Classification of industrial engineering programs in Colombia based on state tests

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.