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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| Format: | Article |
| Language: | English |
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Taylor & Francis
2025-01-01
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| Series: | Research in Statistics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27684520.2024.2433290 |
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| author | Patricio Salas Rodrigo De la Fuente Patricio Sáez Andrés Riquelme |
| author_facet | Patricio Salas Rodrigo De la Fuente Patricio Sáez Andrés Riquelme |
| author_sort | Patricio Salas |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9453599e67f44fc2b3a69900babcd4f1 |
| institution | Kabale University |
| issn | 2768-4520 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis |
| record_format | Article |
| series | Research in Statistics |
| spelling | doaj-art-9453599e67f44fc2b3a69900babcd4f12025-01-24T15:49:34ZengTaylor & FrancisResearch in Statistics2768-45202025-01-013110.1080/27684520.2024.2433290Application of Non-Supervised Learning Tools and Visualization Techniques to Understand the Intrinsic Freshmen Enrollment Segmentation: An application to freshmen engineering students.Patricio Salas0Rodrigo De la Fuente1Patricio Sáez2Andrés Riquelme3Department of Statistics, Universidad de ConcepciónDepartment of Industrial Engineering, Universidad de ConcepciónChileDepartment of Statistics, Universidad de ConcepciónFacultad de Economía y Negocios, Universidad de ConcepciónChileIdentifying 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.https://www.tandfonline.com/doi/10.1080/27684520.2024.2433290Freshmen enrollment analysisData-driven methodologyAcademic information visualizationHigher education institutions |
| spellingShingle | Patricio Salas Rodrigo De la Fuente Patricio Sáez Andrés Riquelme Application of Non-Supervised Learning Tools and Visualization Techniques to Understand the Intrinsic Freshmen Enrollment Segmentation: An application to freshmen engineering students. Research in Statistics Freshmen enrollment analysis Data-driven methodology Academic information visualization Higher education institutions |
| title | Application of Non-Supervised Learning Tools and Visualization Techniques to Understand the Intrinsic Freshmen Enrollment Segmentation: An application to freshmen engineering students. |
| title_full | Application of Non-Supervised Learning Tools and Visualization Techniques to Understand the Intrinsic Freshmen Enrollment Segmentation: An application to freshmen engineering students. |
| title_fullStr | Application of Non-Supervised Learning Tools and Visualization Techniques to Understand the Intrinsic Freshmen Enrollment Segmentation: An application to freshmen engineering students. |
| title_full_unstemmed | Application of Non-Supervised Learning Tools and Visualization Techniques to Understand the Intrinsic Freshmen Enrollment Segmentation: An application to freshmen engineering students. |
| title_short | Application of Non-Supervised Learning Tools and Visualization Techniques to Understand the Intrinsic Freshmen Enrollment Segmentation: An application to freshmen engineering students. |
| title_sort | application of non supervised learning tools and visualization techniques to understand the intrinsic freshmen enrollment segmentation an application to freshmen engineering students |
| topic | Freshmen enrollment analysis Data-driven methodology Academic information visualization Higher education institutions |
| url | https://www.tandfonline.com/doi/10.1080/27684520.2024.2433290 |
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