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A Bayesian non-parametric modeling to estimate student response to ICT investment

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  • Stefano Cabras
  • Juan de Dios Tena Horrillo

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.

Suggested Citation

  • Stefano Cabras & Juan de Dios Tena Horrillo, 2016. "A Bayesian non-parametric modeling to estimate student response to ICT investment," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(14), pages 2627-2642, October.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:14:p:2627-2642
    DOI: 10.1080/02664763.2016.1142946
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    Cited by:

    1. Gregorio Gimenez & Luis Vargas-Montoya, 2021. "ICT Use and Successful Learning: The Role of the Stock of Human Capital," Mathematics, MDPI, vol. 9(14), pages 1-15, July.
    2. Ferraro, Simona, 2018. "Is information and communication technology satisfying educational needs at school?," MPRA Paper 86175, University Library of Munich, Germany.
    3. Vargas-Montoya, Luis & Gimenez, Gregorio & Fernández-Gutiérrez, Marcos, 2023. "ICT use for learning and students' outcomes: Does the country's development level matter?," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    4. Prieto-Latorre, Claudia & Lopez-Agudo, Luis Alejandro & Luque, Mariano & Marcenaro-Gutierrez, Oscar David, 2022. "The ideal use of the internet and academic success: Finding a balance between competences and knowledge using interval multiobjective programming," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).

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