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.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Astin, A. W. (1993). What matters in college? Four critical years revisited. San Francisco: Jossey-Bass.
Bean, J. P. (1985). Interaction effects based on class level in an explanatory model of college student dropout syndrome. American Educational Research Journal,22(1), 35–64.
Caison, A. L. (2006). Analysis of institutionally specific retention research: A comparison between survey and institutional database methods. Research in Higher Education,48(4), 435-451.
Herzog, S. (2006). Estimating student retention and degree-completion time. Decision trees and neural networks vis-à-vis regression. New Directions for Institutional Research,131, 17-33.
Pascarella, E., and Terenzini, P. (2005). How College Affects Student: Volume 2, A Third Decade of Research.San Francisco: Jossey-Bass.
Sujitparapitaya, S. (2006). Considering student mobility in retention outcomes. New Directions for Institutional Research,131, 35-51.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89-125.
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
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.
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)
Director of Advanced Technology &
Lead Architect of ‘STAR’
University of Hawaii at Manoa