Using High Performance Computing and Open Source Technologies for Solving Behaviour Analytics Problems in E-Learning


  • Laimonis Zacs Liepaja University (LV)
  • Anita Jansone Liepaja University (LV)



Apache Hadoop, Big Data, E-Learning technologies, High Performance Computing, online learning platform, open-source software


In this paper the authors describe solution for solving various analytical problems in E-learning, Course Management Systems like Moodle by using HPC (High Performance Computing) and Apache Hadoop open source technologies in Liepaja University. The problem is that nowadays there are collecting huge amounts of analytics data from several gigabytes to petabytes, which is hard to store, process, analyse and visualize. This article reflects one of the solutions concerning distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that can store and process the data, can scale without limits and provides technological opportunities of reliable, scalable and distributed computing.



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AWStats (2015). What is AWStats. Retrieved from

Cloudera (2015). Hadoop and Big Data. The Platform for Big Data and the Leading Solution for Apahe Hadoop in the Enterprise – Cloud Retrieved from

Drazdilova P., Obadi G., Slaninova K., Al-Dubaee S., Martinovic J., and Snasel V. (2010). Computational intelligence methods for data analysis and mining of elearning activities. In F. Xhafa, S. Caballe, A. Abraham, T. Daradoumis, and J. Perez, editors, Studies in Computational Intelligence For Technology Enhanced Learning, volume 273, pages 195–224. Heidelberg, Germany: Springer-Verlag

GISMO (2015). Graphical Interactive Student Monitoring Tool for Moodle. Retrieved from

Graf, S., List, B. (2005). An evaluation of open source e-learning platforms stressing adaption issues. In Proceedings of 5th IEEE International Conference on Advanced Learning Technologies (pp. 163-165).

Hadoop (2015). Hadoop 1.1.2 Documentation. Cluster Setup. Retrieved from

Mazza R., Milani C. (2005). Exploring Usage Analysis in Learning Systems: Gaining Insights from Visualisations. AIED Workshops (AIED’05) (2015). Current Moodle Statistics. Retrieved from

Ninoriya Suman, Chawan P.M., Meshram B.B. (2011). CMS, LMS and LCMS For eLearning. IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 2, ISSN (Online): 1694-0814,

Rogers P., Berg G., Boettcher J., Howard C., Justice L., Schenk.Hershey (editors). (2009). Course Management Meets Social Networking in Moodle. M. Crosslin. The Encyclopedia of Distance Learning, Second Edition. Information Science Reference New York, Idea Group Inc (IGI).

Rosenberg M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age. New York: McGraw-Hill.

Zhang H., Almeroth K., Knight A., Bulger M., Mayer R. (2007). C. Montgomerie & J. Seale (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2007 (pp. 4415-4422). Chesapeake, VA: AACE.




How to Cite

Zacs, L., & Jansone, A. (2015). Using High Performance Computing and Open Source Technologies for Solving Behaviour Analytics Problems in E-Learning. SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference, 4, 529-538.