知识追踪是教育数据挖掘领域中的一项关键任务，旨在建模学生随时间不断变化的知识状态，以推断学生对知识点的掌握程度。然而，现有知识追踪方法大多忽略了基于学生-习题-知识点关系构造的学生-知识点空间的不可靠性和高维稀疏性，并且未结合学生在习题上的作答情况生成习题的可靠响应表示。针对上述问题，提出可靠响应表示增强的知识追踪方法。具体地，首先根据学生的作答记录细粒度地划分学生-习题空间，并基于习题-知识点空间得到不同划分下的学生-知识点空间；其次，从学生-知识点空间的相对可靠性和绝对可靠性2方面获得学生-知识点空间的可靠性，并采用维数约减方法得到可靠且低维的学生-知识点空间；再次，结合学生在习题上的作答情况和习题表示方法得到习题在2种作答下的可靠响应表示；最后，利用长短期记忆网络和得到的可靠响应表示评估学生在不同时刻的知识状态。在4个真实数据集上验证了本文方法的有效性和可解释性。

Knowledge Tracing (KT) is a key task in educational data mining, aiming at modeling students changing knowledge states over time to infer students proficiency on concepts. However, most of existing knowledge tracing methods ignore the reliability and high-dimensional sparsity of the student-concept space based on the student-exercise-concept relationship, and do not combine the students response to the exercise to generate a reliable response representation. To address the above issues, a reliable response representation enhanced knowledge tracing method is proposed. Specifically, firstly, the student-exercise space is divided into fine-grained student-exercise spaces based on the student’s response records, and different student-concept spaces are obtained based on the exercise- concept space; secondly, the reliability of the student-concept space is obtained from both the relative reliability and absolute reliability of the student-concept space, and a reliable and low-dimensional student-concept space is obtained using dimensionality reduction methods; thirdly, the reliable response representation of the exercise is obtained by combining the students response to the exercise and the exercise representation method under two response conditions; finally, the students knowledge state at different timesteps is evaluated using a long short-term memory network and the obtained reliable response representation. Experimental results on four real datasets demonstrate the effectiveness and interpretability of the proposed method.

相关文章

[1]	郭艺, 何廷年, 李爱斌, 毛君宇. 融合GA-CART和Deep-IRT的知识追踪模型[J]. 计算机工程与科学, 2023, 45(09): 1691-1700.
[2]	王堃, 郑晨, 张立中, 陈志刚. 一种基于SARIMA-LSTM模型的电网主机负载预测方法[J]. 计算机工程与科学, 2022, 44(11): 2064-2070.
[3]	王文涛, 马慧芳, 舒跃育, 贺相春. 基于上下文表示的知识追踪方法[J]. 计算机工程与科学, 2022, 44(09): 1693-1701.