Advertisement

Educational Big Data Mining: How to Enhance Virtual Learning Environments

  • Pietro Ducange
  • Riccardo Pecori
  • Luigi Sarti
  • Massimo Vecchio
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)

Abstract

The growing development of virtual learning platforms is boosting a new type of Big Data and of Big Data Stream, those ones that can be labeled as e-learning Big Data. These data, coming from different sources of Virtual Learning Environments, such as communications between students and instructors as well as pupils tests, require accurate analysis and mining techniques in order to retrieve from them fruitful insights. This paper analyzes the main features of current e-learning systems, pointing out their sources of data and the huge amount of information that may be retrieved from them. Moreover, we assess the concept of educational Big Data, suggesting a logical and functional layered model that can turn to be very useful in real life.

Keywords

Big Data Educational data mining e-learning Virtual Learning Environment 

References

  1. 1.
    Schneider, B., Blikstein, P.: Unraveling students interaction around a tangible interface using multimodal learning analytics. J. Educ. Data Min. 7(3), 89–116 (2015)Google Scholar
  2. 2.
    Clement, B., Roy, D., Oudeyer, P.Y., Lopes, M.: Multi-armed bandits for intelligent tutoring systems. J. Educ. Data Min. 7(2), 20–48 (2015)Google Scholar
  3. 3.
    Vidal Alonso, O.A.: Big data and elearning: a binomial to the future of the knowledge society. Int. J. Interact. Multimedia Artif. Intell. 3(6), 29–33 (2016)CrossRefGoogle Scholar
  4. 4.
    Knowles, J.E.: Of needles and haystacks: building an accurate statewide dropout early warning system in Wisconsin. J. Educ. Data Min. 7(3), 18–67 (2015)Google Scholar
  5. 5.
    Hegazi, M.O., Abugroon, M.A.: The state of the art on educational data mining in higher education. Int. J. Comput. Trends Techn. 31(1), 46–56 (2016)CrossRefGoogle Scholar
  6. 6.
    Ivanova, M., Grosseck, G., Holotescu, C.: Researching data privacy models in eLearning. In: 2015 International Conference on Information Technology Based Higher Education and Training, pp. 1–6. IEEE, June 2015Google Scholar
  7. 7.
    Fernández, A., Peralta, D., Benítez, J.M., Herrera, F.: E-learning and educational data mining in cloud computing: an overview. Int. J. Learn. Technol. 9(1), 25–52 (2014)CrossRefGoogle Scholar
  8. 8.
    Sin, K., Muthu, L.: Application of big data in education data mining and learning analytics-a literature review. ICTACT J. Soft Comput. Spec. Issue Soft Comput. Models Big Data 5(4), 1035–1049 (2015)CrossRefGoogle Scholar
  9. 9.
    Miguel, J., Caballe, S., Xhafa, F.: A knowledge management process to enhance trustworthiness-based security in on-line learning teams. In: 2015 International Conference on Intelligent Networking and Collaborative Systems, pp. 272–279 (2015)Google Scholar
  10. 10.
    Pecori, R., Veltri, L.: Trust-based routing for kademlia in a sybil scenario. In: 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 279–283. IEEE (2014)Google Scholar
  11. 11.
    Nithya, P., Umamaheswari, B., Umadevi, A.: A survey on educational data mining in field of education. Int. J. Adv. Res. Comput. Eng. Technol. 5, 69–78 (2016)Google Scholar
  12. 12.
    Avgeriou, P., Retalis, S., Skordalakis, M.: An architecture for open learning management systems. In: Manolopoulos, Y., Evripidou, S., Kakas, A.C. (eds.) PCI 2001. LNCS, vol. 2563, pp. 183–200. Springer, Heidelberg (2003). doi: 10.1007/3-540-38076-0_13 CrossRefGoogle Scholar
  13. 13.
    Bohl, O., Scheuhase, J., Sengler, R., Winand, U.: The sharable content object reference model (scorm) - a critical review. In: Proceedings of IEEE 2002 International Conference on Computers in Education, pp. 950–951 (2002)Google Scholar
  14. 14.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  15. 15.
    White, T.: Hadoop: The Definitive Guide, 4th edn. O’Reilly Media, Sebastopol (2015)Google Scholar
  16. 16.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, p. 10 (2010)Google Scholar
  17. 17.
    Chodorow, K.: MongoDB: The Definitive Guide. O’Reilly Media, Sebastopol (2013)Google Scholar
  18. 18.
    Evans, R.: Apache storm, a hands on tutorial. In: 2015 IEEE International Conference on Cloud Engineering, p. 2 (2015)Google Scholar
  19. 19.
    Feng, T., Zhuang, Z., Pan, Y., Ramachandra, H.: A memory capacity model for high performing data-filtering applications in Samza framework. In: 2015 IEEE International Conference on Big Data, pp. 2600–2605 (2015)Google Scholar
  20. 20.
    Galyardt, A., Goldin, I.: Move your lamp post: recent data reflects learner knowledge better than older data. J. Educ. Data Min. 7(2), 83–108 (2015)Google Scholar
  21. 21.
    Shum, B.: Learning analytics policy brief. UNESCO Institute for Information Technology in Education (2012)Google Scholar
  22. 22.
    Landset, S., Khoshgoftaar, T.M., Richter, A.N., Hasanin, T.: A survey of open source tools for machine learning with big data in the hadoop ecosystem. J. Big Data 2(1), 1–36 (2015)CrossRefGoogle Scholar
  23. 23.
    Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 421–451. Springer, New York (2015)CrossRefGoogle Scholar
  24. 24.
    Pozzi, F., Manca, S., Persico, D., Sarti, L.: A general framework for tracking and analysing learning processes in computer-supported collaborative learning environments. Innovations Educ. Teach. Int. 44(2), 169–179 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pietro Ducange
    • 1
  • Riccardo Pecori
    • 1
  • Luigi Sarti
    • 1
    • 2
  • Massimo Vecchio
    • 1
  1. 1.SMART Engineering Solutions and Technologies (SMARTEST) Research CentreeCampus UniversityNovedrate (CO)Italy
  2. 2.Istituto per le Tecnologie Didattiche del Consiglio Nazionale delle RicercheGenova (GE)Italy

Personalised recommendations