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Classification of industrial engineering programs in Colombia based on state tests.

Adel Mendoza-Mendoza¹, Enrique De La Hoz-Domínguez², Delimiro Visbal-Cadavid³

¹Program of Industrial Engineering, Universidad del Atlántico, Barranquilla, Colombia.

|May 24, 2023

Summary

This study classifies Colombian industrial engineering programs using academic performance data from state tests. Data Envelopment Analysis (DEA) and cluster analysis grouped higher education institutions (HEIs) effectively.

Keywords:

Classification Cluster analysis Data envelopment analysis Higher education institutions

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Area of Science:

Industrial Engineering Education
Higher Education Assessment
Quantitative Analysis

Background:

Evaluating higher education institutions (HEIs) is crucial for academic quality.
Colombia's industrial engineering programs require robust classification methods.
Standardized test data offers a basis for performance evaluation.

Purpose of the Study:

To propose and validate an approach for classifying industrial engineering programs in Colombian HEIs.
To leverage student academic performance data for institutional classification.
To assess the efficacy of Data Envelopment Analysis (DEA) combined with cluster analysis for this purpose.

Main Methods:

Employed Data Envelopment Analysis (DEA) to measure the efficiency of 93 HEIs based on 5318 industrial engineering students' Saber11 and SaberPro test scores.
Utilized cluster analysis to validate the DEA-derived classification of HEIs.
Applied statistical analysis to evaluate classification accuracy.

Main Results:

Classified 93 Colombian HEIs into three distinct groups based on industrial engineering program performance.
Achieved a 77% correct classification rate when validating DEA results with cluster analysis.
Demonstrated the effectiveness of using standardized test data for HEI performance benchmarking.

Conclusions:

The proposed DEA and cluster analysis approach provides a reliable method for classifying industrial engineering programs.
Academic performance metrics derived from state tests are valuable for evaluating HEIs.
This classification framework can inform quality improvement initiatives in higher education.