educational efficiency

23 January 2022

* Organic Law 3/2020, of 29 December, which amends Organic Law 2/2006, of 3 May, on Education (LOMLOE).

This research aims to demonstrate the need for an economic evaluation of the Or­ganic Law that modifies the Organic Law of Education (LOMLOE), especially after the in­vestment of EU Next Generation funds that open new opportunities that were lacking in the initial drafting of the law. The challenge for Public Administrations is to use this addi­tional investment efficiently.

Our analysis shows that artificial intelli­gence models can predict whether educational support programmes will help increase the like­lihood that students who lag behind will pass the 4th grade of ESO (Compulsory Secondary Education). In this way, we can calculate the social return of one of these programmes and contribute to their ex-ante design to achieve higher success rates for students.

To complement the models already used by Public Administrations, we use robust Machine Learning (ML) models such as CHAID decision trees and artificial neural networks to analyse the characteristics of the groups of students and the interven­tion they have been part of. The conclusions allow us to improve educational reinforce­ment programmes in the coming years to support students with lower chances of ac­ademic success.



Please, cite this article as follows: Ballestar, M. T., Sainz, J., & Sanz, I. (2022). Evaluación económica de intervenciones educativas en la LOMLOE: propuestas de mejora con inteligencia artificial |An economic evaluation of educational interventions in the LOMLOE*: Proposals for improvement with artificial intelligence. Revista Española de Pedagogía, 80 (281), xxx-xxx.