Hypergraph-Based De-Conjecture Knowledge Tracing for Intelligent Educational Services

Chenyu Hou, Aochen You, Bin Cao, Dongjing Wang, Jing Fan

Abstract: Knowledge Tracing (KT) has emerged as a critical enabling technology for intelligent educational services, aiming to predict learners' knowledge states by analyzing their interaction histories. As personalized education evolves into a service-oriented paradigm, KT plays a pivotal role in optimizing service delivery, tailoring content recommendations, and enhancing learner engagement through adaptive learning services. However, existing KT methods face significant computational and modeling challenges, such as the insufficient integration of multi-dimensional question attributes, the inability to dynamically model knowledge decay in service-driven environments, and the neglect of conjectural bias, which leads to overestimated learner performance and suboptimal service outcomes. To address these challenges, we propose HDKT (Hypergraph-based De-conjecture Knowledge Tracing), a novel KT framework designed for service-oriented educational platforms. First, the HDKT approach incorporates a Hypergraph-Enhanced Question Representation Module, which explores multiple attributes of questions to obtain comprehensive representations, improving data fidelity for personalized learning services. Second, HDKT utilizes a novel Knowledge State Evolution Module to trace students' knowledge states precisely. This module is a sequential neural network that is augmented with a session-based forgetting mechanism to dynamically model knowledge decay and a de-conjecture mechanism to adjust learners' knowledge states by mitigating conjectural influences, ensuring more accurate assessments. Extensive experiments on three benchmark educational datasets demonstrate that HDKT significantly outperforms eleven state-of-the-art methods in terms of AUC, RMSE, and ACC. The results highlight HDKT's potential as a robust and scalable solution for AI-powered, service-oriented educational platforms, paving the way for more adaptive and personalized learning services. The full code and data can be found at https://anonymous.4open.science/r/HDKT-Code.

External IDs:dblp:conf/icws/HouYCWF25