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A Web Services-Based Application for LMS Data Extraction and Processing for Social Network Analysis

  • Julián Chaparro-PeláezEmail author
  • Emiliano Acquila-Natale
  • Santiago Iglesias-Pradas
  • Ignacio Suárez-Navas
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 222)

Abstract

The emergence of learning analytics as a discipline of its own has given way to a diverse subset of research fields offering very different approximations to the topic. One of the most recent and active approaches is social learning analytics, which focuses primarily on the application of social network analysis (SNA) techniques and visualizations to study and help understanding interactions in online courses as a key pillar of social construction of learning. However, and despite this interest, current tools for analysis and visualization are very limited for advanced social learning analytics, and SNA applications cannot directly process data from learning management systems. This paper presents a technical view of the design and implementation of a web services-based application that aims to overcome these limitations by extracting and processing educational data about forum interactions in online courses to generate the corresponding social graphs and enable advanced social network analysis on SNA software.

Keywords

Learning analytics LMS Social network analysis Data extraction Data processing 

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References

  1. 1.
    Beyer, M.A., Laney, D.: The Importance of ‘Big Data’: A Definition. Gartner (2012)Google Scholar
  2. 2.
    Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics:from big data to big impact. MIS Quarterly 36(4), 1165–1188 (2012)Google Scholar
  3. 3.
    Romero, C., Ventura, S.: Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33, 135–146 (2007)CrossRefGoogle Scholar
  4. 4.
    Romero, C., Ventura, S.: Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 40(6), 601–618 (2010)CrossRefGoogle Scholar
  5. 5.
    van Barneveld, A., Arnold, K.E., Campbell, J.P.: Analytics in Higher Education: Establishing a Common Language. EDUCAUSE ELI Paper 1, 1–11 (2012)Google Scholar
  6. 6.
    Long, P., Siemens, G.: Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review 46(5), 31–40 (2011)Google Scholar
  7. 7.
    Agudo-Peregrina, Á.F., Iglesias-Pradas, S., Conde-González, M.Á., Hernández-García, Á.: Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior 31, 542–550 (2014)CrossRefGoogle Scholar
  8. 8.
    Gómez-Aguilar, D.A., Hernández-García, Á., García-Peñalvo, F.J., Therón, R.: Tap into visual analysis of customization of grouping of activities in eLearning. Computers in Human Behavior 47, 60–67 (2015)CrossRefGoogle Scholar
  9. 9.
    Hernández-García, Á., González-González, I., Jiménez-Zarco, A.I., Chaparro-Peláez, J.: Applying social learning analytics to message boards in online distance learning: A case study. Computers in Human Behavior 47, 68–80 (2015)CrossRefGoogle Scholar
  10. 10.
    Bandura, A.: Social learning theory. General Learning Press, New York (1971)Google Scholar
  11. 11.
    Berger, P.L., Luckman, T.: The Social Construction of Reality: A Treatise in the Sociology of Knowledge. Penguin Books, Harmondsworth (1967)Google Scholar
  12. 12.
    Lave, J., Wenger, E.: Situated Learning: Legitimate Peripheral Participation. Cambridge University Press, Cambridge (1991)CrossRefGoogle Scholar
  13. 13.
    Buckingham-Shum, S., Ferguson, R.: Social Learning Analytics. Journal of Educational Technology & Society 15(3), 3–26 (2012)Google Scholar
  14. 14.
    Freeman, L.C.: Centrality in social networks: Conceptual clarification. Social Networks 1(3), 215–239 (1979)CrossRefGoogle Scholar
  15. 15.
    Hernández-García, Á.: Usare Gephi per visualizzare la partecipazione nei corsi online: un approccio di Social Learning Analytics. Tecnologie Didattiche 22(3), 148–156 (2014)Google Scholar
  16. 16.
    Edutechnica: LMS Data – Spring 2015 Updates. (2015). http://edutechnica.com/2015/03/08/lms-data-spring-2015-updates
  17. 17.
    Royce, W.: Managing the development of large software systems. In: Proceedings of IEEE WESCON, pp. 1–9 (1970)Google Scholar
  18. 18.
    Brooks Jr., F.P.: No Silver Bullet Essence and Accidents of Software Engineering. Computer 20(4), 10–19 (1987)CrossRefGoogle Scholar
  19. 19.
    Boehm, B.W.: A spiral model of software development and enhancement. Computer 21(5), 61–72 (1988)CrossRefGoogle Scholar
  20. 20.
    Larman, C., Basili, V.R.: Iterative and incremental developments: A Brief History. Computer 36(6), 47–56 (2003)CrossRefGoogle Scholar
  21. 21.
    Dron, J., Anderson, T.: On the design of collective applications. In: Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 4, pp. 368–374 (2009)Google Scholar
  22. 22.
    Rayón, Á.: SCALA: Supporting competency assessment through learning analytics. PhD Dissertation. University of Deusto (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Julián Chaparro-Peláez
    • 1
    Email author
  • Emiliano Acquila-Natale
    • 1
  • Santiago Iglesias-Pradas
    • 1
  • Ignacio Suárez-Navas
    • 1
  1. 1.Departamento de Ingeniería de Organización, Administración de Empresas y EstadísticaUniversidad Politécnica de MadridMadridSpain

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