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As students in online courses usually show differences in their cognitive levels and lack communication with teachers, it is difficult for teachers to grasp student perceptions of the importance of knowledge-points and to develop personalized teaching. Though recent studies have paid attention to this topic, existing methods fail to calculate the importance of every knowledge-point for each student. Moreover, some studies are based on expert analysis, are not data-driven, and hence, are inapplicable to large-scale online scenarios. To address these issues, this article proposes a personal topic rank (PTR) as a solution, which links students and concepts to generate a personalized knowledge concept map. Then, the authors present a novel PTR method to calculate the importance of knowledge-points, wherein student mastery of knowledge-points, student understanding, and the knowledge-point itself are considered simultaneously. This article conducts extensive experiments on a real-world dataset to demonstrate that the method can achieve better results than baselines.

Discovering Knowledge-Point Importance From the Learning-Evaluation Data