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The Bowl Curve Syndrome
I once
observed that the test scores and class averages in many of my low income
classes produced graphs with a shape very different than the stereotypical bell
curve, with a majority of students earning C’s (the top of the bell) and fewer
students earning A’s, B’s, D’s and F’s (the sides of the bell). In my classes,
the curves were shaped like inverted bells, or bowls, with very few C’s, slight
higher numbers of A’s and B’s, and much higher numbers of D’s and F’s.
My
interpretation of these results was that I had very few students who were
academically and socially ready for college-preparatory level biology (the A’s,
B’s and C’s) and a majority of students who were not ready (the D’s and F’s). Indeed,
this hypothesis was supported by other data (e.g., high percentages of my
students were reading far below grade level, failing other courses, and/or
deficient in graduation credits). Furthermore, the students with D’s and F’s had
significantly higher rates of absenteeism and were less likely to complete
their assignments.
While my
hypothesis made a lot of sense, it did not explain why there were fewer C’s
than A’s or B’s. However, I did observe that many of these C students ebbed and
flowed depending on who they were teamed up with in the class. When grouped
with higher performing students, they tended to rise to the occasion and do
better in the class, while the opposite occurred when they were grouped with
lower performing students.
This was not
just a case of riding on their peers’ coat tails or putting their names on a
“group” activity that was completed mostly by the higher performing students.
Rather, their tests scores, lab reports and other individual assignments began
to improve. It was if their peers’ self-efficacy and optimism inspired and
motivated them (e.g., “if they can do, maybe I can, too, with a little more
effort”). In contrast, when surrounded by students who were struggling and
failing, it appeared to them as if no one was succeeding and the system must
therefore be rigged and impossible, so why bother?
Academic Success is Infectious
My
hypothesis was recently confirmed by research led by Hiroki Sayama, director of the Collective Dynamics
of Complex Systems research group at Binghamton University in Binghamton, N.Y.
and published in the Public Library of Science (PLoS).
Sayama’s
research team asked students at Maine-Endwell High School in Endwell, N.Y. to
rank their classmates as either a “best friend,” a “friend,” an “acquaintance”
or someone they didn’t know. They then examined their grade point averages and
how these changed between January 2011 and January 2012, finding a linear
relationship between students’ grades and those of their peer group. For
example, if a student’s grade were initially higher than their peers’ grades,
the student’s grade tended to decline over the course of the year until it was
more in line with the peers. Likewise, if the student’s initial grades were
lower than their peers’, their grades tended to rise over the year. They also
found a correlation in happiness, obesity and other traits between students and
their peer groups. The correlations were strongest at the “friend” level, and
much less significant at the “best friend” and “acquaintance” levels.
There were a number of variables and
biases in the experiment that are worth noting. The sample size was relatively
small (only 158 students and only one school were examined). Consequently, the
results may have been skewed due to the small sample size or the possible
concentration of students of similar socioeconomic background (e.g., they were
predominantly white and suburban). Also, the social networks were constructed
based on self-reporting by students. This could also skew the data, as people
do not always respond truthfully or accurately on self-reporting surveys.
Furthermore, the categories to which students assigned their peers (e.g., best
friend, friend, acquaintance, etc.) are subjective and difficult to quantify.
One other significant bias inherent in this research is that students may be
choosing their “friends” based on preexisting similarities in behavior,
academic readiness, motivation and attitudes about school. Thus, a correlation
in academic achievement could be explained by behaviors that were coincidental
rather than influential.
Take Home Message: Desegregate Schools
and Classrooms
While the research
indicates that “friends” had a greater influence on students than did
acquaintances, my own experience has been that even acquaintances can have a
measurable influence. The problem is that grouping students with “friends” or
“acquaintances” will have a negative effect if the “friends” and
“acquaintances” are performing more poorly. Thus, the solution should be to surround
each low performing student with more successful peers. However, in low income
schools, which have higher percentages of low performing students, this would
be impossible.
One way around this
dilemma is to desegregate schools based on socioeconomic background and academic
performance (i.e., reassigning students across a school district such that all
schools have similar numbers of low performing and high performing students). Of
course, this would have little effect in a district that is composed entirely
of lower income students. However, in cities like San Francisco, where there
are wide socioeconomic, ethnic and achievement gaps between schools within the
same school district, such a reassigning of students could go a long way toward
increasing the ratio of successful students per classroom and thus the chances
that a lower performing student is grouped with or makes friends with a higher
achieving student.
Of course this is
much easier said than done, as San Francisco has discovered each time it has
attempted to (and failed) desegregate its schools.
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