Stream Processing for Performance Analysis of Identifying Dropout Students utilizing Different Decision Tree Based Methods

Authors

  • Sumita Guddin  PG Student, Department of CSE/SDMCET/Dharwad, Karnataka, India
  • Dr. R. N. Yadawad  Professor Department of CSE/SDMCET/Dharwad, Karnataka, India
  • Dr. U.P. Kulkarni  Professor Department of CSE/SDMCET/Dharwad, Karnataka, India

DOI:

https://doi.org//10.32628/CSEIT228417

Keywords:

Important attributes, Decision tree, various decision tree-based approaches, dropouts.

Abstract

A person's ability to lead a stable, affluent life is made possible through education. In the same way, a country's development may be influenced by the proportion of its population with a higher level of education. This number does, however, decline because of early schooling dropouts. Furthermore, a nation's resources are diminished if a student cannot continue because of a dropout. Although the number of dropouts is constantly falling, it is still very challenging for educational institutions to identify these individuals. An educational institution's first priority is to improve student Performance; therefore, it makes sure that every student graduates on time. Nevertheless, a significant barrier that has a negative effect on this goal is student dropout. Understanding the causes of dropouts is necessary to finding a solution. The causes differ from one student to another; some are connected to the student's workload and mental fortitude. Various ways using Decision Tree (DT) methodologies have been suggested and studied in this study.

References

  1. A. Bowers, and R. Sprott, “Why tenth graders fail to finish high school: a dropout typology latent class analysis,” Journal of Education for Students Placed at Risk (JESPAR), vol. 17, no. 3, pp. 129-148, 2012.
  2. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “Knowledge discovery and data mining: Towards a unifying framework,” in the 2nd International Conference on Knowledge Discovery and Data Mining (KDD), Aug. 1996, pp. 82-88.
  3. C. J. Carmona, P. Gonzales, M. J. Jesus, and F. Herrera, “NMEEF-SD: Non-dominated Multi-objective Evolutionary algorithm for Extracting Fuzzy Rules in Subgroup Discovery,” in IEEE international conference on fuzzy systems, pp. 1706-1711, 2010.
  4. Afterschool, A. (2011). STEM learning in afterschool: An analysis of impact and outcomes. Retrieved from  http://www.afterschoolalliance.org/STEM-Afterschool-Outcomes.pdf
  5. Z. Kovacic, “Early prediction of student success: Mining students’ enrolment data.” Proceedings of Informing Science & IT Education Conference, 2010. 
  6. Zwedin, S. 2014. Computing Degrees and Enrollment Trends: From the 2012-2014 CRA Talbee Survey. Computing Research Association, Washington D.C.
  7. Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education, 39(4), 361-377.
  8. Y. Zhao, and Y. Zhang, “Comparison of Decision Tree Methods for finding active objects,” National Astronomical Observation, vol. 41, pp. 1955-1959, 2008.
  9. W. N. H. W. Mohamed, M. N. M. Salleh, and A. H. Omar, “A Comparative Study of Reduced Error Pruning Method in Decision Tree Algorithms,” In IEEE International Conference on Control System, Computing and Engineering, 23 - 25 Nov. 2012.
  10. W. Chen, X. Xie, J. Wang, B. Pradhan, H. Hong, D. T. Bui, Z. Duan, and J. Ma, “A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility,” Catena 151, pp. 147-160, 2017
  11. T. Fawcett, “An introduction to ROC analysis,” Pattern recognition letters, vol. 27, no. 8, pp. 861-874, 2006.
  12. A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machine learning algorithms,” Pattern recognition, vol. 30, no. 7, pp. 1145-1159, 1997.
  13. K. H. Walse, R. V. Dharaskar, and V. M. Thakare, “A study of human activity recognition using AdaBoost classifiers on WISDM dataset,” The Institute of Integrative Omics and Applied Biotechnology Journal, 2016 Jan 1;7(2):68-76
  14. K. Shaleena and S. Paul, “Data mining techniques for predicting student performance,” in Engineering and Technology (ICETECH), 2015 IEEE International Conference on. IEEE, 2015, pp. 1–3.
  15. M. Kumar, A. Singh, and D. Handa, “Literature survey on educational dropout prediction,” IJ Education and Management Engineering, vol. 2, pp. 8–19, 2017.
  16. M. Samner, E. Frank, and M. Hall, “Speeding up Logistic Model Tree Induction,” In European Conference on Principles of Data Mining and Knowledge Discovery. Springer, Berlin, Heidelberg.
  17. J. R. Quinlal, “Bagging, Boosting, and C4.5”, In AAAI/IAAI, Vol. 1, pp. 725-730. 1996.

Downloads

Published

2022-08-30

Issue

Section

Research Articles

How to Cite

[1]
Sumita Guddin, Dr. R. N. Yadawad, Dr. U.P. Kulkarni, " Stream Processing for Performance Analysis of Identifying Dropout Students utilizing Different Decision Tree Based Methods, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 4, pp.150-156, July-August-2022. Available at doi : https://doi.org/10.32628/CSEIT228417