This study analyzed data on 25,922 students at Kennesaw State University to examine the relationship between student age and GPA. A segmented regression found that for students ages 17-25.5, age correlated negatively with GPA, but for ages 25.5-61, age correlated positively with GPA. Further analysis showed that while non-traditional students performed better on average than traditional students, many lost eligibility for HOPE scholarships after 7 years out of high school, despite being high-performing students in need of financial assistance. The findings suggest the HOPE scholarship criteria should be re-evaluated to reinstate eligibility for non-traditional students.

1. “Is There HOPE For Non-Traditional Students?”
Presenters: Nicole Moutos, Reuben Hilliard, Kevin Robinson
Faculty Advisor: Dr. Jennifer Priestley
We began our study by conducting an exploratory analysis of the original data set. The raw data file contained information on the GPA, Age,
major, cumulative hours completed, and gender for 26,278 students at Kennesaw State University. Through our preliminary analysis, we
discovered that there is a relationship between the age of a student and their academic performance. This led us to address a problem within the
HOPE scholarship system that prevents older students, who have been out of high school for 7 or more years from receiving the scholarship. This
is a problem that has affected each of us individually, who are all high performing students in pursuit of a degree to enrich the lives of ourselves and
our families.
Through further analysis we created two categorical variables, which allowed us to segment students based on their age and their level of academic
performance. With these variables, we constructed 100% stacked bar charts to be able to make meaningful and scaled comparisons of each
segment. Our explanatory variable was the student’s age and our response variable was their GPA. When we created our first bar chart, we saw an
interesting trend reversal between the third and fourth age categories. This encouraged us to do a deeper segmented regression analysis to uncover
any breakpoints in the trend that we were missing in a basic linear regression model. We found that between ages 25 and 26 there is in fact a trend
reversal. From ages 17 to 25.5, there is a negative correlation between age and GPA, and from 25.5 to 61 the trend reverses to show a positive
correlation between age and GPA. We then zoomed in on the stacked bar chart to look at the individual ages from 20 to 31, where the breakpoint
was also captured.
*We cleaned our data to reflect only the students who could potentially get HOPE, irrespective of their age. This reduced our number of observations to 25,922 students.
ABSTRACT
RESULTS
In 2011, the HOPE Scholarship Fund enacted an eligibility clause preventing students who have been out of high school for seven or more years
the opportunity to receive the scholarship.1 Notably, this clause came after the 2008 financial crisis when unemployment rates and the number of
scholarship awards increased simultaneously.2,3 Since the clause was enacted, previously eligible non-traditional students now have to find
alternative ways of paying for school, which include taking out student loans and creating debt for them and their families. This presentation seeks
to provide evidence that on average, older non-traditional students, who are ineligible for HOPE scholarship perform better in school than their
younger counterparts. The data set contains GPA and age information for 25,922 students, enrolled at Kennesaw State University, during the
2013/2014 school year. The student ages range from 17 to 61, and the GPA data is on a 4.0 scale. The response of interest is the academic
performance of the students, measured by their GPA. The explanatory variable is the students’ age. The data will be analyzed through the creation
of age and GPA categories. Age categories will be separated into meaningful segments based on traditional and non-traditional student
classifications. GPA categories will be separated using proper categorizations, as determined by Kennesaw State and the University system. The
categories will be used to create 100% stacked bar charts to evenly scale the different age groups. The quantitative variables will also be used to
create a segmented regression, in order to discover trends and breakpoints that are not evident in a linear regression model. We would like to show
that non-traditional students perform just as well or better than traditional students and should therefore be entitled to the same scholarship funding
that is ultimately determined by academic performance.
INTRODUCTION
METHODS
The acronym HOPE, as it pertains to the scholarship, stands for “Helping Outstanding Pupils Educationally”. It was originally created to help high
performing students fund their education. It was designed to be all- inclusive for Georgia residents and did not discriminate based on age, gender,
race, or high school diploma type. In 2011, the HOPE scholarship bill underwent a number of budgetary-based amendments for the purpose of
keeping the funding alive and well allocated.4 These changes came after three years of increasing scholarship award amounts following the 2008
financial crisis. The aftermath of the financial crisis was sky-rocketing unemployment rates and mounting debt for many individuals. Denying non-
traditional students access to the HOPE scholarship has left them seeking alternative ways to fund their education, while not pushing their families
into more debt. In 2013, 93.27% of incoming non-traditional freshman at KSU were eligible for HOPE scholarship. Approximately 50% of those
HOPE recipients lost their scholarship within the first year.5 According to a study done in 2011, only 27.7% of Kennesaw State students who began
their college career with the HOPE scholarship maintained it until graduation.4 These numbers lead us the question whether the HOPE scholarship
is sacrificing high performing non-traditional students to save the traditional students, who appear to be unprepared for college. Our analysis
provides evidence that on average, non-traditional students do perform better in school than traditional students.
Our findings led us to conclude that while Non-Traditional students, on average, perform better than “Traditional” students, they have been left without HOPE, despite the likelihood that they are the students most desperately in need. While Non-Traditional
students seem to be the most serious about being successful students, taking away HOPE has made their road to self-improvement more difficult, as well as leaving them with a mountain of debt, simply because of their time away from school. A scholarship
purely based on academic merit, should not discriminate based on age, as Non-Traditional students have the same financial needs as Traditional students and quite possibly far more financial obligations.
If the HOPE scholarship’s intent is to help outstanding students improve their ability to excel in school, with the ultimate goal of making them employable, they are doing themselves a disservice by rejecting Non-Traditional Students. The results are clear,
70 percent of hiring managers do report screening applicants based on their GPA, but the largest group say they use a 3.0 as their cutoff.6 All other factors being equal, an employer is more likely to choose the candidate with stellar grades.
The economy is showing multiple signs of recovery, and it is our recommendation that the State Legislature reverse its 2011 decision and once again include Non-Traditional students in the eligibility standards for the HOPE scholarship. In order to offset
the increased costs associated with an influx of Non-Traditional students, the GPA requirement could be raised to 3.25 to accommodate for this measure.
CONCLUSIONS
R CODE
#create contingency table and proportion table for 100% stacked
ctab2_Age1_GPA <- table(StAge,GPA_CV)
rwpt2_Age1_GPA <- prop.table(ctab2_Age1_GPA,1)
#set graphical parameters to provide room for legend and format color and text
par(mfrow=c(1, 1), mar=c(5, 5, 5, 10),font.lab=2,fg="yellow2", bg="gray51",col.main="yellow2",col.axis="yellow2",col.lab="yellow2")
#create formatted 100% stacked bar with legend outside of plot area
SB_AGE1 <- barplot(t(rwpt2_Age1_GPA*100),ylim = c(0,110),border="black",col=c("firebrick3","darkorange2","dark
blue","blue","forestgreen","green1"), main=" Figure 1: 100% Stacked Bar Chart of Age Group by GPA
Category (n=25922)", ylab="Proportion",xlab="Age Category",legend.text = TRUE,args.legend = list(x = 6 +
6.8, y=100, bty = "n"))
#pastes frequencies above bars to show the size of each segment
text(0.7, 103, "3385")
text(1.9, 103, "14396")
text(3.1, 103, "4301")
text(4.3, 103, "1647")
text(5.5, 103, "882")
text(6.7, 103, "576")
text(7.9, 103, "735")
#Sets graphical parameters for segmented regression
par(mfrow=c(1, 1), mar=c(7, 8, 4, 8),font.lab=2,fg="black", bg="gray51",col.main="yellow2",col.axis="yellow2",col.lab="yellow2")
plot(AGE,KSU_ADJUSTED_GPA, ylim=c(2,4),xlim=c(17,62),pch=20)
#install and call segmented package
install.packages("segmented")
library(segmented)
#assign and run linear regression first
lin.mod <- lm(KSU_ADJUSTED_GPA~AGE)
summary(lin.mod)
#assign and run segmented regression, find estimated breakpoint
segmented.mod <- segmented(lin.mod, seg.Z = ~AGE, psi=30)
summary(segmented.mod)
#plot segmented regression with spline at estimated age breakpoint
plot(segmented.mod, add=T, pch=20, interc=TRUE, col="red",lwd=3,conf.level=.95,shade=TRUE)
abline(v=25.5, col= "blue", lwd=3)
title(main = "Figure 3: Segmented Regression of Student GPA by Age (n=29522)")
SUPPORTING DATA
0
500
1000
1500
2000
2500
2003 2004 2005 2006 2007 2008
NumberofStudents
Figure 4: KSU Student 6yr Graduation Rate Trends
With Hope - Graduated within 6 years No Hope - Graduated within 6 years
Incoming w/ Hope Incoming - No Hope
U.S. Unemployment Rates (2002-2014)
Sources: MacroTrends.net and the Bureau of Labor Statistics
RESOURCES
1. "Georgia's HOPE Scholarship Program Overview." GA College 411. Georgia Student Finance Commission, XAP Corporation, n.d. Web.
2. . "Scholarship and Grant Award History." Georgia Student Finance Commission. Gsfc.org, n.d. Web.
3. "Unemployment Rate - Last Ten Years." MacroTrends. Bureau of Labor Statistics, n.d. Web.
4. Diamond, Laura. "Few Hold onto HOPE for Whole Time in College." AJC.com. The Atlanta Journal Constitution, n.d. Web.
5. "Fact Book." Fact Book 2012-2013. Kennesaw State University, n.d. Web.
6. National Association of Colleges and Employers. Job Outlook 2005 survey, n.d. Web
FINDINGS
After seeing there was in fact segmented correlations between age and GPA, we wanted to look for the behavior of HOPE scholars before and after the changes of 2011. Our supporting data shows how the amount of HOPE scholarships awarded, rose
steadily through 2010 - the same time period in which unemployment rates climbed sharply. This suggests it is likely many laid-off workers enrolled in college to improve their chances of finding a good job. When the changes were implemented in
2011, the HOPE scholarships plummeted sharply, but as unemployment rates steadily continued to fall, HOPE scholarships stayed relatively steady, again suggesting that unemployment rates no longer affected HOPE because those now ineligible are
the same people most affected by unemployment.
We then turned our attention to graduation rates of HOPE scholars compared to Non-HOPE students. In Figure 4, we see that not only is the proportion of incoming freshman who receive HOPE much higher than those not receiving it (more than 4:1
in 2013), the ratio of HOPE scholars graduating within six years vs. students who did not receive HOPE, is even more dramatic (34:1 in 2013).