A Data-Driven Transition Model from Stem to UHV STEM: Predictive Analytics for Holistic Student Outcomes Using Clustering

Abstract

Sustainable development is always possible through imparting and acquiring quality education, which should be oriented towards value-based skill development. The Science, Technology, Engineering, and Mathematics (STEM) education model adopted by countries definitely enhances the skills across the generations from the last many years, but this model has become a failure in giving a sustainable, holistic life to the people. For the alignment towards the sustainable development goals (SDG), the education model ought to be Universal Human Values (UHV)-based STEM education (UHV-STEM), which will integrate the values with the skill acquisition.So, this study will provide a path to move from STEM education to UHV-STEM education. In this, a data-driven analysis through using clustering of Educational Data Mining (EDM) has been adopted and analyzed a model of education that is UHV-STEM. The study identifies patterns and correlations that demonstrate the UHV impact on STEM education. K-means, DBSCAN, and other clustering algorithms are used to group data, providing insights into the effectiveness of UHV-STEM. The findings validate the need for incorporating UHV into STEM education, leading to a more inclusive, value-driven learning environment.