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Hechinger Report
A ‘wildly intrusive’ way to help older college students get their degrees
An experiment at John Jay College to get seniors over the final hurdle to graduation is worth watching
By Jill Barshay
January 6, 2020
The John Jay College for Criminal Justice is known for training New
York City’s future police officers and as Dara Byrne rose through the
ranks of the college’s administration, she noticed a mystery right on
her campus: why were 2,000 seniors, with only one year left to
graduate, not enrolling in the fall?
“Students in that 90-credit zone, really close to graduation, were
leaving,” said Byrne, when I talked to her by phone. “I couldn’t
understand why because it’s counter to what you would think. You’re
almost there. You’ve got only three, four, five, eight classes left.”
Many of these students intended to return. “But data showed that very
few of them came back to us,” Byrne said. “Thousands of students a
year, never getting a degree.”
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In 2018, Byrne and her colleagues decided to play detective using
predictive analytics, which tracks student data to predict who is
likely to graduate and who is not. In addition to being dean of
undergraduate studies, Byrne has a second job as associate provost for
undergraduate retention and it is her job to make sure John Jay’s
students — mostly low-income and many the first in their families to
attend college — graduate. Typically, colleges work on improving
graduation rates by investing more in students when they first arrive
on campus. But Byrne’s observation convinced her that she needed to
turn this model around and nurture older, nontraditional students at
the end of their college careers.
Byrne’s colleagues presented the results of their recent experiment in
using predictive analytics at the Complete College America annual
conference in Phoenix, Ariz., in December 2019.
Predictive analytics helped Byrne and her colleagues figure out which
seniors were most likely to leave before getting their degrees. John
Jay College sent more than 10 years of student data to a nonprofit
organization, DataKind, and its data scientists figured out which
attributes were associated with dropping out at the very end of a
college career. Twenty-four data points, such as financial aid status,
plus a dozen calculations, such falling grades, rose to the top as
important indicators.
In the spring of 2018, DataKind ranked more than 1,100 rising seniors
according to their risk of dropping out of school. The school
especially focused on 380 students with the highest risk scores. Byrne
hired three employees plus a student liaison to give these students
extra academic advising and financial aid.*
Byrne calls this level of advising “wildly intrusive,” with advisers
telephoning and forcing these high-risk students to come in for
face-to-face meetings to learn about their problems. Many of the
students had already spent more than four years in college and had
exhausted their federal and state financial aid. Many had taken on more
part-time work to pay for school.
“It’s exactly those financial pressures that make students think about
not coming back,” Byrne said. “Emergency funds are critical…It’s
amazing to see that $300 is what is standing between someone and a
college degree.”
John Jay gave many of these students aid that they otherwise wouldn’t
have qualified for to pay small, unpaid tuition bills. Even mediocre
students received these “emergency funds.”
“There’s not a lot of infrastructure for students whose performance is
declining over time,” Byrne said, noting that more than half of the
school’s graduates go into public service, from police and firefighting
to social work and youth nonprofits. “We think a 2.5 student [C+
student] is just as valuable as 3.0 [B student].”
The “wildly intrusive” advisers were encouraged to remove
administrative roadblocks, sometimes bending college rules on removing
and repeating courses. Byrne told me about one student who needed to
retroactively withdraw from a class where she did not do well. The
school allows students to withdraw from classes if they are
experiencing mental health challenges but this student didn’t have any
mental health documentation of the domestic violence she suffered that
semester. Her adviser advocated for her and helped to gather other
forms of documentation. That student’s exemption from the rules
eventually led to a policy change that allows for the program’s
advisers to advocate for students instead of requiring students to pay
for a mental health professional to document a mental health crisis.**
After one year of Byrne’s interventions, which John Jay calls the
Completion for Upper Division Students Program, 51 percent of the 380
students identified as high risk and targeted for help graduated in the
2018-19 academic year. In the previous year, before the college had
started this experiment, only 47 percent of the students that the
DataKind algorithm would have identified as high-risk seniors completed
a college degree on their own within a year. That’s a
four-percentage-point jump in the graduation rate for this group.
The gains were primarily achieved by an even smaller subgroup of 180
students who had the very highest risks of dropping out, according to
the algorithmic predictions. More than half of them were Hispanic, much
higher than the 46 percent Hispanic population of the overall student
body. And they were notably older: 38 percent of this very
high-risk group was at least 25 years of age. This very high-risk group
saw a six-percentage-point improvement in their graduation rate, from
33 percent previously to 39 percent after the data-driven intervention.
(The remaining 200 students in the intervention actually saw their
graduation rate deteriorate slightly from 64 percent to 62 percent.)
Among the larger group of 1,100 students who had originally been
analyzed for risk, nearly 73 percent graduated within a year. Without
the program, the school had expected only 54 percent to graduate within
two years.***
Those jumps helped increase the overall graduation rate at John Jay
College, which has 13,000 undergraduates, by 2 percentage points,
according to the college’s estimates. It calculates that nearly 38
percent of its students who started in 2015 received a bachelor’s
degree within four years and almost 52 percent of the students who
started in 2013 received a bachelor’s degree within six years.
Graduation rates at John Jay were already trending upward because of
other things the college has been doing. But without this predictive
analytics experiment, John Jay estimates that its four-year and
six-year graduation rates would have grown to less than 36 percent and
50 percent, respectively. It’s one of the first times I’ve seen a
college try to measure the effectiveness of predictive analytics and
compare it to what might have happened otherwise.
Of course, predictive analytics, counseling and financial aid come with
price tags. Two foundations, MasterCard and Robin Hood, footed John
Jay’s bill for DataKind. (Companies typically charge colleges $300,000
a year for providing predictive analytics services.) And the Price
Family Foundation**** gave John Jay $800,000 to hire extra staff and
give students emergency grants.
Because John Jay is part of the City University of New York system,
Byrne is well aware of the financial pressures at public institutions.
She argues that it can be affordable if colleges hunt for cheaper
open-source data analysis and reallocate existing advisers. Getting
more seniors to graduate seems worth the effort. But ultimately,
sleuthing out and solving each student’s obstacles one at a time is
slow, painstaking work.
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