Experimental results and performance comparison.

Knowledge tracing is a technology that models students’ changing knowledge state over learning time based on their historical answer records, thus predicting their learning ability. It is the core module that supports the intelligent education system. To address the problems of sparse input data, lack of interpretability and weak capacity to capture the relationship between exercises in the existing models, this paper build a deep knowledge tracing model DKVMN&MRI based on the Dynamic Key-Value Memory Network (DKVMN) that incorporates multiple relationship information including exercise-knowledge point relations, exercise-exercise relations, and learning-forgetting relations. In the model, firstly, the Q-matrix is utilized to map the link between knowledge points and exercises to the input layer; secondly, improved DKVMN and LSTM are used to model the learning process of learners, then the Ebbinghaus forgetting curve function is introduced to simulate the process of memory forgetting in learners, and finally, the prediction strategies of Item Response Theory (IRT) and attention mechanism are used to combine the similarity relationship between learners’ knowledge state and exercises to calculate the probability that learners would correctly respond during the subsequent time step. Through extensive experiments on three real-world datasets, we demonstrate that DKVMN&MRI has significant improvements in both AUC and ACC metrics contrast with the latest models. Furthermore, the study provides explanations at both the exercise level and learner knowledge state level, demonstrating the interpretability and efficacy of the proposed model.

知识追踪（Knowledge Tracing）是一类基于学生历史答题记录，对其学习过程中动态变化的知识状态进行建模，进而预测其学习能力的技术，亦是智能教育系统的核心支撑模块。针对现有模型存在的输入数据稀疏、可解释性不足以及难以捕捉习题间关联关系等问题，本文基于动态键值记忆网络（Dynamic Key-Value Memory Network，DKVMN）构建了融合习题-知识点关联、习题-习题关联以及学习-遗忘关联等多维度关系信息的深度知识追踪模型DKVMN&MRI。该模型首先利用Q矩阵（Q-matrix）将知识点与习题之间的映射关系引入输入层；其次，改进后的DKVMN与长短期记忆网络（Long Short-Term Memory，LSTM）被用于建模学习者的学习过程，并引入艾宾浩斯遗忘曲线（Ebbinghaus Forgetting Curve）函数模拟学习者的记忆遗忘规律；最后，结合项目反应理论（Item Response Theory，IRT）与注意力机制的预测策略，将学习者知识状态与习题间的相似性关系进行融合，计算学习者在后续时间步内正确作答的概率。通过在三个真实世界数据集上开展的大量对比实验，本文证明DKVMN&MRI在受试者工作特征曲线下面积（Area Under Curve，AUC）与准确率（Accuracy，ACC）两项指标上均较当前最新模型取得了显著提升。此外，本研究分别从习题层面与学习者知识状态层面给出了解释，验证了所提模型的可解释性与有效性。