Evaluation of Sustainable Development Business Strategies of Higher Education Institutions using Data Mining and Machine Learning Techniques

Kizhakechethipuza, Mary Ann Antony (2025) Evaluation of Sustainable Development Business Strategies of Higher Education Institutions using Data Mining and Machine Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Sustainable Development Goals when introduced by United Nations provide a much needed structure to streamline sustainability-based efforts. Higher Education Institutions (HEIs) provide a conducive environment where research related to sustainability can flourish. They are in a unique position to lead by example and build knowledge on sustainability that can be harnessed by future generations. This project pursues the direction of using THE-IR data to evaluate the sustainable development-based strategies of HEI using well established machine learning models like KMeans Clustering, Logistic Regression and Random Forest Classifier. The feasibility of pursuing the addition of a technological dimension of sustainability is also addressed using Bibliometric analysis. Key findings include that THE-IR data can be used in evaluation of sustainability-based strategies as it works well with traditional models employed in the education data mining domain. Digital Transformation emerged as a major theme surrounding technological sustainability. More research is needed to identify if there is feasibility in including the technological dimension to the traditional triple bottom line framework.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rustam, Furqan UNSPECIFIED
Subjects:	L Education > LB Theory and practice of education > LB2300 Higher Education Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	01 Jul 2026 11:15
Last Modified:	01 Jul 2026 11:15
URI:	https://norma.ncirl.ie/id/eprint/9432

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