00507nas a2200121   4500008004100000245006000041210006000101260009700161100001900258700000700277700001500284856008600299       2007                        eng d00aPartial order knowledge structures for CAT applications0 aPartial order knowledge structures for CAT applications  aD. J. Weiss (Ed.), Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing.1 aDesmarais, M C1 aPu1 aBlais, J-G  uhttp://mail.iacat.org/content/partial-order-knowledge-structures-cat-applications02243nas a2200205   4500008004100000020004600041245009500087210006900182260002700251300001200278490000700290520149400297653002701791653003001818653001701848653002501865100001901890700001001909856011801919       2005                        eng d  a1560-4292 (Print); 1560-4306 (Electronic)00aA Bayesian student model without hidden nodes and its comparison with item response theory0 aBayesian student model without hidden nodes and its comparison w  bIOS Press: Netherlands  a291-3230 v153 aThe Bayesian framework offers a number of techniques for inferring an individual's knowledge state from evidence of mastery of concepts or skills. A typical application where such a technique can be useful is Computer Adaptive Testing (CAT). A Bayesian modeling scheme, POKS, is proposed and compared to the traditional Item Response Theory (IRT), which has been the prevalent CAT approach for the last three decades. POKS is based on the theory of knowledge spaces and constructs item-to-item graph structures without hidden nodes. It aims to offer an effective knowledge assessment method with an efficient algorithm for learning the graph structure from data. We review the different Bayesian approaches to modeling student ability assessment and discuss how POKS relates to them. The performance of POKS is compared to the IRT two parameter logistic model. Experimental results over a 34 item Unix test and a 160 item French language test show that both approaches can classify examinees as master or non-master effectively and efficiently, with relatively comparable performance. However, more significant differences are found in favor of POKS for a second task that consists in predicting individual question item outcome. Implications of these results for adaptive testing and student modeling are discussed, as well as the limitations and advantages of POKS, namely the issue of integrating concepts into its structure. (PsycINFO Database Record (c) 2007 APA, all rights reserved)10aBayesian Student Model10acomputer adaptive testing10ahidden nodes10aItem Response Theory1 aDesmarais, M C1 aPu, X  uhttp://mail.iacat.org/content/bayesian-student-model-without-hidden-nodes-and-its-comparison-item-response-theory