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Author Desmarais, Michel C. ♦ Pu, Xiaoming
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Hidden Node ♦ Item Response Theory ♦ Bayesian Student Model ♦ Graph Structure ♦ Bayesian Modeling Scheme ♦ Knowledge State ♦ Prevalent Cat Approach ♦ Parameter Logistic Model ♦ Efficient Knowledge Assessment Method ♦ Computer Adaptive Testing ♦ Different Bayesian Approach ♦ Typical Application ♦ Construct Item ♦ Knowledge Space ♦ Traditional Item Response Theory ♦ Bayesian Framework ♦ Student Ability Assessment
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 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.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Publisher Date 2005-01-01