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.

IntroductionSDG 4.7 mandates university contributions to the United Nations (UN) Sustainable Development Goals (SDGs) through their education provisions. Hence, universities increasingly assess their curricular alignment to the SDGs. A common approach to the assessment is to identify keywords associated with specific SDGs and to analyze for their presence in the curriculum. An inherent challenge is associating the identified keywords as used in the diverse set of curricular contexts to relevant sustainability indicators; hence, the urgent need for more systematic assessment as SDG implementation passes its mid-cycle.MethodIn this study, a more nuanced technique was evaluated with notable capabilities for: (i) computing the importance of keywords based on the term frequency-inverse document frequency (TF-IDF) method; (ii) extending this computation to the importance of courses to each SDG and; (iii) correlating such importance to a statistical categorization based on the Association for the Advancement of Sustainability in Higher Education (AASHE) criteria. Application of the technique to analyze 5,773 modules in a university's curriculum portfolio facilitated categorization of the modules/courses to be “sustainability-focused” or “sustainability-inclusive.” With the strategic objective of systematically assessing the sustainability content of taught curricula, it is critical to evaluate the precision and accuracy of the computed results, in order to attribute text with the appropriate SDGs and level of sustainability embeddedness. This paper evaluates this technique, comparing its results against a manual and labor-intensive interpretation of expert informed assessment of sustainability embeddedness on a random sample of 306 modules/courses.Results and discussionExcept for SDGs 1 and 17, the technique exhibited a reasonable degree of accuracy in predicting module/course alignment to SDGs and in categorizing them using AASHE criteria. Whilst limited to curricular contexts from a single university, this study indicates that the technique can support curricular transformation by stimulating enhancement and reframing of module/course contexts through the lens of the SDGs.

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