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- W2626150207 abstract "An adaptive test is a computer-based testing technique which adjusts the sequence of questions on the basis of the estimated ability level of the test taker. We suggest the use of credal networks, a generalization of Bayesian networks based on sets of probability mass functions, to implement adaptive tests exploiting the knowledge of the test developer instead of training on databases of answers. Compared to Bayesian networks, these models might offer higher expressiveness and hence a more reliable modeling of the qualitative expert knowledge. The counterpart is a less straightforward identification of the information-theoretic measure controlling the question-selection and the test-stopping criteria. We elaborate on these issues and propose a sound and computationally feasible procedure. Validation against a Bayesian-network approach on a benchmark about German language proficiency assessments suggests that credal networks can be reliable in assessing the student level and effective in reducing the number of questions required to do it." @default.
- W2626150207 created "2017-06-23" @default.
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- W2626150207 date "2017-01-01" @default.
- W2626150207 modified "2023-09-27" @default.
- W2626150207 title "Reliable Knowledge-Based Adaptive Tests by Credal Networks" @default.
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- W2626150207 doi "https://doi.org/10.1007/978-3-319-61581-3_26" @default.
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