IEICE TRANSACTIONS on Information
Estimating Knowledge Category Coverage by Courses Based on Centrality in Taxonomy
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.5 pp.928-938
- Publication Date
- 2020/05/01
- Publicized
- 2019/12/26
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019DAP0002
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Yiling DAI
Kyoto University
Masatoshi YOSHIKAWA
Kyoto University
Yasuhito ASANO
Kyoto University
Keyword
Latest Issue
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Yiling DAI, Masatoshi YOSHIKAWA, Yasuhito ASANO, "Estimating Knowledge Category Coverage by Courses Based on Centrality in Taxonomy" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 928-938, May 2020, doi: 10.1587/transinf.2019DAP0002.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019DAP0002/_p
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@ARTICLE{e103-d_5_928,
author={Yiling DAI, Masatoshi YOSHIKAWA, Yasuhito ASANO, },
journal={IEICE TRANSACTIONS on Information},
title={Estimating Knowledge Category Coverage by Courses Based on Centrality in Taxonomy},
year={2020},
volume={E103-D},
number={5},
pages={928-938},
abstract={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.},
keywords={},
doi={10.1587/transinf.2019DAP0002},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Estimating Knowledge Category Coverage by Courses Based on Centrality in Taxonomy
T2 - IEICE TRANSACTIONS on Information
SP - 928
EP - 938
AU - Yiling DAI
AU - Masatoshi YOSHIKAWA
AU - Yasuhito ASANO
PY - 2020
DO - 10.1587/transinf.2019DAP0002
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - 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.
ER -
English
