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ERIC ED608051: Variational Item Response Theory: Fast, Accurate, an... |
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by ERIC |
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Item Response Theory (IRT) is a ubiquitous model for |
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understanding humans based on their responses to |
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questions, used in fields as diverse as education, |
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medicine and psychology. Large modern datasets offer |
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opportunities to capture more nuances in human behavior, |
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potentially improving test scoring and better informing |
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public policy. Yet larger datasets pose a difficult |
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speed/accuracy challenge to contemporary algorithms for |
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fitting IRT models. We introduce a variational Bayesian |
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inference algorithm for IRT, and show that it is fast and |
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scaleable without sacrificing accuracy. Using this |
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inference approach we then extend classic IRT with |
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expressive Bayesian models of responses. Applying this |
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method to five large-scale item response datasets from |
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cognitive science and education yields higher log |
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likelihoods and improvements in imputing missing data. |
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The algorithm implementation is open-source, and easily |
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usable. [For the full proceedings, see ED607784.] |
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Date Published: 2022-07-15 04:12:08 |
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Identifier: ERIC_ED608051 |
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Item Size: 15225203 |
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Language: english |
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Media Type: texts |
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# Topics |
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ERIC Archive; ERIC; Wu, Mike\nDavis, ... |
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# Collections |
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ericarchive |
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additional_collections |
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# Uploaded by |
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@chris85 |
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