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Ios press a bayesian student model without hidden nodes and its comparison with item response theory (2008).
| Content Provider | CiteSeerX |
|---|---|
| Author | Desmarais, Michel C. Pu, Xiaoming |
| Abstract | Abstract. The 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. |
| File Format | |
| Publisher Date | 2008-01-01 |
| Access Restriction | Open |
| Subject Keyword | Hidden Node Item Response Theory Io Press Bayesian Student Model Significant Difference Item French Language Test Show Traditional Item Response Theory Student Modeling Adaptive Testing Individual Question Item Outcome Effective Knowledge Assessment Method Construct Item-to-item Graph Structure Different Bayesian Approach Bayesian Modeling Scheme Prevalent Cat Approach Efficient Algorithm Parameter Logistic Model Item Unix Test Typical Application Graph Structure Student Ability Assessment Knowledge Space Comparable Performance Bayesian Framework Computer Adaptive Testing Individual Knowledge State Experimental Result Second Task |
| Content Type | Text |
| Resource Type | Article |
