Presentation Outline. The Future of Institutional Research (IR) & Technology in improving first-year students’ success. Example 1: Demonstration of an IR innovation. Example 2: Demonstration of a Technology innovation . The Future of IR and Technology.
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1. Get Freshmen Data.
(i.e. We used fall 2009 & 2010 data to build our “training” data set.)
3. Apply model parameters to new data.
(i.e. model validation, scoring)
2. Build Model.
4. Check the actual 2011retention outcomes to see how well the model performed.
On Campus Employment
Student Life Activities
Average Class Size
Need Based Aid
Non-need Based Aid
% of Aid Met
High School GPA & Rank
# Transfer Credits
Credit Completion Ratio
First Term GPA
High Failure Rate Courses
Courses Taken (including Math & English)
Ethnicity by Geographic Origin
Employment by Housing
High School GPA by First Term GPA
Residency by Need Based Aid
Ratio of Successful Adds to Drops
7 Strongest Predictors of Retention
These variables account for approximately 39% of the variance in a student’s likelihood of returning for a third semester (Pseudo R Square = .387).
RETENTION IN YEAR 1
*Wald statistic (sig.)
The Wald test statistic was used to indicate strength of the variable instead of the coefficient, standardized beta. Because of the nature of the logistic regression, the coefficient is not easily interpretable to indicate strength.
Predictors in Regression Equation
Pseudo Rsquare = .387
p = exp(a+b1x1+b2x2+b3x3+b4x4….)
1 + exp(a+b1x1+b2x2+b3x3+b4x4….)
Where:p = probability of enrollment/non-enrollment
exp = base of natural logarithms (~ 2.72)
a = constant/intercept of the equation
b = coefficient of predictors (parameter estimates)
Example: John is at risk of dropping
Impact on Campus
Example 2: ‘STAR’ Technology
Director of Advanced Technology &
Lead Architect of ‘STAR’
University of Hawaii at Manoa
Vice Chancellor for Academic Affairs
Director of Advanced Technology
University of Hawaii at Manoa