Although recent studies have used the Classroom Observation Protocol for Undergraduate STEM (COPUS) to make claims about faculty reform, important questions remain: How should COPUS measures be situated within existing reform frameworks? Is there a universal sampling intensity that allows for valid inferences about the frequency of…

Employing nested sequences of models is a common practice when exploring the extent to which one set of variables mediates the impact of another set. Such an analysis in the context of logistic regression models confronts two challenges: (1) direct comparisons of coefficients across models are generally biased due to the changes in scale that…

The purpose of this article is to present an analysis to identify the reasoning processes and representations that preschool students develop about sound based on the inferential-representational approach. Participants were 18 preschool students between the ages of four and five attending three rural schools located in the Sierra Norte of Puebla,…

Planned missing data designs allow researchers to have highly-powered studies by testing only a fraction of the traditional sample size. In two-method measurement planned missingness designs, researchers assess only part of the sample on a high-quality expensive measure, while the entire sample is given a more inexpensive, but biased measure. The…

Transitioning out of college for NCAA Division III student athletes is a unique experience. Through the lens of the athletic identity theory, this study examined how Division III student athletes describe their experience transitioning out of college. Participants included former Division III student athletes from a university in the southwest…

This study examines factors that influence readers' cognitive processing (i.e., inference generation) and the development of a mental representation of text: comprehension skill and working memory (WM). Elementary students (N = 61) participated in causal questioning conditions with narrative texts to examine text- and knowledge-based inferences…

Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…

This study was conducted to examine whether newly enrolled 9th grade students were ready to directly engage in the theoretical discursive process from the perspective of Duval's Cognitive Model. The sample of the study was comprised of 51 newly enrolled 9th grade students between the ages of 14 and 15, who had not received any prior geometry…

Conceptual challenge is often considered a necessary ingredient for promoting deep learning in an inquiry-based environment. However, challenge alone does not support conceptual development. In this paper, we draw on complexity theory as a theoretical lens to explore how a primary teacher facilitated students' conceptual change through repeated…

In this experiment we investigated whether the presentation of domain principles within one text facilitates reading to learn from multiple exemplar texts that feature the importance of the principles. There were five texts about natural selection: a principles text, which described principles of natural selection, and four exemplar-based texts…

Discourse-based interviews allow researchers to gather data about a writer's understanding of what informs a task. This method was essential for a research team seeking to understand the impact of programmatic learning objectives on student writing development. Three decisions in the approach to this research project sought to center the student…

Drawing on discourse analysis and interview data, this case study uncovers how a lesson study team, supported by a curriculum specialist and an experienced teacher, anchored anticipating of student learning in sound curriculum deliberation, which enabled novice teachers to teach effectively inferencing skills, a highly demanding domain, through an…

Categorical data become increasingly ubiquitous in the modern big data era. In this dissertation, we propose novel statistical learning and inference methods for large-scale categorical data, focusing on latent variable models and their applications to psychometrics. In psychometric assessments, the subjects' underlying aptitude often cannot be…

Analyzing heterogeneous treatment effects (HTEs) plays a crucial role in understanding the impacts of educational interventions. A standard practice for HTE analysis is to examine interactions between treatment status and preintervention participant characteristics, such as pretest scores, to identify how different groups respond to treatment.…

A critical component of cognitive diagnostic models (CDMs) is a Q-matrix that stipulates associations between items of a test and their required attributes. The present study aims to develop and empirically validate a Q-matrix for the listening comprehension section of the International English Language Testing System (IELTS). To this end, a…