Knowledge structure enhanced graph representation learning model for attentive knowledge tracing
Knowledge structure enhanced graph representation learning model for attentive knowledge tracing
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DOI：
10.1002/int.22763
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发表时间：
2021-11
影响因子：
7
通讯作者：
Wenbin Gan;Yuan Sun;Yi Sun
中科院分区：
文献类型：
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作者：
Wenbin Gan;Yuan Sun;Yi Sun
知识追踪(KT)是在线学习系统中为学习者提供个性化辅导的一种基本技术。最近的KT方法采用了灵活的基于深度神经网络的模型，这些模型在这一任务中表现出色。然而，由于学习者练习数据的稀疏性，KT的充分性仍然受到挑战。为了缓解稀疏性问题，大多数现有的KT研究都是在技能层面上进行的，而不是在问题层面上进行的，因为问题往往很多，与更少的技能相关。然而，在技能层面，KT忽略了与问题本身以及它们之间的关系有关的独特信息。在这种情况下，模型不能准确地推断学习者的知识状态，并且可能无法捕捉到练习序列中的长期依赖关系。在知识领域，技能自然以图形的形式联系在一起(边是教学概念之间的前提关系)。我们将这样的图称为知识结构(KS)。将知识结构引入知识结构转换过程可以潜在地解决稀疏性和信息丢失问题，但这一途径还没有得到充分的探索，因为获得领域的完整知识结构是具有挑战性的和劳动密集型的。在本文中，我们提出了一种新的具有注意机制的KS增强的图表示学习模型(KSGKT)。我们首先探索了八种方法，这些方法可以从学习者的反应数据中自动推断出领域KS，并将其集成到KT过程中。利用图表示学习模型，我们从KS增强的图中获得问题和技能嵌入。为了融入更多关于问题的独特信息，我们从每个学习者的学习历史中提取了认知问题的难度。然后，我们提出了一种融合这些离散特征的卷积表示方法，从而获得了每个问题的综合表示。这些表示被输入到所提出的KT模型中，而长期依赖关系由注意机制来处理。该模型最终预测了学习者在新问题上的表现。在三个真实世界的数据集上，从六个角度进行了广泛的实验，证明了该模型用于学习者绩效建模的优越性和可解释性。基于KT结果，我们还提出了模型的三个潜在应用。
Knowledge tracing (KT) is a fundamental personalized‐tutoring technique for learners in online learning systems. Recent KT methods employ flexible deep neural network‐based models that excel at this task. However, the adequacy of KT is still challenged by the sparseness of the learners' exercise data. To alleviate the sparseness problem, most of the exiting KT studies are performed at the skill‐level rather than the question‐level, as questions are often numerous and associated with much fewer skills. However, at the skill level, KT neglects the distinctive information related to the questions themselves and their relations. In this case, the models can imprecisely infer the learners' knowledge states and might fail to capture the long‐term dependencies in the exercising sequences. In the knowledge domain, skills are naturally linked as a graph (with the edges being the prerequisite relations between pedagogical concepts). We refer to such a graph as a knowledge structure (KS). Incorporating a KS into the KT procedure can potentially resolve both the sparseness and information loss, but this avenue has been underexplored because obtaining the complete KS of a domain is challenging and labor‐intensive. In this paper, we propose a novel KS‐enhanced graph representation learning model for KT with an attention mechanism (KSGKT). We first explore eight methods that automatically infer the domain KS from learner response data and integrate it into the KT procedure. Leveraging a graph representation learning model, we then obtain the question and skill embeddings from the KS‐enhanced graph. To incorporate more distinctive information on the questions, we extract the cognitive question difficulty from the learning history of each learner. We then propose a convolutional representation method that fuses these disctinctive features, thus obtaining a comprehensive representation of each question. These representations are input to the proposed KT model, and the long‐term dependencies are handled by the attention mechanism. The model finally predicts the learner's performance on new problems. Extensive experiments conducted from six perspectives on three real‐world data sets demonstrated the superiority and interpretability of our model for learner‐performance modeling. Based on the KT results, we also suggest three potential applications of our model.