文献类型：期刊文献

英文题名：A Novel Deep Knowledge Tracing Model with Problem Complexity and State Stability

作者：Li, Xinxin[1];Luo, Fei[1];Ouyang, Junhai[1];Pino, Luis Rojas[2];Li, Wenhai[3];Ding, Weichao[1];Gu, Chunhua[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Meilong Rd 130, Shanghai 200237, Peoples R China;[2]Univ San Sebastian, Sch Engn, Recoleta 7, Santiago 8320000, Chile;[3]Wuhan Univ, Sch Comp Sci, Bayi Rd 299, Wuhan 430072, Hubei, Peoples R China

年份：2025

卷号：35

期号：5

起止页码：3270

外文期刊名：INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION

收录：EI(收录号：20252918803452);WOS:【ESCI(收录号:WOS:001529112700001)】；

语种：英文

外文关键词：Entropy-based complexity; Knowledge tracing; Contrastive learning in education; Knowledge state stability

摘要：Accurately modeling learners' knowledge states is crucial for advancing personalized intelligent education. However, existing knowledge tracing methods often overlook the influence of problem complexity on students' answering strategies, leading to unstable and inaccurate predictions. To address these challenges, we propose PSKT, a novel deep knowledge tracing model that integrates problem complexity and state stability. PSKT incorporates (1) a quantitative representation of problem complexity using information entropy and accuracy, (2) dynamic adjustments of knowledge states based on perceived and actual problem difficulty, and (3) a contrastive learning-based mechanism to stabilize predictions and reduce information bias. Experiments on four public datasets-ASSIST2009, ASSIST2015, Algebra05, and Statics2011-demonstrate that PSKT outperforms six state-of-the-art models, achieving up to 3.67% higher AUC and improved robustness across all datasets. These results highlight the potential of PSKT to enhance predictive performance and provide more reliable insights into students' learning processes, making it a valuable tool for personalized education systems.