This experimental study explores how source use features impact raters' judgment of argumentation in a second language (L2) integrated writing test. One hundred four experienced and novice raters were recruited to complete a rating task that simulated the scoring assignment of a local English Placement Test (EPT). Sixty written responses were…

Considering the increasing severity of environmental disasters and the scarcity of studies centered on children's perspectives, this article explores context-based learning to create spaces of hope. Constructing explanations fosters meaning creation and knowledge integration. Fourth graders' self-explanations about contamination in a degraded…

A non-concurrent multiple baseline design across four early intervention providers was used to examine functional relations between job-embedded professional development (PD), including practice-based coaching (PBC), and providers' fidelity of implementation of home visiting practices for embedded intervention. Four providers with one to two…

Background: There is growing interest in research understanding the individual-specific predictors of child callous-unemotional (CU) traits, particularly in early childhood. Objective: This study reviewed evidence from studies that investigated the relationship between early child temperament factors (between 0 and 3 years) and CU traits in…

While white ignorance is primarily produced and reproduced through social-structural processes, philosophy of education scholarship has focused on agent-centered educational solutions. This article argues that agent-centered solutions are ineffective and that education for disrupting white ignorance must be structure-centered. Specifically, the…

This paper develops a novel approach called Outcome Trajectory Evaluation (OTE) in response to the long-causal-chain problem confronting the evaluation of research for development (R4D) projects. OTE strives to tackle four issues resulting from the common practice of evaluating R4D projects based on theory of change developed at the start. The…

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal…

Recent critiques of physics education research (PER) studies have revoiced the critical issues when drawing causal inferences from observational data where no intervention is present. In response to a call for a "causal reasoning primer" in PER, this paper discusses some of the fundamental issues in statistical causal inference. In…

Over 2000 years ago, Aristotle wrote of four distinct causes at play in the world we know. Those causes, the material cause, the formal cause, the efficient cause, and the final cause, were meant to refer to ontological and, by extension, epistemological concerns, and were powerful enough to be seized upon and used in some form by those of very…

Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a a treatment or cause (intervention, program, drug, etc). To interpret differences between groups causally we need to ensure that they have been constructed in such a way that the comparisons are "fair." This can be…

Qualitative Comparative Analysis (QCA) is a relatively young method of causal inference that continues to diffuse across the social sciences. However, recent methodological research has found the conservative (QCA-CS) and the intermediate solution type (QCA-IS) of QCA to fail fundamental tests of correctness. Even under conditions otherwise ideal…

Generic language (e.g., "tigers have stripes") leads children to assume that the referenced category (e.g., tigers) is inductively informative and provides a causal explanation for the behavior of individual members. In two preregistered studies with 4- to 7-year-old children (N = 497), we considered the mechanisms underlying these…

Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and…

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well.…

The consequence of a change in school leadership (e.g., principal turnover) on student achievement has important implications for education policy. The impact of such an event can be estimated via the popular Difference in Difference (DiD) estimator, where those schools with a turnover event are compared to a selected set of schools that did not…