Big Data, the Next Step in the Evolution of Educational Data Analysis

  • W. Villegas-Ch
  • Sergio Luján-Mora
  • Diego Buenaño-Fernandez
  • X. Palacios-Pacheco
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)

Abstract

This paper presents an analysis of new concepts such as big data, smart data and a data lake. It is to sought integrate learning management systems with these platforms and contribute to education by making it personalised and of quality. For this study, the data and needs of a university in Ecuador have been considered. This university has set its goals to the discovery of patterns, using data mining techniques applied to cubes generated in a data warehouse. However, the institution wants to integrate all the systems and sensors that contribute to the educational development of the student. Integrating more systems into the data warehouse has compromised the veracity of the data and the processing capabilities have been surpassed by the volume of data. The paper proposes the use of one of the platforms analysed and its tools to generate knowledge and to help the students to learn.

Keywords

Analysis of data Big data Data lake Data mining Data warehouse Smart data 

References

  1. 1.
    Dalsgaard, Ch.: Social software: e-learning beyond learning management systems. Eur. J. Open Distance E-Learn. 9(2), 1–7 (2006)Google Scholar
  2. 2.
    Davenport, T.H., Barth, P., Bean, R.: How big data is different. MIT Sloan Manage. Rev. 54(1), 4346 (2012). https://search.proquest.com/docview/1124397830?accountid=33194Google Scholar
  3. 3.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)Google Scholar
  4. 4.
    Higdon, S.J., Devost, D., Higdon, J., Brandl, B., Houck, J., Hall, P., Green, J.: The SMART data analysis package for the infrared spectrograph* on the spitzer space telescope. Publ. Astron. Soc. Pac. 116(824), 975 (2004)CrossRefGoogle Scholar
  5. 5.
    Lavalle, S., Lesser, E., Shocley, R.: Big data, analytics and the path from insights to value. MIT Sloan Manage. Rev. 52(2), 21 (2011)Google Scholar
  6. 6.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.: Big data: the next frontier for innovation, competition, and productivity, pp. 27–36 (2011). http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation
  7. 7.
    Dougiamas, M., Taylor, P.: Moodle: using learning communities to create an open source course management system. In: Proceedings of ED-MEDIA World Conference on Educational Multimedia, Hypermedia and Telecommunications, pp. 171–178. Association for the Advancement of Computing in Education, Honolulu (2003)Google Scholar
  8. 8.
    O’leary, D.: Embedding AI and crowdsourcing in the big data lake. IEEE Intell. Syst. 29(5), 70–73 (2014)CrossRefGoogle Scholar
  9. 9.
    Sagiroglu, S., Sinanc, D.: Big data: a review. In: International Conference on Collaboration Technologies and Systems (CTS), pp. 42–47 (2013)Google Scholar
  10. 10.
    Snijders, C., Matzat, U., Reips, U.: Big Data: big gaps of knowledge in the field of internet science. Int. J. Internet Sci. 7(1), 1–5 (2012)Google Scholar
  11. 11.
    Terrizzano, I.G., Schwarz, P.M., Roth, M., Colino, J.E.: Data wrangling: the challenging Yourney from the wild to the lake. In: Conference on Innovative Data Systems Research (CIDR), pp. 1–9 (2015)Google Scholar
  12. 12.
    Thusoo, A., Shao, Z., Anthony, S., Borthakur, D., Jain, N., Sen Sarma, J., Liu, H.: Data warehousing and analytics infrastructure at Facebook. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 1013–1020. ACM (2010)Google Scholar
  13. 13.
    Trautsch, F., Herbold, S., Makedonski, P., Grabowski. J.: Adressing problems with external validity of repository mining studies through a smart data platform. In: Proceedings of the 13th International Conference on Mining Software Repositories MSR, pp. 97–108. ACM (2016)Google Scholar
  14. 14.
    Villars, R.L., Carl, W., Matthew, E.: Big data: what it is and why you should care. White Paper IDC 14, 1–14 (2011)Google Scholar
  15. 15.
    Villegas-Ch, W., Luján-Mora, S.: Systematic review of evidence on data mining applied to LMS platforms for improving e-learning. In: International Technology, Education and Development Conference (INTED), pp. 6537–6545 (2017)Google Scholar
  16. 16.
    Villegas-Ch, W., Luján-Mora, S.: Analysis of data mining techniques applied to LMS for personalized education. In: World Engineering Education Conference (EDUNINE), pp. 85–89. IEEE (2017)Google Scholar
  17. 17.
    Walker, J.S.: Big data: a revolution that will transform how we live, work, and think. Int. J. Advertising 33(1), 181–183 (2014)CrossRefGoogle Scholar
  18. 18.
    Widom, J.: Research problems in data warehousing. In: Proceedings of the Fourth International Conference on Information and Knowledge Management (CIKM), pp. 25–30 (1995)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • W. Villegas-Ch
    • 1
  • Sergio Luján-Mora
    • 2
  • Diego Buenaño-Fernandez
    • 1
  • X. Palacios-Pacheco
    • 3
  1. 1.Facultad de Ingeniería y Ciencias AgropecuariasUniversidad de Las AméricasQuitoEcuador
  2. 2.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain
  3. 3.Departamento de SistemasUniversidad Internacional del EcuadorQuitoEcuador

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