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Guher Gorgun; Okan Bulut – Educational Measurement: Issues and Practice, 2025
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…
Descriptors: Artificial Intelligence, Quality Control, Technology Uses in Education, Automation
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Jung Yeon Park; Sean Joo; Zikun Li; Hyejin Yoon – Educational Measurement: Issues and Practice, 2025
This study examines potential assessment bias based on students' primary language status in PISA 2018. Specifically, multilingual (MLs) and nonmultilingual (non-MLs) students in the United States are compared with regard to their response time as well as scored responses across three cognitive domains (reading, mathematics, and science).…
Descriptors: Achievement Tests, Secondary School Students, International Assessment, Test Bias
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Sanford R. Student; Derek C. Briggs; Laurie Davis – Educational Measurement: Issues and Practice, 2025
Vertical scales are frequently developed using common item nonequivalent group linking. In this design, one can use upper-grade, lower-grade, or mixed-grade common items to estimate the linking constants that underlie the absolute measurement of growth. Using the Rasch model and a dataset from Curriculum Associates' i-Ready Diagnostic in math in…
Descriptors: Elementary School Mathematics, Elementary School Students, Middle School Mathematics, Middle School Students
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Stella Y. Kim; Sungyeun Kim – Educational Measurement: Issues and Practice, 2025
This study presents several multivariate Generalizability theory designs for analyzing automatic item-generated (AIG) based test forms. The study used real data to illustrate the analysis procedure and discuss practical considerations. We collected the data from two groups of students, each group receiving a different form generated by AIG. A…
Descriptors: Generalizability Theory, Automation, Test Items, Students
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Ye Ma; Deborah J. Harris – Educational Measurement: Issues and Practice, 2025
Item position effect (IPE) refers to situations where an item performs differently when it is administered in different positions on a test. The majority of previous research studies have focused on investigating IPE under linear testing. There is a lack of IPE research under adaptive testing. In addition, the existence of IPE might violate Item…
Descriptors: Computer Assisted Testing, Adaptive Testing, Item Response Theory, Test Items
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Mo Zhang; Paul Deane; Andrew Hoang; Hongwen Guo; Chen Li – Educational Measurement: Issues and Practice, 2025
In this paper, we describe two empirical studies that demonstrate the application and modeling of keystroke logs in writing assessments. We illustrate two different approaches of modeling differences in writing processes: analysis of mean differences in handcrafted theory-driven features and use of large language models to identify stable personal…
Descriptors: Writing Tests, Computer Assisted Testing, Keyboarding (Data Entry), Writing Processes
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Katherine E. Castellano; Daniel F. McCaffrey; Joseph A. Martineau – Educational Measurement: Issues and Practice, 2025
Growth-to-standard models evaluate student growth against the growth needed to reach a future standard or target of interest, such as proficiency. A common growth-to-standard model involves comparing the popular Student Growth Percentile (SGP) to Adequate Growth Percentiles (AGPs). AGPs follow from an involved process based on fitting a series of…
Descriptors: Student Evaluation, Growth Models, Student Educational Objectives, Educational Indicators