Educational Measurement-Issues and Practice最新文献

What are foundational competencies in educational measurement? We published a framework for these foundational competencies in this journal (Ackerman et al. 2024) and were grateful to receive eight commentaries raising a number of important questions about the framework and its implications. We identified five cross-cutting recommendations among the eight commentaries relating to (1) our process and purpose, (2) Artificial Intelligence, (3) ethical competencies, (4) qualitative, critical, and culturally responsive commentaries, and (5) intersecting professions in, for example, classroom assessment and content development. In this rejoinder, we respond to these five recommendations and to each of the eight commentaries in turn. We hope that discussion and consensus about foundational competencies in educational measurement continue to advance in our journals and our field.

The “Foundational Competencies in Educational Measurement” framework aims to shape the field's future. However, the absence of emerging scholars and graduate students in the task force highlights a gap in representing those most familiar with current educational landscape. As early career scholars, we offer perspectives to enhance this framework by focusing on making educational measurement more inclusive, collaborative, and culturally responsive. Drawing on our diverse backgrounds and experiences, we propose expanding the framework to empower measurement professionals, diversify measurement practices, and integrate ethical considerations. We also advocate for a new visual representation of the framework as a budding plant, symbolizing the organic and evolving nature of foundational skills in educational measurement. This commentary aims to refine the foundational competencies to better prepare future professionals for meaningful, equitable educational contributions.

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 facilitate fine-grained reporting on skills that students have mastered. In this article, we describe how DCMs can be leveraged to support better decision-making. We first provide a high-level overview of DCMs. We then describe different methods for reporting results from DCM-based assessments that support decision-making for different stakeholder groups. We close with a discussion of considerations for implementing DCMs in an operational setting, including how they can inform decision-making at state and local levels, and share future directions for research.

Multidimensional forced-choice (MFC) items have been found to be useful to reduce response biases in personality assessments. However, conventional scoring methods for the MFC items result in ipsative data, hindering the wider applications of the MFC format. In the last decade, a number of item response theory (IRT) models have been developed, majority of which are for MFC items with binary responses. However, MFC items with polytomous responses are more informative and have many applications. This paper develops a polytomous Rasch ipsative model (pRIM) that can deal with ipsative data and yield estimates that measure construct differentiation—a latent trait that describes the degree to which the personality constructs (e.g., interests) distinguish between each other. The pRIM and its simpler form are applied to a career interests assessment containing four-category MFC items and the measures of interests differentiation are used for both intra- and interpersonal comparisons. Simulations are conducted to examine the recovery of the parameters under various conditions. The results show that the parameters of the pRIM can be well recovered, particularly when a complete linking design and a large sample are used. The implications and application of the pRIM in the personality assessment using MFC items are discussed.

There is a growing integration of technology in the workplace (World Economic Forum), and with it, organizations are increasingly relying on advanced technological approaches for improving their human capital processes to stay relevant and competitive in complex environments. All professions must keep up with this transition and begin integrating technology into their tools and processes. This paper centers on how advanced technological approaches (such as natural language processing (NLP) and data mining) have complemented a traditional practice analysis of the accounting profession. We also discuss strategic selection and use of subject-matter experts (SMEs) for more efficient practice analysis. The authors have adopted a triangulation process—gathering information from traditional practice analysis, using selected SMEs, and confirming findings with a novel NLP-based approach. These methods collectively contributed to the revision of the Uniform CPA Exam blueprint and in understanding accounting trends.

This article is based on my 2023 NCME Presidential Address, where I talked a bit about my journey into the profession, and more substantively about comparable scores. Specifically, I discussed some of the different ways ‘comparable scores’ are defined, highlighted some areas I think we as a profession need to pay more attention to when considering score comparability, and emphasized that comparability in this context is a matter of degree which varies according to the decisions we plan to make on particular scores.