8192430
Description
Mind Map by Danilo Rojas, updated more than 1 year ago
|
Created by Danilo Rojas
over 7 years ago
|
|
An Information Processing Approach to
Student Retention: A Multivariate
perspective
- Identification of most relevant factors for
student retention
- First year student
- Adams, Banks, Davis & Dickson, 2010;
- Hodges et al., 2013;
- Willcoxson et al., 2011).
- Kairamo,
2012
- Adams, Banks, Davis & Dickson, 2010;
- Minority groups
- Gonyea, Shoup & Kuh, 2008
- Carter,
2006
- Heilig & Darling-Hammond, 2008
- Allen (1999)
- Gonyea, Shoup & Kuh, 2008
- Womens in engineering
- student retention/attrition
- Grebennikov & Shah, 2012;
- Lodge,
2011
- Roberts, McGill & Hyland, 2012
- Demetriou & Schmitz-Sciborski,
2011
- Troxel, 2010
- Grebennikov & Shah, 2012;
- Student retention models
- Spady,
1970,
- Tinto, 1975,
1993
- Bean’s Student Attrition Model (1980, 1982
- Bean & Metzner, 1985
- Spady,
1970,
- First year student
- Multivariate techniques for student
retention
- Factors identifications
- Correlation Analysis
- Correlation Analysis
- Prediction
- Machine Learning and Data Mining
- 2016 - Use Educational Data Mining to Predict
Undergraduate Retention
- 2016 - Use Educational Data Mining to Predict
Undergraduate Retention
- Neuronal Networks
- Regression alaysis
- Astin and his colleagues (1987)
- Tross et al. (2000)
- Tross et al. (2000)
- Astin and his colleagues (1987)
- Logistic regression modeling
- Survival Analysis
- 2016 - Survival Analysis based Framework for Early
Prediction of Student Dropouts
- 2016 - Predictors of Retention and Achievement in a Massive Open
Online Course
- 2016 - Survival Analysis based Framework for Early
Prediction of Student Dropouts
- Machine Learning and Data Mining
- Factors identifications
Want to create your own Mind Maps for free with GoConqr? Learn more.