Evaluation of UN SDG-related formal learning activities in a university common core curriculum
International Journal of Sustainability in Higher Education
ISSN: 1467-6370
Article publication date: 11 December 2023
Issue publication date: 25 April 2024
Abstract
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.
Keywords
Acknowledgements
The authors would like to thank Mr Jeremy T.D. Ng, Professor Gray Kochhar-Lindgren, Dr Jack Tsao and Ms Xinyi Liang for their time and effort on commenting the study. The comments have led to a substantial improvement on the quality of the report. The authors also want to express their gratitude to Ms Xiaodong Hu and Ms. Xue Qian for their help in training data collection and modeling. In addition, we highly appreciate Mr Chun Yin Cham for their support in sending the teachers’ surveys to the teaching staff.
Geolocation information: Pokfulam Road, Hong Kong.
Disclosure statement: The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding details: This research was supported by the University Grants Committee (UGC) of Hong Kong under the UGC Special Grant for Strategic Development of Virtual Teaching and Learning and UGC Teaching Development and Language Enhancement Grant.
Statement on ethics: Informed consent was obtained from all study participants. All collected data were treated confidentially. This research has been approved by the Human Research Ethics Committee of the University of Hong Kong (EA210317).
Data availability statement: Coding and data for classifications can be found in the Github repository (https://github.com/HKU-SDG-Classification/). Results of teachers’ surveys can be provided upon request.
Data disposition: Coding and data for classifications can be found in the Github repository (https://github.com/HKU-SDG-Classification/).
Citation
Lei, C.-U., Chan, W. and Wang, Y. (2024), "Evaluation of UN SDG-related formal learning activities in a university common core curriculum", International Journal of Sustainability in Higher Education, Vol. 25 No. 4, pp. 821-837. https://doi.org/10.1108/IJSHE-02-2023-0050
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited