CPRL (Concept Prerequisite Relation Learning) is a method introduced by Jia, Shen, Tang, Sun, and Lu at NAACL 2021 for predicting prerequisite relations between educational concepts. It builds a heterogeneous graph over concepts together with auxiliary node types such as learning objects, applies a heterogeneous graph neural network to obtain concept representations, and feeds those representations together with concept-pairwise features to a classifier that predicts whether one concept is a prerequisite of another. The authors further extend CPRL to weakly supervised settings, leveraging learning-object dependencies and data programming to generate training labels when explicit concept-pair annotations are scarce. CPRL reports state-of-the-art results across four prerequisite-relation benchmarks and is a standard recent reference in the prerequisite-learning literature alongside Liang et al. (2015) and Roy et al. (2019).