{"title":"Mapping the landscape of a wide interdisciplinary curriculum: a network analysis of a Korean university and the lessons learnt","authors":"H. Ryu, Jieun Kim","doi":"10.1017/dsj.2022.1","DOIUrl":null,"url":null,"abstract":"Abstract Interdisciplinary programmes have become common in universities and research groups’ curricula. This study conducted a network analysis on a Korean university’s undergraduate curriculum and used several visualisation tools to assess keywords across courses and departments, revealing epistemological distances between the courses/departments and their concepts of study. This data-driven methodology defined the characteristics of close or neighbouring departments, making it possible to implement narrow interdisciplinarity through common subjects within the courses. Interestingly, a further projected network could determine the implicit relations between departments that are not considered close, which would make it possible to implement a wide interdisciplinary curriculum. The data-driven network analysis conducted in this study contributes to searching for new programmes for specific levels of interdisciplinarity on an empirical basis.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":" ","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Design Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dsj.2022.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Interdisciplinary programmes have become common in universities and research groups’ curricula. This study conducted a network analysis on a Korean university’s undergraduate curriculum and used several visualisation tools to assess keywords across courses and departments, revealing epistemological distances between the courses/departments and their concepts of study. This data-driven methodology defined the characteristics of close or neighbouring departments, making it possible to implement narrow interdisciplinarity through common subjects within the courses. Interestingly, a further projected network could determine the implicit relations between departments that are not considered close, which would make it possible to implement a wide interdisciplinary curriculum. The data-driven network analysis conducted in this study contributes to searching for new programmes for specific levels of interdisciplinarity on an empirical basis.