A Learning-path based Supervised Method for Concept Prerequisite Relations Extraction in Educational Data

In educational data mining, concept prerequisite relations extraction determines which concepts need to be learned before learning another concept. It plays a crucial role in pedagogical practices, such as learning path planning and curriculum design. Deep neural networks, especially graph neural networks, have recently made significant strides in concept prerequisite relations extraction. However, existing methods face two primary limitations. (1) Methods with better performance construct heterogeneous complete graphs, leading to higher model complexity and training cost. Meanwhile, the performance of low-complexity methods is inferior to the former. (2) A disregard for temporal context, essential for learning, limits both the performance and the application of these methods. To address these issues, we propose a novel graph-based approach, called Learning-path based Concept Prerequisite Relations Extraction (LCPRE). LCPRE constructs a lightweight sparse graph in a simple manner, which reduces complexity from quadratic to linear and captures the temporal feature through learning-path, a comprehensible learning approach from one concept to another. Experimental results on three benchmark datasets demonstrate that LCPRE outperforms existing methods, establishing a new state-of-the-art in concept prerequisite relations extraction.