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An Information Processing Approach to Student Retention: A Multivariate perspective
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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
- Minority groups
- Gonyea, Shoup & Kuh, 2008
- Carter, 2006
- Heilig & Darling-Hammond, 2008
- Allen (1999)
- Womens in engineering
- student retention/attrition
- Grebennikov & Shah, 2012;
- Lodge, 2011
- Roberts, McGill & Hyland, 2012
- Demetriou & Schmitz-Sciborski, 2011
- Troxel, 2010
- Student retention models
- Spady, 1970,
- Tinto, 1975, 1993
- Bean’s Student Attrition Model (1980, 1982
- Bean & Metzner, 1985
- First year student
Multivariate techniques for student retention
- Factors identifications
- Correlation Analysis
- Prediction
- Machine Learning and Data Mining
- 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)
- 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
- Machine Learning and Data Mining
- Factors identifications
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- Mapa mental Examenes calificadores
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- Danilo Rojas
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