Evaluation of UN SDG-related formal learning activities in a university common core curriculum

Bibliographic Data

ID21720771
AuthorsChi‐Un Lei (0000-0002-6189-6424, University of Hong Kong), Chi-Un Lei, Wincy Chan (Chinese University of Hong Kong), Yuyue Wang (0000-0002-6598-7753, University of Hong Kong)
Year2024
Volume25
Issue4
Pages821-837
Publication date2024-04-25
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Sustainability in Higher Education (JOURNAL)
Journal identifiersISSN: 1467-6370 • E-ISSN: 1758-6739
PublisherEmerald (PUBLISHER)
DOI10.1108/ijshe-02-2023-0050
OpenAlexW4389481920
LanguageEN
Citations received3
References cited12

Purpose Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs. Design/methodology/approach In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions. Findings The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data. Research limitations/implications The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs. Originality/value The proposed approach explores the possibility of using machine learning for SDG classifications in scale

Curriculum · Machine learning · Mathematics education · Pedagogy · Syllabus · Computer Science · Environmental Education and Sustainability · Mathematics · Service-Learning and Community Engagement · Sustainability in Higher Education · Artificial Intelligence

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