Big Data Irruption in Education

  1. Antonio Matas Terrón 1
  2. Juan José Leiva Olivencia 1
  3. Pablo Daniel Franco Caballero 1
  1. 1 Universidad de Málaga
    info

    Universidad de Málaga

    Málaga, España

    ROR https://ror.org/036b2ww28

Revista:
Pixel-Bit: Revista de medios y educación

ISSN: 1133-8482

Any de publicació: 2020

Número: 57

Pàgines: 59-90

Tipus: Article

DOI: 10.12795/PIXELBIT.2020.I57.02 DIALNET GOOGLE SCHOLAR

Altres publicacions en: Pixel-Bit: Revista de medios y educación

Resum

The objective is to analyse the production of scientific articles on Big Data in Education from 2013 to 2018, as well as to identify the most frequently used keywords in those articles. The publications of the Scopus database were consulted using a search algorithm based on pre-established criteria. Through a quantitative procedure, including text mining, different aspects of the production of research articles on Big Data in Education were analysed: citations, authors, journals, and topics covered. The results show an increase in production over Big Data in Education from 2015, as well as a change in trend in the subjects dealt with, going from studies focused on Psychology and Behaviour to studies focused on Education. From this point, there is a real interest in this field of research, and the usage in the Educational System will change the pedagogical mentality and in the training centres. .

Referències bibliogràfiques

  • Anshari, M., Alas, Y., & Guan, L. S. (2016). Developing online learning resources: Big data, social networks, and cloud computing to support pervasive knowledge. Education and Information Technologies, 21(6), 1663-1677. https://doi.org/10.1007/s10639-015-9407-3
  • Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Arranz, O., & Alonso, V. (2013). Big Data & Learning Analytics: A Potential Way to Optimize eLearning Technological Tools. Recuperado de https://bit.ly/2VweSsw7
  • Borgman, C. L. (2015). Big data, little data, no data: scholarship in the networked world. Cambridge, Massachusetts: The MIT Press.
  • Boyd, D., & Crawford, K. (2011). Six Provocations for Big Data. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1926431
  • Boyd, D., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15(5), 662-679. https://doi.org/10.1080/1369118X.2012.678878
  • Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. https://doi.org/10.1007/s11036-013-0489-0
  • Chiu, W.-T., & Ho, Y.-S. (2005). Bibliometric analysis of homeopathy research during the period of 1991 to 2003. Scientometrics, 63(1), 3-23. https://doi.org/10.1007/s11192-005-0201-7
  • Chryssolouris, G., Mavrikios, D., & Rentzos, L. (2016). The Teaching Factory: A Manufacturing Education Paradigm. Procedia CIRP, 57, 44-48. https://doi.org/10.1016/j.procir.2016.11.009
  • Conway, M., & O’Connor, D. (2016). Social media, big data, and mental health: current advances and ethical implications. Current Opinion in Psychology, 9, 77-82. https://doi.org/10.1016/j.copsyc.2016.01.004
  • Crossland, T., Stenetorp, P., Riedel, S., Kawata, D., Kitching, T. D., & Croft, R. A. C. (2019). Towards Machine-assisted Meta-Studies: The Hubble Constant. arXiv:1902.00027 [astro-ph]. Recuperado de https://bit.ly/2Ow7YC1
  • Crossley, M. (2014). Global league tables, big data and the international transfer of educational research modalities. Comparative Education, 50(1), 15-26. https://doi.org/10.1080/03050068.2013.871438
  • Daniel, B. K. (2019). Big Data and data science: A critical review of issues for educational research: Critical issues for educational research. British Journal of Educational Technology, 50(1), 101-113. https://doi.org/10.1111/bjet.12595
  • Davis, J. C., & Gonzalez, J. G. (2003). Scholarly Journal Articles about the Asian Tiger Economies: authors, journals and research fields, 1986-2001. Asian-Pacific Economic Literature, 17(2), 51-61. https://doi.org/10.1046/j.1467-8411.2003.00131.x
  • Dede, C. J. (2016). Next steps for “Big Data” in education: Utilizing data-intensive research. Educational Technology. Recuperado de https://bit.ly/31ZiEwK
  • Delgado-López-Cózar, E., & Repiso-Caballero, R. (2013). The Impact of Scientific Journals of Communication: Comparing Google Scholar Metrics, Web of Science and Scopus. Comunicar, 21(41), 45-52. https://doi.org/10.3916/C41-2013-04
  • Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., … Zupan, B. (2013). Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research, 14, 2349-2353.
  • Ellaway, R. H., Pusic, M. V., Galbraith, R. M., & Cameron, T. (2014). Developing the role of big data and analytics in health professional education. Medical Teacher, 36(3), 216-222. https://doi.org/10.3109/0142159X.2014.874553
  • Ferguson, R., & Shum, S. B. (2012). Social learning analytics: five approaches. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12, 23. https://doi.org/10.1145/2330601.2330616
  • Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031
  • Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. https://doi.org/10.1007/s11528-014-0822-x
  • George, G., Haas, M. R., & Pentland, A. (2014). Big Data and Management. Academy of Management Journal, 57(2), 321-326. https://doi.org/10.5465/amj.2014.4002
  • Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human Geography, 3(3), 255-261. https://doi.org/10.1177/2043820613513121
  • Hilbert, M. (2016). Big Data for Development: A Review of Promises and Challenges. Development Policy Review, 34(1), 135-174. https://doi.org/10.1111/dpr.12142
  • Khan, M. A., Uddin, M. F., & Gupta, N. (2014). Seven V’s of Big Data understanding Big Data to extract value. Proceedings of the 2014 Zone 1 Conference of the American Society for Engineering Education, 1-5. https://doi.org/10.1109/ASEEZone1.2014.6820689
  • Kyriakidis, M., Happee, R., & de Winter, J. C. F. (2015). Public opinion on automated driving: Results of an international questionnaire among 5000 respondents. Transportation Research Part F: Traffic Psychology and Behaviour, 32, 127-140. https://doi.org/10.1016/j.trf.2015.04.014
  • Laude, H. (2017). Data scientist y lenguaje R: guía de autoformación para el uso de Big Data (F. J. Piqueres Juan, Trad.). Barcelona: Eni.
  • Long, P., & Siemens, G. (2011, septiembre 12). Penetrating the Fog: Analytics in Learning and Education. EduCause Review, 46(5), 6.
  • Macfadyen, L. P., Dawson, S., Pardo, A., & Gaševic, D. (2014). Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge. Research & Practice in Assessment, 9, 17-28.
  • Mayer-Schönberger, V., & Cukier, K. (2018). Aprender con big data. Madrid: Turner.
  • Modoni, G. E., Doukas, M., Terkaj, W., Sacco, M., & Mourtzis, D. (2017). Enhancing factory data integration through the development of an ontology: from the reference models reuse to the semantic conversion of the legacy models. International Journal of Computer Integrated Manufacturing, 30(10), 1043-1059. https://doi.org/10.1080/0951192X.2016.1268720
  • Puyol, J. (2014). Una aproximación a Big Data. Revista de Derecho de la UNED (RDUNED), 14, 471-506. https://doi.org/10.5944/rduned.14.2014.13303
  • Rao, D., & McMahan, B. (2019). Natural language processing with PyTorch: build intelligent language applications using deep learning. Sebastopol, CA: OReilly Media.
  • Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Computer Networks, 101, 63-80. https://doi.org/10.1016/j.comnet.2015.12.023
  • Reyes, J. A. (2015). The skinny on big data in education: Learning analytics simplified. TechTrends, 59(2), 75-80. https://doi.org/10.1007/s11528-015-0842-1
  • Silge, J., & Robinson, D. (2017). Text mining with R: a tidy approach. Beijing; Boston: O’Reilly.
  • Viedma-Del-Jesus, M. I., Perakakis, P., Muñoz, M. Á., López-Herrera, A. G., & Vila, J. (2011). Sketching the first 45 years of the journal Psychophysiology (1964-2008): A co-word-based analysis: Forty-five years of Psychophysiology. Psychophysiology, 48(8), 1029-1036. https://doi.org/10.1111/j.1469-8986.2011.01171.x
  • Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77-84. https://doi.org/10.1111/jbl.12010
  • Williamson, B. (2016). Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments. Journal of Education Policy, 31(2), 123-141. https://doi.org/10.1080/02680939.2015.1035758
  • Williamson, B. (2017). Big data in education: the digital future of learning, policy and practice. Thousand Oaks, CA: SAGE Publications.
  • Zablith, F. (2015). Interconnecting and Enriching Higher Education Programs Using Linked Data. Proceedings of the 24th International Conference on World Wide Web, 711–716. https://doi.org/10.1145/2740908.2741740