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Educational Data Analysis for Decision-Making: Performance in the Saber Pro Test among Early Childhood Education Programs in Colombia

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  • Mayerly Llanos-Redondo
  • Sonia Maritza Mendoza Lizcano
  • Pastor Ramírez Leal
  • Andrés Llanos-Redondo

Abstract

This study applies descriptive and exploratory data analysis techniques to examine the performance of students from 59 undergraduate programs in Early Childhood Education and Pedagogy in Colombia on the 2022 Saber Pro standardized test. Official datasets from ICFES were processed and analyzed using SPAD 5.6 and SPSS 28, focusing on five generic competencies: critical reading, quantitative reasoning, written communication, English, and citizenship competencies. Performance levels were examined in relation to institutional, sectoral, and regional variables. The findings reveal consistently low performance in quantitative reasoning, advantages associated with accredited institutions, and regional disparities. This research highlights the potential of data science tools—such as data mining and statistical visualization—for guiding evidence-based educational strategies and policy-making. It underscores the value of educational data systems as foundations for improving academic outcomes and reducing inequality through informed decision-making.

Suggested Citation

Handle: RePEc:dbk:datame:v:4:y:2025:i::p:1034:id:1056294dm20251034
DOI: 10.56294/dm20251034
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