File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Evaluation of UN SDG-related formal learning activities in a university common core curriculum

TitleEvaluation of UN SDG-related formal learning activities in a university common core curriculum
Authors
KeywordsClassification
Curriculum analysis
Machine-learning classification
SDGs
Sustainable development education classification
Sustainable development goals
Issue Date11-Dec-2023
PublisherEmerald
Citation
International Journal of Sustainability in Higher Education, 2023 How to Cite?
Abstract

Purpose – Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

Design/methodology/approach – In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification.


Persistent Identifierhttp://hdl.handle.net/10722/340094
ISSN
2023 Impact Factor: 3.0
2023 SCImago Journal Rankings: 0.830
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLei, Chi-Un-
dc.contributor.authorChan, Wincy-
dc.contributor.authorWang, Yuyue-
dc.date.accessioned2024-03-11T10:41:37Z-
dc.date.available2024-03-11T10:41:37Z-
dc.date.issued2023-12-11-
dc.identifier.citationInternational Journal of Sustainability in Higher Education, 2023-
dc.identifier.issn1467-6370-
dc.identifier.urihttp://hdl.handle.net/10722/340094-
dc.description.abstract<p><b>Purpose</b> – Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.</p><p><b>Design/methodology/approach</b> – In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification.</p>-
dc.languageeng-
dc.publisherEmerald-
dc.relation.ispartofInternational Journal of Sustainability in Higher Education-
dc.subjectClassification-
dc.subjectCurriculum analysis-
dc.subjectMachine-learning classification-
dc.subjectSDGs-
dc.subjectSustainable development education classification-
dc.subjectSustainable development goals-
dc.titleEvaluation of UN SDG-related formal learning activities in a university common core curriculum-
dc.typeArticle-
dc.description.naturepreprint-
dc.identifier.doi10.1108/IJSHE-02-2023-0050-
dc.identifier.scopuseid_2-s2.0-85178905706-
dc.identifier.isiWOS:001116598900001-
dc.identifier.issnl1467-6370-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats