A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

Journal of Education for Business

Volumn 85, Issue 5, 2010, Pages 258-263

  Schumacher, Phyllis ^a   Olinsky, Alan ^a   Quinn, John ^a   Smith, Richard ^a  

^a BRYANT UNIVERSITY   (United States)

Author keywords

actuarial mathematics; classification trees; data mining; logistic regression; neural networks

Indexed keywords

EID: 84922770420     PISSN: 08832323     EISSN: 19403356     Source Type: Journal    
DOI: 10.1080/08832320903449477     Document Type: Article

References (14)

1. Agdelen, Z., Haydar, A., and Kanani, A., Analyzing the factors affecting the success in university entrance examination through the use of artificial neural networks. Paper presented at the 7th International Educational Technology (IETC) Conference, Nicosia, Turkish Republic of Northern Cyprus. Retrieved April 2, 2010, from http://www.eric.ed.gov/ERICWebPortal/contentdelivery/servlet/ERICServlet?accno=ED500088
2. Hardgrave, B. C., Wilson, R. L., and Kent, K. A., 1994. Predicting graduate student success: A comparison of neural networks and traditional techniques. Computers & Operations Research, 21: 249–263.
3. Herzog, S., 2006. Estimating student retention and degree completion time: Decision trees and neural networks vis-à-vis regression. New Directions for Institutional Research, 131: 17–33.
4. Hoefer, P., and Gould, J., 2000. Assessment of admission criteria for predicting students’ academic performance in graduate business programs. Journal of Education for Business, 75: 225–229.
5. Ma, X., 2005. Growth in mathematics achievement: Analysis with classification and regression trees. Journal of Educational Research, 99: 78–86.
6. Naik, B., and Ragothaman, S., 2004. Using neural network to predict MBA student success. College Student Journal, 38: 143–149.
7. Ranjan, J., and Malik, K., 2007. Effective educational process: A data mining approach. VINE, 37: 502–515.
8. SAS Institute. 2006. SAS (version 5.2), Cary, NC: Author.
9. Sauer, P., and O’Donnell, J., 2006. “ The impact of new major offerings on student retention ”. In Journal of Marketing for Higher Education Vol. 16, 135–155.
10. Smith, R., and Schumacher, P., 2005. Predicting success for actuarial students in undergraduate mathematics courses. College Student Journal, 39: 165–177.
11. Smith, R., and Schumacher, P., 2006. Academic attributes of college freshmen that lead to success in actuarial studies in a business college. Journal of Education for Business, 81: 256–260.
12. SPSS. 2006. SPSS Clementine 10.1 node reference, Chicago: Author.
13. Vandamme, J.-P., Meskens, N., and Superby, J.-F., 2007. Predicting academic performance by data mining methods. Education Economics, 15: 405–419.
14. Veitch, W., Identifying characteristics of high school dropouts: Data mining with a decision tree model. Paper presented at the 62nd Annual Meeting of the American Educational Research Association. San Diego, California. Retrieved March 20, 2009, from http://www.eric.ed.gov/ERICWebPortal/contentdelivery/servlet/ERICServlet?accno=ED490086