There are various methods in knowledge space theory for building knowledge structures or surmise relations from data. Few of them have been thoroughly analyzed, making it difficult to decide which of these methods provides good results and when to apply each of the methods. In this paper, we investigate the method known as inductive item tree analysis and discuss the advantages and disadvantages of this algorithm. In particular, we introduce some corrections and improvements to it, resulting in two newly proposed algorithms. These algorithms and the original inductive item tree analysis procedure are compared in a simulation study and with empirical data. © 2009 Elsevier B.V. All rights reserved.
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