The article describes practical suggestions for measurement researchers and psychometricians to respond to calls for social responsibility in assessment. The underlying assumption is that personalizing large-scale assessment improves the chances that assessment and the use of test scores will contribute to equity in education. This article…

In this digital ITEMS module, Dr. Stella Kim provides an overview of multidimensional item response theory (MIRT) equating. Traditional unidimensional item response theory (IRT) equating methods impose the sometimes untenable restriction on data that only a single ability is assessed. This module discusses potential sources of multidimensionality…

The remarkable strides in artificial intelligence (AI), exemplified by ChatGPT, have unveiled a wealth of opportunities and challenges in assessment. Applying cutting-edge large language models (LLMs) and generative AI to assessment holds great promise in boosting efficiency, mitigating bias, and facilitating customized evaluations. Conversely,…

COVID-19 is disrupting assessment practices and accelerating changes. With special focus on K-12 and credentialing exams, this article describes the series of changes observed during the pandemic, the solutions assessment providers have implemented, and the long-term impact on future practices. Additionally, this article highlights the importance…

The onset of the coronavirus pandemic forced schools and universities across the nation and world to close and move to distance learning rather immediately. Almost two years later, colleges and universities have reopened, and most students have returned to campuses, but distance learning still occurs at a much higher rate than before the beginning…

Professor Gordon argues for a significant reorientation in the focus and impact of assessment in education. For the types of assessment activities that he advocates to prosper and positively impact education, serious attention must be paid to two important topics: (1) the conceptual underpinnings of the assessment practices we develop and use to…

In this ITEMS module, we introduce the generalized deterministic inputs, noisy "and" gate (G-DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G-DINA…

Educators and researchers strive to build policies and practices on data and evidence, especially on academic achievement scores. When assessment scores are inaccurate for specific student populations or when scores are inappropriately used, even data-driven decisions will be misinformed. To maximize the impact of the research-practice-policy…

In early spring 2020 the vast majority of US colleges and schools closed for the year due to COVID-19 with no clear direction on when or how these institutions will reopen for in-person instruction. School closures and the associated health concerns haulted large scale admissions testing and required alternative models such as remote proctoring at…

In this digital ITEMS module, Dr. Brian Leventhal and Dr. Allison Ames provide an overview of "Monte Carlo simulation studies" (MCSS) in "item response theory" (IRT). MCSS are utilized for a variety of reasons, one of the most compelling being that they can be used when analytic solutions are impractical or nonexistent because…

Test score users often demand the reporting of subscores due to their potential diagnostic, remedial, and instructional benefits. Therefore, there is substantial pressure on testing programs to report subscores. However, professional standards require that subscores have to satisfy minimum quality standards before they can be reported. In this…

Three examples of extant testing practices (i.e., a classroom instructor's use of a simple pre-post design, the practice of teaching to the test, and the think aloud verbal protocol) are discussed to illustrate the contention that assessment in the service of testing and learning does not necessarily involve radically different assessment…

In this digital ITEMS module, Dr. Roy Levy describes Bayesian approaches to psychometric modeling. He discusses how Bayesian inference is a mechanism for reasoning in a probability-modeling framework and is well-suited to core problems in educational measurement: reasoning from student performances on an assessment to make inferences about their…

The goal in personalized assessment is to best fit the needs of each individual test taker, given the assessment purposes. Design-in-Real-Time (DIRTy) assessment reflects the progressive evolution in testing from a single test, to an adaptive test, to an adaptive assessment "system." In this article, we lay the foundation for DIRTy…

Contemporary views on learning highlight that deep learning occurs not simply by accumulating knowledge, but by using and applying knowledge as one engages in disciplinary activity. Increasingly, those concerned with education policy and practice are shifting priorities toward supporting deeper learning by emphasizing the importance of students'…