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Hongwen Guo; Matthew S. Johnson; Luis Saldivia; Michelle Worthington; Kadriye Ercikan – ETS Research Institute, 2025
ETS scientists developed a human-centered AI (HAI) framework that combines data on how students interact with assessments--such as task navigation and time spent--with their performance, providing deeper insights into student performance in large-scale assessments.
Descriptors: Artificial Intelligence, Student Evaluation, Evaluation Methods, Measurement
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Yi-Ling Wu; Yao-Hsuan Huang; Chia-Wen Chen; Po-Hsi Chen – Journal of Educational Measurement, 2025
Multistage testing (MST), a variant of computerized adaptive testing (CAT), differs from conventional CAT in that it is adapted at the module level rather than at the individual item level. Typically, all examinees begin the MST with a linear test form in the first stage, commonly known as the routing stage. In 2020, Han introduced an innovative…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Format, Measurement
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Kylie Gorney; Mark D. Reckase – Journal of Educational Measurement, 2025
In computerized adaptive testing, item exposure control methods are often used to provide a more balanced usage of the item pool. Many of the most popular methods, including the restricted method (Revuelta and Ponsoda), use a single maximum exposure rate to limit the proportion of times that each item is administered. However, Barrada et al.…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Item Banks
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Kayla V. Campaña; Benjamin G. Solomon – Assessment for Effective Intervention, 2025
The purpose of this study was to compare the classification accuracy of data produced by the previous year's end-of-year New York state assessment, a computer-adaptive diagnostic assessment ("i-Ready"), and the gating combination of both assessments to predict the rate of students passing the following year's end-of-year state assessment…
Descriptors: Accuracy, Classification, Diagnostic Tests, Adaptive Testing