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Is information and communication technology satisfying educational needs at school?

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  • Ferraro, Simona

Abstract

This paper assesses how the integration of ICT in education has affected the mathematics test scores for Italian students measured by the Programme for International Student Assessment 2012 data. The problem of endogeneity that affects survey data in this area, is addressed by applying the Bayesian Additive Regression Trees (BART) methodology as in Cabras & Tena Horrillo (2016). The BART methodology needs a prior and likelihood functions using the Markov Chain Monte Carlo (MCMC) algorithm to obtain the posterior distribution. Controlling for socioeconomic, demographic and school factors, the predicted posterior distribution implies an increase, on average, of 16 points in the test scores. The result indicates that the use of ICT at school has a positive and strong impact on mathematic test scores.

Suggested Citation

  • Ferraro, Simona, 2018. "Is information and communication technology satisfying educational needs at school?," MPRA Paper 86175, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:86175
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    References listed on IDEAS

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    6. Tommaso Agasisti & María Gil-Izquierdo & Seong Won Han, 2020. "ICT Use at home for school-related tasks: what is the effect on a student’s achievement? Empirical evidence from OECD PISA data," Education Economics, Taylor & Francis Journals, vol. 28(6), pages 601-620, November.
    7. Stephen Machin & Sandra McNally & Olmo Silva, 2007. "New Technology in Schools: Is There a Payoff?," Economic Journal, Royal Economic Society, vol. 117(522), pages 1145-1167, July.
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    9. Joshua Angrist & Victor Lavy, 2002. "New Evidence on Classroom Computers and Pupil Learning," Economic Journal, Royal Economic Society, vol. 112(482), pages 735-765, October.
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    Cited by:

    1. Melchor Gómez-García & Hossein Hossein-Mohand & Juan Manuel Trujillo-Torres & Hassan Hossein-Mohand, 2020. "The Training and Use of ICT in Teaching Perceptions of Melilla’s (Spain) Mathematics Teachers," Mathematics, MDPI, vol. 8(10), pages 1-19, September.
    2. Ivanildo Viana Moura & Lauro Brito de Almeida & Wesley Vieira da Silva & Claudimar Pereira da Veiga & Flaviano Costa, 2020. "Predictor Factors of Intention to Use Technological Resources: A Multigroup Study About the Approach of Technology Acceptance Model," SAGE Open, , vol. 10(4), pages 21582440209, October.
    3. Martínez-Gautier, Daniel & Garrido-Yserte, Rubén & Gallo-Rivera, María-Teresa, 2021. "Educational performance and ICTs: Availability, use, misuse and context," Journal of Business Research, Elsevier, vol. 135(C), pages 173-182.

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    More about this item

    Keywords

    ICT Bayesian additive regression tree Posterior distribution; PISA;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I20 - Health, Education, and Welfare - - Education - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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