基于神经网络的认知诊断方法

2010 Second International Conference on Computational Intelligence and Natural Computing Pub Date : 2010-11-22 DOI:10.1109/CINC.2010.5643883

Jiayuan Yu

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引用次数: 0

摘要

统计认知诊断方法复杂，不能很好地判断认知缺陷。为了解决这一问题，提出了一种结合主成分分析、自组织羽毛图和概率神经网络的混合方法。应用于认知诊断。数据来源于488名参加汉语测试的高中生。结果表明，主成分分析可以有效地降低SOM输入数据的维数，得到认知属性。SOM网络可以对被试进行分类，识别不同类别的认知不足。概率神经网络能准确判断新生的认知缺陷。这是一种有价值的认知诊断方法。

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Neural network-based cognitive diagnostic method

Statistics cognitive diagnostic methods were complex and could not judge cognitive bugs very well. To solve this problem, a hybrid method combining principal component analysis, self-organizing feather map and probabilistic neural networks was promoted. It was applied in cognitive diagnostic. The data was got from 488 students of high school who took in Chinese language test. The results showed the principal component analysis could reduce the dimensions for SOM input data, and get the cognitive attributes. SOM network could divide the subjects into categories, and identify the cognitive shortages of different categories. Probabilistic neural network could judge cognitive bugs of the new students accurately. It is a valuable cognitive diagnostic method.

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2010 Second International Conference on Computational Intelligence and Natural Computing

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