Double-scoring constructed-response items is a common but costly practice in mixed-format assessments. This study explored the impacts of Targeted Double-Scoring (TDS) and random double-scoring procedures on the quality of psychometric outcomes, including student achievement estimates, person fit, and student classifications under various…

In recent years, educators, administrators, policymakers, and measurement experts have called for assessments that support educators in making better instructional decisions. One promising approach to measurement to support instructional decision-making is diagnostic classification models (DCMs). DCMs are flexible psychometric models that…

Large language models are increasingly used in educational and psychological measurement activities. Their rapidly evolving sophistication and ability to detect language semantics make them viable tools to supplement subject matter experts and their reviews of large amounts of text statements, such as educational content standards. This paper…

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,…

Automatic item generation may supply many items instantly and efficiently to assessment and learning environments. Yet, the evaluation of item quality persists to be a bottleneck for deploying generated items in learning and assessment settings. In this study, we investigated the utility of using large-language models, specifically Llama 3-8B, for…

This article provides a history of the two large-scale assessment consortia that were funded in 2010 as part of the Race to the Top Competition, the Partnership for the Assessment of Readiness for College and Career (PARCC), and the Smarter-Balanced Assessment Consortium (SBAC). I compare the goals the consortia were funded to meet between 2011…

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…

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…