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- W2895537499 abstract "As the popularity of rich assessment scenarios increases so must the availability of psychometric models capable of handling the resulting data. Dynamic Bayesian networks (DBNs) offer a fast, flexible option for characterizing student ability across time under psychometrically complex conditions. In this article, a brief introduction to DBNs is offered, followed by a review of the existing literature on the use of DBNs in educational and psychological measurement with a focus on methodological investigations and novel applications that may provide guidance for practitioners wishing to deploy these models. The article concludes with a discussion of future directions for research in the field." @default.
- W2895537499 created "2018-10-12" @default.
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- W2895537499 date "2018-10-02" @default.
- W2895537499 modified "2023-10-14" @default.
- W2895537499 title "Dynamic Bayesian Networks in Educational Measurement: Reviewing and Advancing the State of the Field" @default.
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- W2895537499 doi "https://doi.org/10.1080/08957347.2018.1495217" @default.
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