A comparative analysis of machine learning techniques for student retention management

Decision Support Systems

Volumn 49, Issue 4, 2010, Pages 498-506

  Delen, Dursun ^a  

^a OKLAHOMA STATE UNIVERSITY   (United States)

Author keywords

Classification; Machine learning; Prediction; Retention management; Sensitivity analysis; Student attrition

Indexed keywords

ANALYTICAL MODEL; CLASSIFICATION; COMPARATIVE ANALYSIS; DATA MINING TECHNIQUES; DATA SETS; DECISION MAKERS; ENROLLMENT MANAGEMENT; FINANCIAL VARIABLES; HIGHER EDUCATION INSTITUTIONS; INDIVIDUAL MODELS; MACHINE LEARNING TECHNIQUES; MACHINE-LEARNING; PREDICTION; RETENTION MANAGEMENT; STUDENT RETENTION; WELLBEING;

DATA MINING; FORECASTING; LEARNING SYSTEMS; MATHEMATICAL MODELS; SENSITIVITY ANALYSIS; STUDENTS; TEACHING;

EDUCATION COMPUTING;

EID: 78049421754     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2010.06.003     Document Type: Article

References (46)

1. A. Astin What Matters in College? Four Critical Years Revisited 1993 Jossey-Bass San Francisco
2. J.B. Berger, and J.M. Braxton Revising Tinto's interactionalist theory of student departure through theory elaboration: examining the role of organizational attributes in the persistence process Research in Higher Education 39 2 1998 103 119
3. J.B. Berger, and J.F. Milem The role of student involvement and perceptions of integration in a causal model of student persistence Research in Higher Education 40 6 1999 641 664
4. L. Breiman Random forests Machine Learning 45 1 2001 5 32
5. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and Regression Trees 1984 Wadsworth and Brooks/Cole Advanced Books and Software Monterey, CA
6. A.F. Cabrera, A. Nora, and M.A. Castaneda College persistence: structural equations modeling test of an integrated model of student retention Journal of Higher Education 64 2 1993 123 139
7. A.L. Caison Analysis of institutionally specific retention research: a comparison between survey and institutional database methods Research in Higher Education 48 4 2007 435 449
8. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 2000 Cambridge University Press London, U.K
9. G. Davis Sensitivity analysis in neural net solutions IEEE Transactions on Systems, Man, and Cybernetics 19 1989 1078 1082
10. S.M. Deberard, G.I. Julka, and L. Deana Predictors of academic achievement and retention among college freshmen: a longitudinal study College Student Journal 38 1 2004 66 81
11. D. Delen, G. Walker, and A. Kadam Predicting breast cancer survivability: a comparison of three data mining methods Artificial Intelligence in Medicine 34 2 2004 113 127
12. D. Delen, R. Sharda, and P. Kumar Movie forecast guru: a web-based DSS for Hollywood managers Decision Support Systems 43 4 2007 1151 1170
13. A.M. Gansemer-Topf, and J.H. Schuh Institutional selectivity and institutional expenditures: examining organizational factors that contribute to retention and graduation Research in Higher Education 47 6 2006 613 642
14. T. Hastie, R. Tibshirani, and J. Friedman The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2001 Springer Publishing New York, NY
15. S. Haykin Neural Networks and Learning Machines 3rd ed. 2008 Prentice Hall Publishing Englewood Cliffs, New Jersey
16. J.C. Hermaniwicz College Attrition at American Research Universities: Comparitive Case Studies 2003 Agathon Press New York
17. S. Herzog Measuring determinants of student return vs. dropout/stopout vs. transfer: a first-to-second year analysis of new freshmen Research in Higher Education 46 8 2005 883 928
18. S.K. Hochstein, and R.R. Butler The effects of the composition of a financial aids package on student retention Journal of Student Financial Aid 13 1 1983 21 27
19. K. Hornik, M. Stinchcombe, and H. White Universal approximation of an unknown mapping and its derivatives using multilayer feed-forward network Neural Networks 3 1990 359 366
20. T.T. Ishitani Studying attrition and degree completion behavior among first-generation college students in the United States Journal of Higher Education 77 5 2006 861 885
21. M.Y. Kiang A comparative assessment of classification algorithms Decision Support Systems 35 2003 441 454
22. R. Kohavi A study of cross-validation and bootstrap for accuracy estimation and model selection The Proceedings of the 14th International Conference on AI (IJCAI) 1995 Morgan Kaufmann San Mateo, CA 1137 1145
23. L.K. Lau Institutional factors affecting student retention Education 124 1 2003 126 137
24. A. Lemmens, and C. Croux Bagging and boosting classification trees to predict churn Journal of Marketing Research 43 2 2006 276 286
25. X. Li, G.C. Nsofor, and L. Song A comparative analysis of predictive data mining techniques International Journal of Rapid Manufacturing 1 2 2009 150 172
26. M.A. Mannan Student attrition and academic and social integration: application of Tinto's model at the university of Papua New Guinea Higher Education 53 2 2007 147 165
27. T.E. Miller, and C.H. Herreid Analysis of variables: predicting sophomore persistence using logistic regression analysis at the University of South Florida College and University 85 1 2010 2 11
28. T.E. Miller, and T.M. Tyree Using a model that predicts individual student attrition to intervene with those who are most at risk College and University 84 3 2009 12 21
29. K.B. Porter Current trends in student retention: a literature review Teaching and Learning in Nursing 3 1 2008 3 15
30. J. Quinlan Induction of decision trees Machine Learning 1 1986 81 106
31. J. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann San Mateo, CA
32. A. Saltelli Making best use of model evaluations to compute sensitivity indices Computer Physics Communications 145 2002 280 297
33. A. Saltelli, S. Tarantola, F. Campolongo, and M. Ratto Sensitivity Analysis in Practice - A Guide to Assessing Scientific Models 2004 John Wiley and Sons Hoboken, NJ
34. G. Seni, J. Elder, and R. Grossman Ensemble Methods in Data Mining: Improving Accuracy through Combining Predictions 2010 Morgan and Claypool Publishers San Rafael, CA
35. R. Sharda, and D. Delen Predicting box-office success of motion pictures with neural networks Expert Systems with Applications 30 2 2006 243 254
36. C. Shearer The CRISP-DM model: the new blueprint for data mining Journal of Data Warehousing 5 2000 13 22
37. SPSS SPSS PASW Modeler (formerly Clementine) User Manual. A Comprehensive Data Mining Toolkit 2010
38. J.O. Stampen, and A.F. Cabrera Exploring the effects of student aid on attrition Journal of Student Financial Aid 16 2 1986 28 37
39. E.H. Thomas, and N. Galambos What satisfies students? Mining student opinion data with regression and decision tree analysis Research in Higher Education 45 3 2004 251 269
40. V. Tinto Leaving College: Rethinking the Causes and Cures of Student Attrition 1987 University of Chicago Press Chicago
41. V. Tinto Leaving college: Rethinking the Causes and Cures of Student Attrition Second ed. 1993 The University of Chicago Press Chicago
42. V. Tinto Classroom as communities: exploring the educational character of student persistence Journal of Higher Education 68 6 1997 599 623 (Pubitemid 127145621)
43. J.P. Vandamme, N. Meskens, and J.F. Superby Predicting academic performance by data mining methods Education Economics 15 4 2007 405 419
44. C.P. Veenstra A strategy for improving freshman college retention Journal for Quality and Participation 31 4 2009 19 23
45. J.N. Wetzel, D. O'Toole, and S. Peterson Factors affecting student retention probabilities: a case study Journal of Economics and Finance 23 1 1999 45 55
46. R.L. Wilson, and R. Sharda Bankruptcy prediction using neural networks Decision Support Systems 11 1994 545 557