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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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Presentation outline
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
The Future of IR and Technology

  • IR’s future is moving beyond reporting to analysis. This means converting data into ‘actionable’ information that FYE personnel can use.

  • Technology’s future is moving beyond data management to production of tools that directly facilitate and improve student success.


Example 1 student at risk prediction model
Example 1:Student-at-Risk Prediction Model

  • Also known as a predictive model, or enrollment forecasting model.

  • Helps answer questions like:

    • Which student variables are most useful for predicting freshmen retention?

    • What is the “best” combination of variables to optimize predictions?

    • How useful is this combination for identifying at-risk students?


R elevant previous research
Relevant Previous Research

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.


4 steps to modeling retention
4 Steps to Modeling Retention

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.

RETENTION

4. Check the actual 2011retention outcomes to see how well the model performed.


Examples of student variables analyzed
Examples of Student Variables Analyzed

On Campus Employment

Housing

Student Life Activities

Athletics

STAR Usage

Average Class Size

Gender

Age

Ethnicity

Residency

Geographic Origin

CAMPUS EXPERIENCE

DEMOGRAPHICS

Need Based Aid

Non-need Based Aid

Pell Grant

Work Study

% of Aid Met

High School GPA & Rank

SAT

AP CLEP

Educational Goals

Transfer GPA

# Transfer Credits

FINANCIAL NEED

PRE-COLLEGE

PERSISTENCE

Credits earned

Credits attempted

Credit Completion Ratio

Math/English Enrollment/Completion

Continuous Enrollment

Milestone metrics

Major

Credit Load

Credits Earned

First Term GPA

Distance Education

Dual Enrollment

High Failure Rate Courses

Courses Taken (including Math & English)

MILE-STONES

ACADEMIC

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

INTERACTIONS


7 Strongest Predictors of Retention

Strongest

These variables account for approximately 39% of the variance in a student’s likelihood of returning for a third semester (Pseudo R Square = .387).

262.804 (.000)*

101.368 (.000)*

20.292 (.000)*

13.486 (.000)*

RETENTION IN YEAR 1

7.817 (.005)*

7.134 (.008)*

4.419 (.036)*

3.791 (.052)*

*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.

Weakest


Predictors in Regression Equation

Pseudo Rsquare = .387


Scoring Students

  • Scoring of relative dropout/retention risk

    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

  • John:

    • is from the continental U.S. (0)

    • has a below average high school GPA (2.65)

    • is enrolled in 9 credits (9)

    • has a low % of financial need met (.45)

    • isn’t not working on campus (0)

    • isn’t enrolled in CAS 110 (0)

    • didn’t specify any educational goals in survey (0)

  • Probability of Dropping: 0.77


Sample data for fye advisors
Sample Data for FYE Advisors


Impact on Campus

  • 407 freshmen from 2011 dropped out in year one.

  • Retaining just 22students from 2011 would have improved Mānoa’s overall retention rate from 78.8% to 80%.

  • Additional Revenue from Tuition and Fees =$210,000 (for 16 HI, 6 WUE, excludes out-of-state!).

  • Are there 22 students in this group that we can help/retain?


Example 2: ‘STAR’ Technology

Gary Rodwell

Director of Advanced Technology &

Lead Architect of ‘STAR’

University of Hawaii at Manoa




Mahalo

Reed Dasenbrock

Vice Chancellor for Academic Affairs

John Stanley

Institutional Analyst

Gary Rodwell

Director of Advanced Technology

University of Hawaii at Manoa

Questions: [email protected]


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