It is no longer a question whether technology should be integrated into the classroom. The focus has shifted to how to use it to enable and promote effective learning. For better or for worse, technology is pervasive in our lives, and educational settings are no exception. However, it is not sufficient to employ educational technology simply because it is available. How technology is deployed, when and for what purposes it is used, what kind of learning it is applied to, and which categories of students it affects, are now of prime importance. This paper presents findings of a meta-analysis (M-A) that investigated differences between teacher-centered and student-centered (T-C vs. S-C) pedagogical practices in their effect on educational technology use as measured by student achievement outcomes. To describe S-C strategies, eleven instructional dimensions were identified from our previous work. Findings, based on 168 independent effect sizes (ESs) comparing T-C with S-C revealed a weighted average of g+=0.402 indicating that educational technology moderately increases learning achievement outcomes. Significant findings are reported, with four dimensions -Course design, Problem type, Conceptual level, and Peer collaboration - strengthening the impact of educational technology on students’ achievement, and in one dimension - Pacing/Flexibility - weakening it.

This is a study of two populations of learners/teachers: Pre-service teacher students (i.e., formal education for teaching certification) and In-service teachers (i.e., engaged in professional development), and involves an examination of their use of internet-based instructional applications. In these studies, the technologies are: a) Online Learning [OL]; b) Blended Learning [BL] and c) Flipped Classrooms [FC]. Treatments were compared to standard face-to-face classroom instruction (CI) on three dependent measures analysed separately to produce 77 achievement measures in the first meta-analysis, 21 attitude/satisfaction measures in the second, and 22 studies of self-efficacy in the third. Achievement data yielded a statistically significant, moderate effect size (i.e., g+= 0.44) in favor of the combined OL, BL and FC approaches versus CI, although OL showed only a small positive effect versus CI. Attitude yielded a non-significant effect size comparing CI with technology-supported strategies (g+= 0.12). Interestingly, self-efficacy produced a significant and moderate average random effect size of g+ = 0.45. Perhaps the most impactful finding of this analysis is related to improved performance of BL/FC when contrasted with OL, yielding significantly positive differences in all three measures. This confirms that improved outcomes are the result of better pedagogy, not the mere presence of technology. Finally, self-efficacy was found to be meaningfully improved when using BL/FC. This is the first meta-analysis with this target population implicating self-efficacy. These two outcomes offer important implications for institutions regarding the future design of instructional delivery for both pre-and in-service teachers.

Objective To determine the effectiveness of mindfulness-based programmes (MBPs) on the mental health of elite athletes. Design Systematic review and meta-analysis. Data sources Eight online databases (Embase, PsycINFO, SPORTDiscus, MEDLINE, Scopus, Cochrane CENTRAL, ProQuest Dissertations & Theses and Google Scholar), plus forward and backward searching from included studies and previous systematic reviews. Eligibility criteria for selecting studies Studies were included if they were randomised controlled trials (RCTs) that compared an MBP against a control, in current or former elite athletes. Results Of 2386 articles identified, 12 RCTs were included in this systematic review and meta-analysis, comprising a total of 614 elite athletes (314 MBPs and 300 controls). Overall, MBPs improved mental health, with large significant pooled effect sizes for reducing symptoms of anxiety (hedges g=-0.87, number of studies (n)=6, p=0.017, I (2)=90) and stress (g=-0.91, n=5, p=0.012, I (2)=74) and increasing psychological well-being (g=0.96, n=5, p=0.039., I (2)=89). Overall, the risk of bias and certainty of evidence was moderate, and all findings were subject to high estimated levels of heterogeneity. Conclusion MBPs improved several mental health outcomes. Given the moderate degree of evidence, high-quality, adequately powered trials are required in the future. These studies should emphasise intervention fidelity, teacher competence and scalability within elite sport. PROSPERO registration number CRD42020176654.

As the empirical literature in educational technology continues to grow, meta-analyses are increasingly being used to synthesise research to inform practice. However, not all meta-analyses are equal. To examine their evolution over the past 30 years, this study systematically analysed the quality of 52 meta-analyses (1988–2017) on educational technology. Methodological and reporting quality is defined here as the completeness of the descriptive and methodological reporting features of meta-analyses. The study employed the Meta-Analysis Methodological Reporting Quality Guide (MMRQG), an instrument designed to assess 22 areas of reporting quality in meta-analyses. Overall, MMRQG scores were negatively related to average effect size (i.e., the higher the quality, the lower the effect size). Owing to the presence of poor-quality syntheses, the contribution of educational technologies to learning has been overestimated, potentially misleading researchers and practitioners. Nine MMRQG items discriminated between higher and lower average effect sizes. A publication date analysis revealed that older reviews (1988–2009) scored significantly lower on the MMRQG than more recent reviews (2010–2017). Although the increase in quality bodes well for the educational technology literature, many recent meta-analyses still show only moderate levels of quality. Identifying and using only best evidence-based research is thus imperative to avoid bias. Implications for practice or policy: Educational technology practitioners should make use of meta-analytical findings that systematically synthesise primary research. Academics, policymakers and practitioners should consider the methodological quality of meta-analyses as they vary in reliability. Academics, policymakers and practitioners could avoid misleading bias in research evidence by using the MMRQG to evaluate the quality of meta-analyses. Meta-analyses with lower MMRQG scores should be considered with caution as they seem to overestimate the effect of educational technology on learning.

This meta-analysis investigates the effects of four instructional dimensions rated on a scale from more Teacher-centered (T-C) to more Student-centered (S-C) plus several coded moderator variables on the achievement of undergraduate students in science education courses. More student-centered conditions served as the ‘treatment’ while more teacher-centered conditions were considered the ‘control.’ Hedges’ g, operationalized as the adjusted standardized differences between treatment and control means, served as the outcome measure. The weighted average difference between groups was g̅ = 0.34, k = 140 (random effects analysis), indicating an overall difference in favor of student-centered instruction. Out of four rated dimensions (Pacing, Teacher’s Role, Flexibility, and Adaptation) only Flexibility was significant in metaregression as a negative predictor of effect size. Two demographic variables (i.e., class size & subject matter), and one instructional moderator variables (i.e., technology use) were also significant when added to Flexibility, producing a model that accounted for 36% of total variation in effect size.

Introduction. This paper provides an overview of the information retrieval strategy employed for two meta-analyses, conducted by a systematic review team at Concordia University (Montreal, QC, Canada). Both papers draw on standards first articulated by H.M. Cooper and further developed by the Campbell Collaboration, which promote a comprehensive approach to systematically searching an extensive array of resources (bibliographic databases, print resources, citation indices, etc.) in order to locate both published and unpublished research. The goal is to verify if searching comprehensively through multiple resources retrieves studies that are unique, and hence, improve the overall representativeness of a diverse body of literature. We also analyze the sensitivity and specificity of the results by data source. Methods. In order to determine the source sensitivity, we consider percentage of results from each source retrieved for full-text review. In order to determine the source specificity, we derive a percentage from the total number of studies included in the final meta-analysis compared against the overall number of initial results found. Results. Results demonstrate the need to search beyond the subject-specific databases of a particular discipline as unique results can be found in many places. Databases for related disciplines provided 129 unique includes to each meta-analysis, and multidisciplinary databases provided 44 and 99 unique includes for the two meta-analyses in question respectively. Manual search techniques were much more sensitive and specific than electronic searches of databases and yield a higher percentage of final includes. Discussion. The results demonstrate the utility of a comprehensive information retrieval methodology like that proposed by the Campbell Collaboration, which goes beyond the main subject databases to locate the full range of information sources, including grey literature.

Campbell Systematic ReviewsVolume 15, Issue 1-2 e1017 SYSTEMATIC REVIEWOpen Access Twenty-first century adaptive teaching and individualized learning operationalized as specific blends of student-centered instructional events: A systematic review and meta-analysis Robert M. Bernard, Corresponding Author Robert M. Bernard robert.bernard@concordia.ca Centre for the Study of Learning and Performance, Concordia University, Montreal, Canada Correspondence Robert M. Bernard, Centre for the Study of Learning and Performance, Concordia University, Montreal, QC H3G 1M8, Canada. Email: robert.bernard@concordia.caSearch for more papers by this authorEugene Borokhovski, Eugene Borokhovski Centre for the Study of Learning and Performance, Montreal, CanadaSearch for more papers by this authorRichard F. Schmid, Richard F. Schmid Department of Education (Educational Technology), Centre for the Study of Learning and Performance, Concordia University, Montreal, CanadaSearch for more papers by this authorDavid I. Waddington, David I. Waddington Department of Education (Educational Studies), Centre for the Study of Learning and Performance, Concordia University, Montreal, CanadaSearch for more papers by this authorDavid I. Pickup, David I. Pickup Centre for the Study of Learning and Performance, Montreal, CanadaSearch for more papers by this author Robert M. Bernard, Corresponding Author Robert M. Bernard robert.bernard@concordia.ca Centre for the Study of Learning and Performance, Concordia University, Montreal, Canada Correspondence Robert M. Bernard, Centre for the Study of Learning and Performance, Concordia University, Montreal, QC H3G 1M8, Canada. Email: robert.bernard@concordia.caSearch for more papers by this authorEugene Borokhovski, Eugene Borokhovski Centre for the Study of Learning and Performance, Montreal, CanadaSearch for more papers by this authorRichard F. Schmid, Richard F. Schmid Department of Education (Educational Technology), Centre for the Study of Learning and Performance, Concordia University, Montreal, CanadaSearch for more papers by this authorDavid I. Waddington, David I. Waddington Department of Education (Educational Studies), Centre for the Study of Learning and Performance, Concordia University, Montreal, CanadaSearch for more papers by this authorDavid I. Pickup, David I. Pickup Centre for the Study of Learning and Performance, Montreal, CanadaSearch for more papers by this author First published: 19 July 2019 https://doi.org/10.1002/cl2.1017Citations: 3 Linked article: Plain language summary on the Campbell website Protocol AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat 1 PLAIN LANGUAGE SUMMARY Adaptive teaching and individualization for K-12 students improve academic achievement 1.1 The review in brief Teaching methods that individualize and adapt instructional conditions to K-12 learners' needs, abilities, and interests help improve learning achievement. The most important variables are the teacher's role in the classroom as a guide and mentor and the adaptability of learning activities and materials. What is the aim of this review? This Campbell systematic review assesses the overall impact on student achievement of processes and methods that are more student-centered versus less student-centered. It also considers the strength of student-centered practices in four teaching domains. Flexibility: Degree to which students can contribute to course design, selecting study materials, and stating learning objectives. Pacing of instruction: Students can decide how fast to progress through course content and whether this progression is linear or iterative. Teacher's role: Ranging from authority figure and sole source of information, to teacher as equal partner in the learning process. Adaptability: Degrees of manipulating learning environments, materials, and activities to make them more student-centered. 1.2 What is this review about? Teaching in K-12 classrooms involves many decisions about the appropriateness of methods and materials that both provide content and encourage learning. This review assesses the overall impact on student achievement of processes and methods that are more student-centered versus less student-centered (and thus more teacher-centered, i.e., more under the direct control of a teacher). It also considers in which instructional dimensions the application of more of these student-centered practices is most appropriate, and the strength of student-centered practices in each of four teaching domains. 1.3 What is this review about? 1.3.1 What studies are included? This review presents evidence from 299 studies (covering 43,175 students in a formal school setting) yielding 365 estimates of the impact of teaching practices. The studies spanned the period 2000–2017 and were mostly carried out in the United States, Europe, and Australia. What is the overall average effect of more versus less student-centered instruction on achievement outcomes? Which demographic variables moderate the overall results? More student-centered instructional conditions have a moderate positive effect on student achievement compared to less student-centered. Which dimensions of instruction are most important in promoting better achievement through the application of more versus less student-centered instruction? Do these dimensions interact? The teacher's role has a significantly positive impact on student achievement; more student-centered instruction produces better achievement. Pacing of instruction/learning—where learners have more choice over setting the pace and content navigation of learning activities—has a significant effect in the opposite direction; i.e., a significantly negative relationship. There is no relationship between adaptability and flexibility and student achievement. There are interactive effects. The teacher's role combined with adaptability produces stronger effects, whereas flexibility (greater involvement of students in course design and selection of learning materials and objectives) has the opposite effect; it reduces the effectiveness of teacher's role on learning outcomes. Special education students perform significantly better in achievement compared to the general population. Three other factors—grade level; Science, technology, engineering, and mathematics (STEM) versus non-STEM subjects; individual subjects—do not have any effect on the impact of the intervention. 1.4 What do the findings of this review mean? This review confirms previous research on the effectiveness of student-centered and active learning. It goes further in suggesting the teacher's role promotes effective student-centered learning, and excessive student control over pacing appears to inhibit it. An important element of these findings relates to the significant combination of teacher's role and adaptability, in that it suggests the domain in which the teacher's role should focus. Since adaptability relates to increasing the involvement of students in more student-centered activities, the evidence suggests that instruction that involves activity-based learning, either individually or in groups, increases learning beyond the overall effect found for more student-centered versus less student-centered activities. Various student-centered approaches, such as cooperative learning and peer-tutoring, have been found to accomplish this goal. 1.5 How up-to-date is this review? This meta-analysis contains studies that date from 2000–2017. 2 EXECUTIVE SUMMARY/ABSTRACT 2.1 Background The question of how to best deliver instruction to k-12 students has dominated the educational conversation, both in terms of theory and practice, since before 1960. Two predominant models have clashed: (a) Traditional teacher-directed instruction (referred to here as teacher-centered Teacher-Centered instruction), where there is little methodological adaptation for individual differences in ability, skills, interests, etc. among students; and (b) so-called student-centered instruction (referred to here as Student-Centered instruction), deriving much of its theoretical justification and methodological intricacies from constructivist thought embodied in the works of Jean Piaget, Lev Vygotsky, Jerome Burner, and many others. While radical constructivism has never become dominant in k-12 schooling (except in a relatively small number of demonstration schools), there has been considerable interest in embedding some of the principles of constructivism into k-12 schooling. This is often referred to as individualized or adaptive instruction, meaning an operational concern for individual students, their abilities, interests, etc., which is nearly the opposite of Teacher-Centered instruction. A great deal of research has demonstrated that approaches to individualism, such as mastery learning, collaborative and cooperative learning, problem-based learning, peer tutoring, and computer-based instruction, are effective in promoting achievement and attitudinal gains, as contrasted with Teacher-Centered instruction, where mastery of content or subject matter is of the greatest concern, and the teacher is the "delivery mechanism." More recently, this has been extended to include video-based lectures often delivered through the internet, as proposed by proponents of blended learning and its variant the flipped classroom (e.g., Baepler, Walker, and Driessen (2014). Research has also demonstrated that Teacher-Centered instruction is particularly useful in developing basic skills in areas such as reading, spelling, and math (Stockard, Wood, Coughlin, & Khoury, 2018). More recent theory and practice concerning Teacher-Centered (more conventional) and Student-Centered (more adaptive and individualized) instruction suggest that neither perspective is entirely sufficient and that some combination of Teacher-Centered and Student-Centered instruction is possibly more productive. This notion of combined teaching methods (i.e., Teacher-Centered plus Student-Centered) is one of the defining characteristics of the flipped classroom (Baepler et al., 2014). Certainly, students need to acquire skills and knowledge, but they also need to develop their own personal preferences, creativity, problem-solving abilities, and evaluative and self-evaluative perspectives. The current meta-analysis aims to determine if the advantage endowed by Student-Centered instruction also affects content achievement (i.e., content achievement is the outcome measure in this meta-analysis). The current meta-analysis was designed to explore teaching and learning in k-12 classrooms and the achievement benefit that derives from more Student-Centered versus less Student-Centered classrooms. Several perspectives informed the basis for the research approach described here, but none more so than the words of Gersten et al. (2008) while exploring through meta-analysis the question of Teacher-Centered versus Student-Centered instructional practices in elementary mathematics instruction. In the final report of their study, the group stated: "The Task Group found no examples of studies in which learners were teaching themselves or each other without any teacher guidance; nor did the Task Group find studies in which teachers conveyed … content directly to learners without any attention to their understanding or response. The fact that these terms, in practice, are neither clearly nor uniformly defined, nor are they true opposites, complicates the challenge of providing a review and synthesis of the literature …" (p. 12). The current meta-analysis intends to investigate variations of more versus less Student-Centered instruction and the four domains of the instructional process in which they are more or less profitable. 2.2 Objectives (research questions) There are three primary objectives that this meta-analysis intends to address (research questions that this study explores): Overall, does more Student-Centered instructional practices lead to a significant advantage in the acquisition of content (subject matter) knowledge (i.e., measured learning achievement)? Do any of the four primary (substantive) moderator variables (entered into multiple meta-regression), Teacher's Role, Pacing, Adaptability, and Flexibility, predict an increase or decrease in achievement across degrees of Student-Centered use (From less Student-Centered to more Student-Centered)? Is there a difference in categorical levels of less Student-Centered to more Student-Centered for each of the dimensions of instructional practice listed above, tested in mixed moderator variable analysis? Do any of the secondary (demographic) moderator variables interact with each other (i.e., combine) to produce more versus less Student-Centered instructional practices? 2.3 Search methods Following the guidelines of the Campbell Collaboration (Kugley et al., 2017), in order to retrieve a broad base of studies to review, we started by having an experienced Information Specialist search across an array of bibliographic databases, both in the subject area and in related disciplines. The following databases were searched for relevant publications: ABI/Inform Global (ProQuest), Academic Search Complete (EBSCO), ERIC (EBSCO), PsycINFO (EBSCO), CBCA Education (ProQuest), Education Source (EBSCO), Web of Knowledge, Engineering Village, Francis, ProQuest Dissertations & Theses Global, ProQuest Education Database, Linguistics and Language Behavior Abstracts (ProQuest). The search strategy was tailored to the features of each database, making use of database-specific controlled vocabulary and search filters, but based on the same core key terms. Searches were limited to the year 2000–2017 and targeted a k-12 population. Database searching was supplemented by using the Google search engine to locate additional articles, but principally grey literature (research reports, conference papers, theses, and research published outside conventional journals). 2.4 Selection criteria The overall set of inclusion/exclusion criteria (i.e., selection) for the meta-analysis contained the following requirements: Be publicly available and encompass studies from 2000 to the present; Feature at least two groups of different instructional strategies/practices that can be compared according to the research question as Student-Centered and Teacher-Centered instruction; Include course content and outcome measures that are compatible with the groups that form these comparisons; Contain sufficient descriptions of major instructional events in both instructional conditions; Satisfy the requirements of either experimental or high-quality quasi-experimental design; Be conducted in formal k-12 educational settings eventually leading to a certificate, diploma, degree, or promotion to a higher grade level; Contain legitimate measures of academic achievement (i.e., teacher/researcher-made, standardized); and Contain sufficient statistical information for effect size extraction. 2.5 Data collection and analysis 2.5.1 Effect size extraction and calculation One of the selection criteria was "Contain sufficient statistical information for effect size extraction," so that an effect size could be calculated for each independent comparison. This information could take several forms (in all cases sample size data were required): Means and standard deviations for each treatment and control group; Exact t value, F value, with an indication of the ± direction of the effect; Exact p value (e.g., p = .011), with an indication of the ± direction of the effect; Effect sizes converted from correlations or log odds ratios; Estimates of the mean difference (e.g., adjusted means, regression β weight, gain score means when r is unknown) Estimates of the pooled standard deviation (e.g., gain score standard deviation, one-way ANOVA with three or more groups, ANCOVA); Estimates based on a probability of a significant t test using α (e.g., p < .05); and Approximations based on dichotomous data (e.g., percentages of students who succeeded or failed the course requirements). Effect sizes were initially calculated as Cohen's d (Cohen, 1988) and then converted to Hedges'g (i.e., correction for small samples; Hedges & Olkin, 1985). Standard errors (SEd) were calculated for d and then converted to standard errors of SEg applying the correction formula for g. Hedges' g, SEg, and sample sizes (i.e., treatment and control) were entered into Comprehensive Meta-Analysis 3.3.07 (Borenstein, Hedges, Higgins, & Rothstein, 2014) where statistical analyses were performed. The effect sizes were coded for precision and these data were analyzed in moderator variable analysis. 2.5.2 Statistical analyses Analyses were conducted using the following statistical tests: Overall weighted random effects analysis with the statistics of g ¯ , SEg, Vg, upper and lower limits of the 95th confidence interval, zg, and p value; Homogeneity is estimated using Q-Total, df, and p value. I2 (i.e., percentage of error variation) and tau2 (i.e., average heterogeneity) is also calculated and reported. Meta-regression (single and multiple) is used to determine the relationship between covariates and effect sizes; and Mixed-model (i.e., random and fixed) moderator variable analysis is used to compare levels (categories) of each coded moderator variable. Q-Between, df, and p value are used to make decisions about the significance of each categorical variable. 2.6 Results The results are presented here in relationship to the four research questions previously described. Question 1: Overall, does more Student-Centered instructional practices lead to a significant advantage in the acquisition of content (subject matter) achievement (i.e., measured learning). Result: Answering the basic question, more Student-Centered instructional conditions (i.e., the treatment described above) outperform less Student-Centered to a moderate extent. The average effect, g ¯ = 0.44, k = 365, z = 4.56, p < .00, SE = 0.03, Q = 3,095.89, I2 = 88.22, tau2 = 0.27, between the mean of the more Student-Centered treatment and the less Student-Centered control, suggesting that teachers who promote and enact active classroom processes (more Student-Centered instruction), can expect to see better student achievement than in classrooms where teachers employ less Student-Centered instruction. Also, a linear trend was found in meta-regression when Hedges' g ¯ was regressed on degree of Student-Centered instruction (β = 0.04, SE = 0.02, z = 2.41, p = .032). The distribution remains significantly heterogeneous. Question 2: Do any of the four moderator variables (entered into multiple meta-regression), Teacher's Role, Pacing, Adaptability, and Flexibility, predict an increase or decrease in achievement across degrees of Student-Centered use (From less Student-Centered to more Student-Centered)? Result: In meta-regression, Teacher's role produces a significant linear trend (β = 0.06, SE = 0.04, z = 4.42, p < .001) and Pacing (β = −0.14, SE = 0.04, z = 3.18, p = .002). Adaptability, and Flexibility are not significant (p > .05). However, the trend for Teacher's role and Pacing is opposite (note the opposite signs on β). Teacher's role is significantly positive (i.e., more Student-Centered instruction produced higher achievement), while Pacing produces the reverse (i.e., a significantly negative trend). For Pacing, more Student-Centered methods produce lower achievement. Question 3: Do any of the moderator variables interact with each other (i.e., combine) to produce more versus less Student-Centered instructional practices? Result: Yes, Teacher's Role compared to two dimensions added to the Teacher's Role produce significantly different results (Q-Between = 7.76, df = 3, p = .02: Teacher's Role and Teacher's Role plus Adaptability significantly outperformed Teacher's Role plus Flexibility. Question 4: Is there a difference in categorical levels of less Student-Centered to more Student-Centered for each of the dimensions of instructional practice listed above, tested in mixed moderator variable analysis? Result: Only one of five moderator variables produced a significant differentiation among levels. Among four moderator variables (i.e., grade level; STEM versus Non-STEM subjects; individual subjects; and ability profile) only ability profile significantly differentiated among levels. Special education students demonstrated significantly higher achievement compared to the General population of students. 2.7 Authors' conclusions This meta-analysis provides strong evidence that Student-Centered instruction leads to improvements in learning with k-12 students. Not only is the overall random effects average effect size of medium strength ( g ¯ = 0.44), but there is also a demonstrated (subtle but significant) linear relationship between more Student-Centered classroom instruction and effect size (p = .03). Taken together, these results support the efficacy of allowing students to engage in active learning or other forms of Student-Centered enterprise as part of a comprehensive educational experience. 3 BACKGROUND 3.1 Adaptive teaching and individualization for k-12 students improve academic achievement: A meta-analysis of classroom studies The question of how to provide the best-quality instructional conditions for students of all grade levels has been scrutinized extensively since the early 1960s, principally from two major perspectives: Teacher-centeredness (Teacher-Centered) and student-centeredness (Student-Centered). Student-Centered education initially arose from the writings of early progressive educators like John Dewey, and was carried on subsequently, in various forms, by Jean Piaget, Lev Vigotsky, Jerome Bruner, and Carl Rogers, to name only a few. The ideas were radical when first introduced, but the notion of Student-Centered education resonated in educational circles, where lecturing and rote memorization was still the standard for quality education and led to vast amounts of theorizing and research to show that students could succeed in learning of all sorts without a strongly transmissive approach on the part of the teacher. Today, the terms individualized instruction and adaptive teaching have become a popular expression for current practice and are used nearly synonymously with Student-Centered learning. However, since their inception, Student-Centered practices have inspired resistance, both from the public and from educational theorists. Thus, after Student-Centered practices were widely introduced, a dichotomy arose in the literature, with one side promoting the continuation of Teacher-Centered learning and on the other side the adopting Student-Centered learning practices. This was argued as a dichotomy for many years. However, the arguments have abated somewhat now with the general recognition that there is value in both approaches. Generally speaking, educators no longer aspire to a pure implementation of either approach, but now discuss questions of which method, when, and for what purpose is best. 3.1.1 Individualized learning and adaptive student-centered education (Student-Centered) Conceptual understanding of individualized learning and adaptive teaching varies broadly, encompassing a multitude of instructional strategies, approaches, and activities. It stretches from accounts of specific systems of instruction such as mastery learning (Bloom, 1968) and scaffolded adaptive feedback in computer-based instruction (e.g., Azevedo & Bernard, 1995) to more general conceptions of active learning and individualization that involve approaches such as cooperative learning (e.g., Johnson & Johnson, 2002; Johnson, Johnson, & Maruyama, 1983), collaborative learning (e.g., Bernard, Rojo de Rubalcava, & St-Pierre, 2000), problem-based learning (e.g., Zhang et al., 2015), and project-based learning (e.g., Bernard & Lundgren-Cayrol, 2001). It also includes educational concepts, largely derived from elements of constructivism, such as discovery learning, inquiry-based learning, activity-based learning, experiential learning, and other forms of Student-Centered education (Tobias & Duffy, 2009). Notions of unguided Student-Centered learners have not been free from detractors. Dewey criticized this approach in Experience and Education (Dewey, 1938), and, more recently, Kirschner, Sweller, and Clark (2006) published an influential piece that argued that the practice of turning kids loose to learn defies many of the tenets of the psychological principles of working memory and that guided instruction is both more efficient and ultimately more profitable to long-term learning outcomes. A flurry of responses and rejoinders ensued with no clear resolution, but the educational community was left with the strong impression that a teacher's role in Student-Centered learning was better as a guide on the side rather than a silent witness (King, 1993). The learning sciences have further contributed to the distinction between social constructivism and individual constructivism providing a theoretical grounding for teacher versus learner-based strategies (Kolodner, 2004). Current and developing applications, informed by pedagogical principles espoused by case-based learning (e.g., Kolodner et al., 2003). Research on more individualized and adaptive education The earliest large-scale research project, aimed at exploring the efficacy of so-called progressive education, was conducted between 1933 and 1941 by the Progressive Education Association (funded by the General Education Board and other foundations). Twenty-nine model schools were selected for curricular experimentation with the security that over 200 colleges and universities would accept their students upon recommendation by their principals. Changes in these schools included more individualized instruction and more access to alternative and cross-disciplinary programs, which emphasized greater access to arts and extracurricular programs. Results indicated that students graduating from the 200 schools scored on par in basic courses (e.g., mathematics and science) with students from traditionally oriented schools and that there was more activity in artistic, political, and social engagement in students from the alternative experimental schools. The long-term impact of these experiments is generally described as influence on its participants and subsequent reformers rather than dramatic change. The intervening conservatism brought about by World War II and the ensuing Cold War are often cited as deterrents to widespread change in the overall educational system in the United States (Aiken, 1942). Examples of further attempts to make teaching and learning more individualized and adaptive can be found in both the early and current research literature. They include, but are not limited to, mastery learning (e.g., Bloom, 1968), Personalized System of Instruction (PSI; e.g., Keller, 1968), assorted forms of peer instruction (e.g., Mazur, 1997), various practices of reciprocal reading/writing activities (e.g., Huang & Yang, 2015), collaborative and cooperative learning, problem and project-based learning and, more recently, Intelligent Tutoring Systems (ITS; e.g., Huang & Shiu, 2012). Several of these approaches are summarized in the following paragraphs and a number of the most common group-based Student-Centered approaches are depicted in a Venn Diagram (Figure 2) that shows their inter-relationship and approximate overlap (Bishop & Verleger, 2013, p. 6). The benefits and limitations of so-called systems of instruction (i.e., mastery learning, PSI, and ISI) are summarized separately in both qualitative and quantitative reviews. In the late 1970s and early 1980s, several relevant meta-analyses were published on mastery learning and its variant PSI. First, Lysakowski and Walberg (1982), Guskey and Gates (1986), Guskey and Pigott (1988), Slavin (1987), and Kulik, Kulik, and Bangert-Drowns (1990) each performed successive meta-analyses (Slavin's was the best evidence synthesis) on the efficacy of mastery learning. The studies produced equivocal and highly debatable findings. Kulik, Kulik, and Cohen (1979) reviewed 75 individual comparative studies of Keller's Personalized System of Instruction (PSI is a spin-off of mastery learning) college teaching method. In comparison to conventional instruction, the PSI approach was demonstrated to have a positive effect on student achievement and course perception (mean effect size of nearly 0.70sd for both). Bangert and Kulik (1982) looked at the effectiveness of the Individualized Systems of Instruction (ISI, a spin-off of PSI) in secondary school students. They broadened the list of outcomes to account not only for student achievement (e.g., final exams), but also critical thinking, attitudes toward subject matter, and student self-concept. For all outcome types, the findings were inconclusive. For example, for the achievement data, only 8 out of 49 studies demonstrated statistically significant results in favor of ISI (four studies favored more conventional teaching methods and the rest were inconclusive). Finally, Kulik (1984) attempted a wider research synthesis (encompassing over 500 individual studies) of t

s (WilsonLine), Education: A SAGE Full-text Collection, Francis (CSA), Medline (PubMed), ProQuest Digital Dissertations & Theses, PsycINFO (EBSCO), Australian Policy Online, British Education Index, and Social Science Information Gateway. In addition, Google Internet searches were performed to help identify gray literature, including a search for conference proceedings. Review articles and previous meta-analyses were used for branching, and the tables of contents of major journals in the field of educational technology (e. g., Educational Technology Research & Development) were manually searched. Effect size calculation and synthesis A d-type standardized mean difference effect size was used as the common metric (i. e., Cohen’s d), and then was transformed into Hedges’ g metric [15] to provide necessary correction for small sample sizes. The random effects model [6] was the main analytical approach for this meta-analysis. A mixed effects model was used to test the difference in levels of moderator variables. In a mixed analysis, average effect sizes for categories of the moderator are calculated using a random effects model. The variance component Q-Between is calculated across categories using a fixed effect model [6]. All analyses, including sensitivity and publication bias analysis, were performed in Comprehensive Meta-AnalysisTM 2.2.048 [5]. Results The findings of the meta-analysis of effects of classroom technology integration in higher education on student achievement outcomes are presented, first, overall, and then by individual research sub-question as outlined earlier. More detailed information regarding each of these follow-up meta-analyses can be found in respective publications. Overall findings The overall random-model results of the [24] study are shown in Table 1. The total of 879 effect sizes produced a weighted average effect size of 0.27 that was significantly greater than zero. The collection is significantly heterogeneous, based on findings from the fixed model where heterogeneity is tested in terms of the magnitude of Q-Total (i. e., total between-study variability). An effect size of 0.27 is considered to be small and represents a difference of 0.27sd between the mean of the treatment condition and the control condition, amounting to about an 11 % difference. These results suggest that technology-supported РОССИЙСКИЙ ПСИХОЛОГИЧЕСКИЙ ЖУРНАЛ • 2016 ТОМ 13 No 4 294 RUSSIAN PSYCHOLOGICAL JOURNAL • 2016 VOL. 13 # 4 instruction is advantageous compared to either non-use or limited use, but that this advantage is relatively modest. Table 1. Overall weighted average effect size (Random Effects Model) Population Estimates k g SE Lower 95 th Upper 95th Final Collection 879 0.27* 0.02 0.24 0.31 ** Heterogeneity Analysis QT = 3,183.10 (df = 878), p < .001, I2 = 72.42 * p < .01; ** Based on the fixed effect model for k = 879 Major function of technology use The results become differentiated when effect sizes are divided by pedagogical application. These results from [24] indicate that technology that is used to support student cognition outperforms all other categories, but especially presentational support (Table 2). This effect is interpreted as a difference primarily between “technology used by students” (for content understanding) and “technology used by teachers” (for content delivery). Other functions, such as support for communication and a mixture of cognitive and presentational support, fall in between these two. Table 2. Instructional moderator variable analysis: Major function of technology use Levels of Technology Use k g Lower 95th Upper 95th QBetween Cognitive Support (CS) 186 0.36 0.28 0.44 Presentational Support (PS) 113 0.15 0.07 0.23 Communication Support 27 0.24 0.12 0.35 Mixture (CS plus PS) 485 0.25 0.21 0.30 Between Groups, df = 3 13.28, p = .004 Contrast: Cog. Supp. vs. Presentational Supp., z = 5.14, p < .0001 RUSSIAN PSYCHOLOGICAL JOURNAL • 2016 VOL. 13 # 4 295 РОССИЙСКИЙ ПСИХОЛОГИЧЕСКИЙ ЖУРНАЛ • 2016 ТОМ 13 No 4 Blended learning The effects of blended learning (i. e., partly in class and partly online) were derived from the [24] database and analyzed and reported in [3]. As evident from Table 3, the random-effects weighted average effect size (= 0.334, k = 117) is larger than the overall average effect of technology use in the original metaanalysis and is in line with the findings from the other meta-analyses of blended learning [21, 26]. Apparently, there is an advantage that accrues from balancing face-to-face instruction with online learning outside of class. The mechanisms of this effect have not been determined, so one of the challenges of educational technology research of the future will be to tease out the effects of variables such as amount of time devoted to each pattern of instruction, the most effective learning strategies, and the teacher’s role in the online portion of blended learning. Table 3. Weighted average effects for blended learning Analytical Models K g SE Lower 95th Upper 95th Random Effect Model 117 0.334* 0.04 0.26 0.41 Fixed Effect Model 117 0.316** 0.02 0.28 0.36 Heterogeneity Q-total = 372.91, df = 116, p < .001 I-squared = 68.89 % τ2 = 0.11 * z = 8.62, p < .001; ** z = 15.68, p < .001. Interaction treatments Interaction treatments were defined by [4] as instructional setups in distance education that are intended to facilitate and promote interaction among students, between students and teachers, and between students and the content. This definition was used to code studies in the [24] study in classroom setting. Table 4 shows the results of this basic analysis. Conditions where interactions were greater in the treatment group, compared to the control condition, produced results that were significantly higher than when the control group was higher in the potential for interaction. As expected, when the two conditions were roughly the same (i. e., = 0.29, k = 703), the outcome was not significantly different from the former condition (i. e., = 0.34 vs. 0.29). РОССИЙСКИЙ ПСИХОЛОГИЧЕСКИЙ ЖУРНАЛ • 2016 ТОМ 13 No 4 296 RUSSIAN PSYCHOLOGICAL JOURNAL • 2016 VOL. 13 # 4 Table 4. Mixed effects analysis of the degree of student-student interaction Levels k g SE Lower 95th Upper 95th QBetween Equal in control and experimental groups 703 0.29 0.02 0.25 0.33 Control group higher 127 0.16 0.04 0.07 0.24 Experimental group higher 48 0.34 0.07 0.20 0.48 Between Groups, df = 2 8.93, p = .012

Introduction. This meta-analytical study of primary research on early literacy explores and summarizes patterns of correlation between performance on Rapid Automatized Naming (RAN) task and measures of specific reading skills. This is the first large-scale meta-analysis intended to verify claims of the double-deficit hypothesis of relative independence of naming speed and phonological awareness factors in developmental dyslexia and to systematically map specific connection between RAN performance and various literacy competencies. Method. Two-hundred-forty-one primary studies identified through systematic searches of related empirical literature yielded 1551 effect sizes of two types – cross-sectional (correlations at the same time) and longitudinal (when measures of RAN and reading were considerably separated in time), reflecting RAN-to-reading correlations for seven independent outcome types. Results. The overall weighted average effect sizes were: r+ = 314, k = 1254 and r+ = 343, k = 297, respectively. Subsequent moderator variable analyses further explored RAN-to-reading associations dependent on RAN type, particular reading skills, age of learners and other factors. Among the strongest and most consistent in both sub-collections were correlation between symbolic RAN and reading speed and between non-symbolic RAN and reading comprehension, whereas both RAN types were strongly associated with decoding skills and reading composite measures. Discussion. Patterns of RAN-to-reading correlation provided insufficient support for the double-deficit hypothesis, but were suggestive of perceiving RAN as a measure of “pre-reading” skills, an “equal among equals” correlate of reading performance. The study also emphasizes the important role of both automatic and controlled cognitive processes for successful RAN task performance in its connection to reading competency.

Conceptual understanding of individualized learning and adaptive teaching varies broadly, encompassing a multitude of instructional strategies, approaches, and activities. It stretches from accounts as narrow and specific as scaffolding adaptive feedback in computer-based instruction (e.g., Atkinson, Renkl & Merrill, 2003) to more general conceptions, such as cooperative and collaborative learning (e.g., Johnson & Johnson, 2002). It also includes educational concepts derived from elements of constructivism, such as discovery learning, inquiry-based learning, experiential learning, problem-based learning and other forms of student-centered education. These instructional forms, which have been broadly described from the 1960s (e.g., Summerhill), were recently criticized by Kirshner, Sweller & Clark (2006) with a response published by Tobias & Duffy (2009). The learning sciences have further contributed to the distinction between social constructivism and individual constructivism (i.e., instructional system designs) providing a theoretical grounding for teacher vs. learner-based strategies (Kolodner, 2004). Current and developing applications, informed by pedagogical principles espoused by case-based learning (e.g., Kolodner et al., 2008), exemplify the transformation of learning environments which apply Bruner's concept of not just discovery for the student, but co-discovery on the part of the teacher. Examples of various attempts to make teaching and learning more adaptive can be found in both the early and current research literature. They include, though not limited to, mastery learning (e.g., Bloom, 1968), Personalized Systems of Instruction or PSI (e.g., Keller, 1968; Gifford & Vicks, 1982; Davies, 1981), assorted forms of peer instruction (e.g., Mazur, 1997), various reciprocal reading/writing activities (e.g., Huang & Yang, 2015; MacArthur, Schwartz & Graham, 1991), adaptive hypermedia (Brusilovsky, 2001), accommodation for individual learning styles (e.g., Özyurt & Özyurt, 2015) and more recent Intelligent Tutoring Systems or ITS (e.g., Huang & Shiu, 2012; VanLehn, 2011). To some extent, findings of primary research on these and related instructional practices have been summarized in two rather sparse collections of meta-analyses separated in time by almost three decades. In the late 1970s and early 1980s, several relevant meta-analyses were published. First, Lysakowski and Walberg (1982), Gusky and Gates (1986), Slavin (1986) and Kulik, Kulik and Bangert-Drowns (1990) each performed successive meta-analyses (Slavin's was a best evidence synthesis) on the efficacy of mastery learning. The studies produced equivocal findings. Also, Kulik, Kulik & Cohen (1979) reviewed 75 individual comparative studies of Keller's PSI (a spin-off of mastery learning) college teaching method. In comparison to conventional instruction the PSI was demonstrated to have a positive effect on student achievement and course perception (mean effect size of nearly 0.70 for both). Aiello and Wolfle (1980) summarized research on individualized instruction in science compared with traditional lectures and found that individualized instruction was more effective. Horak's (1981) study of self-paced modular instruction of elementary and secondary school math produced a wide variety of both positive and negative effect sizes. Bangert and Kulik (1982) looked at the effectiveness of the Individualized Systems of Instruction (ISI) in secondary school students. They broadened the list of outcomes to account not only for student achievement (e.g., final exam), but also critical thinking, attitudes toward subject matter, and student self-concept. For all outcome types the findings were unsettled. For example, for the achievement data only eight out of 49 studies demonstrated statistically significant results in favour of ISI (four studies favoured more conventional teaching methods and the rest were inconclusive). Finally in 1984, Kulik attempted a wider research synthesis (encompassing over 500 individual studies) of effectiveness of programmed instruction and ISI, paying special attention to the moderator variables of study dates and grade levels. Among the most promising findings, the author indicated that more recent studies showed higher effects than the earlier and that college-level students benefited significantly from using ISI compared with elementary and secondary school students. In summary, these meta-analyses produced inconclusive results. Moreover, they are rather outdated – practically none of the above-mentioned instructional methods exists now in its original form (e.g., Eyre, 2008 was able to identify fewer then 50 studies of PSI for the period between 1990 and 2006 in the PsycInfo database). All this suggests the need for a more refined (both methodologically and substantively) update of systematic reviews in the field, especially taking into account how much the methodology of meta-analysis itself has evolved since then. Several meta-analyses addressed the topic of individualized instruction, though in very specific narrowly focused forms. Cole (2014) examined the effectiveness of cooperative, collaborative, and peer tutoring for English language learners. A low-to-moderate average effect size of = 0.49 was found in favour of peer tutoring over individualized or teacher-centred comparison instructional conditions. The effect size tended to be relatively small in middle school students, but higher at elementary and high school levels. More in line with the already mentioned earlier meta-analyses of various forms of computer-assisted instruction, Ma, Adesope, Nesbit and Liu (2014) meta-analysed studies of Intelligent Tutoring Systems (ITS) in a variety of subject matters, from reading and math to law and medical education. The list of moderator variables included the type of both experimental and comparison treatments, as well as outcome type, student academic level, discipline studies, etc. The highest achievement effects of using ITS were found in comparison with non-ITS computer-based instruction ( = 0.57) and teacher-centred, large-group instruction ( = 0.42), whereas in comparison with human tutoring it was even negative ( = -0.11), though not statistically significant. ITS-based practices were similarly effective when used either alone or in combination with various forms of teacher-led instruction in many subject domains. In summary, research evidence concerning the effects of adaptive teaching and individualized learning remain relatively inconclusive, while there is obviously a need for better understanding how K-12 formal education may be more successful in addressing students’ personal needs and interests, accounting for their diverse abilities with the main goal of advancing their learning. The proposed systematic review will not only rely on the most rigorous and comprehensive methodology of meta-analysis, but also should be conceptually sound (i.e., thoroughly exploring educational practices to find consistent links among a multitude of individual pedagogical approaches) and timely (i.e., account for the most recent developments in education). The main research question of the proposed meta-analysis is: Can more Student-Centred (SC) (i.e., more adaptive and individualized) approaches to K-12 instruction be distinguished from more Teacher-Centred (TC) approaches in terms of their effect on student achievements and what substantive and demographic factors moderate these effects? For better understanding and more successful practical application, educational practices subsumed under this generic pedagogical idea of adaptive teaching and individualized learning deserve a valid conceptual working model, both inclusive enough to account for various forms of personalized/individualized instruction and sufficiently sensitive to fluctuations due not only to the influence of numerous moderator variables, but also to nuanced qualities of particular instructional approaches themselves. SC instructional strategies could, in our view, serve such an overarching conceptual framework with adequate explanatory power, but only if operationalized properly to avoid an oversimplified dichotomy of inductive vs. deductive education (constructivism vs. direct instruction). Indeed, we are less interested in deciding between these two extremes and more interested in understanding the circumstances or combination of circumstances that optimize teaching and learning. Gresalfi and Lester (2009) for mathematics teaching, and Klahr (2009) for science teaching argue that the goal of instruction should be to achieve curricular and process objectives by choosing the most appropriate method based on student age, ability, prior knowledge, level of content, etc. In this regard, we would like to avoid the conceptual error of falsely dichotomizing pedagogical environments as either TC or SC since neither instructional practice likely exists in its pure form. As Gersten et al. (2008) observed in their systematic review of mathematics teaching practices: “[We] found no examples of studies in which students were teaching themselves or each other without any teacher guidance; nor did the Task Group find studies in which teachers conveyed … content directly to students without any attention to their understanding or response. The fact that these terms, in practice, are neither clearly nor uniformly defined, nor are they true opposites, complicates the challenge of providing a review and synthesis of the literature …” (p. 12). Since SC pedagogical practices try to emphasize guidance over direct instruction, the question becomes how much and what kind of guidance is offered to students and who takes responsibility for the design and implementation of various components of learning experience to make them truly adaptive/individualized and, hence, more effective. To define the key quality of instruction as “adaptive” and “individualized,” for the purposes of the proposed systematic review, we suggest deconstructing teaching and learning according to the events associated with them (e.g., setting objectives, implementing instructional methods, assessing learning). Accordingly, a more SC (more adaptive) classroom is one in which students play a more central role in the conduct of the instructional events. If these events can be isolated in reports of primary classroom research, they can be rated individually on a TC to SC continuum. Each event could then be: 1) examined separately to determine their individual strengths; 2) examined in clusters as combinations of events; or 3) collapsed into a multi-dimensional composite that would yield a “greater-than to lesser-than” distinction between two different instructional settings. This approach avoids problems associated with either subjectively defining instructional conditions as SC vs. TC or vaguely labeling them, such as PSI, mastery learning, etc. It also has the advantage of allowing us to examine instructional events in isolation and in various combinations in the search for optimal instructional practices. Most of the significant effects from the meta-analyses described in the first section of this protocol on the topic cluster around 0.40SD, but the data also reflect a wide range of effects, depending on the whole spectrum of moderator variables. In other words, the picture painted by these meta-analyses remains in large part as inconclusive as it was several decades ago in the 1980s. Of special concern to us is the fact that both earlier and recent meta-analyses are rather limited in scope and focus of interest, addressing very specific instructional practices and technological tools. There were no serious attempts to find and conceptualize pedagogical commonalities among the interventions in question that would allow treating them within the same class of phenomena broadly depicted as individualized learning and adaptive teaching. Thus, the need for a review that would be broad in scope, summarize research evidence up to date, and have a conceptually sound foundation is pressing. The main objective of the proposed review is to summarize research data on the effectiveness (in terms of learning achievement outcomes) of adaptive and individualized instructional interventions operationally defined here as more SC pedagogical approaches. The overall weighted average effect size will be an indication of that. Additionally, and no less important, the review aims to better understand under what circumstances (e.g., with what populations of learners, for what subject matters) the effects of adaptive and individualized instruction rich their highest potential, and what conditions may depress them. To explore the latter, a set of substantive and demographic study features will be coded and subjected to moderator variable analyses. The review outcomes will inform education practitioners and research community of the best instructional practices, preconditions for their successful implementation and potential pitfalls, as well as of directions for further empirical research in the area. The review will include studies that are experimental (i.e., RCT) or high-quality quasi-experimental (i.e., statistically verified group equivalence or adjustment) in design that address adequate to the research question group comparisons, contain legitimate measures of academic achievement (i.e. teacher-made, standardized), and report sufficient statistical information for effect size extraction. Students in K-12 formal educational settings (approximate age 5-18), i.e., eventually leading to a certificate, diploma, degree, or promotion to a higher level. Educational interventions may take place either in the classroom (F2F), via distance education (DE), or as a blended (various combinations of F2F and DE) intervention. The highest total rating across these dimensions will determine an Intervention condition to be compared on achievement outcomes to a Comparison condition, lowest in total rating That is differential total across all dimensions, as some of them may be higher in ratings for one group and some – for another. At the same time, keeping individual positive and negative differentials will allow identifying consistent clusters of instructional events that are more/less likely to work to the advantage of students’ learning. All types of objective measures of academic achievements are to be considered. Their psychometric features (e.g., standardized, non-standardized teacher/researcher-made assessment tools) and type of representativeness (e.g., cumulative final examinations or averages of several performance tasks covering various components of the course/unit content) will be documented and used in subsequent moderator variable analyses. Self-assessments are to be excluded, as well as attitudinal and behavioural measures. Data of their prevalence in the reviewed primary literature will be collected to inform further reviews in the area with a potential focus on those types of outcomes. To maximize coverage of primary research, fully compatible in terms of outcome measures, only immediate post-test (that is assessment administered at the end of treatment implementation) results will be considered. Various forms of delayed post-tests will be documented and their time lags categorized to inform further reviews. K-12 formal educational settings (approximate age 5-18), in educational programs eventually leading to a certificate, diploma, degree, or advancement to the next academic level/grade are of interest to the current meta-analysis. Other settings (i.e., home schooling, auxiliary programs, summer camps, etc.) are to be excluded. Addressing studies from 2000 onward seems to strike a reasonable balance of covering various approaches to individualized/adaptive learning prominent throughout several decades (studies published in early 2000s would still reflect most interesting pedagogies of 1990s), while primarily focusing on those that retain relevance in most recent educational practices. In order to retrieve a broad base of studies to review, we will begin by having an experienced Information Specialist search across an array of bibliographic databases, both in the subject area and in related disciplines. The following databases will be targeted: ABI/Inform Global (ProQuest) Academic Search Complete (EBSCO) ERIC (EBSCO) PsycINFO (EBSCO) CBCA Education (ProQuest) Australian Education Index British Education Index Education Source (EBSCO) Web of Knowledge Scopus Engineering Village Francis (EBSCO) Medline ProQuest Dissertations & Theses Global ProQuest Education Database Linguistics and Language Behavior Abstracts (ProQuest) Database searching will be supplemented by searches of the Web using Google and Bing to locate additional articles, but also grey literature (research reports, conference papers, theses and research published outside the conventional journals). We will also search the OpenGrey.eu and the Learn Tech Lib online collections for grey literature, and will consult the Campbell guide (Hammerstrøm, Wade, & Jørgensen, 2010) for other useful online resources. Finally, the reference lists of identified literature reviews will be ‘branched’ for additional relevant studies using a citation search approach. The most recent issues of the top journals (based on inclusion rate) will be searched manually toward the end of the search process to catch any recent publications that match our screening criteria. When possible, we will contact noted experts in the field to ensure we have all their relevant research. Although the search strategy will be tailored to the features of the various databases, i.e. making use of database-specific controlled vocabulary and search filters, the following is representative of what the overall search statement would look like. (“student cent*” OR “learner cent*” OR “learner control” OR constructivi* OR “individualized instruction” OR “discovery learning” OR “active learning” OR scaffold* OR “experiential learning” OR “teacher guid*” OR “self-direct*” OR “problem based learning” OR inquiry OR “humanistic education” OR “democratic education” OR “progressive education” OR “adaptive learning” OR “adaptive education” OR “adaptive class*” OR “adaptive teach*” OR differentiation) AND (“creative teaching” OR “instructional innovation” OR “instructional effectiveness” OR “teaching methods” OR “program effectiveness” OR “program evaluation”) AND (compar* OR contrast* OR “control group” OR experiment* OR “matched group*” OR quasiexperiment* OR posttest OR “post test” OR “comparative case study”) True experimental and quasi-experimental studies are to be included as far as they feature two educational interventions covering the same content (required knowledge acquisition and/or skill development) as assessed on compatible outcome measures, where one group (experimental) is higher in student-centred qualities (as described earlier) compared to the other (control) group. Reporting quantitative data sufficient for an effect size extraction is a necessary condition for study inclusion. There are several major threats to the independence of findings. These are: 1) repeated use of data coming from the same participants; 2) reporting multiple outcomes of the same type; and 3) aggregating outcomes of different types representing the same sample of participants (does not apply to the proposed review, as it is limited to learning achievement outcomes only). The means that we will use for ensuring data independence are presented in the Within Study Synthesis sub-section below. In addition to coding dimensions of SC pedagogical qualities that would determine proper comparisons for effect size extraction, the following groups of study coding categories will be used in the proposed review. First, study methodological quality will be assessed for features such as design type, fidelity of treatment implementation, attrition, and the unit of assignment/analysis (Cooper, Hedges, & Valentine, 2009). Within the same category, we will code for outcome source and psychometric quality of the assessment tools, as well as for the precision of procedures used for effect size extraction and for equivalence of instructor and study materials. Jointly, these methodological study features, used in moderator variable analyses will inform us of any potential threat to all types of study validity (Cooper et al., 2009). Substantive study features will further clarify description of SC pedagogical qualities by specifying theoretical models underlying instructional practices under review, treatment duration, instructor's experience, provision of professional development for teachers and training for students, whenever is required by specific instructional intervention. Demographic study features will encompass learners’ age, educational background and ability level, as well as, subject matter studied. All these study features will be subsequently analysed as moderators for their potential impact on treatment effects. All coding activities (i.e., abstract screening, full-text review, study features coding, as well as effect size extraction) will be carried out by two reviewers working independently and discussing and resolving disagreements, when necessary eliciting a third opinion of the project P.I. Reliability rates (initial judgement), i.e., Pearson's r and Cohen's kappa – for continuous and ordinal data, respectively, will be calculated and reported. Effect Size Computation: Our review aims to include and summarize quantifiable achievement outcomes of primary empirical studies of high methodological quality that compare effectiveness of more SC (i.e., more adaptive and individualized) versus more TC (i.e., more conventional undifferentiated) instructional interventions. The following are the primary metrics and procedures that will be used for the effect sizes extraction and subsequent analyses. For studies that report descriptive statistics for continuous measures of student achievement outcomes, the post-intervention mean of the control group will be subtracted from the post-intervention mean of the intervention group and the resulting difference will be divided by the pooled standard deviation of both groups (Cohen's d). Outcome data are likely to be reported in a variety of formats. For studies that report inferential statistics such as t, F, or p-values only, the appropriate conversion formula will be applied to calculate the d-index as the effect size estimate (Lipsey & Wilson, 2001; Hedges, Shymansky, & Woodworth, 1989; Hedges & Olkin, 1985). To introduce proper corrections for the small sample size bias, all d-indices will be converted into the unbiased Hedge's g statistics using the following formula: To test for statistical significance of calculated effect sizes we will use standard error of g, and the 95% confidence intervals. In all calculations, to aggregate effect sizes across studies and for moderator variable analyses, we will use Comprehensive Meta Analysis 3.0 (Borenstein, Hedges, Higgins, & Rothstein, 2005) software package. Within Study Synthesis This review will focus on learning achievement outcomes. There is a possibility, however, of finding several measures representing the same outcome type in the same study based on the same sample of participants. When it happens we will either decide in favour of the most representative measure – typically, cumulative final exams or post-tests or when no single outcome fully reflects learning performance throughout the unit of instruction – we will average effects deriving from different (complementary or equally representative) achievement measures. Also, the same sets of participants are never to be used to calculate multiple effect sizes of the same type. Whenever the same group of participants is used repeatedly (for example, when the same control group compared to two different treatment groups, each unique in its SC qualities – for the purposes of retaining as much of explanatory capacity of the review), its sample size will be reduced proportionally in order to avoid any overrepresentation (i.e., disproportionally high weighs of the respective effects) of the same participants in the final data set. Across Study Synthesis Independent effect sizes will be aggregated across studies using the random effects model (Borenstein, Hedges, Higgins, & Rothstein, 2010), as neither of the assumptions (conceptually grounded uniformity of interventions and access to the entire population of relevant studies) for applying the fixed effect model is met. Reflecting on the nature of our review as a random (though comprehensive) sampling of various populations of empirical studies in education, the random effects model provides a more accurate measure of the treatment effectiveness. The results obtained from a random effects model analysis will represent the overall effect of a diverse collection of SC instructional intervention on student learning across age groups, subject matters, etc. Fixed effect model will be used to assess heterogeneity of the distribution of effect sizes in which the Q- statistic and I2 to determine the collective extent to which studies deviate from the fixed effect average for the collection. Also the I2, a statistic derived from Q, indicates the proportion of true heterogeneity (i.e., variability exceeding what would be expected based on the sampling error estimate) associated with each distribution of effect sizes. If the observed heterogeneity is above sampling error, coded study features, as potential sources of systematic variation, will be further explored through moderator variable analysis under the mixed effects model. It is necessary to assess potential bias that may be associated with out-of-range individual calculated effect sizes and may potentially distort the overall interpretation of the findings. Sensitivity analysis (Hedges & Olkin, 1985) is intended to determine whether the removal of a certain effect size increases the fit of the remaining effect sizes in a homogeneous distribution while not substantially affecting the interpretation of the recalculated mean effect size. Various approaches to identifying potential outliers will be used, including visual examination of data organized into a forest plots and also performing “one study removed” CMA routine. Identified outliers will be examined with the potential to remove them from the final dataset. Potential sources of bias, such as study design, type of treatment, publication source, missing data, sample size, or attrition, will be carefully examined through the corresponding moderator variable analyses. There is widely recognized concern that relying on published studies alone may substantially distort (misrepresent) the overall intervention effect. To assess potential publication bias in this review, we will visually inspect the resulting funnel plot and run the Duval & Tweedie's (2000) trim and fill routine in CMA (Borenstein et al., 2005). Also, the classical Fail-Safe N test will help determine the number of null effect studies needed to raise the p-value associated with the average effect above a specified level of α. Orwin's (1983) Fail Safe N will also be used to determine how many studies missing studies that, when added to the analysis, will bring the combined Hedges’ g below a specified threshold. For studies that do not report complete outcome data (that is, in exceptional cases when missing information from otherwise perfectly suitable studies is minimal – e.g., sample size), the first author of the study will be contacted to retrieve missing information. If needed data are unavailable, a process of data imputation may be conducted, where appropriate. In such cases, a sensitivity analysis needs to be conducted to assess the impact of the imputed data on the overall analysis and synthesis of the results. No qualitative research will be reviewed within the framework of this project. Methodological Study Features: Study research design: 1 = RCT 2 = Quasi-experimental Instructor equivalence: 1 = Same 2 = Different 999 = Missing information Content (study materials) equivalence: 1 = Same (highly compatible) 2 = Different (marginally compatible) 999 = Missing information Psychometric quality of the outcome assessment tool: 1 = Standardized test 2 = Modified standardized/Piloted (validated) origina measure 3 = Teacher/Researcher-made test 4 = Average of two of the above (When ES is calculated based on averaging several outcome measures) Source of outcome data: 1 = One-time cumulative measure (e.g., final exam) 2 = Composite measure reported in the study (e.g., course grades) 3 = Average of equally representative non-cumulative measures (e.g., series of projects/assignments) 4 = A single selected (most representative out of a number reported) measure Effect size extraction precision: 0 = Calculated from descriptive statistic 1 = Calculated from inferential statistics 2 = Estimated from exact p-values (i.e., no assumptions) 3 = Estimated with reasonable assumptions (e.g., sample size equivalence) 4 = Reported in the study (without an option of verifying/recalculating) Substantive (Instructional) Study Features: Instruction delivery mode (coded separately for experimental and control groups): 1 = F2F (Classroom Instruction) 2 = DE (Distance Education) 3 = BL (Blended Learning,) 4 = Computer automated program: in lab, class or on campus, with or without the presence of a lab assistant 5 = Computer automated program (at distance) 999 = Missing information Conceptual (pedagogical) framework: An open entry reflecting an explicitly stated theoretical framework which, the treatment is based upon (modelled after) – to be categorized for subsequent moderator variable analyses when all data are collected Instructor's experience with (training for) implementing the corresponding instructional intervention (coded separately for experimental and control groups): 1 = Yes 2 = No 999 = Missing information Treatment duration: Specify the number of weeks of treatment implementation. Demographic Study Features: Academic level (learners’ age) 1 = Kindergarten 2 = Elementary school (Grades 1-5) 3 = Secondary/Middle school (Grades 6-8) 4 = High school (Grades 9-12) 999 = Missing information Subject matter (discipline): An open entry reflecting an explicitly named course – to be categorized in various ways (e.g., STEM/non-STEM, natural/social sciences) for subsequent moderator variable analyses when all data are collected Learners’ ability (as defined in the study by splitting sample in sub-groups – e.g., by the results of a pre-test): 1 = High achievers 2 = Average achievers 3 = Low achievers 4 = No split (in vast majority of studies) Learners’ profile (characteristic of the entire sample in the study): 1 = Gifted (talented) students 2 = General (average, “garden variety”) population – assume when not specified otherwise 3 = Special needs students – specify which type of (e.g., 3: Learning disability or 3: Autistic children) Learners SES (similarly to the above): 1 = Privileged category 2 = General population 3 = Underprivileged – specify (if available) Study settings: 1 = Urban 2 = Rural 999 = Missing information Study geographic region: An open entry – specify the country. * The final composition of the list is a matter of actual frequencies of included studies published in the corresponding journals as established through the review process. Lead review author: The lead author is the person who develops and co-ordinates the review team, discusses and assigns roles for individual members of the review team, liaises with the editorial base and takes responsibility for the on-going updates of the review. Affiliation: Centre for the Study of Learning and Performance, Concordia University Affiliation: Centre for the Study of Learning and Performance, Concordia University Affiliation: Centre for the Study of Learning and Performance, Concordia University Affiliation: Centre for the Study of Learning and Performance, Concordia University Address: Room GA 2.133; 1211 St. Mathieu Email: dpickup@education.concordia.ca Please give brief description of content and methodological expertise within the review team. The recommended optimal review team composition includes at least one person on the review team who has content expertise, at least one person who has methodological expertise and at least one person who has statistical expertise. It is also recommended to have one person with information retrieval expertise. Richard F. Schmid has expertise in and has published on topics such as: application of technologies to improve pedagogy and training in the workplace and schools; analysis of learning strategies and collaborative techniques in in-class and distance education contexts; cognitive information processing using technologies, especially with young learners. The Centre for the Study of Learning and Performance (CSLP) Systematic Review Team (Leader: Robert M. Bernard) has been active since 2001 and has a long list of accomplishments. We have published five major meta-analyses in Review of Educational Research, AERA's premier review journal (1st/219 in Educational Research with an Impact Factor of 5.00). The team has also published seven other meta-analyses and systematic reviews in other journals and has presented papers in many scholarly venues, with at least one presentation per year at AERA's annual meeting. Members of the team have given workshops, some for the Campbell Collaboration, short courses, and invited methodological presentations, etc. in the U.S., Canada, Great Britain, Dubai, UAE, and several European countries, and have published articles about meta-analysis methodology (e.g., Bernard et al., 2014; Abrami & Bernard, 2012) in prominent research journals. David Pickup is an Information Specialist with 7 years experience working on systematic review projects. He has previously served (2009-2010) as the Education Trials Search Adviser for the Campbell Collaboration, providing consultations and peer review of search strategies. He continues to provide peer review services for Campbell protocols and reviews on an ad hoc basis. Bernard, R. M. [PI], Borokhovski, E., Schmid, R. M., Waddington, D. I., & Pickup, D. Jacobs Foundation and the Campbell Collaboration. “A Meta-Analysis of 21st Century Adaptive Teaching and Individualized Learning Operationalized as Specific Blends of Student-Centered Instructional Events.” ≈$50,000USD Abrami, P. C. [PI], Bernard, R. M. with other CSLP members. Fonds Québécois de la Recherche sur la Société et la Culture (FQRSC) “Instruments du savoir pour l'apprentissage. Soutien aux quips de recherché.” — Infrastructure Support: $708,000. There are no conflicts of interest. We expect to fully complete the review by December of 2017. By completing this form, you accept responsibility for preparing, maintaining and updating the review in accordance with Campbell Collaboration policy. The Campbell Collaboration will provide as much support as possible to assist with the preparation of the review. A draft review must be submitted to the relevant Coordinating Group within two years of protocol publication. If drafts are not submitted before the agreed deadlines, or if we are unable to contact you for an extended period, the relevant Coordinating Group has the right to de-register the title or transfer the title to alternative authors. The Coordinating Group also has the right to de-register or transfer the title if it does not meet the standards of the Coordinating Group and/or the Campbell Collaboration. You accept responsibility for maintaining the review in light of new evidence, comments and criticisms, and other developments, and updating the review at least once every five years, or, if requested, transferring responsibility for maintaining the review to others as agreed with the Coordinating Group. The support of the Coordinating Group in preparing your review is conditional upon your agreement to publish the protocol, finished review, and subsequent updates in the Campbell Library. The Campbell Collaboration places no restrictions on publication of the findings of a Campbell systematic review in a more abbreviated form as a journal article either before or after the publication of the monograph version in Campbell Systematic Reviews. Some journals, however, have restrictions that preclude publication of findings that have been, or will be, reported elsewhere and authors considering publication in such a journal should be aware of possible conflict with publication of the monograph version in Campbell Systematic Reviews. Publication in a journal after publication or in press status in Campbell Systematic Reviews should acknowledge the Campbell version and include a citation to it. Note that systematic reviews published in Campbell Systematic Reviews and co-registered with the Cochrane Collaboration may have additional requirements or restrictions for co-publication. Review authors accept responsibility for meeting any co-publication requirements. I understand the commitment required to undertake a Campbell review, and agree to publish in the Campbell Library. Signed on behalf of the authors:

Although the overall research literature on the application of educational technologies to classroom instruction tends to favor their use over their non-use, these results vary considerably depending on what kind of technology is used, who it is used with and, more importantly, under what circumstances and for what instructional purposes it is used. Relatively recent, but well-developed and powerful methodology of systematic reviews, particularly quantitative syntheses (also known as meta-analyses) is especially suitable for addressing questions of that type by systematically summarizing research evidence in given areas of interest in social sciences.This meta-analysis summarizes data from 674 independent primary studies that compared higher degrees of technology use in the experimental condition with less technology in the control condition, in terms of their effects on student learning outcomes in postsecondary education. The result was an overall average weighted effect size of = 0.27 (k = 879, p < .01), indicating low but significant positive effect of technology integration on learning. The follow-up analyses revealed the influence of educational technology used for cognitive support and blended learning instructional settings designed interaction treatments, and technology integration in teacher training, especially when student-centered pedagogical frameworks are used. These findings are of potentially high interest and applied value for educational practitioners, including teachers and school administrators, as well as for instructional designers and developers of educational software.

The present study extends the results of a larger meta-analysis that addressed the effects of technology use on student achievement and attitudes in postsecondary education. The focus of the current meta-analysis is the use of technology to enable instructional conditions that promote collaborative interactions among learners. More specifically, it aims to compare the impact of designed interaction treatments (i.e., collaborative activities intentionally built into course design) and contextual interaction treatments (i.e., course conditions that result in high levels of student student interaction but are not intentionally designed to promote collaboration) on student learning outcomes. Results indicate that designed treatments outperform contextual treatments ((g) over bar = 0.52, k = 25 vs. (g) over bar = 0.11, k = 20, Q(Between) = 7.91, p < .02) on measures of achievement, emphasizing the importance of planning and instructional design in technology integration in postsecondary education. The results are discussed in relation to the literature of student student interaction and collaborative learning. (C) 2015 Elsevier Ltd. All rights reserved.

This paper summarizes the results of a pan-Canadian online survey study that investigates the extent to which school practitioners ( N = 1,153) use research to inform their practice. The self-reports indicate that the majority of the respondents used educational research, yet this engagement was infrequent. Although the respondents shared neutral attitudes about research, their comments add rather negative connotation to their perceptions. This study’s findings are relevant to school leadership organizations, teacher education institutions and research-generating bodies as they point to the necessity of increasing research relevance and accessibility, cultivating teaching as a research-based profession, and building school capacity to use research.