中文摘要：

根据现有的研究使用神经网络进行认知诊断的方法,改变神经网络的建模方式,实现基于神经网络的认知诊断计算机化自适应测验.使用Monte Carlo方法生成模拟数据,训练并测试神经网络,在此基础上将神经网络用于自适应测试中,知识状态估计采用极大似然估计,选题策略采用后验加权的KL信息量指标.模拟结果表明,基于BP神经网络的CD-CAT的判准率较为理想,并且因为神经网络的训练过程可以在小样本情况下实现,这种CD-CAT更适合在课堂评估中使用.

英文摘要：

Current neural network-based cognitive diagnosis was altered to realize cognitive diagnostic computerized adaptive testing.Monte Carlo method was used to generate datasets.The BP network was tralned through tralning datasets.Prediction accuracy of tralned network is computed with testing datasets. Such tralned and tested BP networks were used to realize CD-CAT,with maximum likelihood method to estimate knowledge state and posterior weighted kullback leibler (PWKL)information as the item selection strategy.Classification accuracy for respondents was computed and the outcome was found to be desirable.