融合状态关系的知识追踪模型

知识追踪模型建模学习者对每个知识点的状态推断其知识总状态，预测其未来的学习表现。但现有研究在建模知识总状态时，没有融合知识点状态之间的关系，影响了最终的预测效果。针对这一问题，提出一种融合知识点状态关系的知识追踪模型。首先向量化表示数据集中的知识点，构建知识点表示图；其次将知识点表示图扩散至潜式空间以反映其内在结构和本质信息；融合当前时刻的习题与知识点作为引导向量，从知识点表示图的潜式表示中提取知识点状态图；以知识点状态图为基础，推导知识总状态，预测当前习题的表现。通过在三个数据集上对比四个相关模型的实验证明，提出的模型在AUC、ACC和表示质量方面均取得了一定的优势。其中，在 ASSISTments2009数据集上表现最佳，与对比模型中的最优值和最低值比较，AUC分别提升了1.17%、10.57%，ACC分别提升了3.23%、12.17%，表示质量分别提升了1.95%、10.40%。进一步地，可视化地展示了知识点状态及其关系的内部推导过程，以及它们与真实答题结果之间的对应关系，说明模型具备一定的可解释性。同时，将该模型应用于三门课程以预测学生的表现，与相关模型对比取得了更好的结果，说明模型具备一定的实用性。

State relationships incorporated knowledge tracing models

Knowledge tracing models the learner''s state of each knowledge point, infers overall knowledge state, and predicts their future learning performance. However, existing research does not incorporate the relationships between knowledge point states when modeling the overall knowledge state, which affects the final prediction accuracy. To address this problem, this paper proposed a knowledge tracing model that integrated the relationships between knowledge point states. Firstly, it vectorized the knowledge points in the dataset, and constructed a knowledge point representation graph. Secondly, it diffused the knowledge point representation graph to the latent space to reflect its inherent structure and essential information. Thirdly, it fused the current exercises and knowledge points as guiding vectors to extract the knowledge point state graph from the latent representation of the knowledge point representation graph. Fourthly, based on the knowledge point state graph, it derived the overall knowledge state. Through experiments comparing four related models on three datasets, the proposed model demonstrated certain advantages in AUC, ACC, and DOA. Among them, it performed best on the ASSISTments2009 data set. Compared with the optimal value and the lowest value in the comparison model, AUC increased by 1.17% and 10.57% respectively, and ACC increased by 3.23% and 12.17% respectively, indicates quality increased by 1.95% and 10.40% respectively. Furthermore, the internal reason process of the knowledge point state and its relationships are visualized, as well as their correspondence with the actual test results, demonstrating the model''s interpretability. Additionally, when applied to predicting student performances in three courses and compared with related models, the proposed model achieves better results, demonstrating its practicality.