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An Information Processing Approach to Student Retention: A Multivariate perspective

Danilo Rojas Calificadores Spanish

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An Information Processing Approach to Student Retention: A Multivariate perspective

  1. 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
  2. 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

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