Central China Normal University Wollongong Joint Institute, Central China Normal University, Wuhan, China.
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China.
PLoS One. 2023 Dec 22;18(12):e0295808. doi: 10.1371/journal.pone.0295808. eCollection 2023.
Knowledge tracing models have gained prominence in educational data mining, with applications like the Self-Attention Knowledge Tracing model, which captures the exercise-knowledge relationship. However, conventional knowledge tracing models focus solely on static question-knowledge and knowledge-knowledge relationships, treating them with equal significance. This simplistic approach often succumbs to subjective labeling bias and lacks the depth to capture nuanced exercise-knowledge connections. In this study, we propose a novel knowledge tracing model called Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing. Our model mitigates the impact of subjective labeling by fine-tuning the skill relation matrix and Q-matrix. Additionally, we employ Graph Convolutional Networks (GCNs) to capture intricate interactions between students, exercises, and skills. Specifically, the Knowledge Relation Importance Rank Calibration method is employed to generate the skill relation matrix and Q-matrix. These calibrated matrices, alongside heterogeneous interactions, serve as input for the GCN to compute exercise and skill embeddings. Subsequently, exercise embeddings, skill embeddings, item difficulty, and contingency tables collectively contribute to an exercise relation matrix, which is then fed into an attention mechanism for predictions. Experimental evaluations on two publicly available educational datasets demonstrate the superiority of our proposed model over baseline models, evidenced by enhanced performance across three evaluation metrics.
知识追踪模型在教育数据挖掘中得到了广泛的关注，例如 Self-Attention Knowledge Tracing 模型，它捕捉了练习与知识之间的关系。然而，传统的知识追踪模型仅关注静态的问题-知识和知识-知识关系，对它们的处理方式是同等重要的。这种简单的方法往往受到主观标签偏差的影响，并且缺乏捕捉细微的练习-知识联系的深度。在本研究中，我们提出了一种名为 Knowledge Relation Rank enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing 的新型知识追踪模型。我们的模型通过微调技能关系矩阵和 Q 矩阵来减轻主观标签的影响。此外，我们还采用了图卷积神经网络（Graph Convolutional Networks，GCNs）来捕捉学生、练习和技能之间复杂的相互作用。具体来说，我们使用 Knowledge Relation Importance Rank Calibration 方法生成技能关系矩阵和 Q 矩阵。这些经过校准的矩阵与异质相互作用一起作为 GCN 的输入，用于计算练习和技能的嵌入。随后，练习嵌入、技能嵌入、项目难度和交叉表共同构成了一个练习关系矩阵，然后将其输入到注意力机制中进行预测。在两个公开的教育数据集上的实验评估表明，我们提出的模型优于基准模型，在三个评估指标上都表现出了更好的性能。
