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von Davier, Matthias; Tyack, Lillian; Khorramdel, Lale – Educational and Psychological Measurement, 2023
Automated scoring of free drawings or images as responses has yet to be used in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a TIMSS 2019 item. We are comparing classification accuracy of convolutional and feed-forward approaches. Our…
Descriptors: Scoring, Networks, Artificial Intelligence, Elementary Secondary Education
Wang, Wen-Chung; Chen, Hui-Fang; Jin, Kuan-Yu – Educational and Psychological Measurement, 2015
Many scales contain both positively and negatively worded items. Reverse recoding of negatively worded items might not be enough for them to function as positively worded items do. In this study, we commented on the drawbacks of existing approaches to wording effect in mixed-format scales and used bi-factor item response theory (IRT) models to…
Descriptors: Item Response Theory, Test Format, Language Usage, Test Items
Huang, Hung-Yu; Wang, Wen-Chung – Educational and Psychological Measurement, 2014
In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…
Descriptors: Item Response Theory, Hierarchical Linear Modeling, Computation, Test Reliability