Education and Information Technologies

, Volume 23, Issue 6, pp 2783–2800 | Cite as

Big data for online learning systems

  • Karim DahdouhEmail author
  • Ahmed Dakkak
  • Lahcen Oughdir
  • Fayçal Messaoudi


In recent years, Online learning systems have met big challenges, especially due to rapid changes in technology, the gigantic amounts of data to be stored and manipulated, the large number of learners and the diversity of educational resources. As a result, e-learning platforms must change their mechanisms for data processing and storage to be smarter. In this context, big data is the relevant paradigm for the distributed and parallel processing of large data sets through thousands of clusters. It also offers a rich set of tools in order to improve data collection, storage, analysis, processing, optimization, and visualization. This article introduces the big data concept, its characteristics, and focuses in particular on the integration of it in a computing environment for human learning dedicated to online learning systems, and how the new methods, technologies, and tools of big data can enhance the future of online learning. Moreover, it proposes an approach for smoothly adapting the traditional e-learning systems to be suitable for big data ecosystems in cloud computing. Furthermore, it provides a methodology and architecture to incorporate the e-learning storage and computing in a Hadoop software library. Finally, the benefits and advantages associated with implementing big data in future online learning systems are discussed.


Computing environment for human learning (CEHL) Big data Cloud computing Online learning E-learning 


  1. Adhikari, B. K., Zuo, W., & Maharjan, R. (2017). A performance analysis of openstack cloud vs real system on hadoop clusters (pp. 194–201). IEEE.
  2. Anshari, M., Alas, Y., Sabtu, N. I. P. H., & Hamid, M. H. S. A. (2016). Online learning: trends, issues and challenges in the big data era. Journal of E-Learning and Knowledge Society, 12(1):121–134.Google Scholar
  3. Apache Flume. (2018). Retrieved from: Accessed 7 February 2018.
  4. Apache Sqoop. (2018). Retrieved from: Accessed 7 February 2018.
  5. Ashraf, A., El-Bakry, H., M. Abd El-razek, S., & El-Mashad, Y. (2015). Handling big data in e-learning. International Journal of Advanced Research in Computer Science & Technology (IJARCST 2015), pp. 47–51.Google Scholar
  6. Birjali, M., Beni-Hssane, A., & Erritali, M. (2018). Learning with big data technology: The future of education. In A. Abraham, A. Haqiq, A. Ella Hassanien, V. Snasel, & A. M. Alimi (Eds.), Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016 (Vol. 565, pp. 209–217). Cham: Springer International Publishing. Scholar
  7. Buyya, R., Calheiros, R. N., & Dastjerdi, A. V. (2016). Big data: Principles and paradigms. Cambridge: Elsevier/Morgan Kaufmann.Google Scholar
  8. Calheiros, R. N. (2016). Big data: principles and paradigms (1st ed.). Cambridge: Elsevier.Google Scholar
  9. Dahdouh, K., Dakak, A., & Oughdir, L. (2017). Integration of the cloud environment in E-learning systems. Transactions on Machine Learning and Artificial Intelligence, 5(4).
  10. Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51, 107.CrossRefGoogle Scholar
  11. Duygu, S. T., Demirezen, U., & Sagiroglu, S. (2016). Evaluations of big data processing. Services Transactions on Big Data.Google Scholar
  12. Fortino, G., Badica, C., Malgeri, M., & Unland, R. (2012). Proceedings of the 6th International Symposium on Intelligent Distributed Computing, Calabria, Italy, (1st ed.). Springer. Scholar
  13. Hadoop. (2018). Retrieved from: Accessed 30 April 2018.
  14. HDFS. (2018). Retrieved from: Accessed 7 February 2018.
  15. Hive. (2018). Retrieved from: Accessed 7 February 2018.
  16. Hwang, K. (2017). Big-data analytics for cloud, IoT and cognitive learning [electronic resource] (1st ed.). Wiley.Google Scholar
  17. Lin, H.-M., Chen, W.-J., & Nien, S.-F. (2014). The study of achievement and motivation by e-learning–a case study. International Journal of Information and Education Technology, 4(5), 421–425. Scholar
  18. NIST Big Data Public Working Group Definitions and Taxonomies Subgroup. (2015). NIST Big Data Interoperability Framework: Volume 1, Definitions (No. NIST SP 1500–1). National Institute of Standards and Technology.
  19. Pappas, C. (2014). Big Data in eLearning: The Future of eLearning Industry. (2014, July 24). eLearning Industry. Retrieved from: Accessed 6 February 2018.
  20. Podesta, J., Pritzker, P., Moniz, E., Holdren, J., & Zients, J. (2014). Big data: seizing opportunities, preserving values. Executive office of the president. Washington, D.C: The White House Office of Science & Technology.Google Scholar
  21. Scott, T. (2017). Using big data to improve workplace learning. eLearning Industry. Retrieved from: Accessed 1 May 2018.
  22. Spark. (2018). Retrieved from: Accessed 7 February 2018.
  23. YARN. (2018). Retrieved from: Accessed 7 February 2018.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Engineering Sciences LaboratoryFPT, Sidi Mohamed Ben Abdellah UniversityTazaMorocco
  2. 2.Research Laboratory in Management, Finance and Audit of OrganizationsENCG, Sidi Mohamed Ben Abdellah UniversityFezMorocco

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