Article

Unveiling Educational Archetypes of High Achievers: A K-Prototypes Clustering Analysis of Academic Pathways

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Abstract

While individual stories of successful people are often celebrated, a systematic, data-driven understanding of common educational pathways to high achievement remains underdeveloped. This is partly due to the analytical challenges of studying datasets with mixed numerical and categorical attributes. This study addresses this gap by identifying distinct educational archetypes from a diverse group of 108 high-achieving individuals using a computational approach. The research leverages the K-Prototypes clustering algorithm, a method specifically designed for mixed-attribute data, to analyze a dataset detailing the educational backgrounds of successful people. A comprehensive preprocessing pipeline was developed to clean, standardize, and transform features such as degree, field of study, university ranking, and GPA into a format suitable for clustering. The optimal number of clusters was determined using the Elbow method, balanced with a focus on the practical interpretability of the resulting groups. The analysis successfully identified four distinct and meaningful educational archetypes: (1) The Elite US STEM Achiever, characterized by advanced degrees from top-ranked American universities; (2) The Elite US Business & Law Professional, a similar high-prestige path focused on MBA and JD degrees; (3) The Global Entrepreneurial Path, a more internationally diverse route where institutional prestige and formal awards were less critical; and (4) The International STEM Scholar, defined by scholarship-funded education at a global range of institutions. The primary conclusion is that success is not predicated on a single educational model. The existence of these varied archetypes challenges monolithic definitions of prestigious education and provides a more nuanced understanding of the diverse foundations of high achievement. These findings have significant implications for the development of AI-driven educational guidance systems, which can be enhanced to provide more personalized and globally-aware recommendations.

Keywords: AI in Education, Clustering, Educational Data Mining, K-Prototypes, Success Pathways

How to Cite:

Mashao, D. & Olanipekun, A., (2025) “Unveiling Educational Archetypes of High Achievers: A K-Prototypes Clustering Analysis of Academic Pathways”, Artificial Intelligence in Learning 1(3), 258-270. doi: https://doi.org/10.63913/ail.v1i3.35