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

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.

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

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.

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

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

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?

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

DESCRIPTIVE Gender

DESCRIPTIVE Race & Ethnicity or

DESCRIPTIVE Enrollment Level

DESCRIPTIVE College

DESCRIPTIVE Campus

DESCRIPTIVE Hall Type Corridor Hullabaloo

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

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%

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

CONNECT WITH US Academic Support Initiatives, Residence Life Hullabaloo Hall, Suite 125 academics@housing.tamu.edu Tel. 979.458.0343

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), 219-225. 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), 527-538. 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), 215-226. Pritchard, M.E., & Wilson, G.S. (2003). Using Emotional and Social Cognitive Factors to Predict Student Success. Journal of College Student Development, 44(1), 18-28. Thompson, J., Samiratedu, V., Rafter, J. (1993). The Effects of On-Campus Residence on First-Time College Students. NASPA Journal, 31(1), 41-47. Tinto, V. (1975). Dropout From Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research, 45(1), 89-125.