试题知识点预测:一种教研知识强化的卷积神经网络模型

在各类在线学习系统中,为了给学生提供优质的学习服务,一个基础性的任务是试题知识点预测,即预测一道试题所考察的知识概念、能力等。在这个任务中,已有方法通常基于人工专家标注或者传统机器学习方法。然而,这些传统方法要么耗时耗力,要么仅关注试题资源的浅层特征,忽略了试题文本和知识点之间的深层语义关联。因此,这两类方法在实际应用中均受到了限制。为此,该文提出一种教研知识强化的卷积神经网络方法进行试题知识点预测。首先,结合教育学经验,定义和抽取试题的浅层特征。然后,利用一个卷积神经网络对试题的深层语义进行理解和表征。然后,考虑到教研先验与试题词句之间的关联,提出一种基于注意力机制的方法能够自动识别和计算不同教研先验对试题的重要性程度。最后,设计了一个融合知识点决策和试题语义约束的模型训练目标。该文在大规模数据上进行了充分的实验。实验结果表明,所提出的方法能够有效地进行试题知识点预测,具有很好的应用价值。

Predicting Knowledge Points of Questions: an Expertise-Enriched CNN Model

In online learning systems, to offer students better learning services, a fundamental task is predicting questions’ knowledge points, i.e., predicting the knowledge concepts or skills of a question. Existing methods for this task usually rely on human labeling or traditional machine learning methods, They are defected in either labor intensive or focusing only on shallow features without capturing the deep semantic relations between questions and knowledge points. In this paper, we propose an Expertise-enriched Convolutional Neural Network(ECNN)to predict questions’ knowledge points. Specifically, we first define and extract question features under the guidance of educational experience. Then, we leverage a convolutional neural network to exploit question representations from deep sematic perspective. After that, considering the relations between questions and expertise priors, we develop an attention based method for calculating the importance of expertise for questions. At last, we design an objective function for model learning that constrains both knowledge points and semantics. Extensive experiments on a large-scale dataset demonstrate the effectiveness of the proposed model, showing a good application value.