Program-level assessment is a holistic approach for arranging assessments throughout a degree program that supports sequential development of discipline knowledge, transferable skills and career readiness. Currently, the modular arrangement of courses means that student learning is partial, limited to passing the assessment and compartmentalized…

Mathematical modeling (MM) -- a cyclical process that involves using mathematics to make-sense of and analyze relevant, real-world situations -- has the potential to advance equity and challenge spaces of marginalization in the elementary mathematics classroom. When informed by culturally responsive teaching practices, MM creates opportunities to…

Participation in educational settings is a universal right of every child, consigned by the United Nations Convention on the Rights of the Child. This right encompasses the need to protect and encourage young children's active participation and decision-making in early childhood education and care. Research Findings: This qualitative study,…

We extended current knowledge by examining the performance of several Bayesian model fit and comparison indices through a simulation study using the confirmatory factor analysis. Our goal was to determine whether commonly implemented Bayesian indices can detect specification errors. Specifically, we wanted to uncover any differences in detecting…

A standard assumption of latent class (LC) analysis is conditional independence, that is the items of the LC are independent of the covariates given the LCs. Several approaches have been proposed for identifying violations of this assumption. The recently proposed likelihood ratio approach is compared to residual statistics (bivariate residuals…

One of the essential goals of science learning is to lead students to master scientific concepts or ideas and apply them to explain relevant everyday phenomena. Such mastery should help students to work with various representations. The objective of this study was to determine the effectiveness of the Learning Cycle Multiple Representation (LCMR)…

Educational research outcomes frequently rely on an assumption that measurement metrics have interval-level properties. While most investigators know enough to be suspicious of interval-level claims, and in some cases even question their findings given such doubts, there is a lack of understanding regarding the measurement conditions that create…

In using large language models (LLMs) for education, such as distractors in multiple-choice questions and learning by teaching, error-containing content is used. Prompt tuning and retraining LLMs are possible ways of having LLMs generate error-containing sentences in the learning content. However, there needs to be more discussion on how to tune…

Background: Clustered randomized controlled trials are commonly used to evaluate the effectiveness of treatments. Frequently, stratified or paired designs are adopted in practice. Fogarty (2018) studied variance estimators for stratified and not clustered experiments and Schochet et. al. (2022) studied that for stratified, clustered RCTs with…

Background: Randomized controlled trials (RCTs), or experiments, are the gold standard for intervention evaluation. However, the main appeal of RCTs--the clean identification of causal effects--can be compromised by interference, when one subject's actions can influence another subject's behavior or outcomes. In this paper, we formalize and study…

Background: The emergence of causal research in education was almost strictly quantitative twenty years ago, however, that landscape has changed considerably. The number of intervention studies fielded and completed annually has increased substantially, and the quality of the evaluations is much more robust, including paying much greater attention…

Inequities in STEM persist, notably in underrepresented groups relating to gender, race, socio-economic status, and disability. The purpose of this study was to explore correlations between STEM identities, personal identities, mentor-related experiences, and academic/career persistence of undergraduate students in Idaho public post-secondary…

Given the rapid adoption of machine learning methods by education researchers, and the growing acknowledgment of their inherent risks, there is an urgent need for tailored methodological guidance on how to improve and evaluate the validity of inferences drawn from these methods. Drawing on an integrative literature review and extending a…

This article presents findings from a critical review of 163 peer-reviewed articles on the growth of private higher education (HE) cross-nationally. Our review finds that the vast majority of studies on the development of private HE are country-specific case studies, with few comparative or cross-national studies. Moreover, most studies endorse a…

This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit,…