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Estimating Knowledge Category Coverage by Courses Based on Centrality in Taxonomy
Yiling DAI Masatoshi YOSHIKAWA Yasuhito ASANO
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2020/05/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Data Engineering and Information Management)
knowledge category coverage, course, taxonomy, centrality,
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The proliferation of Massive Open Online Courses has made it a challenge for the user to select a proper course. We assume a situation in which the user has targeted on the knowledge defined by some knowledge categories. Then, knowing how much of the knowledge in the category is covered by the courses will be helpful in the course selection. In this study, we define a concept of knowledge category coverage and aim to estimate it in a semi-automatic manner. We first model the knowledge category and the course as a set of concepts, and then utilize a taxonomy and the idea of centrality to differentiate the importance of concepts. Finally, we obtain the coverage value by calculating how much of the concepts required in a knowledge category is also taught in a course. Compared with treating the concepts uniformly important, we found that our proposed method can effectively generate closer coverage values to the ground truth assigned by domain experts.