AI in Education
Research Asks If Machine Learning Can Make Better Enrollment Predictions
- By Dian Schaffhauser
artificial intelligence help school districts better predict their
fall enrollments? That was the question a team
of researchers asked on behalf of the School
District of Philadelphia.
school system, like many others, offers residents school choice.
Families may choose to send their students to the neighborhood school
or to another one elsewhere in the city, including public charters or
private schools. That flexibility poses a planning problem: Each year
schools have no idea about the size of their incoming cohorts. While
spring planning may tell district and site leaders that students
intend to go to their local school, in the intervening months
families may choose to send
their kids elsewhere.
district allocates resources in March for the upcoming school year
and then reallocates resources as needed after October 1, once it has
taken measure of discrepancies between the prospective spring
enrollment and actual attendance in the fall, a process the district
calls "leveling." The October leveling process can result
in the elimination of classrooms, reassignment of students to
different teachers and shifting of teachers to different schools or
grade levels. This kind of disruption can have a big impact on
classroom engagement for students
see if there was a better way to plan for fall enrollment, the
district turned to the Regional
Educational Laboratory Mid-Atlantic, part of the
of Education Sciences network. In a project
administered by Mathematica,
researchers developed three machine learning algorithms for doing
student enrollment prediction, to see if the results were any better
than a "simple regression model" —
figuring out which variables matter and which ones don't, and then
applying the ones with impact to the calculations.
AI didn't do any better. According to a recently published report
all four methods had similar accuracy, differing from actual fall
cohort sizes by about six students, on average, including across
schools "with larger proportions of black
students, economically disadvantaged students and English learners."
Each method resulted in the need to reallocate between 20% and 30% of
students to different teachers in the October recount.
machine learning didn't generate any better results, the research
project did come up with some guidance:
districts with high rates of mobility, predictions would probably be
refined if the schools gathered additional data later in the spring
and early summer; and
any of the methods would generate similar predictions, regression
models are easier and less expensive for districts to implement.
while the research used 259 predictors from multiple schools
(2016-2017 through 2018-2019), just a handful of variables really
makes a difference:
enrollment for each grade, which had a predictor importance of 0.97;
The number of
students with more than five in-school suspensions (0.91);
The number of
students with more than five out-of-school suspensions (0.83); and
The number of
students with fewer than six absences (0.65).
the researchers offered a caveat: The results may not be applicable
in years to come since COVID-19 has wreaked havoc. The pandemic, the
report stated, "might have fundamentally altered patterns of
attrition and new entrants in a way that models based on historic
data are unable to capture."
are openly available on
the Regional Educational Laboratory website.
Dian Schaffhauser is a former senior contributing editor for 1105 Media's education publications THE Journal, Campus Technology and Spaces4Learning.