Papers › BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing

BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing

17 Aug 2021arXiv:2108.07386archive 2025-07-28

Aritra Ghosh, Andrew Lan

Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker. CAT methods adaptively select the next most informative question/item for each student given their responses to previous questions, effectively reducing test length. Existing CAT methods use item response theory (IRT) models to relate student ability to their responses to questions and static question selection algorithms designed to reduce the ability estimation error as quickly as possible; therefore, these algorithms cannot improve by learning from large-scale student response data. In this paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to directly learn a data-driven question selection algorithm from training data. BOBCAT is agnostic to the underlying student response model and is computationally efficient during the adaptive testing process. Through extensive experiments on five real-world student response datasets, we show that BOBCAT outperforms existing CAT methods (sometimes significantly) at reducing test length.

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Syntology Ran 10 of 10 code samples harvested from 2 repositories linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · honoured contract; 3 ran · our draft was wrong; 2 ran · fixture could not drive it; 4 ran with no contract checked.

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arghosh/bobcat officialmentioned in papermentioned on GitHubpytorch report
arghosh/NeurIPSEducation2020 mentioned on GitHubpytorch report

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10 samples harvested; 10 ran; 1 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
3ran · our draft was wrong
2ran · fixture could not drive it
4ran

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MAMLModel arghosh/bobcat/model.py official repository ran MIT (permissive) · 1a5ae3c194622d14 · report
compute_loss arghosh/bobcat/model.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · ddaa3dd8a024013d · report
get_outputs arghosh/bobcat/model.py official repository ran · honoured contract MIT (permissive) · 98d34936f4d8072b · report
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pick_random_sample arghosh/bobcat/model.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 5e6b5e316fc4d0d6 · report
AttentionModel arghosh/NeurIPSEducation2020/model_task_1_2.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · b86faf0fe0426b7f · report
CosinePositionalEmbedding arghosh/NeurIPSEducation2020/model_task_1_2.py community (archive-listed) ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · 7a466f4e8e785035 · report
MultiHeadAttention arghosh/NeurIPSEducation2020/model_task_1_2.py community (archive-listed) ran · metamorphic tier: deterministic MIT (permissive) · e28f6e6239277b44 · report
attention arghosh/NeurIPSEducation2020/model_task_1_2.py community (archive-listed) ran · fixture could not drive it fingerprinted MIT (permissive) · 1abee56c1c82b3be · report
get_inputs identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · bf10b1462929ad81 · report

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Bilevel OptimizationQuestion Selection

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