Big Data Analytics Process Implementation on a Educational Data Set Extracted from Online Testing System

9th International Scientific Conference Technics and Informatics in Education – TIE 2022 (2022) стр. 229-236

АУТОР(И): Gabrijela Dimić, Ivana Milošević, Ljiljana Pecić

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DOI: 10.46793/TIE22.229D


The paper presents an application of the big data analytics process on a data extracted from the educational system for online testing. The procedure of data migration to a non-relational system was implemented. Big data analytics process was realized through query processing in MongoDB and analysis results visualization in Microsoft Power BI. The contribution of the research presented in this paper refers to the importance of applying the big data concept for analyze data set extracted from educational systems in order to discover information and knowledge important for improving the efficiency of the educational process.


big data; education system; data analytics; MongoDB; Power BI visualization


  • [1] Horton, W., Horton, K. (2013). E-learning tools and technologies. A consumer’s guide for trainers, teachers, educators, and instructional designers. Wiley Publishing Inc.
  • [2] Ip, RH., Ang, LM., Seng, KP., Broster, JC., Pratley, JE. (2018, August). Big data and machine learning for crop protection. Computers and Electronics in Agriculture. 151, 376-83. doi: 10.1016/j.compag.2018.06.008.
  • [3] Seng, KP., Ang, LM., Ooi, CS.(2016, Jul). A combined rule-based & machine learning audio-visual emotion recognition approach. IEEE Transactions on Affective Computing, 9(1), 3-13.doi:10.1109/TAFFC.2016.2588488.
  • [4] James, M. (2011, May). Big Data: The next frontier for innovation, competition and productivity. McKinsey Global Institute., (accessed, 2022, Jun).
  • [5] Mostow, J., Beck, J. (2006, Jun). Some useful tactics to modify, map and mine data from intelligent tutors. Natural Language Engineering, 12(2), 195-208.
  • [6] Rice, W.H. (2006). Moodle e-learning course development. A complete guide to succeful learning using Moodle. Packt publishing.
  • [7] Alblawi, A.S., Alhamed, A.A. (2017, Nov 16). Big data and learning analytics in higher education: Demystifying variety, acquisition, storage, NLP and analytics. In2017 IEEE conference on big data and analytics (ICBDA), 124-129.
  • [8] Machova, R., Komarkova, J., Lnenicka, M. (2016, Oct 10). Processing of big educational data in the cloud using apache Hadoop. 2016 International Conference on Information Society (i-Society), 46 – 49.
  • [9] Swathi, R., et al. (2017, Jun 15). Systematic approach on big data analytics in education systems. 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 420-423.
  • [10] Chen, Jinhua, et al. (2017, Mar 10). Research on architecture of education big data analysis system. 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 601-605.
  • [11] Michalik, P., Štofa, J., Zolotova, I., (2014). Concept definition for Big Data architecture in the education system. In2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 331-334.
  • [12] Klašnja‐Milićević, A., Ivanović, M., Budimac, Z., (2017, Nov). Data science in education: Big data and learning analytics. Computer Applications in Engineering Education, 25(6), 1066-78.
    [13] Muhammad, R.N., Tasmin, R., Aziati, A.N., (2020, Apr). Sustainable competitive advantage of big data analytics in higher education sector: An Overview. InJournal of Physics: Conference Series, 1529(4), 042100.
    [14] Diebold, F.X., (2012). On the Origin (s) and Development of the Term’Big Data.
  • [15] Diebold, F.X., (2003, Feb). Big data dynamic factor models for macroeconomic measurement and forecasting. InAdvances in Economics and Econometrics: Theory and Applications, Eighth World Congress of the Econometric Society, 115-122.
  • [16] EDPS (2015). Meeting the challenges of big data. European Data Protection Supervisor (EDPS). Retrieved April 14, 2017.
  • [17] Erl, T., Khattak, W., Buhler, P., (2015). Big Data Fundamentals Concepts, Drivers and Techniques. Prentice Hall.
  • [18] Yu, S., Guo, S.,(2016). Big Data Concepts, Theories, and Applications. Springer.
  • [19], (accessed, 2022, July).
  • [20] Demchenko, Y., Zhao, Z., Grosso, P., Wibisono, A., & De Laat, C. (2012, December). Addressing big data challenges for scientific data infrastructure. In4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, 614-617.
  • [21] Moniruzzaman, A. B. M., & Hossain, S. A. (2013). Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv preprint arXiv:1307.0191.
  • [22] Prokin, D., Čoko, D., Dimić, G., Đenić, S., Savić, A., Bogojević, B., (2016). Online aplikacija za testiranje učenika za pripremu prijemnog ispita. Proc. INFOTEH, Jahorina, Bosnia and Herzegovina, 15, 710-713.
  • [23] Shannon, B., Eoin, B., Kristina, C., (2109, December). MongoDB: The Definitive Guide: Powerful and Scalable Data Storage. O’Reilly Media; 3rd edition.
  • [24] Li, Y., & Manoharan, S. (2013, August). A performance comparison of SQL and NoSQL databases. In 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), 15-19.
  • [25]