Author ORCID Identifier

0000-0002-9759-9712

Document Type

Article

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING

Publication Details

Frontiers in Sustainability, 03 August 2022

doi:10.3389/frsus.2022.909676

Supplementary material for this article can be found at https://www.frontiersin.org/articles/10.3389/frsus.2022.909676/full#supplementary-material

Abstract

The United Nations (UN) considers universities to be key actors in the pursuit of the Sustainable Development Goals (SDGs). Yet, efforts to evaluate the embeddedness of the SDGs in university curricula tend to rely on manual analyses of curriculum documents for keywords contained in sustainability lexica, with little consideration for the diverse contexts of such keywords. The efficacy of these efforts, relying on expert co-elicitation in both subject-matter contexts and sustainability, suffers from drawbacks associated with keyword searches, such as limited coverage of key concepts, difficulty in extracting intended meaning and potential for greenwashing through “keyword stuffing.” This paper presents a computational technique, derived from natural language processing (NLP), which develops a sustainability lexicon of root keywords (RKs) of relative importance by adapting the Term Frequency–Inverse Document Frequency (TF-IDF) method to a corpus of sustainability documents. Identifying these RKs in module/course descriptors offers a basis for evaluating the embeddedness of sustainability in 5,773 modules in a university's curricula using classification criteria provided by the Association for the Enhancement of Sustainability in Higher Education's (AASHE). Applying this technique, our analysis of these descriptors found 286 modules (5%) to be “sustainability focused” and a further 769 modules (13%) to be “sustainability inclusive,” which appear to address SDGs 1, 17, 3, 7, and 15. Whilst this technique does not exploit machine learning methods applied to large amounts of trained data, it is, nevertheless, systemic and evolutive. It, therefore, offers an appropriate trade-off, which faculty with limited analytics skills can apply. By supplementing existing approaches to evaluating sustainability in the curriculum, the developed technique offers a contribution to benchmarking curricular alignment to the SDGs, facilitating faculty to pursue meaningful curricular enhancement, whilst complying with sustainability reporting requirements. The technique is useful for first-pass analyses of any university curriculum portfolio. Further testing and validation offer an avenue for future design-science research.

DOI

https://doi.org/10.3389/frsus.2022.909676

Funder

Higher Education Authority of Ireland, SFI Research Centre for Energy, Climate and Marine, European University of Technology

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