Abstract
This research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysis. Finally, a machine learning model is trained and validated to forecast the categories of academic efficiency. The study population corresponds to 256 university engineering programs in Colombia and the data correspond to the national examination of the quality of education in Colombia in 2018. In the results, two university profiles were identified with efficiency levels of 92.3% and 97.3%, respectively. The Random Forest model presents an Area under ROC value of 95.8% in the prediction of the efficiency profiles. The proposed structure evaluates and predicts university programs’ academic efficiency, evaluating the efficiency between institutions with similar characteristics, avoiding a negative bias toward those institutions that host students with low educational levels.
Highlights
The teaching of science, engineering, technology, and mathematics (STEM) is a critical aspect of countries’ development
How to define university profiles of engineering education considering state exams at the secondary and university level? What is the academic efficiency of the identified engineering profiles? How to predict through machine learning the efficiency category of a university program belonging to the engineering training profiles created? the main objective of this study is to evaluate and forecast the academic performance of Colombian engineering programs, creating a replicable and reproducible method, offering objective guidelines for decision-making in a higher education environment
With the previously refined and selected information, the following phases were carried out: i) A cluster analysis using the unsupervised learning algorithm k-means to identify the formation of homogeneous groups in the data, associated with the results of the SABER tests; ii) An academic efficiency analysis was developed under an exit optimization approach to determine academic efficiency profiles (AEP); iii) A predictive model was defined to classify and predict belonging to an academic efficiency profile of an engineering program through Random Forest (RF) and Decision Tree (DT)
Summary
The teaching of science, engineering, technology, and mathematics (STEM) is a critical aspect of countries’ development. Corlu and Aydin (2016) show that teaching in STEM areas generates higher levels of business creation. It is essential to generate objective assessment tools for teaching STEM-related careers. This study presents a databased model to analyze the fundamental characteristics and relationships of engineering education programs and the results of a standardized assessment to achieve academic efficiency. To avoid the biases that represent the different levels in the basic academic competencies with which students access university education, the comparison of the programs must be fair, that is, comparing between equals. This study identifies homogeneous groups of engineering programs to analyze and forecast their level of efficiency within their reference group. Long and Siemens (2014) show that strategic and operational decision processes are developed
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