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Examining Gender Differences in TIMSS 2019 Using a Multiple-Group Hierarchical Speed-Accuracy-Revisits Model
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Dihao Leng; Ummugul Bezirhan; Lale Khorramdel; Bethany Fishbein; Matthias von Davier – Educational Measurement: Issues and Practice, 2024
This study capitalizes on response and process data from the computer-based TIMSS 2019 Problem Solving and Inquiry tasks to investigate gender differences in test-taking behaviors and their association with mathematics achievement at the eighth grade. Specifically, a recently proposed hierarchical speed-accuracy-revisits (SAR) model was adapted to…
Descriptors: Gender Differences, Test Wiseness, Achievement Tests, Mathematics Tests
Proposal for Modifying Procedures for Declaring Significant Trends in TIMSS
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Henry Isaiah Braun; Matthias von Davier; Jihang Chen – Large-scale Assessments in Education, 2025
International large-scale assessments (ILSA) are an important source of information for education policymakers across the globe. Despite sponsors' warnings, when results are published, media attention focuses on country rankings and changes in scores. Score changes are evaluated using a two-sided z-statistic, with statistical significance declared…
Descriptors: Educational Trends, Elementary Secondary Education, Foreign Countries, Achievement Tests
Combining Machine Translation and Automated Scoring in International Large-Scale Assessments
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Ji Yoon Jung; Lillian Tyack; Matthias von Davier – Large-scale Assessments in Education, 2024
Background: Artificial intelligence (AI) is rapidly changing communication and technology-driven content creation and is also being used more frequently in education. Despite these advancements, AI-powered automated scoring in international large-scale assessments (ILSAs) remains largely unexplored due to the scoring challenges associated with…
Descriptors: Artificial Intelligence, Automation, Scoring, International Assessment
Improving Context Scale Interpretation Using Latent Class Analysis for Cut Scores
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Liqun Yin; Ummugul Bezirhan; Matthias von Davier – International Electronic Journal of Elementary Education, 2025
This paper introduces an approach that uses latent class analysis to identify cut scores (LCA-CS) and categorize respondents based on context scales derived from largescale assessments like PIRLS, TIMSS, and NAEP. Context scales use Likert scale items to measure latent constructs of interest and classify respondents into meaningful ordered…
Descriptors: Multivariate Analysis, Cutting Scores, Achievement Tests, Foreign Countries