Automated Mapping of Environmental Higher Education Ranking Systems Indicators to SDGs Indicators using Natural Language Processing and Document Similarity

Buzaboon, Anwaar; Alboflasa, Hanan; Alnaser, W.E.; Shatnawi, Safwan; Albinali, Khawla

doi:10.1109/3ict53449.2021.9581693

Cited by 2 publications

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“…A more systematic approach could deploy natural language processing (NLP) techniques to interrogate curricular texts based on a lexica of keywords or phrases associated with the SDGs (e.g., Varshney and Mojsilovic, 2019;Buzaboon et al, 2021;Yang and Cormican, 2021) and then to apply machine learning to iterate better search techniques (Hajikhani and Suominen, 2022). The central challenge with this approach is in bridging the gap between keywords and meaning during the mapping process.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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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