IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i13p5470-d381350.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/13/5470/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/13/5470/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sundarakani, Balan & Ajaykumar, Aneesh & Gunasekaran, Angappa, 2021. "Big data driven supply chain design and applications for blockchain: An action research using case study approach," Omega, Elsevier, vol. 102(C).
    2. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    3. Antonio Matas-Terrón & Juan José Leiva-Olivencia & Cristina Negro-Martínez, 2020. "Tendency to Use Big Data in Education Based on Its Opportunities According to Andalusian Education Students," Social Sciences, MDPI, vol. 9(9), pages 1-12, September.
    4. Amankwah-Amoah, Joseph, 2019. "Big data analytics and business failures in data-Rich environments: An organizing framework," MPRA Paper 91264, University Library of Munich, Germany.
    5. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    6. Candice WALLS & Brian BARNARD, 2020. "Success Factors of Big Data to Achieve Organisational Performance: Theoretical Perspectives," Expert Journal of Business and Management, Sprint Investify, vol. 8(1), pages 1-16.
    7. Jacqmin, Julien, 2018. "Why are some online courses more open than others?," MPRA Paper 89929, University Library of Munich, Germany.
    8. Emilie Dargaud & Frédéric Jouneau-Sion, 2019. "The good MOOC and the universities," Working Papers halshs-01996582, HAL.
    9. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    10. Shamim, Saqib & Zeng, Jing & Khan, Zaheer & Zia, Najam Ul, 2020. "Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    11. Maniyassouwe Amana & Pingfeng Liu & Mona Alariqi, 2022. "Value Creation and Capture with Big Data in Smart Phones Companies," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    12. Pei Zhang & Peiran Chen & Fan Xiao & Yong Sun & Shuyan Ma & Ziwei Zhao, 2022. "The Impact of Information Infrastructure on Air Pollution: Empirical Evidence from China," IJERPH, MDPI, vol. 19(21), pages 1-17, November.
    13. Arnold, René & Hildebrandt, Christian & Taş, Serpil, 2020. "Europäische Datenökonomie: Zwischen Wettbewerb und Regulierung. Endbericht," Study Series, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, number 251537, December.
    14. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    15. Osterrieder, Philipp & Budde, Lukas & Friedli, Thomas, 2020. "The smart factory as a key construct of industry 4.0: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 221(C).
    16. Umit Can & Bilal Alatas, 2017. "Big Social Network Data and Sustainable Economic Development," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    17. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    18. Tiago Carneiro & Winnie Ng Picoto & Inês Pinto, 2023. "Big Data Analytics and Firm Performance in the Hotel Sector," Tourism and Hospitality, MDPI, vol. 4(2), pages 1-13, April.
    19. Emilie Dargaud & Fréedéeric Jouneau-Sion, 2020. "The good MOOC and the universities," Education Economics, Taylor & Francis Journals, vol. 28(5), pages 474-490, September.
    20. Jean-Luc Pradel Mathurin Augustin & Shu-Yi Liaw, 2020. "Exploring the Relationship between Perceived Big Data Advantages and Online Consumers’ Behavior: An Extended Hierarchy of Effects Model," International Business Research, Canadian Center of Science and Education, vol. 13(6), pages 1-73, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5470-:d:381350. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.