Measuring Sustainable Development Goals (SDGs) in Higher Education through Semantic Matching
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| Title: | Measuring Sustainable Development Goals (SDGs) in Higher Education through Semantic Matching |
|---|---|
| Language: | English |
| Authors: | Daniel D. Prior (ORCID |
| Source: | Studies in Higher Education. 2025 50(7):1556-1569. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Sustainable Development, Higher Education, Educational Objectives, Measurement, Foreign Countries, Universities, Learning Objectives, Undergraduate Study, Graduate Study, Course Descriptions, Methods, Reports |
| Geographic Terms: | Australia |
| DOI: | 10.1080/03075079.2024.2386625 |
| ISSN: | 0307-5079 1470-174X |
| Abstract: | 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1502757 |
| Database: | ERIC |
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| Abstract: | 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. |
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| ISSN: | 0307-5079 1470-174X |
| DOI: | 10.1080/03075079.2024.2386625 |
