Dynamic modeling of PISA achievement scores: comparative analysis of artificial neural networks and differential equation systems approaches.

ABSTRACT: In this study, the effects of nine key educational problems in schools on student achievement in mathematics, science, and reading in the Programme for International Student Assessment (PISA) were examined using artificial neural networks and differential equation systems. A linear differential equation model was used to describe instantaneous changes over time for twelve variables. Furthermore, the structure in which the nine education-related variables were defined as inputs and the PISA scores as outputs was analyzed through a neural network model with a tangent hyperbolic activation function. Model parameters were estimated using a least-squares curve fitting procedure, and numerical simulations were implemented to evaluate model accuracy. The performance of the models was evaluated us

SUBMITTER: Dasbasi B 

PROVIDER: S-EPMC12748620 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature