title: Cognitive diagnostic assessment with different weight for attribute: based on the Dina model.
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.
authors: 1 National Key Laboratory of Cognitive Neuroscience and Learning.
authors: Lei
authors: L
authors: Guo
authors: Yu
authors: Y
authors: Bao
authors: Zhuoran
authors: Z
authors: Wang
authors: Yufang
authors: Y
authors: Bian
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003072:Cognition Disorders / Q000175:diagnosis* / Q000523:psychology; D006801:Humans; D016018:Least-Squares Analysis; D016015:Logistic Models; D015233:Models, Statistical; D009483:Neuropsychological Tests / Q000706:statistics & numerical data*; D011336:Probability; D011594:Psychometrics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results