Educational Measurement-Issues and Practice最新文献

The Focal Article (Ackerman et al., 2024) provides a comprehensive framework and detailed guidelines to faculty, students, and measurement professionals. We used a data-driven approach that examines recent job posts (including full-time positions at academic institutions, nonprofit organizations, and for-profit companies, as well as internship positions) to evaluate the match between the skills required by the jobs and the Foundational Competencies and provide summaries and recommendations thereof.

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.

Job and practice analysis is a commonly used method for determining examination content specifications. However, difficulties arise when many domains are present, as mainstream approaches do not fully adhere to the essence of the weighing process, namely a “comparison-evaluation-decision” framework for assigning percentage values to the content. Stemming from the principle of comparing multiple criteria for making decisions, the Analytic Hierarchy Process (AHP) provides an appropriate solution that circumvents the aforementioned obstacle. We propose using an extended version of AHP called Group AHP (GAHP) to weight content specifications for standardized medical education assessment. Specifically, GAHP is integrated with the Delphi method and expected to aid exam developers in integrating feedback from diverse experienced physicians when determining content specifications for the National Medical Licensing Examination (NMLE) in China. The complete flow of the proposed approach was demonstrated in this study with an application to the NMLE.

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.