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Discovering Knowledge-Point Importance From the Learning-Evaluation Data
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DOI：10.4018/IJDET.302012.png)
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摘要
En 中文
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.
Keyword:
Concept Map
Distance Education
Learning Analysis
Learning-Evaluation Data
Online Courses
Personalized Difference
Personalized Teaching
Random Walk
期刊
IF:
0.7
论文数:
83
被引数:
233
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引用论文
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NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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