Visualization Improvement in Learning Analytics Using Semantic Enrichment

Conference paper
Part of the Lecture Notes in Educational Technology book series (LNET)

Abstract

This chapter presents a learning analytics and knowledge representation framework to support analysis and visualization of data extracted from MOOC (Massive Open Online Course). Data analytics provides methods and tools to analyze big data sets. In the context of learning, these methods have helped analyze MOOC data, mainly to characterize groups of learners. On the other hand, semantic Web and eLearning research groups are working on ontological representation of learning scenario components. Our project joins those efforts, both to discover relations through bottom-up analytics and to organize and focus the analysis with a semantic representation of the learning scenario. The first section states the problem and sketches the solution. The proposed framework is composed of four stages: data cleaning and formatting, bottom-up data analysis, semantic analysis, and finally visualization. The specification and implementation of each step is described in the following sections. The last section presents the evaluation of the project and conclusions.

Keywords

Learning analytics Ontology Semantic representation MapReduce and visualization 

References

  1. 1.
    Technology and the Completion Agenda @insidehighered [Online]. Available: https://www.insidehighered.com/news/2010/11/09/completion. Accessed October 02, 2014.
  2. 2.
    Russom, P. (2014). Big data analytics [Online]. Available: http://public.dhe.ibm.com/common/ssi/ecm/en/iml14293usen/IML14293USEN.PDF. Accessed October 02, 2014.
  3. 3.
    Daradoumis, T., Bassi, R., Xhafa, F., Caballe, S. (2013). A review on massive e-Learning (MOOC) design, delivery and assessment. In 2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (pp. 208–213).Google Scholar
  4. 4.
    Academic Analytics. The uses of management information and technology in higher education [Online]. Available: http://net.educause.edu/ir/library/pdf/ecar_so/ers/ers0508/EKF0508.pdf. Accessed October 02, 2014.
  5. 5.
    Baker, R. S. J. D., & Yacef, K. (2009, October 1). The state of educational data mining in 2009: A review and future visions. JEDM—Journal of Educational Data Mining, 1(1), 3–17.Google Scholar
  6. 6.
    About SoLAR | Society for learning analytics research (SoLAR) [Online]. Available: http://solaresearch.org/about/. Accessed October 02, 2014.
  7. 7.
    Drachsler, H., Dietze, S., Herder, E., d’Aquin, M., & Taibi, D. (2014). The learning analytics and knowledge (LAK) data challenge 2014. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge—LAK ’14 (pp. 289–290).Google Scholar
  8. 8.
    Blanco, Á. F., García-Peñalvo, F. J., & Sein-Echaluce, M. (2013). A methodology proposal for developing adaptive cMOOC. In Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality—TEEM ’13 (pp. 553–558).Google Scholar
  9. 9.
    Russom, P. (2014). Big data analytics [Online]. Available: http://public.dhe.ibm.com/common/ssi/ecm/en/iml14293usen/IML14293USEN.PDF. Accessed October 02, 2014.
  10. 10.
    Siemens G., & Gasevic, D. (2012). Guest editorial – learning and knowledge analytics.Google Scholar
  11. 11.
    Goldstein, P. J. (2008). Academic analytics: The uses of management information and technology in higher education (8th ed.).Google Scholar
  12. 12.
    Baker, R. S. J. D., & Yacef, K. (2009, October 1). The state of educational data mining in 2009: A review and future visions. JEDM—Journal of Educational Data Mining, 1(1), 3–17.Google Scholar
  13. 13.
    SOLAR. About SoLAR | Society for Learning Analytics Research (SoLAR) [Online]. Available: http://solaresearch.org/about/. Accessed October 02, 2014.
  14. 14.
    Paquette, G., Mariño, O., Rogozan, D., & Léonard, M. (2015). Competency-based personalization for massive online learning. Smart Learning Environment, 2(1), 4.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.TICSW Research Group, Systems and Computing Engineering DepartmentUniversidad de Los AndesBogotáColombia

Personalised recommendations