A systematic review of research on online teaching and learning from 2009 to 2018.

Systematic reviews were conducted in the nineties and early 2000's on online learning research. However, there is no review examining the broader aspect of research themes in online learning in the last decade. This systematic review addresses this gap by examining 619 research articles on online learning published in twelve journals in the last decade. These studies were examined for publication trends and patterns, research themes, research methods, and research settings and compared with the research themes from the previous decades. While there has been a slight decrease in the number of studies on online learning in 2015 and 2016, it has then continued to increase in 2017 and 2018. The majority of the studies were quantitative in nature and were examined in higher education. Online learning research was categorized into twelve themes and a framework across learner, course and instructor, and organizational levels was developed. Online learner characteristics and online engagement were examined in a high number of studies and were consistent with three of the prior systematic reviews. However, there is still a need for more research on organization level topics such as leadership, policy, and management and access, culture, equity, inclusion, and ethics and also on online instructor characteristics.

Evaluating the Effects of Missing Data Handling Methods on Scale Linking Accuracy.

For large-scale assessments, data are often collected with missing responses. Despite the wide use of item response theory (IRT) in many testing programs, however, the existing literature offers little insight into the effectiveness of various approaches to handling missing responses in the context of scale linking. Scale linking is commonly used in large-scale assessments to maintain scale comparability over multiple forms of a test. Under a common-item nonequivalent group design (CINEG), missing data that occur to common items potentially influence the linking coefficients and, consequently, may affect scale comparability, test validity, and reliability. The objective of this study was to evaluate the effect of six missing data handling approaches, including listwise deletion (LWD), treating missing data as incorrect responses (IN), corrected item mean imputation (CM), imputing with a response function (RF), multiple imputation (MI), and full information likelihood information (FIML), on IRT scale linking accuracy when missing data occur to common items. Under a set of simulation conditions, the relative performance of the six missing data treatment methods under two missing mechanisms was explored. Results showed that RF, MI, and FIML produced less errors for conducting scale linking whereas LWD was associated with the most errors regardless of various testing conditions.

An Empirical Investigation of Variance Design Parameters for Planning Cluster-Randomized Trials of Science Achievement.

BACKGROUND: Prior research has focused primarily on empirically estimating design parameters for cluster-randomized trials (CRTs) of mathematics and reading achievement. Little is known about how design parameters compare across other educational outcomes. OBJECTIVES: This article presents empirical estimates of design parameters that can be used to appropriately power CRTs in science education and compares them to estimates using mathematics and reading. RESEARCH DESIGN: Estimates of intraclass correlations (ICCs) are computed for unconditional two-level (students in schools) and three-level (students in schools in districts) hierarchical linear models of science achievement. Relevant student- and school-level pretest and demographic covariates are then considered, and estimates of variance explained are computed. Subjects: Five consecutive years of Texas student-level data for Grades 5, 8, 10, and 11. MEASURES: Science, mathematics, and reading achievement raw scores as measured by the Texas Assessment of Knowledge and Skills. Results: Findings show that ICCs in science range from .172 to .196 across grades and are generally higher than comparable statistics in mathematics, .163-.172, and reading, .099-.156. When available, a 1-year lagged student-level science pretest explains the most variability in the outcome. The 1-year lagged school-level science pretest is the best alternative in the absence of a 1-year lagged student-level science pretest. CONCLUSION: Science educational researchers should utilize design parameters derived from science achievement outcomes.

An application of a new evidence grading system to research on the chronic care model.

Methodological quality undergirds all evidence-based medicine because without strong evidence supporting or refuting the efficacy of an intervention, the movement toward basing medical decisions and practice on scientific evidence is not sustainable. Recently, the consensus that had existed regarding the hierarchy of evidence produced by a study design was challenged on the basis that existing guidelines failed to properly define key terms, weight the merits of certain non-randomized controlled trials, and employ a comprehensive list of study design limitations to render evaluative conclusions, to name a few of the challenges. The present study introduces a new grading system that overcomes, or at the very least greatly diminishes, these challenges. This new method is applied to the literature on the Chronic Care Model and the results are then compared to several of the most popular grading guidelines currently in use. These results revealed substantial differences between the guidelines in accordance with previous research that challenged existing methods. Furthermore, the present study lends support to the proposed grading guideline although further research into its validity and reliability is needed.