Abstract
An attribute weight calculation method which used a Bayesian network and the least squares distance method was proposed to assign different weights to different attributes in cognitive diagnosis. This method is independent of any specific cognitive diagnostic models, so it is practicable to consider attribute weight not only in the models with explicit expression but also in item response theory-based cognitive diagnostic methods. Simulation studies showed that the data fit for the least squares distance method was excellent and the weighted status can yield higher correct classification rates than the unweighted status. The weighted status had a promising performance in recognizing the knowledge states of examinees for various slippage probabilities under different attribute hierarchies. The numbers of items and attributes could also affect the examinees' classification accuracy.
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