1. DEVELOPING PREDICTIVE ANALYTICS TO IMPROVE RESIDENT GPA PERFORMANCE
CORIE DEPUE, RESEARCH ASSISTANT
NOT PRESENT:
DUSTIN K. GRABSCH, MANAGER FOR ACADEMIC SUPPORT
LORI L. MOORE, ASSOCIATE PROFESSOR

2. ABSTRACT
Predictive equations have traditionally been used to anticipate academic standing in college students using variables such as scores on the American College Test (ACT) and/or School and College Ability Test (SAT), high-school rank, gender, ethnicity, social cognitive factors, etc.
While the use of predictive equations in higher education has expanded to include variables of identity, such as gender and socioeconomic status, and social and emotional factors, these elements have seldom been explored in the context of housing and the residential environment and their impact on academic performance.
This program seeks to present findings from research that seeks to fill this gap by evaluating GPA performance against historical residential demographic data at Texas A&M University.

3. AGENDA Abstract Introduction Methodology Findings/Data
Literature Review
Research Questions
Methodology
Findings/Data
Discussion/Conclusions
Questions
Connect with Us

4. INTRODUCTION
Predictive equations have traditionally been used to anticipate academic standing.
(Bowers, 1970; Hackett, Betz, Casas, & Rocha-Singh, 1992; Noble & Sawyer, 2002)
Use of predictive equations in higher education has expanded to include variables of identity.

5. INTRODUCTION
These elements have seldom been explored in the context of housing and the residential environment and their impact on academic performance
(Thompson, 1993)

6. THE WHY
Focus academic support initiatives resources and support to students who may struggle with GPA Performance

7. RESEARCH QUESTIONS
The objective of this research project is to analyze housing and residential environment as variables in academic success – specifically in terms of GPA performance.
This study aims to expand current understanding of academic success and the factors that positively or negatively impact student GPR.
Are housing variables such as permanent/temporary, building, housing area, hall type, etc. correlated to GPA performance?
To what extent can housing and basic demographic variables such as gender, race and enrollment level explain GPR performance?

8. METHODOLOGY Quantitative Existing Data About the Data
Stepwise Regression
ANOVA
Existing Data
Historical housing database information between 2012 and 2017
Included major, GPA, building, college, enrollment level, gender, and race
Researcher created variables included hall type, semester(s) on- campus, permanent/temporary space
About the Data
98, 738 units of data
Data was extracted from the housing database to form 8 fall/spring semester excel sheets
Included off-campus and on- campus students
Residence Halls
Apartments
Corps of Cadets
Paired cohorts were created to form two complete years
Mid academic year graduates removed

9. DESCRIPTIVE
Gender

10. DESCRIPTIVE
Race & Ethnicity or

11. DESCRIPTIVE
Enrollment Level

12. DESCRIPTIVE
College

13. DESCRIPTIVE
Campus

14. DESCRIPTIVE
Hall Type
Corridor
Hullabaloo

15. FINDINGS THINK-PAIR-SHARE:
WHICH VARIABLE DO YOU THINK MOST SIGNIFICANTLY PREDICTS GPA?
WHICH HOUSING SPECIFIC VARIABLE?

16. FINDINGS
Using stepwise linear regression on the entire dataset of all enrollment levels, we determined 5 variables which aid in explaining spring GPR. Then looked at only U1 on-campus.
Enrollment Level (.058)
Race (.027)
Campus (.014)
Gender (.004)
College (.003)
10.8%
Credit Hours (.282)
Race (.021)
College (.004)
Gender (.002)
Hall Type (.002)
31.1%

17. CONCLUSIONS & DISCUSSION
While no statistical significance was discovered with the included housing variables and the dataset, practical significance may still be meaningful for housing professionals.
The most influential housing variable on GPR performance is campus (on, off, or Corps of Cadets) in the study followed by Housing Type.
Consider the addition of variables such as:
First Generation College Student
In-State/Out-of-State
On-Campus Room Moves

18. CONNECT WITH US Academic Support Initiatives, Residence Life
Hullabaloo Hall, Suite 125
Tel

19. REFERENCES
Bowers, J. (1970). The Comparison of GPA Regression Equations for Regularly Admitted and Disadvantaged Freshman at the University of Illinois. Journal of Educational Measurement, 7(4),
Hackett, G., Betz, N.E., Casas, J.M., & Rocha-Singh, I.A. (1992). Gender, Ethnicity, and Social Cognitive Factors Predicting the Academic Achievement of Students in Engineering. Journal of Counseling Psychology, 39(4),
Noble, Julie, & Sawyer, Richard (2002). Predicting Different Levels of Academic Success in College Using High School GPA and ACT Composite Score. ACT Research Report, 4.
Pascarella, E.T., & Terenzini, P.T. (1983). Predicting Voluntary Freshman Year Persistence/Withdrawal Behavior in a Residential University: A Path Analytic Variation of Tinto’s Model. Journal of Educational Psychology, 75(2),
Pritchard, M.E., & Wilson, G.S. (2003). Using Emotional and Social Cognitive Factors to Predict Student Success. Journal of College Student Development, 44(1),
Thompson, J., Samiratedu, V., Rafter, J. (1993). The Effects of On-Campus Residence on First-Time College Students. NASPA Journal, 31(1),
Tinto, V. (1975). Dropout From Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research, 45(1),