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Eren Can Aybek; Serkan Arikan; Günes Ertas – International Journal of Assessment Tools in Education, 2024
When it is required to estimate item parameters of a large item bank, Multiple Matrix Sampling (MMS) design provides an efficient way while minimizing the test burden on students. The current study exemplifies how to calibrate a large item pool using MMS design for various purposes, such as developing a CAT administration. The purpose of the…
Descriptors: Elementary School Mathematics, Elementary School Students, Grade 4, Item Banks
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Diego Cortes; Dirk Hastedt; Sabine Meinck – Large-scale Assessments in Education, 2025
This paper informs users of data collected in international large-scale assessments (ILSA), by presenting argumentsunderlining the importance of considering two design features employed in these studies. We examine a commonmisconception stating that the uncertainty arising from the assessment design is negligible compared with that arisingfrom the…
Descriptors: Sampling, Research Design, Educational Assessment, Statistical Inference
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Shelby J. Haberman; Sabine Meinck; Ann-Kristin Koop – Large-scale Assessments in Education, 2024
This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal…
Descriptors: Elementary Secondary Education, Foreign Countries, Achievement Tests, Mathematics Achievement
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Anika Alam; A. Brooks Bowden – Society for Research on Educational Effectiveness, 2024
Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…
Descriptors: Dropout Characteristics, Prediction, Artificial Intelligence, At Risk Students