No abstract

No 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.

This research develops an academic production function for the educational process of industrial engineers in Colombia. The proposed function objectively analyses the relationships between the academic competencies obtained in secondary education and the university. The data used correspond to the standardized tests of 4,977 students at the end of high school and university. In the first stage of the model, the structure of the production function was empirically evaluated using a Partial Least Square - Structural Equation Modeling approach. Consequently, in the second stage, the efficiency of the relationships in the academic production function is estimated using Data Envelopment Analysis. The Goodness of Fit index of the empirical model was 0.89, thus, confirming the relationships between the construct's variables. The model validates four transformation relationships and subsequently estimates the efficiency of the interactions in the production function. The average efficiency results of the model in its constant scale are 16.30%, 2.17%, and 5.43%. In conclusion, the model explains the capacity of universities to transform inputs (basic competencies of the secondary school) into desired outputs (professional academic competencies). Additionally, the model analyses professional performance from the interactions among academic competencies.

No abstract