Manipulating basic characteristics of the Rapid Automatized Naming task in search for its most reliable connections to reading performance

Introduction. Connections between Rapid Automatized Naming (RAN) task performance and reading are well documented. Primary empirical studies and meta-analyses established and described associations between specific RAN subtasks and reading outcomes. The cognitive nature of these associations, however, remains largely underexplored. This study attempts to address the issue by explicitly manipulating some critical characteristics of the RAN task (stimuli types, combinations, and familiarity) and conditions of its administration (attention demand) in search for factors that affect RAN performance and underlie its connections to reading competencies. Method. Ten modified RAN subtasks were created by manipulating type and familiarity of the stimuli, size of the stimuli source set, and demand to attention (cognitive controlled processing), involved in RAN performance. Measures of ballistic and efficiency-based automaticity, attention control, and reading rate were collected and analyzed using, ANOVA – with respect to performance on modified RAN subtasks, and correlational and multiple regression analyses – to address interrelations among major independent variables and their connections to reading rate. Results. The study found differential sensitivity of the RAN performance to the explored experimental manipulations. Specifically, significant main effects on naming speed were observed for stimuli type, stimuli familiarity and attention demand. RAN performance on most of the modified subtasks (seven out of ten) was significantly correlated with the measure of attention control, whereas only one correlation between RAN and measures of automaticity was statistically significant. Findings of multiple regression analyses confirmed this pattern of results. Attention factor explained substantially larger portion of variance in performance on modified RAN than both indices of automaticity combined. Reading rate was significantly correlated with bigram-based RAN (supposedly reflecting practice), and its correlations with other modified subtasks were higher for the elevated attention demand conditions, in one case exceeding significance level. Discussion. Understanding the cognitive nature of RAN is important for informing instructional practice of what reading skills might require special attention. This study explored specific conditions to which RAN performance may be especially sensitive. Modified RAN subtasks were markedly influenced by experimental manipulations, especially with regard to attention demand, indicating that attention, more than automaticity, could be a factor underlying naming speed as a predictor of reading

Detecting bias in meta-analyses of distance education research: big pictures we can rely on

© 2014, © 2014 Open and Distance Learning Association of Australia, Inc. This article has two interrelated purposes. The first is to explain how various forms of bias, if introduced during any stage of a meta-analysis, can provide the consumer with a misimpression of the state of a research literature. Five of the most important bias-producing aspects of a meta-analysis are presented and discussed. Second, armed with this information, we examine 15 meta-analyses of the literatures of distance education (DE), online learning (OL), and blended learning (BL), conducted from 2000 to 2014, with the intention of assessing potential sources of bias in each. All of these meta-analyses address the question: “How do students taking courses through DE, OL, and BL compare to students engaged in pure classroom instruction in terms of learning achievement outcomes?” We argue that questions asked by primary researchers must change to reflect issues that will drive improvements in designing and implementing DE, OL, and BL courses

Explorations of the rapid automatized naming (RAN) task : what should the "A" in RAN stand for?

This research explored the cognitive nature of the RAN (Rapid Automatized Naming) task, a test widely used to assess reading development. It addressed automaticity- and attention-based processing and their relative contribution to RAN task performance to better understand why the RAN task has the predictive value for reading development. Study 1 (N=68) utilized two different indices of automatic stimulus recognition and an index of attention control as predictors of naming speed on the four original versions of the RAN task. The study found little support for an automaticity-based account of RAN task performance, but did support an attention-based account. Symbolic and non-symbolic RAN subtasks differed in terms of the role played by automatic and attention-based factors, and in terms of their correlations with reading speed. Study 2 (N=16) used ten modified versions of the RAN task that manipulated attention and memory demands. Naming speed was sensitive to attentional demands and to stimulus familiarity, but not to factors of long-term memory retrieval. Study 3 (N=97) provided additional information on the roles played by automatic and attention-based processing in RAN task performance, using new measures of these constructs. Attention came out as explaining a large proportion of the variance in naming speed; skill in automatic stimulus detection and in lexical access efficiency did not. Working memory was strongly associated with RAN task performance. Finally, a meta-analysis on a representative sample of research data (65 studies reporting 530 coefficients of correlation between RAN tasks performance and different measures of reading, N=8555) revealed the average point estimates were r + = .345 and r + = .398, for cross-sectional and longitudinal research designs respectively. The moderator analyses showed that reading skills more closely associated with RAN task performance required expertise with printed text and depend on applying rules and building and managing associations. These regularities are largely consistent with the results of the three experimental studies. Overall, these indicated that attention-based factors rather than automaticity underlie naming speed as measured by the RAN tasks, and these mechanisms presumably link RAN to reading performance. Implications for further research and educational practices are discussed

An exploration of bias in meta-analysis: the case of technology integration research in higher education

© 2014, Springer Science+Business Media New York. This article contains a second-order meta-analysis and an exploration of bias in the technology integration literature in higher education. Thirteen meta-analyses, dated from 2000 to 2014 were selected to be included based on the questions asked and the presence of adequate statistical information to conduct a quantitative synthesis. The weighted random effects average was g++ = 0.393, p \u3c .000. The article goes on to report an assessment of the methodological quality of the thirteen studies based on Cooper’s (Research synthesis and meta-analysis: a step-by-step approach. Sage, Thousand Oaks, 2010) seven stages in the development of a meta-analysis. Two meta-analyses were found to have five out of seven stages where methodological flaws could potentially create biased results. Five meta-analyses contained two flawed stages and one contained one flawed stage. Four of the stages where methodological flaws can create bias are described in detail. The final section attempts to determine how much influence the methodological flaws exerted on the results of the second-order meta-analysis

A Meta-Analysis of Teacher and Student-Centered Practices and Processes in Undergraduate Science Education

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

The effects of ABRACADABRA on reading outcomes : an updated meta-analysis and landscape review of applied field research

A Balanced Reading Approach for Children Designed to Achieve Best Results for All (ABRA) is an evidence-based suite of interactive multimedia that engages learners in the development of core reading skills. This detailed meta-analysis presents an update on research evidence about the effectiveness of ABRA for elementary students. Offering distinct environments (or modules) for students, teachers and parents, ABRA is neither linear in use nor prescriptive of a single concept or method of teaching and learning to read. The results of the analysis provide positive evidence of the value of ABRA as a tool to promote development of early literacy skills. Includes bibliography

Are contextual and designed student-student interaction treatments equally effective in distance education?

This systematic review draws from and builds upon the results of a meta-analysis of the achievement effects of three types of interaction treatments in distance education: student-student, student-teacher, and student-content (Bernard et al., Review of Educational Research, 79(3), 1243-1289, 2009). This follow-up study considers two forms of student-student interaction treatments, contextual interaction and designed interaction. Typical contextual interaction treatments contain the necessary conditions for student-student interaction to occur, but are not intentionally designed to create collaborative learning environments. By contrast, designed interaction treatments are intentionally implemented collaborative instructional conditions for increasing student learning. Our meta-analysis compared the effect of these two types of interaction treatments on student achievement outcomes. The results favored designed interaction treatments over contextual interaction treatments. Examples of designed interaction treatments and a discussion of study results and their potential implications for research and instruction in distance education and online learning are presented. © 2012 Copyright Open and Distance Learning Association of Australia, Inc

Technology integration in postsecondary education: A summary of findings from a set of related meta-analyses

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 \u3c .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