Chen Liang and collaborators contributed two foundational works to the concept-prerequisite-learning line cited in the paper. The earlier work, Measuring Prerequisite Relations Among Concepts (Liang, Wu, Huang, Giles, EMNLP 2015), introduces a single link-based metric called Reference Distance (RefD) that measures how asymmetrically two concepts cite each other in a reference network, and shows that this unsupervised metric outperforms then-current supervised classifiers across seven domains on two evaluation datasets. The follow-up work, Recovering Concept Prerequisite Relations from University Course Dependencies (Liang, Ye, Wu, Pursel, Giles, AAAI 2017), formalizes the problem of recovering concept-level prerequisite relations from observed course-level prerequisite dependencies, proposes an optimization-based framework that jointly models concept relations and course-concept memberships, and releases the first real dataset built from university computer-science course listings. Together these papers establish a core supply of prerequisite-relation methods and benchmarks that subsequent prerequisite-learning work (e.g., LectureBank, MOOC-CS, hetGNN-style approaches) builds on or compares against, and they are part of the substrate the paper reuses rather than re-derives.