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To do so, courses are formally defined as set of knowledge items with requirement constraints, and associated with a set of exam questions. Moreover, the authors introduce Bayesian net to build a model of cognitive diagnosis, using probabilistic inference on it to help a student understand what knowledge item he\/she does not master, and the recommendations like what should be done next. Experimental results show that the group of students with such understanding can improve their testing performance greatly in an E-learning environment. Although the demo system has been integrated with a specific computerized adaptive testing system, the general technique could be applied to a broad class of intelligent tutoring systems.<\/p>","DOI":"10.4018\/ijisss.2014010103","type":"journal-article","created":{"date-parts":[[2014,5,21]],"date-time":"2014-05-21T11:31:34Z","timestamp":1400671894000},"page":"37-57","source":"Crossref","is-referenced-by-count":0,"title":["Cognitive Diagnosis of Students' Test Performance Based on Probability Inference"],"prefix":"10.4018","volume":"6","author":[{"given":"Junjie","family":"Xu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University, Liaoning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Dalian Maritime University, Liaoning, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijisss.2014010103-0","unstructured":"Beck, J. 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