概念表示增强的知识追踪模型

知识追踪模型以学习者的历史学习行为数据作为输入，通过概念表示来描述学习者的概念掌握状态，从而预测学习者未来的学习表现。然而在概念的外延表示方面，当前知识追踪研究的概念外延信息被限制在一阶相关的范畴内，无法表征概念的一阶以上外延信息。为了解决这一问题，提出方法首先使用图结构描述概念内涵信息及其相互关系；其次使用图神经网络的池化操作等提取概念的外延表示，这保证了概念的外延信息来源于多阶相关关系；再与概念的内涵表示进行融合；最后预测学习者未来的答题情况。为了验证该模型的有效性和效率，选取了四个主流知识追踪模型作为对比模型，在四个常用的知识追踪数据集上进行实验。结果表明，提出模型在若干评价指标上均取得了一定的优势，说明了它的有效性；在模型性能方面，提出模型达到最优评价指标所需的迭代次数最少，说明了它的效率；在实际应用方面，以该模型为基础实现了一个智能学习平台，在三门线下课程的教学过程中判断和预测学习者未来答题情况，取得了优于其他知识追踪模型的表现。

Concept representation enhanced knowledge tracing

Knowledge tracing models take the learner''s historical learning behavior data as input, describe the learner''s concept mastery state through the representation of the concept, and thus predict the learner''s future learning performance. However, in terms of the epitaxial representation of concepts, the conceptual epitaxial information in the current knowledge tracing research is limited to the scope of first-order associations, and cannot represent the epitaxial information of the concept above the first order. To solve this problem, the proposed method first used the graph structure to describe the conceptual connotation information and its interrelationship. Secondly, it used the pooling operation of the graph neural network to extract the epitaxial representation of the concept, which ensured that the epitaxial information of the concept could be derived from the multi-order correlation relationship; then integrated with the connotation representation of the concept. And finally it predicted the learner''s future answers. In order to verify the effectiveness and efficiency of the model, four relevant mainstream knowledge tracing models were selected as comparative models, and experiments were conducted on four commonly used knowledge tracing datasets. The results show that the proposed model has achieved certain advantages in several evaluation indicators, which shows its effectiveness, and in terms of model performance, the proposed model requires the least number of iterations to achieve the optimal evaluation index, which shows its efficiency. In terms of practical application, based on this model, an intelligent learning platform is realized, which judges and predicts the future answers of learners in the teaching process of three offline courses, and achieves better performance than other knowledge tracing models.