CLKT: Optimizing Cognitive Load Management in Knowledge Tracing

With the rise of online adaptive learning, Knowledge Tracing (KT) has become an indispensable component of online education systems. KT assesses the knowledge level of each learner by tracing their learning activities. Managing cognitive load is crucial in the learners' cognitive process; too low a load may lead to a lack of concentration, while excessively high cognitive load can impede information processing. In pursuit of an ideal learning model, this paper proposes the Cognitive Load-based Knowledge Tracing (CLKT) model. This model employs a Heterogeneous Cognitive Graph Convolutional Network (HCGCN) to extract learners' knowledge representations and establish connections between learning tasks or instructional resources and learners, providing the model with interpretable learning path recommendations. By introducing the Attention Concentration (AC) mechanism, the model dynamically processes information and efficiently integrates it into learners' knowledge structures to maintain an appropriate cognitive load level, thus maximizing effective learning. Experiments conducted on the ASSISTMENTS dataset, which contains real-world student interaction data from an online tutoring system, focus on studying the impact of different cognitive loads on the learning process. The experimental results delve into the effects of cognitive load on learner performance, ensuring that learners can engage in learning with appropriate pace and difficulty, thereby enhancing their learning outcomes.