An Investigation on Educational Data Mining to Analyze and Predict the Student’s Academic Performance Using Visualization
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.
KeywordsEDM 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.
- 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
- 4.B.A. Myers, R. Chandhok, A. Sareen, Automatic Data Visualization for Novice Pascal Programmers, pp. 192–198 (1988)Google Scholar
- 6.C. Romero, S. Ventura, Data mining in education. Wiley Interdis. Rev.: Data Min. Knowl. Discovery 3(1), 12–27 (2013)Google Scholar
- 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
- 11.J. Ahrens, B. Geveci, C. Law, Paraview: an end-user tool for large data visualization. Vis. Handb. 717 (2005)Google Scholar
- 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.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.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