Identifying Cognitive Attributes Using Deep Learning Classification Techniques

Shuai Zhao, Xiaoting Huang
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引用次数: 2

Abstract

Cognitive diagnosis is very useful to teachers and students, but its application is limited at present. This is largely because identifying the cognitive attributes of items currently is labor intensive and time-consuming. In this study, we used text classification techniques to automatically identify cognitive attributes. Specifically, two popular deep learning classification models, long-short term memory and bi-directional long-short term memory, were employed in conjunction with word embeddings. As the baseline, support vector machine with feature selection using information gain was also adopted. Experiments based on a sample of 805 third grade math items showed that both the deep learning models performed better than support vector machine, and bi-directional long-short term memory achieved the best performance, yielding the accuracy of 82% and the F1 measure of 80%. Our result indicated that text classification methods, especially deep learning models, have great potential in identifying cognitive attributes efficiently, and in turn, make cognitive diagnostic more feasible to practitioners.
使用深度学习分类技术识别认知属性
认知诊断对教师和学生都有很大的帮助,但目前的应用还很有限。这主要是因为识别物品的认知属性目前是一项劳动密集型且耗时的工作。在本研究中,我们使用文本分类技术来自动识别认知属性。具体来说,两种流行的深度学习分类模型,长短期记忆和双向长短期记忆,与词嵌入结合使用。采用基于信息增益的特征选择支持向量机作为基线。基于805个三年级数学题样本的实验表明,深度学习模型的表现都优于支持向量机,双向长短期记忆的表现最好,准确率为82%,F1测量值为80%。我们的研究结果表明,文本分类方法,特别是深度学习模型,在有效识别认知属性方面具有很大的潜力,从而使认知诊断对从业者来说更加可行。
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