An Investigation on Educational Data Mining to Analyze and Predict the Student’s Academic Performance Using Visualization

  • J. Dheeraj KumarEmail author
  • K. R. Shankar
  • R. A. K. Saravanaguru
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)


Presently, educational institutions compile and store huge volumes of data such as student’s enrollment details, academic history, attendance records, and as well as their examination results. Traditional data mining approaches cannot be directly applied for visualization so we are using Pandas software library framework for preprocessing of the academic’s data and visualization of the data using matplotlib and seaborn libraries are used in this approach to get better results and easily understand and predict the outcomes from the data.


EDM Academic performance MatplotLib Visualization 



We undertake that we have the required permission to use images/dataset in our work from suitable authority and we shall be solely responsible if any conflicts arise in the future.


  1. 1.
    C. Romero, S. Ventura, Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(6), 601–618 (2010)CrossRefGoogle Scholar
  2. 2.
    W. Villegas-Ch, S. Luján-Mora, D. Buenaño-Fernandez, Palacios-Pacheco X, Big Data, The next step in the evolution of educational data analysis, in International Conference on Information Theoretic Security, (Springer, Cham, 2018), pp. 138–147Google Scholar
  3. 3.
    A. Peña-Ayala, Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst. Appl. 41(4), 1432–1462 (2014)CrossRefGoogle Scholar
  4. 4.
    B.A. Myers, R. Chandhok, A. Sareen, Automatic Data Visualization for Novice Pascal Programmers, pp. 192–198 (1988)Google Scholar
  5. 5.
    B. Williamson, Digital education governance: data visualization, predictive analytics, and ‘real-time’policy instruments. J. Educ. Policy 31(2), 123–141 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    C. Romero, S. Ventura, Data mining in education. Wiley Interdis. Rev.: Data Min. Knowl. Discovery 3(1), 12–27 (2013)Google Scholar
  7. 7.
    K. Khare, H. Lam, A. Khare, Educational Data Mining (EDM): Researching Impact on Online Business Education (Springer, Cham, 2018, In On the Line), pp. 37–53zbMATHGoogle Scholar
  8. 8.
    G. Siemens, R.S. d Baker, Learning analytics and educational data mining: towards communication and collaboration, in Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (ACM, USA, 2012), pp. 252–254Google Scholar
  9. 9.
    U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27–34 (1996)CrossRefGoogle Scholar
  10. 10.
    S. Dietze, D. Taibi, M. d’Aquin, Facilitating scientometrics in learning analytics and educational data mining–the LAK dataset. Semant. Web 8(3), 395–403 (2017)CrossRefGoogle Scholar
  11. 11.
    J. Ahrens, B. Geveci, C. Law, Paraview: an end-user tool for large data visualization. Vis. Handb. 717 (2005)Google Scholar
  12. 12.
    R. Rew, G. Davis, NetCDF: an interface for scientific data access. IEEE Comput. Graphics Appl 10(4), 76–82 (1990)CrossRefGoogle Scholar
  13. 13.
    M. Chen, D. Ebert, H. Hagen, R.S. Laramee, R. Van Liere, K.L. Ma, … D. Silver, Data, information, and knowledge in visualization. IEEE Comput. Graphics Appl. 29(1) (2009)CrossRefGoogle Scholar
  14. 14.
    W. Peng, M.O. Ward, E.A. Rundensteiner, Clutter reduction in multi-dimensional data visualization using dimension reordering, in IEEE Symposium on Information Visualization, INFOVIS, pp. 89–96 (2004)Google Scholar
  15. 15.
    M. Khan, S.S. Khan, Data and information visualization methods, and interactive mechanisms: a survey. Int. J. Comput. Appl. 34(1), 1–14 (2011)Google Scholar
  16. 16.
    K. Borner, Y. Zhou, A software repository for education and research in information visualization, in Fifth International Conference on IEEE Information Visualization Proceedings, pp. 257–262 (2001)Google Scholar
  17. 17.
    W. Xing, R. Guo, E. Petakovic, S. Goggins, Participation-based student final performance prediction model through interpretable genetic programming: integrating learning analytics, educational data mining and theory. Comput. Hum. Behav. 47, 168–181 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • J. Dheeraj Kumar
    • 1
    Email author
  • K. R. Shankar
    • 1
  • R. A. K. Saravanaguru
    • 1
  1. 1.School of Computer Science & EngineeringVellore Institute of TechnologyVelloreIndia

Personalised recommendations