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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.

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. 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: (1) Flexibility: Degree to which students can contribute to course design, selecting study materials, and stating learning objectives; (2) Pacing of instruction: Students can decide how fast to progress through course content and whether this progression is linear or iterative; (3) Teacher's role: Ranging from authority figure and sole source of information, to teacher as equal partner in the learning process; and (4) Adaptability: Degrees of manipulating learning environments, materials, and activities to make them more student-centered. 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. 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.

This study involved the analysis of the complex interactions that take place between tutors and preschool children using a computer during early literacy tutoring sessions. Eight five-year-old pre- and early-readers attending a childcare centre participated in daily 20-minute tutoring sessions for two weeks. The literacy software (a beta version) was especially designed to guide tutors while working one-on-one with elementary school students falling into the lower 30% of reading achievement (i.e., at-risk). Parent surveys, videotaped tutor/child sessions, independent observer data, and tutor reports yielded rich descriptions of the tutor/child/computer process. Rigorous grounded theory analyses generated three comprehensive themes: rapport, motivation, and scaffolding. The first focused on interpersonal issues, the latter two on teaching/learning. Implications for practice are discussed.

This review provides a rough sketch of the evidence, gaps and promising directions in e-learning from 2000 onwards, with a particular focus on Canada. The authors searched a wide range of sources and document types to ensure that we represented, comprehensively, the arguments surrounding e-learning. Overall, there were 2,042 entries in their database, of which they reviewed 1,146, including all the Canadian primary research and all scholarly reviews of the literature. In total, there were 726 documents included in the review: 235--general public opinion; 131--trade/practitioners' opinion; 88--policy documents; 120--reviews; and 152--primary empirical research. The Argument Catalogue codebook included the following eleven classes of variables: (1) Document Source; (2) Areas/Themes of e-learning; (3) Value/Impact; (4) Type of evidence; (5) Research design; (6) Area of applicability; (7) Pedagogical implementation factors; 8) A-priori attitudes; (9) Types of learners; (10) Context; and (11) Technology Factors. The authors examined the data from a number of perspectives, including their quality as evidence. In the primary research literature, they examined the kinds of research designs that were used. They found that over half of the studies conducted in Canada are qualitative in nature, while the rest are split in half between surveys and quantitative studies (correlational and experimental). When they looked at the nature of the research designs, they found that 51% are qualitative case studies and 15.8% are experimental or quasi-experimental studies. The findings of this review showed that studies that can increase the general understanding of "what works" in e-learning settings are underrepresented in the Canadian research literature. This article discusses the results of the research as well as implications for K-12 practitioners, post-secondary education, and policymakers.

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? 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.

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 [superscript ++] = 0.393, p <0.000. 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.

This paper serves several purposes. First and foremost, it is devoted to developing a better understanding of the effectiveness of blended learning (BL) in higher education. This is achieved through a meta-analysis of a sub-collection of comparative studies of BL and classroom instruction (CI) from a larger systematic review of technology integration (Schmid et al. in "Comput Educ" 72:271-291, 2014). In addition, the methodology of meta-analysis is described and illustrated by examples from the current study. The paper begins with a summary of the experimental research on distance education (DE) and online learning (OL), encapsulated in meta-analyses that have been conducted since 1990. Then it introduces the Bernard et al. ("Rev Educ Res" 74(3):379-439, 2009) meta-analysis, which attempted to alter the DE research culture of always comparing DE/OL with CI by examining three forms of "interaction treatments" (i.e., student-student, student-teacher, student-content) within DE, using the theoretical framework of Moore ("Am J Distance Educ" 3(2):1-6, 1989) and Anderson ("Rev Res Open Distance Learn" 4(2):9-14, 2003). The rest of the paper revolves around the general steps and procedures (Cooper in "Research Synthesis and Meta-Analysis: A Step-by-Step Approach," 4th edn, SAGE, Los Angeles, CA, 2010) involved in conducting a meta-analysis. This section is included to provide researchers with an overview of precisely how meta-analyses can be used to respond to more nuanced questions that speak to underlying theory and inform practice--in other words, not just answers to the "big questions." In this instance, we know that technology has an overall positive impact on learning (g[superscript +] = +0.35, p < 0.01, Tamim et al. in "Rev Educ Res" 81(3):4-28, 2011), but the sub-questions addressed here concern BL interacting with technology in higher education. The results indicate that, in terms of achievement outcomes, BL conditions exceed CI conditions by about one-third of a standard deviation (g[superscript +] = 0.334, k = 117, p < 0.001) and that the kind of computer support used (i.e., cognitive support vs. content/presentational support) and the presence of one or more interaction treatments (e.g., student-student/-teacher/-content interaction) serve to enhance student achievement. We examine the empirical studies that yielded these outcomes, work through the methodology that enables evidence-based decision-making, and explore how this line of research can improve pedagogy and student achievement.

This study investigated the change in the relationship between pedagogy, computer-use and students' perceptions about course-effectiveness over time. Students from a Canadian university completed a questionnaire in two different years (2003 = 1,834 participants and 2007 = 1,866 participants). Of greatest interest were characteristics of technology that interact with pedagogy to achieve positive learning outcomes. A factor analysis revealed a three-factor solution: "course-structure," "active-learning," and "computer-use." Multiple regression analysis showed that the three variables are predictive of perceived course effectiveness, with "course-structure" being most predictive in both years. "Computer-use" was least predictive with the 2003 sample while it was second in predictive power with the 2007 sample, most likely reflecting increased technology integration in post-secondary education. When comparing use of various applications in "Arts" versus "Science" courses, results indicated higher computer technology use in 2007 for all applications with arts courses while only web-based computer applications increased in use with the science courses. Separate regression analyses were conducted for each type of program while comparing the two different study years with results indicating that "course-structure" is the most stable predictor. Findings reveal that while pedagogy seems to be of highest importance to students, the relationship between computer use and perceived course effectiveness is changing over time. Implications are discussed and suggestions for future research are presented.

Despite its potential, online dialogue (online dialogue) can be superficial. Following Vygotskian (1978) and design experiment approaches (Brown, 1992), this study explores a labelling feature that allows students to tag parts of their messages. Data comes from 4 sessions of a graduate education course. Students engaged in 2-3 graded online activities in groups of 3-4. Students contributed labels for subsequent sessions. Field-notes and descriptive statistics suggested there were 7 labelling groups, 7 non-labelling groups, and 3 difficult-to-categorize groups. Types of labelling use emerged: interactive labelling, elaboration labelling, and interactive elaboration labelling. Labelling correlated to the quality of online dialogue (r=0.410). The Nelson-Denny text-comprehension measure, task-specific motivation, and labelling predicted approximately 25% of the variance in quality online dialogue, F(3,38)=5.149, p less than 0.05; adding labelling was significant. Narrative analyses suggested that some types of labelling more effectively supported online dialogue than did others. Content analyses (n=696 coded "paragraphs") found the interactive elaboration labelling group contributed proportionately more segments coded as critical thinking than did the elaboration labelling group ((M=0.96 vs. M=0.50), especially more analysis and inference. Labelling correlated to performance on the final examination r=0.283. A model including the final, the Nelson-Denny, task-specific motivation, online dialogue marks, and labelling was significant, (F(4, 37)=8.257, R=0.672, R[superscript 2]adj. = 0.415), but adding labelling was not. (Contains 5 figures and 5 tables.)

This paper explores a labelling feature designed to support higher-level online dialogue. It investigates whether students use labels less often during a structured online dialogue than during an unstructured one, and looks at students' reactions to labelling and to both types of tasks. Participants are from three successive course offerings of a Master's-level course (n = 37). All students are allowed but not required to use a labelling feature which enables them to insert phrases such as "Building on your point" directly into their online messages. All students participate in two types of online activities in small groups--first an unstructured online dialogue, then a structured online dialogue. Students tended to use labels significantly less often during the structured dialogue: F(1, 36) = 5.950, p less than 0.05. Sixty-two percent of students used the feature more than once during the unstructured dialogue compared to 46% during the structured dialogue. The maximum number of labels that a student used in the unstructured dialogue was 28 versus 16 in the structured dialogue. Students generally found the structured dialogue to be more interesting and relevant, and to have clearer expectations. Student reactions to the labelling feature were mixed: The mean of satisfaction was 18.35, SD = 3.88 (six items on a 5-point Likert scale). Students did not find labelling as useful during the structured dialogue: Perhaps labelling and the activity provided redundant scaffolding. These results imply that features built into the software should be implemented flexibly with thought to the other pedagogical scaffolds in the environment, particularly to the type of activity.

This study explores a labelling feature that allows students to tag parts of their online messages. Data comes from four sequentially offered sessions of a graduate education course. Students engaged in two to three online activities in groups of three or four. Students (n = 53) contributed from 0 to 56 labels (M = 12.42, SD = 13.50) and 18 to 114 messages (M = 39.70, SD = 18.04). Groups (n = 17) contributed from 0 to 109 labels, and 57 to 227 messages. Field-notes and descriptive statistics suggested there were seven labelling groups, seven non-labelling groups, and three groups difficult to categorize. None of the individual characteristics hypothesized to predict labelling did. Still, categories of users and non-users emerged from qualitative analyses: strategists, trusters, and techies contrasting with fringe participants, surface coasters, techie-shy, and fluid writers/thinkers/readers. Labelling appeared to be largely a family affair--which group a student belonged to correlated to how much he/she labelled. MANOVA gives for labelling usage F(16, 36) = 2.697, p less than 0.01. (Contains 3 tables and 5 figures.)

This paper reports the findings of a Stage I meta-analysis exploring the achievement effects of computer-based technology use in higher education classrooms (non-distance education). An extensive literature search revealed more than 6,000 potentially relevant primary empirical studies. Analysis of a representative sample of 231 studies (k = 310) yielded a weighted average effect size of 0.28 surrounded by wide variability. A mixed effects model was adopted to explore coded moderators of effect size. Research design was found to be not significant across true, quasi- and pre-experimental designs, so the designs were combined. The variable "degree of technology use" (i.e., low, medium, and high) was found to be significant, with low and medium use performing significantly higher than high use. For the variable "type of use" (i.e., cognitive support tools, presentational tools, and multiple uses), cognitive support (g+ = 0.40) was greater than presentational and multiple uses.

This article offers a case study of the evaluation of a redesigned and redeveloped laboratory-based cell biology course. The course was a compulsory element of the biology program, but the laboratory had become outdated and was inadequately equipped. With the support of a faculty-based teaching improvement project, the teaching team redesigned the course and re-equipped the laboratory, using a more learner-centered, constructivist approach. The focus of the article is on the project-supported evaluation of the redesign rather than the redesign per se. The evaluation involved aspects well beyond standard course assessments, including the gathering of self-reported data from the students concerning both the laboratory component and the technical skills associated with the course. The comparison of pre- and postdata gave valuable information to the teaching team on course design issues and skill acquisition. It is argued that the evaluation process was an effective use of the scarce resources of the teaching improvement project. (Contains 2 tables and 2 figures.)

The purpose of this research is to investigate the relationship between computer technology's role and students' perceptions about course effectiveness. Students from two universities (one Canadian, n = 1465; one American, n = 831) completed a 71-item questionnaire addressing different aspects of their learning experience in a given course. Factor analysis revealed a 3-factor solution: "course-structure," "active-learning and time-on-task," and "computer-use." Regression analysis indicated that the 3 variables are predictive of perceived course effectiveness at both sites, with the presence of an interaction between location and "computer-use" and "course-structure" on students' perceptions about course effectiveness. Findings reveal that student perceptions directly reflect the 14 APA learner-centered principles on which the instrument was based. (Contains 5 tables.)

This study investigated the relationship between the amount of computer technology used in post-secondary education courses, students' perceived effectiveness of technology use, and global course evaluations. Survey data were collected from 922 students in 51 courses at both the graduate and undergraduate levels. The survey consisted of 65 items broken down into seven areas, namely: (1) student characteristics, (2) learning experiences and course evaluations, (3) learning strategies, (4) instructional techniques, (5) computer use in course, (6) perceived effectiveness of computer use and (7) personal computer use. Contrary to expectations, no significant relationship was found between computer use and global course evaluations, nor was there a relationship between perceived effectiveness of computer use and global course evaluations. However, the results did yield a positive relationship between global course evaluations and the learning experiences that students engaged in. Students also indicated that they valued the use of computer technology for learning. Descriptive statistics on questions related to personal computer use show a strong favorable response to computer use and: facilitation of learning, value-added aspects such as usefulness to other classes and/or career, learning material in a more meaningful way, and working in groups with other students.

This study investigated the role that computer technology plays in transforming the learning process in higher education. Specifically, we looked at the relationship between computer-technology use, active learning, and perceived course effectiveness. The sample consisted of 1966 students in 81 graduate and undergraduate classes at a large, urban university. The survey categories were: 1) learner preferences; 2) course structure; 3) active learning; 4) time on task; 5) learning with technology; 6) perceived effectiveness of computer use; 7) context of computer use; and 8) overall perceived course effectiveness. Results suggest that there is a relationship between computer technology, active learning, and perceived course effectiveness. Students who use computer technology a lot appear to benefit the most from active learning. (Contains 7 tables and 1 figure.)

Research to date has demonstrated that networking can be an effective strategy for supporting meaningful learning. However, studies have shown that learners encounter difficulties using it. In an attempt to understand and thus reduce these difficulties, this study examined learners' underlying cognitive processes and activities while networking. Detailed process, observational, and post-activity interview data were gathered on 14 participants during and after their networking of an 800-word text. The study showed that learners must possess several forms of prior knowledge, and devote appropriate time and effort in order to derive benefits from networking. The instructional implications of the process and the methodological contribution of this study are discussed.

Studied the effects of a combination of student collaboration, concept mapping, and electronic technologies with 26 students in a graduate level learning theories class. Findings suggest that concept mapping and collaborative learning techniques complement each other, and that students found the combined approach useful. (SLD)

Discusses the role of algorithms in the design of computer-assisted instruction (CAI). Structural and processing characteristics of algorithms are analyzed, instructional algorithms are explained, empirically based design rules are presented, and the use of instructional algorithms in artificial intelligence is described. (44 references) (LRW)

Concept mapping, which requires learners to plot concepts and their interrelationships in a meaningful organizational network, was compared with an established curriculum approach. The usefulness of the technique is discussed in terms of its ability to individualize and raise the quality of learning. (JD)

Abstract This study involved the analysis of the complex interactions that take place between tutors and preschool children using a computer during early literacy tutoring sessions. Eight five-year-old pre- and […]

Graduate students suffer from issues with mental and emotional wellbeing at a high rate. While most commonly understood as an individual issue, the high prevalence of mental health problems suggests that their causes may extend beyond personal factors. A closer look at the environment that a graduate student operates in suggests systemic or organizational factors that exert notable pressures that students have to cope with. This article examines four levels of systems that create a structural environment that negatively impacts mental health and wellbeing among graduate students: (1) the academic system, (2) the institutional and departmental systems, (3) lab or cohort systems, and (4) the social‐economic system. In light of this analysis, we propose a model of how systemic stress affects mental health. This model sheds light on the systems‐level process improvements that can be instituted to help universities prevent mental health issues among graduate students. [ABSTRACT FROM AUTHOR]

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 g? = 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. [ABSTRACT FROM AUTHOR]

This study identified and examined the validity of a Brunerian theory-based, aptitude-enhancing, standardized educational simulation by assessing spatial visualization ability of students assigned to simulation and non-simulation treatment groups. Results indicate the simulation increased high aptitude learners' efficiency (and initially effectiveness) and low aptitude learners' effectiveness. (MBR)

Describes algorithms and shows how they can be applied to the design of instructional systems by relating them to a standard information processing model. Two studies are briefly described which tested serial and parallel processing in learning and offered guidelines for designers. Future research needs are also discussed. (LRW)

Analysis of the process of educational television evaluation focuses on the Programme Evaluation Analysis Computer (PEAC) system. Highlights include affects of evaluation on learning; internal and external validity; variables in the evaluation model; formative evaluation; cognitive and affective objectives; and differences between general broadcast educational television and other instructional programing. (37 references) (LRW)