A Bayesian Student Model without Hidden Nodes and its Comparison with Item Response Theory

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 in Computer Adaptive Testing (CAT). A Bayesian modeling scheme, named 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 graphs structures (without hidden nodes). It aims to offer an efficient knowledge assessment method, while allowing learning of the 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 items UNIX test and a 160 items French language test show that both approaches can classify examinees as maste...

• Type: Journal
• Year: 2005
• Where: AIEDU
• Authors: Michel C. Desmarais, Xiaoming Pu
• Keywords: Adaptive Testing, AIEDU 2005, Bayesian Modeling Scheme, Computer Adaptive Testing