Eliminating Gender Bias in Computer Science Education Materials
In this episode I unpack Medel and Pournaghshband’s (2017) publication titled “Eliminating gender bias in computer science education materials,” which examines three examples of “how stereotypes about women can manifest themselves through class materials” (p. 411)
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Welcome back to another episode of the
CSK8 podcast my name is jared o'leary
every week the podcast alternates
between an interview with guests or
multiple guests and a solo episode where
i unpack some scholarship
this week is going to start a launch of
a little mini series on
gender bias in computer science
education and i'm starting with a paper
titled eliminating gender bias and
computer science education materials
this paper was written by paula medell
and vahab pornashband
in my apologies if i mispronounce any
names in the show notes you can find
a link directly to this particular paper
and if you click on the author last
names you'll be able to
read some of their other works as it
will take you to their google scholar
profile
all right so here's the abstract for
this particular paper
quote low female participation in
computer science is a known problem
studies reveal that female students are
less confident in their cs skills and
knowledge than their male counterparts
despite parallel academic performance
indicators while prior studies focus on
limited
apparent factors causing this lack of
confidence our work is the first
to demonstrate how in cs instructional
materials may lead to the promotion of
gender inequality we use a
multidisciplinary perspective to examine
profound but often subtle portrayals of
gender bias within the course materials
and reveal
their underlying pedagogical causes we
examine three distinct samples of
established cs teaching materials and
explain how they may affect female
students
these samples while not a complete
display of all gender inequalities in cs
curriculum
serve as effective representation of the
established terms a male-centered
representation
imagery and language that may promote
gender inequality
finally we present easily implementable
alternative gender equitable approaches
that maximize gender inclusion
end quote that abstract does a pretty
good job of summarizing the paper itself
now if i
summarize this into a single sentence i
would say that this study examines three
examples of quote
how stereotypes about women can manifest
themselves through class materials
end quote that quotes from page 411 so
the paper begins with a short
introduction that kind of talks about
some of the ways that stereotypes can
negatively harm
women in particular they talk about how
it affects confidence
in relation to computer science so even
when women are performing just as
well as men they're consistently having
lower rates on their confidence rating
in fields like computer science so after
this short introduction
the paper talks about the different
materials that they analyze so in
particular they analyze
how names are represented within cs
materials
they talk about imagery within cs
materials and then they also talk about
pronouns within cs materials
so in the paper it first begins by
describing a common problem
in cryptographic protocols so in this
particular example
it's basically people sending messages
to each other and showing how people can
intercept those messages or change them
or whatever so what they did for the
analysis is they took
the names of the people within this
particular example
and they associated it with either a
positive a negative or a neutral stance
so for example eve was labeled as
an eavesdropper and they were
intercepting messages between two other
people
and able to read those messages that
were being sent
like through the internet so in that
instance eve
being a female name being labeled as an
eavesdropper and doing something
negative
that one would receive a negative
similarly
mallory who was associated with a
man-in-the-middle attack
was also associated negatively however
males
tended to be associated with more
positive things so like walton was the
protective warden
now in these examples they tended to
have whatever the first letter of the
name was
associated with whatever it was that
they were doing whether it was
eavesdropping
or warden or whatever so e for
eavesdropping
w for warden etc now the authors are
arguing well
you could have used any kind of name for
this whether it be
like a general neutral name such as alex
or chris just as easily as you could
have associated
positive traits to females and negative
traits to males
but in general what they were finding is
females were associated with a negative
and males were associated with positive
so one more example of this
that is not only example of some
gender biases but also ableism is the
sybil attack
so the sybil attack was previously known
as pseudo-spoofing
and it's some kind of an attack where
quote identities are forged to support a
reputation system and peer-to-peer
networks end quote
that's from page 412. so an example of a
civil attack might be something like
creating a bunch of false accounts and
giving false reviews
on a service or creating bot accounts
on like something like twitter and then
promoting things
or arguing against things so you're
making it seem like there's this
mass amount of people who are asking for
or recommending or arguing against
something
but really it might just be one person
or a small
number of people who are engaging in
what is commonly referred to as a sybil
attack
now the reason why this particular
example is brought up in this paper
is that quote the name was inspired by
the book sybil about the treatment of a
woman diagnosed with disassociative
identity disorder
as a result of physical and sexual abuse
the representation of a mentally ill
woman
as the field standard term for an
attacker is not only insulting but
harmful
by projecting negative stereotypes about
women unquote from page 412. and by the
way
disassociative identity disorder was
formally referred to as multiple
personality disorder in case you're
unfamiliar with it there's just a
clarification
basically the same idea new term so with
these examples
whether it be the civil attack or eve
the eavesdropper
in the cryptography example the authors
are basically arguing we need to analyze
how we're portraying different genders
within the materials that we're using
now here's the reason why so here's a
quote from page 412 quote
by comparing characters with positively
or negatively associated roles
we found clear gender discrepancies
there are more female characters than
males
however this does not indicate fair
inclusion in fact
of the four characters with positive
connotation only one is female
by comparison of the nine total negative
roles six are female
and three are male thus of eight female
associable characters
less than thirteen percent of them are
good compared to fifty percent of
associable male characters
in quote okay so after kind of laying
down some evidence that supports the
idea that hey there's some gender bias
here in how we're using these names and
the associations we're giving to them
they talk about what are some ways that
we could be more equitable
so one potential solution is to replace
names with gender neutral names however
the authors
argue that there are still associations
with particular genders for different
names
so for example the name alex if i have
a friend who identifies as female named
alex
i might associate alex with female more
so than i do with alex
with male or non-binary but if i have a
friend named alex who identifies as male
i might associate it more with male
so instead of using gender neutral names
the authors actually recommend
using animals so for example the
eavesdropper could be the owl
and instead of going with sybil we could
say chameleon
because chameleons change colors and
assumes varying identities
now the authors do say that quote due to
the universal nature of animal
representations
educators from different cultural and
language backgrounds can use this method
to teach their students in a relatable
way
end quote while i understand what
they're trying to say i disagree
so some cultures view animals
differently than other cultures
for example cultures that use some of
the stories from the bible about snakes
being
sneaky and subversive and manipulative
and whatnot
might differ than other cultures that
represent snakes in a positive light
so as an example some cultures actually
view snakes within a sacred role
or as representation of changes in
cycles
by the shedding of skin in other words
not negatively so that's my one
small minor disagreement with the what
they're indicating
however in general this recommendation
of using animal characters instead of
people names makes sense in relation to
the gender biases that they're trying to
avoid okay so the next particular
example that they talk about
is imagery that is used within materials
so for example there's an image named
lena
that is often used for image processing
examples and it is actually
an image of a woman from a playboy
magazine
and they cropped it so that it's that
person's bare shoulders
and above and this image is frequently
used in presentations publications etc
when discussing image processing
examples now here's a quote from page
quote such imagery objectifies women by
projecting stereotypes that emphasize
their physical appearance rather than
their mental values
objectifying imagery affects women's
confidence and therefore academic
performance in two ways
deteriorating their perceptions of self
and lowering others perceptions of them
end quote now the authors point out that
some people have actually flipped the
image so it was like a
exposed version of a male in a similar
way
in that they cropped it up the shoulders
and above and had a male model
as the example but the authors argue
that this is still an example of
objectifying members of a different
gender so some people have recommended
well instead of using sexualized imagery
how about we instead
have positive imagery of different
genders so for example having a picture
of a woman holding a trophy or a woman
in leadership
but the authors actually recommend
instead of using pictures of people
to instead use pictures of monuments
such as like pyramids
or architecture or things like that the
authors argue that this can
help eliminate gender biases and that
when you need to use
facial images as examples so for example
if you are creating materials that's
talking
about facial recognition and you need a
picture of a face
then recommend that using some kind of a
picture that empowers people rather than
objectifies them
now i totally agree about the point
of avoiding objectification of genders
but i just want to point out that
there's some debate about whether or not
this is a form of objectification or a
form of empowerment
i'm personally not well-versed enough in
that kind of scholarship in that area of
study to be able to explain more nor am
i
a woman or identify as it identify as a
non-binary by the way but i completely
agree that
we should steer away from sexualized
imagery in course materials
as i think that is particularly
problematic or at least can be
depending on the context and whatnot so
the third area that they're analyzing
is language so in particular they talk
about examples of pronoun use
so using only he only she
he or she or the singular use of
they while some people prefer to use he
or she
or he and she when referring to groups
of people
or just some like anonymous pseudo
person in some kind of example
the authors instead recommend that you
use the singular
they pronoun to refer to an unspecified
gender
now as a non-binary individual the
pronoun they is the pronoun that most
aligns with my own gender identity but i
personally
don't have a preference so you can use
he she they with me
so it doesn't really matter to me
however if you use they
it at least moves outside of the binary
it does not put a particular gender
within a positive or negative light it's
more ambiguous
and it again accounts outside of the
gender binary
so that recommendation totally relates
to me and i highly recommend it
now as educators some of the things that
we need to think about is the ways that
we speak with our students so it's not
just in the materials that we submit
so not just the names not just the
imagery not just
the pronouns on the assignments that we
give but how we actually speak to people
so for example a lot of youtubers will
use the what's up guys or
whatever other intro and it's that use
of guys
that can become problematic so i know
some teachers who will avoid that and
say
good morning boys and girls or whatever
something like that but again that then
promotes the
binary assumption with genders and makes
non-binary trans individuals
uncomfortable or at least can so we can
avoid that by saying
like good morning blank just end there
or
something else or using some kind of
other group identity so as an example of
this when i was originally creating
the videos for boot up where i walk
through step by step how to do stuff in
scratch that kids are going to use
it was very intentional with the opening
line that i
started with so every single video i
start with welcome back
fellow coders so it was a very
intentional set of four words
i went with coders because it's gender
neutral
it's also saying hey you are a coder
you are a programmer you can do computer
science and by saying fellow
and saying hey i can program you can too
so it was trying to avoid any kind of
gender associations
basically saying hey welcome back to
this video i'm a coder you're also a
coder
although some people might argue that
i'm thinking way too much about word
choice
it can have a huge impact so speaking of
impact the authors
took their suggestions and they actually
implemented it into
an experimental group that received the
treatment i.e the
replacements of people names with
animals and the
imagery of the sexualized woman with
architecture or structures
and then replacing the pronouns with the
singular they so that's the group that
received all those
treatments and then a control group
which was a class that
just had the normal cs materials with
these gender biases in them
now what they ended up finding is that
there was improvement for
female students in terms of their
confidence while mal students
in either the experimental or the
control group did not have any kind of
statistically significant change in
their confidence so it did not
negatively impact them
but it possibly impacted females in
terms of their confidence
so again as a quick summary of the paper
itself they looked at
names in course materials they looked at
imagery and course materials and they
looked at pronouns and course materials
their overall recommendations were to
avoid names and instead use animals
to avoid imagery of sexualized genders
and instead use
something like a structure ideally a
structure that is not like phallic-like
or gendered and then to use the singular
they
instead of he or she for your pronouns
as always at the end of these unpacking
scholarships i like to share some
lingering questions or thoughts
or sometimes rants like a couple weeks
ago so one question that i have that i
honestly don't know and don't have an
answer for is is the shift towards
animals and monuments a form of
dehumanizing computer science
in other words are we taking the human
aspects out of it are we making this
technological thing
even less human than it already can be
at times and that
i honestly don't know so i'm just kind
of thinking out loud another question
that i have is when creating or sharing
materials with students
what kind of demographic balances do you
strive for
so are you trying to demonstrate equal
relationships match demographic
proportions
or are you leaning more toward
marginalized identities to counter the
trends
so for an example related to gender and
unlike the article i'm going to include
non-binary within this so if you're
trying to go for equal relationships are
you going to have
one-third female representation
one-third non-binary representation and
one-third male representation
or if you're going for matching
demographic proportions are you going to
go for
these are hypothetical numbers 50 female
non-binary and 49 male representation
and if you're going to go lean towards
more marginalized identities to counter
the trends
so for example leaning towards
representation and only 10
male representation to counter balance
the overabundance
of males within cs materials in
whichever direction that you end up
going
when might an approach like this
unintentionally communicate messages
that a certain demographic
is not welcome within the cs community
in other words does the pendulum then
shift the other way
so if we look at gender within cs and
say well there's an over
abundance of representation of males
should we then shift the pendulums that
we mainly focus on
females non-binary representation does
that then unintentionally say
males are unwelcome in cs education and
just like my
question about dehumanizing i don't know
again just thinking out loud
it's something that i would love to see
more research on and more conversations
on
within the field so my last question is
not a question that is tied to this
particular study
but gender imagery analysis in general
so the question is
how might we as a field start engaging
in conversations around gender without
making assumptions about people
now the reason why i say this is because
i sat in a on a presentation once where
somebody started playing a video of the
classroom
and the imagery within it and their
commentary on the imagery was making
assumptions about
the genders that represented now if you
looked at it there were a lot of male
presenting individuals in there in terms
of
the ways that they were dressing in
terms of their hairstyles etc
and the comment was that this was a male
dominated class however i would argue
we actually don't know if that was a
male-dominated class without actually
asking
the people within that imagery what we
don't know is
the class could have in fact been
dominated by non-binary and trans
individuals and we don't know until we
actually
do more than a surface level analysis of
what we're seeing now i say this
to say we should dive deeper into these
gender discussions
but also in recognition of the larger
point was that yes
cs is largely dominated by males
wholeheartedly understand that
so those are just some of my lingering
thoughts related to the overall topic of
this particular paper
i enjoyed reading this paper and i enjoy
these kinds of analyses
so i highly recommend reading it if it
also interests you
again you can find it in the show notes
if you enjoyed this particular episode
please consider sharing it
with a friend or colleague as it helps
spread the word about cs education and
research
stay tuned next week for another
interview and the following week for
another unpacking scholarship episode
i hope you're all having a wonderful
week and are staying safe
Article
Medel, P. & Pournaghshband, V. (2017). Eliminating gender bias in computer science education materials. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE '17). Association for Computing Machinery, New York, NY, USA, 411–416.
Abstract
“Low female participation in Computer Science is a known problem. Studies reveal that female students are less confident in their CS skills and knowledge than their male counterparts, despite parallel academic performance indicators. While prior studies focus on limited, apparent factors causing this lack of confidence, our work is the first to demonstrate how, in CS, instructional materials may lead to the promotion of gender inequality. We use a multidisciplinary perspective to examine profound, but often subtle portrayals of gender bias within the course materials and reveal their underlying pedagogical causes. We examine three distinct samples of established CS teaching materials and explain how they may affect female students. These samples, while not a complete display of all gender inequalities in CS curriculum, serve as effective representations of the established trends of male-centered representation, imagery, and language that may promote gender inequality. Finally, we present easily implementable, alternative gender equitable approaches that maximize gender inclusion.”
Author Keywords
Gender, Diversity, Confidence, Gender Equitable
My One Sentence Summary
This study examines three examples of “how stereotypes about women can manifest themselves through class materials” (p. 411)
Some Of My Lingering Questions/Thoughts
Is the shift toward animals and monuments a form of dehumanizing CS?
When creating or sharing materials with students, what kind of demographic balances do you strive for?
Are you trying to demonstrate equal relationships (e.g., 1/3 female, 1/3 nonbinary, and 1/3 male), match demographic proportions (e.g., 50% female, 1% nonbinary, and 49% male), or lean more toward marginalized identities to counter trends (e.g., 70% female, 20% nonbinary, and 10% male)?
When might an approach like this unintentionally communicate messages that a certain demographic is not welcome in the CS community?
How might we as a field start engaging in conversations around gender without making assumptions about people?
Resources/Links Relevant to This Episode
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In this episode I unpack Mellström’s (2009) publication titled “The intersection of gender, race and cultural boundaries, or why is computer science in Malaysia dominated by women?,” which “points to a western bias of gender and technology studies, and argues for cross-cultural work and intersectional understandings including race, class, age and sexuality” (p. 885).
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