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New Dropout Prediction for Intelligent System

by Md.sarwar Kamal, Linkon Chowdhury, Sonia Farhana Nimmy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 16
Year of Publication: 2012
Authors: Md.sarwar Kamal, Linkon Chowdhury, Sonia Farhana Nimmy
10.5120/5778-8093

Md.sarwar Kamal, Linkon Chowdhury, Sonia Farhana Nimmy . New Dropout Prediction for Intelligent System. International Journal of Computer Applications. 42, 16 ( March 2012), 26-31. DOI=10.5120/5778-8093

@article{ 10.5120/5778-8093,
author = { Md.sarwar Kamal, Linkon Chowdhury, Sonia Farhana Nimmy },
title = { New Dropout Prediction for Intelligent System },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 16 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number16/5778-8093/ },
doi = { 10.5120/5778-8093 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:28.929270+05:30
%A Md.sarwar Kamal
%A Linkon Chowdhury
%A Sonia Farhana Nimmy
%T New Dropout Prediction for Intelligent System
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 16
%P 26-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main purpose of this research is to develop a dynamic dropout prediction model for universities, institutes and colleges. In this work, we first identify dependent and independent variables and dropping year to classify the successful from unsuccessful students. Then we have classify the data using Support Vector Machines(SVM). SVM helped the data set to be properly design and manipulated . The main purpose of applying this identification is to design a Knowledge Base which is sometimes known as joint probability distribution . The concepts of propositional logic helped to build the knowledge Base. Bayes theorem will perform the prediction by collecting the information from knowledge Base. Here we have considered most important factors to classify the successful students over unsuccessful students are gender, financial condition and dropping year. We also consider the socio-demographic variables such as age, gender, ethnicity, education, work status, and disability and study environment that may in-flounce persistence or dropout of students at university level

References
  1. . Al-Radaideh, Q. A. , Al-Shawakfa, E. M. , & Al-Najjar, M. I. (2006). Mining student data using decision trees. In the Proceedings of the 2006 International Arab Conference on Information Technology (ACIT'2006).
  2. . Baker, R. S. J. D. , & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1, 3-17.
  3. . Bean, J. P. , & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55, 485-540.
  4. . Boero, G. , Laureti, T. , & Naylor, R. (2005). An econometric analysis of student withdrawal and progres- sion in post-reform Italian universities. Centro Ricerche Economiche Nord Sud - CRENoS Working Paper 2005/04.
  5. . Cortez, P. , & Silva, A. (2008). Using data mining to predict secondary school student performance. In the
  6. . Proceedings of 5th Annual Future Business Technology Conference, Porto, Portugal, 5-12. Dekker, G. W. , Pechenizkiy, M. , & Vleeshouwers, J. M. (2009). Predicting student drop out: A case study.
  7. . Proceedings of the 2nd International Conference on Educational Data Mining (EDM'09). July 1-3, Cordoba, Spain, 41-50.
  8. . Hastie, T. , Tibshirani, R. , & Friedman, J. (2009). The elements of statistical learning: Data mining, infer- ence and prediction (2nd ed. ). New York: Springer.
  9. . Han, J. , & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed. ). Amsterdam: Elsevier. Herrera, O. L. (2006). Investigation of the role of pre- and post admission variables in undergraduate insti-
  10. . mutational persistence, using a Markov student flow model. PhD Dissertation, North Carolina State Uni-versity, USA.
  11. . Horstmanshof, L. , & Zimitat, C. (2007). Future time orientation predicts academic engagement among first-year university students. British Journal of Educational Psychology, 77 (3): 703-718.
  12. . Ishitani, T. T. (2003). A longitudinal approach to assessing attrition behavior among first-generation stu- dents: Time-varying effects of pre-college characteristics. Research in Higher Education, 44(4), 433-449.
  13. . Ishitani, T. T. (2006). Studying attrition and degree completion behavior among first-generation college students in the United States. Journal of Higher Education, 77(5), 861-885.
  14. . Jun, J. (2005). Understanding dropout of adult learners in e-learning. PhD Dissertation, The University of Georgia, USA.
  15. . Kember, D. (1995). Open learning courses for adults: A model of student progress. Englewood Cliffs, NJ: Education Technology.
  16. . Luan, J. , & Zhao, C-M. (2006). Practicing data mining for enrollment management and beyond. New Di- rections for Institutional Research, 31(1), 117-122.
  17. . Murtaugh, P. , Burns, L. , & Schuster, J. (1999). Predicting the retention of university students. Research inHigher Education, 40(3), 355-371.
  18. . Nandeshwar, A. , & Chaudhari, S. (2009). Enrollment prediction models using data mining. Retrieved Janu- ary 10, 2010,
  19. . Nisbet, R. , Elder, J. , & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Amsterdam: Elsevier.
  20. . Noble, K. , Flynn, N. T. , Lee, J. D. , & Hilton, D. (2007). Predicting successful college experiences: Evi- dence from a first year retention program. Journal of College Student Retention: Research, Theory & Practice, 9(1), 39-60.
  21. . Pascarella, E. T. , Duby, P. B. , & Iverson, B. K. (1983). A test and reconceptualization of a theoretical model of college withdrawal in a commuter institution setting. Sociology of Education, 56, 88-100.
  22. . Pratt, P. A. , & Skaggs, C. T. (1989). First-generation college students: Are they at greater risk for attrition than their peers? Research in Rural Education, 6(1), 31-34.
  23. . Reason, R. D. (2003). Student variables that predict retention: Recent research and new developments. NASPA Journal, 40(4), 172-191.
  24. . Rokach, L. , & Maimon, O. (2008). Data mining with decision trees – Theory and applications. New Jersey: World Scientific Publishing.
  25. . Yu, C. H. , DiGangi, S. , Jannasch-Pennell, A. , Lo, W. , & Kaprolet, C. (2007). A data-mining approach to differentiate predictors of retention. In the Proceedings of the Educause Southwest Conference, Austin, Texas, USA.
  26. . Romero, C. , & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33, 135-146.
  27. . Simpson, O. (2006). Predicting student success in open and distance learning. Open Learning, 21(2), 125-138.
  28. . Siraj, F. , & Abdoulha, M. A. (2009). Uncovering hidden information within university's student enrolment data using data mining. MASAUM Journal of Computing, 1(2), 337-342.
  29. . Strayhorn, T. L. (2009). An examination of the impact of first-year seminars on correlates of college stu- dent retention. Journal of the First-Year Experience & Students in Transition, 21(1), 9-27.
  30. . Tharp, J. (1998). Predicting persistence of urban commuter campus students utilizing student background characteristics from enrollment data. Community College Journal of Research and Practice, 22, 279-294.
  31. . Artificial Intelligence A modern Approach by Stuart Russell and Peter Norvig, ISBN 81-297-0041-7
Index Terms

Computer Science
Information Sciences

Keywords

Intelligent System Dynamic Dropout Prediction Joint Probability Distribution Bayes Theorem Dependent Ad Independent Variables Propositional Logic Knowledge Base mathlab svm

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