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Reseach Article

A Novel Predictive Modeling System to Analyze Students at Risk of Academic Failure

by Lotfi Najdi, Brahim Er-Raha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 6
Year of Publication: 2016
Authors: Lotfi Najdi, Brahim Er-Raha

Lotfi Najdi, Brahim Er-Raha . A Novel Predictive Modeling System to Analyze Students at Risk of Academic Failure. International Journal of Computer Applications. 156, 6 ( Dec 2016), 25-30. DOI=10.5120/ijca2016912482

@article{ 10.5120/ijca2016912482,
author = { Lotfi Najdi, Brahim Er-Raha },
title = { A Novel Predictive Modeling System to Analyze Students at Risk of Academic Failure },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 6 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016912482 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:01:52.707759+05:30
%A Lotfi Najdi
%A Brahim Er-Raha
%T A Novel Predictive Modeling System to Analyze Students at Risk of Academic Failure
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 6
%P 25-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Supporting academic success is a central focus of higher education institutions. To address this challenge, predictive techniques could be applied in order to build models that predict academic performance such as student retention and graduation. This paper presents a predictive system for modeling and scoring students achievements. Based on student historical data, predictive models are developed to classify students who are at risk of dropping out and not graduating, by examining CART and random forest as an ensemble method. Generated models are then applied on freshman student’s data to predict their academic behavior. The examination of the CART and Random forest algorithms on student data resulted tree based models with accuracy of 88%. The result of this work was an interactive system implemented using R language and shiny framework, including data preparation, model building and scoring engine. The predictive system, which is present in this paper, might help decision makers gaining a deeper insight in students’ academic achievements and optimize their human and financial resources toward effective student support services.

  1. C. Romero and S. Ventura, “Educational Data Mining: A Review of the State of the Art,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 6, pp. 601–618, Nov. 2010.
  2. R. S. Baker and K. Yacef, “The state of educational data mining in 2009: A review and future visions,” JEDM-Journal of Educational Data Mining, vol. 1, no. 1, pp. 3–17, 2009.
  3. G. W. Dekker, M. Pechenizkiy, and J. M. Vleeshouwers, “Predicting Students Drop Out: A Case Study.,” International Working Group on Educational Data Mining, 2009.
  4. S. K. Yadav, B. Bharadwaj, and S. Pal, “Mining Education data to predict student’s retention: a comparative study,” International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, 2012.
  5. D. Kabakchieva, “Predicting Student Performance by Using Data Mining Methods for Classification,” Cybernetics and Information Technologies, vol. 13, no. 1, Jan. 2013.
  6. Z. J. Kovacic, “Predicting student success by mining enrolment data,” Research in Higher Education Journal, vol. 15, p. 1, 2012.
  7. S. Pal, “Mining Educational Data to Reduce Dropout Rates of Engineering Students,” International Journal of Information Engineering and Electronic Business, vol. 4, no. 2, pp. 1–7, Apr. 2012.
  8. M. A. Yehuala, “Application Of Data Mining Techniques For Student Success And Failure Prediction (The Case Of Debre_Markos University),” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME, vol. 4, no. 4, 2015.
  9. B. R. Subedi and B. Johnson, “Predicting High School Graduation and Dropout Using a Hierarchical Generalized Linear Model Approach,” 2007.
  10. L. Agnihotri and A. Ott, “Building a student at-risk model: an end-to-end perspective,” in Proc. Int. Conference on Educational Data Mining Conference (EDM), 2014, pp. 209–212.
  11. C. Beeley, Web Application Development with R Using Shiny. Packt Publishing, 2013.
  12. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, 2nd ed. 2009. Corr. 7th printing 2013 edition. New York, NY: Springer, 2011.
  13. M. J. Zaki and W. Meira, Data mining and analysis: fundamental concepts and algorithms. New York, NY: Cambridge University Press, 2014.
  14. A.-L. Boulesteix, S. Janitza, J. Kruppa, and I. R. König, “Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 2, no. 6, pp. 493–507, 2012.
  15. J. Strickland, Predictive Analytics Using R., 2015.
Index Terms

Computer Science
Information Sciences


Educational Data Mining Predictive modeling Random Forest Ensemble learning