"ERIC ED624123: Sparse Factor Autoencoders For Item Response Theory" - Information and Links:

ERIC ED624123: Sparse Factor Autoencoders For Item Response Theory - Info and Reading Options

"ERIC ED624123: Sparse Factor Autoencoders For Item Response Theory" and the language of the book is English.


“ERIC ED624123: Sparse Factor Autoencoders For Item Response Theory” Metadata:

  • Title: ➤  ERIC ED624123: Sparse Factor Autoencoders For Item Response Theory
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  • Language: English

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  • Internet Archive ID: ERIC_ED624123

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"ERIC ED624123: Sparse Factor Autoencoders For Item Response Theory" Description:

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Item response theory (IRT) is a popular method to infer student abilities and item difficulties from observed test responses. However, IRT struggles with two challenges: How to map items to skills if multiple skills are present? And how to infer the ability of new students that have not been part of the training data? Inspired by recent advances in variational autoencoders for IRT, we propose a novel method to tackle both challenges: The Sparse Factor Autoencoder (SparFAE). SparFAE maps from test responses to abilities via a linear operator and from abilities to test responses via an IRT model. All parameters of the model offer an interpretation and can be learned in an efficient manner. In experiments on synthetic and real data, we show that SparFAE is similar in accuracy to other autoencoder approaches while being faster to learn and more accurate in recovering item-skill associations. [For the full proceedings, see ED623995.]

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"ERIC ED624123: Sparse Factor Autoencoders For Item Response Theory" is available for download from The Internet Archive in "texts" format, the size of the file-s is: 9.05 Mbs, and the file-s went public at Wed Jan 22 2025.

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  • Source: Internet Archive
  • Internet Archive Link: Archive.org page
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