Estimating student retention and degree‐completion time: Decision trees and neural networks vis‐à‐vis regression
Author:
- 1 September 2006
- journal article
- research article
- Published by Wiley in New Directions for Institutional Research
- Vol. 2006 (131) , 17-33
Abstract
Focusing on student retention and time to degree completion, this study illustrates how institutional researchers may benefit from the power of predictive analyses associated with data‐mining tools.Keywords
This publication has 10 references indexed in Scilit:
- Measuring Determinants of Student Return VS. Dropout/Stopout VS. Transfer: A First-to-Second Year Analysis of New FreshmenResearch in Higher Education, 2005
- Does College Still Pay?The Economists’ Voice, 2005
- Boosted decision trees as an alternative to artificial neural networks for particle identificationNuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2005
- What Satisfies Students? Mining Student-Opinion Data with Regression and Decision Tree AnalysisResearch in Higher Education, 2004
- Artificial Neural Networks: A New Approach to Predicting Application BehaviorResearch in Higher Education, 2002
- 10.1162/153244304322972694Applied Physics Letters, 2000
- A comparison of conventional linear regression methods and neural networks for forecasting educational spendingEconomics of Education Review, 1999
- Popular Ensemble Methods: An Empirical StudyJournal of Artificial Intelligence Research, 1999
- Automatic Construction of Decision Trees from Data: A Multi-Disciplinary SurveyData Mining and Knowledge Discovery, 1998
- A Comparison of Logistic Regression to Decision-Tree Induction in a Medical DomainComputers and Biomedical Research, 1993