Application of Non-Supervised Learning Tools and Visualization Techniques to Understand the Intrinsic Freshmen Enrollment Segmentation: An application to freshmen engineering students.
Identifying significant subgroups among first-year students is crucial for designing educational policies that foster their academic and personal development. This study presents a data-driven methodology to segment first-year students using sociodemographic factors, admission test scores, and initi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2025-01-01
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| Series: | Research in Statistics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/27684520.2024.2433290 |
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| Summary: | Identifying significant subgroups among first-year students is crucial for designing educational policies that foster their academic and personal development. This study presents a data-driven methodology to segment first-year students using sociodemographic factors, admission test scores, and initial academic performance. Data from 7,866 engineering students enrolled at a Chilean university between 2005 and 2017 were analyzed. By applying Self-Organizing Maps (SOM) in combination with the k-means algorithm, our methodological approach enables the visualization and classification of complex student profiles. SOM provides a two-dimensional representation of the data, while k−means refines the generated clusters, offering a more coherent perspective of intrinsic segmentation. The results provide a robust framework for higher education institutions to develop targeted policies and strategies tailored to the characteristics and needs of different student groups. Although focused on a Chilean context, the proposed methodological approach holds broad applicability across various educational institutions, contributing to the development of evidence-based policies that promote academic progress and educational equity. |
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| ISSN: | 2768-4520 |