Internet Learning Volume 6, Number 1, Spring 2017/Summer 2017 | Page 17

Internet Learning
math requirement?
• Did the student earn an associate’s
degree?
• Did the student repeat a course?
Framework’s common data definitions
provided the means for validating the
results of research conducted at one institution
with results from another institution.
Completion of a math course at a
community college was positively associated
with earning a GPA over 2.0, as
was earning an associate’s degree before
transferring and having a higher GPA
at the Community College. Repeating a
course in community college was negatively
associated with first-term GPA
at a 4-year university. The dataset was
able to reveal that each institution had
unique predictors as well.
The analysis also included determining
whether predictors were the
same at the two institutions for retention.
Retention was defined as whether
the students were still enrolled between
6 and 12 months after they first enrolled.
Only four variables were found
to be positive predictors at both institutions.
The variables in common that
predicted student success included the
number of credits attempted during the
first term at the 4-year institution, the
grade point average of the first term
at the 4-year institution, the number
of credits taken and successfully completed
(credit ratio), and whether the
course was online or face to face. Each
institution also found unique positive
predictors among its students.
The ability to apply results from
one research study based on a local
sample to a larger, national population
has traditionally been compromised
by limited generalizability. The PAR
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Getting to know posttraditional
students
The PAR data science team explored
the dataset and reviewed
the literature to better understand
post-traditional students (Watt
& Wagner, 2016). What it is clear, nationwide,
is that this student segment is
growing. Current data-gathering practices,
whether in federal requirements,
state assessments, or most recruitment
surveys, continue to rely on the firsttime,
full-time cohort. Assessment of
post-traditional students leads to many
related concerns for today’s higher education
ecosystem. For example, students
who vary from the traditional path are
not eligible for many federal financial
aid programs, or they find that the aid
they do receive is not flexible enough to
work with their enrollment plans.
Such antiquated practices do a
disservice to institutions that focus on
recruiting, educating, and graduating
post-traditional students. Similarly, if
more traditional institutions were required
to report on post-traditional student
outcomes, they might alter their
student success practices to be more
inclusive. The common dataset at PAR
offers member institutions an opportunity
to investigate whether post-traditional
students are similar throughout
the membership. Identification of