A Bayesian non-parametric modeling to estimate student response to ICT investment†. Issue 14 (25th October 2016)

• Record Type: Journal Article
• Authors: Cabras, Stefano; Tena Horrillo, Juan de Dios
• Is Part Of: Journal of applied statistics. Volume 43:Issue 14(2016)
• Page Start: 2627
• Page End: 2642
• Publication Date: 2016-10-25
• Subjects: Regression trees -- causality -- ICT -- Bayesian statistics -- BART; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1142946
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: This paper estimates the causal impact of investment in information and communication technologies (ICT) on student performances in mathematics as measured in the Program for International Student Assessment (PISA) 2012 for Spain. To do this we apply a new methodology in this context known as Bayesian Additive Regression Trees that has important advantages over more standard parametric specifications. Results indicate that ICT has a moderate positive effect on math scores. In addition, we analyze how this effect interacts with variables related to school features and student socioeconomic status, finding that ICT investment is especially beneficial for students from a low socioeconomic background.