Open AccessAssessing and Classification of Academic Efficiency in Engineering Teaching Programs
Author(s) -
Enrique Delahoz-Domínguez,
Rohemi Zuluaga-Ortiz,
Adel Mendoza Mendoza
Publication year - 2021
Publication title -
journal on efficiency and responsibility in education and science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.204
H-Index - 7
eISSN - 2336-2375
pISSN - 1803-1617
DOI - 10.7160/eriesj.2021.140104
Subject(s) - data envelopment analysis , cluster (spacecraft) , value (mathematics) , quality (philosophy) , computer science , population , random forest , higher education , phase (matter) , mathematics education , statistics , artificial intelligence , econometrics , machine learning , mathematics , political science , sociology , demography , philosophy , chemistry , organic chemistry , epistemology , law , programming language
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.