205642-28">Teacher Support</hd> <p>When categorized by type of support (individual consultation, self‐monitoring, or small‐group collaborative problem solving and individual consultation), the Q test indicated significant heterogeneity between groups (Q[<reflink idref="bib3" id="ref61">3</reflink>] = 16.75, p < .001). Effects were statistically significant and larger when implementers were provided small‐group collaborative support and individual consultation (g = 0.86) than for individual consultation only (g = 0.46). When categorized by frequency of support, there was also significant heterogeneity between groups (Q[<reflink idref="bib5" id="ref62">5</reflink>] = 21.26, p < .001). Effects were statistically significant and larger when support providers met with special education teachers weekly (g = 0.66) or every two weeks (g = 0.49).</p> <hd id="AN0131205642-29">Student Outcomes</hd> <p>The Q test indicated little heterogeneity between mean effects when categorized by type of outcome measure. Most studies used researcher‐developed measures; that effect was positive and statistically significant (g = 0.27). The effect was larger and statistically significant when student performance was measured by CBM or CBM‐like measures (g = 0.55).</p> <hd id="AN0131205642-30">Publication Bias</hd> <p>Publication bias represents a threat to the validity of meta‐analysis results, as studies are often more likely to be published when they contain significant outcomes (Sutton, [<reflink idref="bib51" id="ref63">51</reflink>] ). To test for publication bias, we generated a funnel plot to examine symmetry of effects around the mean. We also computed the classic fail‐safe N among the independent effect sizes. Funnel plots and classic fail‐safe N are available from the first author. A fail‐safe N of x means an additional x number of studies with an effect size of zero would need to be located to nullify the mean effect. The plots demonstrated symmetry, with effects distributed evenly around the mean. Two DBI Only studies were located in the far right of the funnel, indicating potential bias in studies with small samples reporting relatively large effect sizes. However, the classic fail‐safe N for DBI Only indicated that 327 null studies would be necessary to nullify the effect; for DBI Plus, 23 null studies would be necessary to nullify the mean effect.</p> <hd id="AN0131205642-31">DISCUSSION</hd> <p>Findings of this study provide promising evidence of DBI for improving student outcomes across reading, mathematics, and spelling/writing, with overall effect sizes of g = 0.37 for DBI Only and g = 0.38 for DBI Plus. We were somewhat surprised that DBI Plus effects were not stronger than DBI Only effects, given that DBI Plus incorporated additional information to facilitate teachers’ instructional decision‐making. However, given the relatively small number of studies, further research is needed to address potential benefits of including such supports.</p> <p>Moderator analyses were conducted on DBI Only but not DBI Plus studies, given significant heterogeneity in DBI Only effects but not among DBI Plus effects. These analyses revealed that average effects of DBI Only did not differ reliably by academic area, converging with those of previous syntheses (Fuchs & Fuchs, [<reflink idref="bib21" id="ref64">21</reflink>] ; Stecker et al., [<reflink idref="bib50" id="ref65">50</reflink>] ). However, because the Q tests were likely underpowered for detecting true heterogeneity, given the small number of effect sizes (Borenstein et al., [<reflink idref="bib5" id="ref66">5</reflink>] ), particularly in the areas of mathematics and spelling/writing, these findings should be considered preliminary.</p> <p>Results of moderator analyses revealed differential effects of DBI Only depending on CBM characteristics and supports provided. With respect to CBM characteristics, DBI Only effects were larger in studies that used teacher‐generated CBM (g = 0.80), compared to those that used researcher‐developed CBM (g = 0.39). It may be that teacher‐generated CBM aligned more closely to the instructional programs or curricula that those teachers were using. However, only five effect sizes included teacher‐generated CBM, whereas 31 effect sizes included researcher‐developed CBM; thus, it seems premature to conclude that teacher‐generated CBM leads to stronger DBI effects. Additionally, teacher‐generated CBM tools typically did not have evidence of technical adequacy, raising questions about their appropriateness for progress monitoring.</p> <p>DBI Only effects also varied depending on the frequency of CBM administration, with twice‐weekly administration yielding the strongest effects (g = 0.47). However, these results should be interpreted with caution, given that most effect sizes were drawn from the twice‐weekly variable. This data collection schedule might reflect the time period in which most of the studies were conducted (1980s and 1990s), when twice‐weekly administration was common practice. In addition, in no study was CBM administered less frequently than twice per week, and even more frequent (daily) CBM administration yielded negative effects. Further research is needed to determine the influence of frequency of CBM administration (e.g., weekly).</p> <p>Recently, researchers have begun to address this question. Jenkins, Graff, and Miglioretti ([<reflink idref="bib35" id="ref67">35</reflink>] ) reported that frequency of progress monitoring using CBM in reading could be significantly reduced without compromising the validity of growth estimates. Whereas less frequent progress monitoring might run the risk of detecting a need for instructional change too late, Jenkins and Terjeson ([<reflink idref="bib36" id="ref68">36</reflink>] ) found that reduced frequency, along with ambitious long‐term goals and use of trend‐based decision rules, actually led to more prompts to modify instruction in reading. Further, Jenkins, Schulze, Marti, and Harbaugh ([<reflink idref="bib37" id="ref69">37</reflink>] ) found no evidence that intermittent progress monitoring schedules (e.g., two or three CBM forms administered on one occasion every two to six weeks) would change decision‐making accuracy compared to weekly CBM administration. However, they cautioned that replication is needed. In addition, they did not address twice‐weekly CBM administration, nor were teachers using CBM data for instructional decision‐making during the study; thus, additional research is needed.</p> <p>Finally, effects of DBI Only varied depending on the type and frequency of support provided for teachers. Effects were largest in the one study that incorporated small‐group, collaborative problem solving with individual consultation (g = 0.86; Jung, McMaster, & delMas, [<reflink idref="bib40" id="ref70">40</reflink>] ); effects also were larger for more frequent (weekly or twice‐weekly) support than for less frequent support (every 1–3 weeks g = 0.23; every 2–3 weeks g = ‐0.02). Given that Jung et al. ([<reflink idref="bib40" id="ref71">40</reflink>] ) included almost double the support provided in other DBI studies, type and frequency of support are conflated. Further research is needed to determine the optimal support needed to promote strong student outcomes.</p> <hd id="AN0131205642-32">Limitations and Future Research</hd> <p>Findings of this meta‐analysis should be interpreted in the context of the following limitations, which lead to directions for further research. First, this meta‐analysis included a relatively small sample of studies, limiting conclusions regarding the overall effects of DBI. Some studies had to be excluded because they did not provide sufficient information to compute effect sizes, underscoring the importance of thorough reporting of descriptive study information. Additionally, because of the relatively small number of available effect sizes compared to the number of moderators, we were unable to conduct a meta‐regression, which would have allowed us to examine the effects of each moderator when holding other possible moderators constant, and to estimate the proportion of variance accounted for by the set of moderators. Instead, we were able to examine the influence of only one moderator at a time. As the DBI literature grows, future meta‐analyses should include meta‐regression for a more precise estimate of the influence of potential moderators of DBI's effects (e.g., grade levels or age).</p> <p>Second, we focused only on effects of DBI Only or DBI Plus compared to a business‐as‐usual control, because our purpose was to examine whether DBI produces promising effects in general. We excluded several studies because they compared DBI Only to other types of conditions, such as a CBM‐monitoring only condition (Tichá, [<reflink idref="bib52" id="ref72">52</reflink>] ), DBI Only to DBI Plus (Fuchs et al., [<reflink idref="bib24" id="ref73">24</reflink>] ), or a design that included pairs of students whose teachers used DBI for only one student to plan and deliver instruction for both students in the pair (Stecker & Fuchs, [<reflink idref="bib49" id="ref74">49</reflink>] ). Currently, too few studies exist with such comparisons to aggregate their effects in a meaningful way; further research is needed to determine the active ingredients of DBI.</p> <p>Third, a very small number of studies (n = 14) met the inclusion criteria, and most studies that met inclusion criteria were conducted in the 1980s and 1990s, with only two representing research conducted after 2000. Given the current focus on multitiered systems of support, further research is needed on the use of DBI and other approaches to intensifying intervention for children for whom primary and secondary prevention efforts are insufficient. Of note is that over half of the studies in this review employed computer applications to administer and score CBM tasks, over two‐thirds used software to store and graph data, and over half used software to apply decision rules. Findings of this research, though dated, are promising and should provide guidance for more contemporary research, especially given the advancement of technology in the last couple of decades (Lembke, McMaster, & Stecker, [<reflink idref="bib42" id="ref75">42</reflink>] ).</p> <p>Fourth, all but one study provided ongoing support to teachers who implemented DBI, although various types of support were used. Thus, findings from this meta‐analysis indicate the effects of DBI on student performance when ongoing support is provided to teachers. Additional research is needed to examine the effects of DBI on student performance independent of ongoing support. Given that only one study examining an alternative type of support to individual consultation met inclusion criteria for this review (Allinder, Bolling, Oats, & Gagnon, [<reflink idref="bib3" id="ref76">3</reflink>] ), more research is needed to examine effects of various types of ongoing support.</p> <p>Finally, most studies in this review provided very little detail about specific instructional modifications that teachers made in response to student data. Yet, given the central role of “individualization” in DBI, it seems critical to gain a better understanding of the nature of teachers’ instructional changes in response to student data. The intensification taxonomy proposed by Fuchs, Fuchs, and Malone ([<reflink idref="bib33" id="ref77">33</reflink>] ; see also Fuchs and colleagues’ introduction to this special issue) could provide guidance in this regard. Whereas DBI, by its very nature, addresses the “individualization” dimension of the taxonomy, teachers might make specific modifications by adjusting one or more of the other dimensions (e.g., dosage, comprehensiveness, or attention to transfer). We hoped that we might be able to apply this taxonomy to describe how teachers intensified their instruction in studies for this review, but insufficient information was available to do so. We suggest that future researchers use such a taxonomy to describe the ways that teachers intensify instruction within a DBI framework.</p> <hd id="AN0131205642-33">Implications for Practice</hd> <p>Although it is clear that further research is needed to draw firm conclusions about the effects of DBI on students’ academic outcomes, as well as to understand better the influence of potential moderators of DBI's effects, findings from this review support the following implications for practice. First, no differential effects of DBI were evident across reading, mathematics, or spelling/writing outcomes; thus, we encourage teachers to use DBI to individualize instruction to enhance students’ performance across these academic domains.</p> <p>Second, given that effects of DBI did not vary based on CBM administrator (DBI implementers, computer software, or both), teachers may consider using any of these approaches to CBM administration. Multiple web‐based applications are available (e.g., aimswebPlus [<ulink href="http://www.aimsweb.com">http://www.aimsweb.com</ulink>]; easyCBM [<ulink href="http://www.easycbm.com">http://www.easycbm.com</ulink>]; FAST [<ulink href="http://www.fastbridge.org/">http://www.fastbridge.org/</ulink>]), and these may enhance the feasibility of administration, scoring, and data management. In terms of CBM frequency, one measure twice per week appears to yield stronger effects than more frequent administration. While we have insufficient evidence from this meta‐analysis to recommend less frequent progress monitoring, more frequent administration is not likely necessary.</p> <p>Third, given that DBI effects did not differ reliably by data management or decision‐making features, teachers may have some flexibility regarding how to manage students’ data and how to apply decision rules. Additionally, when teachers received feedback on their instructional decisions via computer, expert, or peer (Fuchs, Fuchs, & Hamlett, [<reflink idref="bib22" id="ref78">22</reflink>] , [<reflink idref="bib23" id="ref79">23</reflink>] ; Fuchs, Fuchs, Hamlett, & Stecker, [<reflink idref="bib32" id="ref80">32</reflink>] ; Jung et al., [<reflink idref="bib40" id="ref81">40</reflink>] ), the effect size was twice as large (g = 0.48) as when they did not receive feedback (g = 0.22). Although this difference was not reliably different, it suggests that teachers might benefit from such ongoing feedback.</p> <p>Finally, findings of this meta‐analysis suggest that frequent (e.g., weekly or twice‐ monthly) support (in the form of individual consultation with or without small‐group collaborative problem solving) might be related to larger effects of DBI. Thus, schools interested in adopting a DBI framework for instructional decision‐making might consider investing resources in such supports. Such support systems might be challenging to initiate, though, as they require individuals with DBI‐related expertise and/or time for colleagues to meet and share data. Ongoing partnerships between schools and researchers may be one promising way to address this challenge, and to establish sustainable systems of successful DBI implementation.</p> <hd id="AN0131205642-34">CONCLUSION</hd> <p>In summary, DBI, which has been examined by researchers over the last several decades, shows promise for improving student outcomes. Whereas effect sizes of 0.37‐0.38 may seem relatively modest, we assert that such effects are important, particularly because they represent effects on outcomes for students with intensive academic needs. Given that a small but persistent proportion of students do not benefit sufficiently from research‐based standard treatment protocols (Wanzek & Vaughn, [<reflink idref="bib54" id="ref82">54</reflink>] ), it is critical to find ways to intensify instruction that meet individual learning needs. DBI offers one way to do so, by providing educators with a validated, data‐based decision‐making process for adjusting instruction systematically over time (cf. Fuchs et al., [<reflink idref="bib33" id="ref83">33</reflink>] ; Fuchs et al., this issue). 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Exceptional Children, 57, 246–256. https://doi.org/10.1177/001440299105700307 </bibtext> </blist> </ref> <aug> <p>By Pyung‐Gang Jung; Kristen L. McMaster; Amy K. Kunkel; Jaehyun Shin and Pamela M. Stecker</p> <p></p> <p>Pyung‐Gang Jung, Ph.D. is a postdoctoral researcher in the Department of Special Education, Ewha Womans University. Her research interests include data‐based individualization and evidence‐based interventions in reading and writing for students at risk, and students with disabilities.</p> <p>Kristen L. McMaster, Ph.D. is a Professor of Special Education in the Department of Educational Psychology, University of Minnesota. Her research focuses on (<reflink idref="bib1" id="ref84">1</reflink>) promoting teachers’ use of data‐based decision making and evidence‐based instruction and (<reflink idref="bib2" id="ref85">2</reflink>) developing intensive, individualized interventions for students for whom generally effective instruction is not sufficient.</p> <p>Amy K. Kunkel, Ph.D. is a lecturer in Special Education in the Department of Educational Psychology, University of Minnesota. Her research and practice include (<reflink idref="bib1" id="ref86">1</reflink>) supporting teachers’ understanding of data‐based decision making and evidence‐based instruction in academics and behavior and (<reflink idref="bib2" id="ref87">2</reflink>) using computer‐assisted instruction to improve the academic performance of struggling learners.</p> <p>Jaehyun Shin, Ph.D. is an assistant professor in the Department of Special Education, Gyeongin National University of Education. His research focuses on (<reflink idref="bib1" id="ref88">1</reflink>) using assessment (Curriculum‐Based Measures) to inform timely identification and effective interventions and (<reflink idref="bib2" id="ref89">2</reflink>) developing and evaluating evidence‐based practices for students at‐risk or with disabilities.</p> <p>Pamela M. Stecker, Ph.D. is a Professor in the Department of Education and Human Development, Clemson University. She prepares educators, both at preservice and in‐service levels, to use data‐based individualization. She also conducts research and development in curriculum‐based measurement.</p> </aug>
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  Data: Effects of Data-Based Individualization for Students with Intensive Learning Needs: A Meta-Analysis
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  Label: Language
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Jung%2C+Pyung-Gang%22">Jung, Pyung-Gang</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-7076-6031">0000-0002-7076-6031</externalLink>)<br /><searchLink fieldCode="AR" term="%22McMaster%2C+Kristen+L%2E%22">McMaster, Kristen L.</searchLink><br /><searchLink fieldCode="AR" term="%22Kunkel%2C+Amy+K%2E%22">Kunkel, Amy K.</searchLink><br /><searchLink fieldCode="AR" term="%22Shin%2C+Jaehyun%22">Shin, Jaehyun</searchLink><br /><searchLink fieldCode="AR" term="%22Stecker%2C+Pamela+M%2E%22">Stecker, Pamela M.</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Learning+Disabilities+Research+%26+Practice%22"><i>Learning Disabilities Research & Practice</i></searchLink>. Aug 2018 33(3):144-155.
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  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
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  Data: Y
– Name: Pages
  Label: Page Count
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  Data: 12
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  Data: 2018
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  Label: Document Type
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  Data: Journal Articles<br />Reports - Research<br />Information Analyses
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Meta+Analysis%22">Meta Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Effect+Size%22">Effect Size</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Analysis%22">Comparative Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+Instruction%22">Individualized Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Student+Relationship%22">Teacher Student Relationship</searchLink>
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  Label: DOI
  Group: ID
  Data: 10.1111/ldrp.12172
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  Label: ISSN
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  Data: 0938-8982
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  Label: Abstract
  Group: Ab
  Data: We examined the mean effect of teachers' use of data-based individualization (DBI) on the performance of students with intensive learning needs across academic areas and factors influencing the effects of DBI on student achievement. A total of 57 effect sizes from 14 studies were categorized into two comparisons: DBI Only (comparisons between DBI and a business-as-usual control) and DBI Plus (comparisons in which DBI implementers had access to additional information on student performance while they implemented DBI, compared to a control). The mean effect of DBI Only on student performance was g = 0.37; the mean effect of DBI Plus was g = 0.38. Differential effects of DBI were found depending on the nature of CBM tasks, frequency of CBM administration, and type and frequency of supports provided to teachers. Findings support the use of DBI to enhance student outcomes across academic areas.
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        Value: 10.1111/ldrp.12172
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      Pagination:
        PageCount: 12
        StartPage: 144
    Subjects:
      – SubjectFull: Meta Analysis
        Type: general
      – SubjectFull: Academic Achievement
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      – SubjectFull: Effect Size
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      – SubjectFull: Comparative Analysis
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      – SubjectFull: Individualized Instruction
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      – SubjectFull: Teacher Student Relationship
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    Titles:
      – TitleFull: Effects of Data-Based Individualization for Students with Intensive Learning Needs: A Meta-Analysis
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