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705 resources
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Using Multi-label Classification Neural Network to Detect Intersectional DIF with Small Sample SizesYale Quan, Chun Wang|May 16th, 2025|journalArticleYale Quan, Chun WangMay 16th, 2025
This study introduces InterDIFNet, a multilabel classification neural network for detecting intersectional differential item functioning (DIF) in educational and psychological assessments, with a focus on small sample sizes. Unlike traditional marginal DIF methods, which often fail to capture the effects of intersecting identities and require large samples, InterDIFNet models uniform and non-uniform DIF across multiple intersectional groups simultaneously. The method utilizes an optimized...
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Pooya Razavi, Sonya J. Powers|May 16th, 2025|conferencePaperPooya Razavi, Sonya J. PowersMay 16th, 2025
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Yashad Samant, Lee Becker, Scott Hellman...|May 16th, 2025|conferencePaperYashad Samant, Lee Becker, Scott Hellman...May 16th, 2025
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Yashad Samant, Lee Becker, Scott Hellman...|May 16th, 2025|presentationYashad Samant, Lee Becker, Scott Hellman...May 16th, 2025
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Mark D. Shermis|May 16th, 2025|conferencePaperMark D. ShermisMay 16th, 2025
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Ladislav Stacho, Edith Aurora Graf, Dieg...|May 16th, 2025|conferencePaperLadislav Stacho, Edith Aurora Graf, Dieg...May 16th, 2025
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Cheng Tang, George Engelhard, Jiawei Xio...|May 16th, 2025|presentationCheng Tang, George Engelhard, Jiawei Xio...May 16th, 2025
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Peter Tran, Ketan ., Stephen G. Sireci|May 16th, 2025|presentationPeter Tran, Ketan ., Stephen G. SireciMay 16th, 2025
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Matthias Von Davier|May 16th, 2025|presentationMatthias Von DavierMay 16th, 2025
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Ting Wang, Ying Du, Karen Hoeve|May 16th, 2025|conferencePaperTing Wang, Ying Du, Karen HoeveMay 16th, 2025
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Ting Wang, Ying Du, Karen Hoeve|May 16th, 2025|presentationTing Wang, Ying Du, Karen HoeveMay 16th, 2025
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Yu Wang, Madhu Gopalakrishnan, Yoav Berg...|May 16th, 2025|presentationYu Wang, Madhu Gopalakrishnan, Yoav Berg...May 16th, 2025
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Yu Wang, Madhumitha Gopalakrishnan, Yoav...|May 16th, 2025|conferencePaperYu Wang, Madhumitha Gopalakrishnan, Yoav...May 16th, 2025
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Joshua Wilson, Corey Palermo, Paul Deane...|May 16th, 2025|presentationJoshua Wilson, Corey Palermo, Paul Deane...May 16th, 2025
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Scott W. Wood|May 16th, 2025|presentationScott W. WoodMay 16th, 2025
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Scott W. Wood|May 16th, 2025|conferencePaperScott W. WoodMay 16th, 2025
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Mingfeng Xue, Yi-Fang Wu|May 16th, 2025|conferencePaperMingfeng Xue, Yi-Fang WuMay 16th, 2025
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Mackenzie Young, Amy Burkhardt, Quinell ...|May 16th, 2025|conferencePaperMackenzie Young, Amy Burkhardt, Quinell ...May 16th, 2025
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Fan Zhang, Joshua Wilson|May 16th, 2025|conferencePaperFan Zhang, Joshua WilsonMay 16th, 2025
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Guher Gorgun, Okan Bulut|Dec 19th, 2024|journalArticleGuher Gorgun, Okan BulutDec 19th, 2024
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 evaluating automatically generated cloze items. The trained large‐language model was able to filter out majority of good and bad items accurately....