VÍTA, Martin, Martin KOMENDA and Andrea POKORNÁ. Exploring Medical Curricula Using Social Network Analysis Methods. Online. In Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems. Warsaw, Los Alamitos: Polskie Towarzystwo Informatyczne, IEEE, 2015, p. 297-302. ISBN 978-83-60810-66-8. Available from: https://dx.doi.org/10.15439/2015F312.
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Basic information
Original name Exploring Medical Curricula Using Social Network Analysis Methods
Authors VÍTA, Martin (203 Czech Republic, belonging to the institution), Martin KOMENDA (203 Czech Republic, guarantor, belonging to the institution) and Andrea POKORNÁ (203 Czech Republic, belonging to the institution).
Edition Warsaw, Los Alamitos, Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, p. 297-302, 6 pp. 2015.
Publisher Polskie Towarzystwo Informatyczne, IEEE
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14110/15:00084515
Organization unit Faculty of Medicine
ISBN 978-83-60810-66-8
ISSN 2300-5963
Doi http://dx.doi.org/10.15439/2015F312
UT WoS 000376494300037
Keywords in English analysis; educational data
Tags EL OK, firank_B, podil
Changed by Changed by: Ing. Mgr. Věra Pospíšilíková, učo 9005. Changed: 2/11/2015 13:22.
Abstract
This contribution demonstrates how to apply concepts of social network analysis on educational data. The main aim of this approach is to provide a deeper insight into the structure of courses and/or other learning units that belong to a given curriculum in order to improve the learning process. The presented work can help us discover communities of similar study disciplines (based on the similarity measures of textual descriptions of their contents), as well as identify important courses strongly linked to others, and also find more independent and less important parts of the curriculum using centrality measures arising from the graph theory and social network analysis.
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