instructors were asked to label which
were bimodal and which were not. The
other half of the instructors saw the
histograms first, and then were asked
to respond to the statements. The deception was that all the histograms
were generated from normal distributions, yet participants who agreed
with the Geek Gene statements were
more likely to identify the distributions as bimodal.
As in all empirical studies involving humans, we can disagree about
the details. How UBC counts withdrawing from a class or failing a class in the
grade distribution is probably different
than many institutions. There is some
possibility that participants might
have seen the histograms, then gone
back to change their answers on the
statements. There can and should be
more studies on these questions.
This paper does not prove there is
no Geek Gene. There may actually be
bimodality in CS grades at some (or
even many) institutions. What this paper does admirably is to use empirical
methods to question some of our long-held (but possibly mistaken) beliefs
about CS education. Through papers
like these, we will learn to measure
and improve computing education,
by moving it from folk wisdom to evi-dence-based decision-making.
Mark Guzdial ( firstname.lastname@example.org) is a professor of
electrical engineering and computer science in the
College of Engineering and a professor of information in
the School of Information at the University of Michigan,
Ann Arbor, MI, USA.
Copyright held by author.
MANY COMPUTER SCIENCE teachers
have told me that some students just
get computer science—and others
do not. We certainly have a lot of evidence that students enter introductory computer science courses with big
differences in skills. Some students
have already had years of programming experience, while others have
never programmed at all. The question is whether those gaps close or diverge further.
Are the differences between stu-
dents in CS classes explained by ex-
perience and background, or are the
differences innate? Innate difference
among CS students has been dubbed
the Geek Gene. Many CS teachers
believe a Geek Gene (or something
similar) is necessary to succeed in
CS, and not everyone has it. A 2007
study found 77% of surveyed CS faculty
strongly disagreed with the statement:
“Nearly everyone is capable of suc-
ceeding in computer science if they
work at it.” CS teachers point to a bi-
modal distribution of grades in their
CS classes as evidence for its exis-
tence. Some students “get it” and do
well, while others do not, which ap-
pears as two peaks in a grade distribu-
tion. Is it real? Are some students born
to be computer scientists, and are oth-
ers unlikely to succeed because they
do not have the right stuff?
There is a long history of researchers trying to discover the variables that
predict student success in computer
science class. Probably the most famous of these had the odd title “The
Camel has Two Humps.” It was never
published in a peer-reviewed venue,
was not replicated in multiple attempts, and was later retracted—but
its power persists because it rings
true to many. The underlying research
questions are important: What skills
and knowledge predict success in CS?
How can we measure them? Can we
teach any missing but necessary skills
and knowledge explicitly?
The following paper “Evidence
that Computer Science Grades Are
Not Bimodal” by Elizabeth Patitsas,
Jesse Berlin, Michelle Craig, and
Steve Easterbrook takes aim at belief
in the Geek Gene. If there is a Geek
Gene, one would expect bimodal
grade distributions in CS classes. If
grades are not bimodal, perhaps the
Geek Gene is just a figment of teachers’ biases. The authors explicitly
check a large corpus of grade data
for bimodality, and then run a study
with CS teachers as participants to
determine if belief in innate differences may itself explain why teachers
see bimodality in grades. This paper
is important for showing student
performance may not be bimodal
and for offering evidence of an alternative, plausible hypothesis.
˲ First, they review all final grades in
every undergraduate class from 1996 to
2013 at the University of British Columbia (UBC). This dataset included over
700 sections and over 30,000 grades.
85% of the grade distributions were
˲ Then, they run a deception study.
They recruit 60 CS instructors. Half
were asked to agree or disagree with
statements like in the 2007 study:
“Some students are innately predisposed to do better at CS than others,”
and then shown a set of histograms
representing grade distributions. The
Is There a Geek Gene?
By Mark Guzdial
To view the accompanying paper,
to be computer
are others unlikely
because they do not
have the right stuff?