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Validity of the “Big Data Tendency in Education” Scale as a Tool Helping to Reach Inclusive Social Development

Author

Listed:
  • Antonio Matas-Terrón

    (Department of Methods of Researching in Education, University of Málaga, 29071 Málaga, Spain)

  • Juan José Leiva-Olivencia

    (Department of Didactic and Organization in School, University of Málaga, 29071 Málaga, Spain)

  • Pablo Daniel Franco-Caballero

    (Department of Methods of Researching in Education, University of Málaga, 29071 Málaga, Spain)

  • Francisco José García-Aguilera

    (Department of Theory and History of Education, University of Málaga, 29071 Málaga, Spain)

Abstract

Big Data technology can be a great resource for achieving the Sustainable Development Goals in a fair and inclusive manner; however, only recently have we begun to analyse its impact on education. This research goal was to analyse the psychometric characteristics of a scale to assess opinions that educators in training have about Big Data besides their related emotions. This is important, as it will be the educators of the future who will have to manage with Big Data at school. A nonprobability sample of 337 education students from Peru and Spain was counted. Internal consistency, as well as validity, were analysed through exploratory and confirmatory factorial analysis. The results show good psychometric values, highlighting as relevant a latent structure of six factors that includes emotional and cognitive dimensions. As a result, the profile defining the participants in relation to Big Data was identified. Finally, the implications of the Big Data for Inclusive Education in a sustainable society are discussed.

Suggested Citation

  • Antonio Matas-Terrón & Juan José Leiva-Olivencia & Pablo Daniel Franco-Caballero & Francisco José García-Aguilera, 2020. "Validity of the “Big Data Tendency in Education” Scale as a Tool Helping to Reach Inclusive Social Development," Sustainability, MDPI, vol. 12(13), pages 1-12, July.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5470-:d:381350
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    References listed on IDEAS

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    1. Paul Belleflamme & Julien Jacqmin, 2016. "An Economic Appraisal of MOOC Platforms: Business Models and Impacts on Higher Education," CESifo Economic Studies, CESifo Group, vol. 62(1), pages 148-169.
    2. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
    3. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    4. Julio Ruiz-Palmero & Ernesto Colomo-Magaña & José Manuel Ríos-Ariza & Melchor Gómez-García, 2020. "Big Data in Education: Perception of Training Advisors on Its Use in the Educational System," Social Sciences, MDPI, vol. 9(4), pages 1-13, April.
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