Probabilistic Models for Computerized Adaptive Testing: Experiments
This paper follows previous research we have already performed in the area of Bayesian networks models for CAT. We present models using Item Response Theory (IRT - standard CAT method), Bayesian networks, and neural networks. We conducted simulated CAT tests on empirical data. Results of these tests are presented for each model separately and compared.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Probabilistic Models for Computerized Adaptive Testing
In this paper we follow our previous research in the area of Computerized Adaptive Testing (CAT). We present three different methods for CAT. One of them, the item response theory, is a well established method, while the…
Balancing Test Accuracy and Security in Computerized Adaptive Testing
Computerized adaptive testing (CAT) is a form of personalized testing that accurately measures students' knowledge levels while reducing test length. Bilevel optimization-based CAT (BOBCAT) is a recent framework that lea…
Bilevel OptimizationQuestion SelectionBOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing
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 response…
Bilevel OptimizationQuestion SelectionAdaptive Querying with AI Persona Priors
We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight query budgets. Classical Bayesian design and computerized adapt…
An Intelligent Testing Strategy for Vocabulary Assessment of Chinese Second Language Learners
Vocabulary is one of the most important parts of language competence. Testing of vocabulary knowledge is central to research on reading and language. However, it usually costs a large amount of time and human labor to bu…