An evaluation of a computational technique for measuring the embeddedness of sustainability in the curriculum aligned to AASHE-STARS and the United Nations Sustainable Development Goals - Lemarchand, Philippe; MacMahon, Cormac; McKeever, Mick; Owende, Philip (Frontiers in Sustainability, 2023)
SDG 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 analyse 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. In 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 categorisation based on the Association for the Enhancement of Sustainability in Higher Education (AASHE) criteria. Application of the technique to analyse 5,773 modules in a university’s curriculum portfolio facilitated categorisation 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 labour-intensive interpretation of expert informed assessment of sustainability embeddedness on a random sample of 306 modules/courses. Except for SDGs 1 and 17 aside, the technique exhibited a reasonable degree of accuracy in predicting module/course alignment to SDGs and in categorising 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.