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- W2120070764 abstract "This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing." @default.
- W2120070764 created "2016-06-24" @default.
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- W2120070764 date "2011-04-01" @default.
- W2120070764 modified "2023-09-23" @default.
- W2120070764 title "A reinforcement learning approach for sequential mastery testing" @default.
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- W2120070764 doi "https://doi.org/10.1109/adprl.2011.5967390" @default.
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