Graph neural networks (GNN) are vital for analyzing real-world problems (e.g., network analysis, drug interaction, electronic design automation, e-commerce) that use graph models. However, efficient GNN acceleration faces with multiple challenges related to high and variable sparsity of input feature vectors, power-law degree distribution in the…

This article is concerned with a subset of numerically stable and scalable algorithms useful to support computationally complex psychometric models in the era of machine learning and massive data. The subset selected here is a core set of numerical methods that should be familiar to computational psychometricians and considers whitening transforms…

Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical…

The goal of creating evidence-based programs is to scale them at sufficient breadth to support population-level improvements in critical outcomes. However, this promise is challenging to fulfill. One of the biggest issues for the field is the reduction in effect sizes seen when a program is taken to scale. This paper discusses an economic…

Innovative staffing models are promising, but challenging to scale up. What does the work of leading strategic staffing involve, and what could make scaling up easier? This report digs deep into the many challenges system leaders face when scaling up innovative staffing solutions. These leaders are trying to address longstanding teacher shortages…

Educational large-scale assessments (LSAs) often provide plausible values for the administered competence tests to facilitate the estimation of population effects. This requires the specification of a background model that is appropriate for the specific research question. Because the "German National Educational Panel Study" (NEPS) is…

Purpose: The purpose of this project was (1) to collaborate with public preschool programs to adapt, test, and revise the implementation model of an innovative early mathematics intervention, "Pre-K Mathematics" and (2) to determine whether this model makes it possible for local programs to sustain their implementation without reducing…

This study proposed a dynamic model of organizational technology adoption within a school institute culture. We described an implementation of a nonhomogeneous hidden Markov model based on a downscaling scheme that can project the cultural factors of the institute onto a teacher's implementation behavior. To reveal the dynamics of cultural…

Extreme response style is the tendency of individuals to prefer the extreme categories of a rating scale irrespective of item content. It has been shown repeatedly that individual response style differences affect the reliability and validity of item responses and should, therefore, be considered carefully. To account for extreme response style…

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…

One major aim of international large-scale assessments (ILSA) like PISA is to monitor changes in student performance over time. To accomplish this task, a set of common items (i.e., link items) is repeatedly administered in each assessment. Linking methods based on item response theory (IRT) models are used to align the results from the different…

In 2015, the American Association of Community Colleges (AACC) announced the AACC Pathways Project, a national initiative designed to support a cohort of community colleges to implement and scale whole-college guided pathways reforms. When the project launched, guided pathways was still a new idea. Yet, momentum was building around a set of…

As measures of general language proficiency, C-tests are ubiquitous in language testing. Speeded C-tests are quite recent developments in the field and are deemed to be more discriminatory and provide more accurate diagnostic information than power C-tests especially with high-ability participants. Item response theory modeling of speeded C-tests…

In 2015, the American Association of Community Colleges (AACC) announced the AACC Pathways Project, a national initiative designed to support a cohort of community colleges to implement and scale whole-college guided pathways reforms. Thirty community colleges from 17 states were selected for the project and embraced the challenge of redesigning…

In 2018 the Center for Universal Education (CUE) at Brookings launched a series of Real-time Scaling Labs (RTSL) to generate more evidence and provide practical recommendations on how to expand, deepen, and sustain the impact of education initiatives leading to transformative change in education systems, especially for the most disadvantaged…