Big Data Irruption in Education / Irrupción del Big Data en la Educación
PDF

Métricas alternativas

Keywords

big data
text mining
education
natural language process
technology

How to Cite

Matas Terrón, A., Leiva Olivencia, J. J., & Franco Caballero, P. D. (2020). Big Data Irruption in Education / Irrupción del Big Data en la Educación. Pi­xel-Bit. Media and Education Journal, 57, 59–90. https://doi.org/10.12795/pixelbit.2020.i57.02

Abstract

Introduction: 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.

Methodology: 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.

Results: 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.

Discussion: 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.

 

Introducción: El objetivo de este documento es analizar la producción de artículos científicos sobre “Big Data” en Educación desde 2013 hasta 2018, además de identificar las palabras clave más frecuentes en esos artículos.

Metodología: Se consultaron publicaciones en la base de datos Scopus usando un algoritmo de búsqueda basado en un criterio pre-establecido. A través de un proceso cuantitativo, incluyendo la minería de textos, fueron analizados diferentes aspectos de la producción de artículos de investigación sobre Big Data en Educación: citas, autores, revistas y temas fueron considerados.

Resultados: Los resultados muestran un incremento de producción sobre Big Data en Educación desde 2015, al igual que un cambio en la tendencia de los temas tratados, partiendo de estudios enfocados en Psicología y comportamiento, llegando a estudios más enfocados en Educación.

Discusión: Hay un gran interés en este campo de investigación y su aplicación en el Sistema Educativo cambiará su mentalidad pedagógica, además de la de los propios centros formativos.

https://doi.org/10.12795/pixelbit.2020.i57.02
PDF

References

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

Downloads

Download data is not yet available.