Evaluating the potential trade-off between students’ satisfaction and school performance using evolutionary multiobjective optimization.

Abstract

In this article, we carry out a combined econometric and multiobjective analysis using data from a representative sample of Andalusian schools. In particular, four econometric models are estimated in which the students’ academic performance (scores in math and reading, and percentage of students reaching a certain threshold in both subjects, respectively) are regressed against the satisfaction of students with different aspects of the teaching-learning process. From these estimates, four objective functions are defined which have been simultaneously maximized, subject to a set of constraints obtained by analyzing dependencies between explanatory variables. This multiobjective programming model is intended to optimize the students’ academic performance as a function of the students’ satisfaction. To solve this problem we use a decomposition-based evolutionary multiobjective algorithm called Global WASF-GA with different scalarizing functions which allows generating an approximation of the Pareto optimal front. In general, the results show the importance of promoting respect and closer interaction between students and teachers, as a way to increase the average performance of the students and the proportion of high performance students.