Measuring sustainable development goals (SDGs) in higher education through semantic matching

Bibliographic Data

ID4097056
AuthorsDaniel D Prior (0000-0002-4365-2100, UNSW Canberra, University of New South Wales, corresponding author), Sandeep Mysore Seshadrinath (0000-0002-0241-5352, UNSW Business School, University of New South Wales), Michael Zhang (0000-0002-8747-2603, UNSW Canberra, University of New South Wales), Matthew Mccormack (0000-0001-5926-2759, UNSW Canberra, University of New South Wales)
Year2025
Volume50
Issue7
Pages1556-1569
Publication date2025-07-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueStudies in Higher Education (JOURNAL)
Journal identifiersISSN: 0307-5079 • E-ISSN: 1470-174X
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/03075079.2024.2386625
OpenAlexW4401218799
LanguageEN
Citations received7
References cited49

Over the past twenty years, higher education institutions (HEIs) including universities, polytechnics, and vocational education providers, have become major conduits for the teaching and dissemination of the sustainable development goals (SDGs). The Times Higher Education Impact rankings, other rankings schemes and external accreditation requirements also pressure HEIs to report their progress towards the SDGs periodically. For large HEIs, reporting requirements can equate to significant resource burdens, and these can be insurmountable. In this paper, we develop a novel approach to measuring SDG content in the course descriptions and learning outcomes of 5461 courses offered by a major Australian university. Our method utilises semantic matching, an AI-based technique that assesses similarities between key terms in the dataset (including course descriptions and course learning outcomes) and those in each SDG. Our results achieve a 75% fit to the data and show that Quality Education (SDG 4) and Partnerships for the Goals (SDG 17) are the most common SDGs covered at the case university and that these reflect course learning outcomes that focus on pedagogy. Where SDGs are more domain-specific, there is a much less consistent coverage across the university, and this is observable at the Faculty and School levels. This highlights that it is not possible or desirable to cover all SDG content in all educational offerings consistently. Our algorithm is a potential solution for larger HEIs that seek to capture and report the SDG contributions of their education offerings holistically using existing textual datasets, without the need for excessive resource investments

Accreditation · Business · Economic growth · Economics · Higher education · Knowledge management · Mathematics education · Medical education · Pedagogy · Political science · Sociology · Statistics · Sustainable development · Vocational education · Computer Science · Mathematics · Medicine · Psychology · Sustainability in Higher Education

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