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Pan et al. (2017) Prerequisite Relation Learning for Concepts in MOOCs

Prerequisite Relation Learning for Concepts in MOOCs is the canonical method paper by Pan, Li, Li, and Tang (ACL 2017) that formulates and supervises concept-level prerequisite-relation prediction over Massive Open Online Course materials. Course concepts are first extracted from Coursera lecture captions (in Data Structures & Algorithms and Machine Learning), and each ordered pair of concepts is represented by contextual, structural, and semantic features, including two link-style cues introduced by the paper: a video reference distance and a sentence reference distance that exploit the order in which concepts are first introduced across videos and sentences. A binary classifier (with logistic regression and SVM variants) is trained on manually labeled concept pairs to predict whether concept AA is a prerequisite of concept BB. The work establishes the supervised feature-based baseline for MOOC concept-prerequisite learning and releases the MOOC-CS dataset that the same paper introduces and that later work treats as the standard MOOC prerequisite benchmark.

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Updated 2026-05-18

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Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls

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