{"title":"Exploring medical curricula using social network analysis methods","authors":"Martin Vita, M. Komenda, A. Pokorná","doi":"10.15439/2015F312","DOIUrl":null,"url":null,"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.","PeriodicalId":276884,"journal":{"name":"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15439/2015F312","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
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.