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Assessing Children's Understanding of the Work of Computer Scientists: The Draw-a-Computer-Scientist Test

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We developed the Draw-A-Computer-Scientist-Test (DACST) to better understand elementary school students' conceptions of computer scientists and the nature of their work. By understanding how young children perceive computer scientists, we can broaden their ideas about the activities and images of computer scientists. We administered the DACST to 87 fourth-grade students (ages 8-9) as a pre- and post-assessment to a computer science curriculum. All students attended the same school and were taught by the same female teacher. Before the curriculum, we found that students most often drew male computer scientists working alone, and featured actions that were connected to technology in general (e.g., typing, printing), but not specific to computer science. After the curriculum, more female students drew female computer scientists than before, and the featured actions were more specific to computer science (e.g., programming a game). We also share insights about the classroom-learning environment that may have contributed to changes in students' understanding of computer scientists and their work.
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Assessing Children’s Understanding of the Work of
Computer Scientists: The Draw-a-Computer-Scientist Test
Alexandria K. Hansen§, Hilary A. Dwyer§, Ashley Iveland§, Mia Talesfore§, Lacy Wright§,
Danielle B. Harlow§, Diana Franklin
§Gevirtz School of Education
UC Santa Barbara
Santa Barbara, CA 93106-9490
{akillian, hdwyer, aockey, dharlow}
@education.ucsb.edu
miatalesfore@gmail.com
lacywright@umail.ucsb.edu
󲙈UChicago STEM Education
University of Chicago
1100 E. 58th St.
Chicago, IL 60637
dmfranklin@uchicago.edu
ABSTRACT
We developed the Draw-A-Computer-Scientist-Test (DACST) to
better understand elementary school students’ conceptions of
computer scientists and the nature of their work. By understanding
how young children perceive computer scientists, we can broaden
their ideas about the activities and images of computer scientists.
We administered the DACST to 87 fourth-grade students (ages 8-
9) as a pre- and post-assessment to a computer science
curriculum. All students attended the same school and were taught
by the same female teacher. Before the curriculum, we found that
students most often drew male computer scientists working alone,
and featured actions that were connected to technology in general
(e.g., typing, printing), but not specific to computer science. After
the curriculum, more female students drew female computer
scientists than before, and the featured actions were more specific
to computer science (e.g., programming a game). We also share
insights about the classroom-learning environment that may have
contributed to changes in students’ understanding of computer
scientists and their work.
Keywords Computer science education; computer scientists;
elementary school
1. INTRODUCTION
Computer programming provides avenues for young students to
creatively express their ideas and interests using technology. With
the recent increase of graphical, block-based programming
environments, more students are starting to code at a younger age
[1, 2]. This fervor around coding recently increased with President
Obama’s call for “Computer Science for All.” This initiative aims
to increase the available opportunities that young students have to
code, empowering the next generation of American students with
the computing skills necessary to thrive in a digital economy [3].
Much work has been done to ensure that computer programming
environments are developmentally appropriate for younger age
groups [4, 5, 6, 7], however, concerns still exist around ensuring
novice programming experiences are equitable for all students,
regardless of gender, ethnicity, language, socioeconomic status or
previous exposure to technology [8, 9, 10]. This is in part due to
the fact that the field of computer science is still vastly
unrepresentative of the larger population in terms of ethnicity and
gender. To encourage underrepresented populations to pursue
computer science requires that these students both have the
confidence in their abilities to do so (hence, our focus on teaching
programming in schools), and an understanding of computer
science as a field that will be interesting and welcoming to them.
While many studies have reported positive results when teaching
introductory programming to small groups of students in isolated
settings [7, 11], more attention needs to focus on uncovering what
young students think about computer scientists and the nature of
computer science. Research indicates that early interest in the
STEM disciplines greatly increases a student’s likelihood to
pursue a STEM career in the future [12]. By understanding what
young students think about computer scientists, we can broaden
their ideas about the activities and perceptions of computer
scientists, potentially allowing students to more readily see
themselves as a computer scientist (or, at the very least, capable of
pursuing computer science in the future, should they choose).
In order to gauge how students perceived computer scientists, we
adapted the Draw-A-Scientist-Test (DAST) [13] for computer
science. The DAST and later the Draw-A-Scientist-Checklist
(DAST-C) have been used to understand students’ perceptions of
professional scientists, albeit often stereotypical perceptions [14].
This activity has also been adapted for engineers [15, 16, 17].
With similar goals to the DAST, we sought to investigate how
young students thought about computer scientists, particularly
images of computer scientists and attributes of computer
scientists’ work. We piloted a computer science version in earlier
work [18]. In this paper, we introduce a revised version of the
instrument and report on findings from using the tool with 4th
graders (ages 9-10) before and after these students participated in
a computer science curriculum.
The paper is organized as follows. In section 2, we share relevant
and related work. Section 3 discusses the research methods and
data analysis. Finally, findings are presented in Section 4, with
implications shared in the discussion.
2. RELATED WORKS
In the following section, we provide an overview of related
literature that informed our research design in this study.
2.1 Drawing a scientist and engineer
Over the past fifty years, a growing body of research has been
conducted on how people, especially students, perceive scientists
and their work [14]. In early work by Mead and Metraux [19],
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DOI: http://dx.doi.org/10.1145/3017680.3017769
279
35,000 high school students were asked to write an essay to
describe their views of a scientist. The resulting analysis found
that students often reported stereotypical images of scientists (e.g.,
White/European American, male, lab coat, working in a lab).
Building off two decades of research in this area, Pion and Lipsey
[20] concluded that often these images of science and scientists
distorted the reality of who scientists actually are and what they
do in the field, possibly discouraging students from pursuing a
career in science if they differed from the stereotypical images.
To assess conceptions of younger students, Chambers [13]
developed the Draw-a-Scientist-Test (DAST). Chambers asked
4,807 elementary students (grades K-5; ages 5-11) to draw a
scientist. Researchers analyzed the drawings for the
presence/absence of certain features: symbols of research,
symbols of knowledge, visible attributes of the scientist (e.g.,
gender, ethnicity, clothing), and mythic stereotypes (e.g.,
Frankenstein, “mad scientists”). This analysis worked to refine
common attributes that young students associate with being a
scientist, primarily male (only 28 out of 4,807 students drew
females), white lab coats, eyeglasses, facial hair, scientific
instruments, technology, and signs of secrecy or danger [13].
The Draw-an-Engineer-Test (DAET) modified after the DAST
prompts students to draw an engineer and include written
responses to describe their engineer [17]. Fralick et al. [21] later
used the DAET with over 1,800 elementary school students.
Analysis revealed that when students included a person in their
drawing, 50% were male while only 13% were female. The rest
did not clearly indicate a gender. Additionally, common physical
attributes included laborer’s clothing (e.g., overalls), glasses or
goggles, and unkempt (“crazy”) hair. Students included objects
such as vehicles, structures, and tools, and they drew engineers
building with their hands or engaging in activities representative
of blue-collar jobs such as auto mechanics and construction [21]
This suggests that young students often have inaccurate
perceptions of what engineers do, potentially impacting their later
career choices [22, 23, 24].
2.2 Student perceptions of computers
More recently, Grover et al. [25] investigated 20 middle school
students’ perceptions of what a computer is by asking students
enrolled in an introductory CS course to post responses in an
online discussion board. Researchers were particularly interested
in understanding commonly held notions of what a computer is,
and what types of devices qualify as “computers.” The middle
school students initially tended to report that a computer must be a
certain size (i.e., a desktop computer is much larger than a phone,
so a phone must not be a computer), although opinions changed
throughout the online discussion. General consensus was reached,
with middle school students concluding that a computer was
based on its features (e.g., wifi connectivity, multiple uses), rather
than size.
Grover compared the middle school responses to 117 high school
students enrolled in the Exploring Computer Science (ECS)
curriculum. On an end-of-unit assessment, students were asked if
they thought a microwave qualified as a computer. Results
indicated that 90% of students thought a microwave was a
computer, but only 2% were able to list correct characteristics as
justification (e.g., accepts input, stores information, etc.). Overall,
researchers concluded that asking students about a computer
caused a misplaced focus on the device, as opposed to the act of
computing, and that the definition of a computer is confusing
when considering varied types of devices.
2.3 Student perceptions of computer users
While we found no existing studies that asked young students to
draw computer scientists, Mercier et al. [26] analyzed middle
school students’ (aged 11-14) drawings of computer users. The
most common item drawn was a man wearing glasses. Follow-up
interviews conducted with a subset of students revealed that over
75% of students described a “computer-type” person. Such
individuals were knowledgeable about computers, motivated to
learn about computers, and spent a great deal of time working on
computers. The majority of students did not view themselves as
“computer-type” persons.
2.4 Student perceptions of computer scientists
Some studies have investigated student perceptions of computer
scientists at the university level. Cheryan et al. [27] asked 118
undergraduates to “Describe a computer science major” on a
questionnaire. They identified a number of stereotypes about
computer scientists, such as that computer science majors were
technology-oriented, singularly focused on computers, lacked
interpersonal skills, intelligent, male, and with specific physical
features (pale skin, abnormal body weight) and attire (glasses).
Additionally, Cheryan et al. [27] found that undergraduate
descriptions of computer science majors consisted of traits that
may be incompatible with the female gender role, such as anti-
social behavior and an unhealthy fixation on computers. This is
similar to the arguments made by Lewis [28] regarding the
development of the computing profession, which has a long
history of deterring women from entering the profession.
Martin [29] asked incoming freshmen enrolled in an introductory
computer science course: (1) what is computer science? and (2) to
draw a computer scientist. Results indicated that these students
most often drew “white males in various degrees of ‘geekiness.”’
Drawings that were labeled as including levels of “geekiness”
were described as including features such as glasses, pocket
protectors, messy hair, acne, etc. Martin concluded that, “CS has a
fundamental image problem.
Hewner and Guzdial [30] analyzed autobiographical accounts of
computer science majors compared to non-computer science
majors. Computer science majors with a positive conception of
computer science often viewed computing as fun and useful, as an
interesting technology, and as intellectually stimulating.
However, it was indicated that introductory computer science
courses did not have a large effect on changing negative attitudes
about computing, indicating that more work is needed earlier on
in academic careers to expose individuals to computer science in a
positive manner.
Grover et al. [31] investigated perceptions of computer scientists
among 26 middle school students (ages 12-14) before and after
these students had completed a 6-week computer science
curriculum. Pre-assessments revealed that many students
associated computer science with notions of fixing, building, and
studying; whereas post-assessment results revealed that more
students were apt to depict computer science as problem solving.
This indicates that computer science curriculum can help younger
students more accurately understand the nature of computer
science.
Across all these studies is the consistent thread that computer
scientists, computer science majors, and computer users are white,
male and associated with “geekiness.” Yet, with one exception of
the study of middle school students, these studies were all
conducted with young adults or older. Understanding younger
children’s views will help identify when this stereotype begins.
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3. Methods
During the 2014-2015 academic year, we created and piloted the
Draw-A-Computer-Scientist-Test (DACST) with 185 students in
fourth-sixth grade (ages 8-11) [9]. These students completed the
DACST after participating in approximately 12 hours of an
introductory computer science curriculum [6] that featured block-
based programming, similar to Scratch [7].
In the subsequent academic year (2015-2016), we repeated a
similar activity and analysis with students, but with two main
changes. First, the prompt was modified. Instead of asking
students to draw a computer scientist programming, we asked
students to draw a computer scientist working. We hoped this
change would better elicit the students’ ideas about what
computer scientists did. Second, the activity was given to students
as a pre-assessment, before they began any official computer
science curriculum in their classrooms. We hoped this change
would decrease the impact that our curriculum had on the
responses students provided. Additionally, this allowed us to also
give the same activity as a post-assessment, after students had
completed the curriculum. Our research questions for the follow-
up study presented here were as follows:
How do elementary school students conceptualize
computer scientists?
How do elementary school students conceptualize the
work of computer scientists?
How does our computer science curriculum impact
student conceptions of a computer scientist?
The revised prompt was given to 87 fourth grade students (aged 8-
9) who attended the same school and were taught computer
science by the same female teacher. These students had no prior
experience with our computer science curriculum. The DACST
was administered by the classroom teacher, without researchers
present. The participating school had approximately equal
numbers of male and female students. The majority of enrolled
students identified as either Hispanic (46%) or White (47%).
Approximately 40% of students at this school qualify for free or
reduced lunch (a proxy for socioeconomic status).
3.1 Data Analysis
A preliminary coding scheme was developed for analysis after
reviewing a subset of the drawings, which was further refined
through group discussion. Multiple rounds of coding were
conducted on subsets of the drawings by researchers, with each
round refining the coding scheme. In the final coding scheme,
drawings were coded for the following: observable demographic
information (e.g., gender), worn accessories (e.g., glasses),
emotionality (as depicted in speech or thought bubbles),
technologies included, the setting (e.g., classroom, garage), the
title (e.g., computer scientist, scientist), actions (e.g., coding,
fixing), and the object of the actions (e.g., computer, website).
The final scheme was used by multiple researchers individually
before coming together to check for inter-rater reliability. When
codes differed, researchers discussed until consensus was reached.
Finally, all drawings were coded for the presence or absence of
the characteristics described above.
It is important to note that researchers refrained from coding their
personal assumptions or inferences. For example, gender was only
coded if a student included an explicit gender-specific pronoun in
the written description (e.g., he, she). Additionally, student
drawings were sometimes coded more than once per category. For
example, a student who wrote their computer scientist was
programming a game for a website was coded for “programming”
as the action, but as both “game” and “website” for the object.
4. FINDINGS
In this section, we share the results of our analysis. Results are
organized by research question, and separated into two main
sections: pre-CS curriculum and post-CS curriculum.
4.1 Pre-CS Curriculum
Before students completed any formal instruction in computer
science, they were asked to draw an image of a computer scientist
working and describe what was going on in the picture. Pre-
assessment results are shared below.
4.1.1 Who is a computer scientist?
Depictions of computer scientists matched common stereotypes.
Seven percent of student drawings featured a bald computer
scientist, and 4% of drawings featured a computer scientist
wearing glasses. Other findings are shared below. Please note that
while the following subheadings may seem definitive, there was,
of course, variation observed across student responses. The
subheadings reflect common student responses.
Computer scientists are male
On the pre-assessment, 71% of students drew a male computer
scientist, while only 27% drew a female computer scientist. The
remaining 3% of student drawings were not coded for gender
because the images were unclear (e.g., stick figure drawings and
no gender-specific pronoun in the written description.) These
results are consistent with the results from the pilot study
conducted the previous academic year.
Computer scientists have a mean age of 25
Despite not prompting students to include an age of their
computer scientist, 17% of students did so. When a specific age
was mentioned, the mean age was 25 years old, and the mode was
30. Several students referred to their computer scientist as a
“college student.” In one example, a student wrote, “A computer
scientist can be anyone. A boy, a girl, or a kid.”
4.1.2 What do computer scientists do?
Figure 1. Drawings of a computer scientist working alone
(left) and collaboratively (right).
Computer scientists work alone
Similar to results from the pilot study, 90% of students drew
computer scientists working alone, in isolation. In one extreme
example, a student drew a computer scientist working in an empty
computer lab, and wrote, “She is the only one working.” In
contrast, only 10% of students drew more than one computer
scientist working together. See Figure 1 for examples of both.
This finding may be a result of the prompt that asked students to
draw “a computer scientist," implying one individual.
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Computer scientists predominantly use computers
When considering the technologies that students included in their
drawings, not surprisingly 82% of students drew a computer
(either a desktop or a laptop), and 27% of students drew multiple
computers. Fewer students drew phones (3%), printers (2%) and
tablets (1%).
Computers scientists perform a vague set of tasks
Based on the verbs used in the written descriptions of pictures,
commonly reported actions of computer scientists included
working (23%), coding (18%), making (16%), typing (9%), doing
(7%), looking (7%), fixing (7%), and testing (6%). When
considering the object that the computer scientists were working
on/coding/making, common responses included computers (26%),
ideas (18%), games (7%), and science experiments (7%).
Computer scientists are often scientists who use computers
A compelling finding that was not present in the pilot study was
that roughly 25% of students referred to their computer scientist
as a scientist, without the qualifier of “computer”. Many of these
students also drew objects (e.g., lab coats, beakers filled with
chemicals, explosions, etc.) that indicated they confused the term
computer scientist with other types of scientists (see Figure 2).
Figure 2. A drawing demonstrating the confusion between
“scientist” and “computer scientist.”
4.2 Post-CS Curriculum
After students completed roughly 12 hours of programming
instruction within a block-based environment, they were asked to
repeat the same activity: draw a computer science working, and
describe what is going on in the picture. Note that the curriculum
did not address explicitly who could be a computer scientist or
stereotypes. Post-assessment results are shared below, with
considerations for how the results differed from the pre-
assessment and how computer science curriculum within the
school may have impacted the differences observed.
4.2.1 Who is a computer scientist?
Computer scientists can be male or female
After the computer science instruction, the percentage of drawings
featuring a male computer scientist dropped from 71% to 51%,
whereas the number featuring females increased from 27% to
31%. One possible explanation for this could be related to the
gender of the teacher; a female teacher taught all participating
students computer science. There was also an increase in the
number of drawings that were unclear for gender (3% to 20%).
For some of these drawings, only a computer screen was visible;
in others, non gender-specific pronouns were used in the written
description, and, thus, were not coded for gender.
Seven percent of students changed from an initial drawing with a
male to a drawing with a female; all of these students were
female. Considering that computer science is a male-dominated
profession, this is a promising finding because it indicates that
computer science experiences in school can change student
perceptions about the field, allowing students to more readily see
themselves as computer scientists. Figure 3 depicts a pre- and
post-assessment for one student who displayed this change. In the
pre-assessment, this female student drew a picture of a male
computer scientist working and wrote, This man is working on
this computer writing some notes.” In her post-assessment
drawing, this student drew a female computer scientist, and wrote,
“In my picture, she is working on Sandbox (part of the
programming environment used by students in the classroom).
Figure 3. Pre- (left) and post-assessment (right) results for one
student who changed the gender depicted.
Computer scientists are increasingly bald and bespectacled
The number of bald computer scientists increased from 7% in the
pre-assessment to 17% in the post-assessment. The number of
computer scientists that featured glasses also increased from 4%
to 7% in the post-assessment. This increase was surprising, but
could possibly be explained based on videos that the teacher
showed students during class, some of which featured computer
scientists who were bald or wearing glasses.
Computer scientists have positive emotions
Interestingly, while we were unable to code for emotional
expression in most pre-assessment drawings, 7% of students
indicated positive emotions in their post-assessment drawings.
For example, these students often included speech or thought
bubbles in their drawings with exclamations such as, “I love
coding!” or “This is fun.” This is promising because it indicates
the learning experiences that occur in a classroom can impact and
shape students’ perceptions of computer science. Unlike the pilot
study, no students included any indication of a negative emotion
(such as frustration or anger) related to computer science.
4.2.2 What do computer scientists do?
Similar to the pre-assessment results, the vast majority of students
still drew computer scientists working in isolation. While the
number of drawings featuring collaboration increased from 10%
to 14%, the increase was not significant. While this may be an
artifact of the instrument prompt, it is potentially indicative of
how the classroom was structured and operated. Typically,
students worked through each programming activity individually.
While they were not prevented from sharing ideas or discussing
with a nearby peer, it was not a requirement to do so. This might
imply that young students should be encouraged to work
collaboratively through programming activities to create more
282
accurate conceptions (and prevent possible stereotypes that might
deter some students from pursuing computer science).
Computer scientists use computers, some with peripherals
When considering the technologies that students included in their
drawings, more students (93%) included a computer in the post-
assessment than the pre-assessment (82%). While fewer students
(15%) included multiple computers on the post-assessment, there
was an increase in other types of technologies drawn. For
example, 10% of students included a technology that was outside
of the original coding scheme (such as speakers and headphones),
which was an increase from the pre-assessment. This could be due
to the fact that students were provided headphones during
programming activities in class so they could use the read-aloud
function for task instructions.
Computer scientists mostly program
Student conceptions of the work of computer scientists increased
in sophistication between the pre- and post-assessment, as
indicated by their vocabulary in the written descriptions. In the
pre-assessment, students often used general words to describe the
actions of computer scientists; such as working, playing, typing,
researching or fixing. In the post-assessment, while the frequency
of working and making remained similar, the use of coding and
programming increased to 40% (vs. 18%). These more specific
vocabulary words were connected to reasonable items (such as
coding on a computer, or programming a website). We noticed
this change in 27% of students’ drawings when comparing their
pre- and post-assessment results.
When considering the product of the actions depicted, results
differed from the pre-assessment. The percentage of students who
explicitly wrote their computer scientist was working on a
computer increased from 26% to 47%. Additionally, more
students (16%) mentioned games compared to pre-assessment
results (7%), as well as websites (24% vs. 4%). Recall that
students’ drawings were sometimes coded as including more than
one product, so reported percentages may not equal 100.
Figure 4. A post-assessment student drawing depicting a
teacher leading a computer science lesson.
Computer scientists program in block-based languages
Many students drew the website and/or programming language
that was taught to them during computer class. Perhaps an
unsurprising finding, in the pre-assessment results there was no
mention of the specific curriculum or programming language used
with the students, yet 29% of students included this in their post-
assessment, which is similar results to the initial pilot study.
Additionally, in contrast to the pre-assessment results, 11% of
students indicated that their computer scientist was either teaching
or learning computer science (see Figure 4). This speaks to the
importance of what and how computer science is taught in school.
Early experiences in computer science can motivate a student’s
desire to pursue this discipline later in life, which is why it is
crucial to understand student conceptions at an early age and how
classroom-learning experiences may shape this.
Computer scientists are not scientists
When considering pre-assessment results, 38% of student
drawings were coded as potentially including scientific symbols
or written descriptions (e.g., beakers, chemicals, researching
animals or space, etc.). Additionally, roughly 25% of these
students used the word scientist instead of computer scientist in
their written descriptions. However, these numbers decreased in
the post-assessment. Only 18% of students used the term scientist
instead of computer scientist in their written descriptions.
Furthermore, only 6% of these students’ drawings were coded as
including scientific content (e.g., lab coats, beakers). This
discrepancy suggests that students demonstrated a more accurate
understanding of the work of computer scientists, despite some
students still using the term scientist. Figure 5 is an example of a
student drawing that used the word scientist, but was coded as
depicting actions of a computer scientist.
Figure 5. Post-assessment drawing that used the term scientist
to describe work associated with computer scientists. Student
wrote, “This scientist is programming a drone.”
5. DISCUSSION
As coding becomes more prevalent, and integrated into the school
day, it is crucial that we understand young students’ conceptions
of computer scientists. By understanding student perceptions of
stereotypical computer scientists, we can broaden their ideas
about the activities and images of computer scientists. This work
serves as another step in uncovering what, specifically, young
children think about computer science. It is particularly
noteworthy because this instrument was administered before and
after a computer science curriculum, indicating how the specific
curriculum, programming environment, and classroom may have
impacted student conceptions.
We also consider the instrument itself, the DACST, as an
important contribution to the field of computer science education.
One possible revision that may elicit additional information from
students is to have students write a story about their computer
scientists to accompany the picture, instead of just a short
description. Writing a narrative may elicit increased information
that was missed in a short description.
6. ACKNOWLEDGMENTS
This work is supported by the National Science Foundation CE21
Award CNS-1240985. Any opinions, findings, and conclusions or
recommendations expressed are those of the authors and do not
necessarily reflect those of the National Science Foundation. We
would also like to thank all of the teachers, students, and schools
involved in this project.
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... This exploratory study uses a Draw-a-Programmer (DAPT) instrument (adapted from similar instruments; Draw-a-Scientist, Engineer, Computer Scientist Tests) to investigate how preservice teachers perceive and visualize CS and CT (Hansen et al., 2017;Knight & Cunningham, 2004;Miller et al., 2018). Here, we detail the development and testing of this tool, and then analyze and consider our findings as they apply to elementary teacher education. ...
... The "Draw a Scientist Test" (DAST) and "Draw an Engineer Test" (DAET) have been widely implemented to assess students' ideas about scientists and engineers and the work they do (Chambers, 1983;Knight & Cunningham, 2004;Miller et al., 2018). More recently, scholars have developed the Draw-a-Computer Scientist Test (DACST) to understand children's views of computer scientists (Hansen et al., 2017). Each of these instruments require respondents to draw and use words to describe their views of these respective disciplines and professions (Hansen et al., 2017;Knight & Cunningham, 2004;Miller et al., 2018). ...
... More recently, scholars have developed the Draw-a-Computer Scientist Test (DACST) to understand children's views of computer scientists (Hansen et al., 2017). Each of these instruments require respondents to draw and use words to describe their views of these respective disciplines and professions (Hansen et al., 2017;Knight & Cunningham, 2004;Miller et al., 2018). As such, these instruments provide teachers and researchers valuable and multifaceted insights into their students' understandings of science, engineering, and/or computer science. ...
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Recent US science standards conceptualize science as a set of shared multidisciplinary ideas and practices in common with engineering and computer science (CS). At the core, this portrayal requires an understanding of CS as a viable career path and set of discrete knowledge and skills, including those related to computer programming. However, research repeatedly shows inservice and preservice teachers to be unfamiliar or uncomfortable with reform-based instruction and CS-related careers. This exploratory study uses a Draw-a-Programmer Test (DAPT) instrument (adapted from the Draw-a-Scientist [DAST], Engineering [DAET], and Computer Scientist [DACST]) to investigate how preservice teachers understand and visualize computer programming. Here, we detail the development and testing of this tool across two preservice elementary science and technology courses. Participants in this study included 52 preservice teachers in the last semester of their teacher preparation program enrolled in these courses. Data were collected using the DAPT instrument and were analyzed using open coding of respondents’ depictions and written descriptions of computer programming. Findings revealed that participants held somewhat stereotypical, yet distinct conceptions of CS and computer programming (i.e., separate from science and engineering) which may provide concrete entry points into fostering computational thinking skills. Implications are discussed as they relate to elementary teacher education and research.
... 52), are socially awkward (Beyer et al. 2003), intelligent (Ehrlinger et al., 2018) and male (Cheryan & Plaut, 2010). This stereotype is also evident in younger children: 71% of elementary school children drew a male when asked to draw a 'computer scientist' (Hansen et al., 2017). These stereotypes do not just exclude females but candidates who identify with stereotypically 'feminine' characteristics, regardless of their sex. ...
... them. An area that educational practitioners and policy-makers could explore further is the availability and diversity of STEM role models available to school students, particularly at an early age, when stereotypes and gender roles are beginning to develop (Chick, Heilman-Houser & Hunter, 2002 (Hansen et al., 2017). Allowing children and young people opportunities to access a variety of STEM role models, from a wide range of backgrounds, might challenge any existent stereotypes and promote the idea that STEM fields are inclusive and not accessible only by those who fit a certain stereotype. ...
Thesis
Despite recent government initiatives, there continues to be a shortage of individuals working in Science, Technology, Engineering and Mathematics (STEM) industries. There is a particular underrepresentation of female STEM workers, with females opting out of STEM fields at each step of the ‘STEM pipeline’, from classroom to boardroom. This thesis identifies and explores the impact of different factors on interest in choosing STEM subjects at post-16 level and how gender identity and stereotypes impact upon computer science enrolment interest. A systematic review of the literature that explores influences on STEM subject choice at post-16 level highlighted thirteen key factors that predict STEM subject choice; these factors could be categorised as either intrinsic or extrinsic to the individual. A fourteenth factor, an individual’s sex, interacted with the majority of these identified factors. This systematic literature review highlights the insufficiency of theories of decision-making in explaining the decision-making that occurs during STEM subject choice, since an individual’s biological sex appears so influential. The empirical study investigates whether gender identity and other well-evidenced influences predict enrolment interest in computer science. It aims to explore whether stereotypical cues in a learning environment affect students’ interest. Year 9 students (n= 168) completed measures assessing gender identity. They were shown either a stereotypical or a non-stereotypical computer science classroom and completed measures assessing their enrolment interest in computer science, belonging, stereotype threat, self-efficacy and utility value. Femininity significantly predicted enrolment interest, and this relationship was mediated by stereotype threat. The stereotypicality of the classroom did not moderate the mediation of stereotype threat on femininity and enrolment interest. This empirical study extends previous research by showing that it is one’s gender identity, rather than simply their sex, that predicts enrolment interest. We highlight the need to consider and challenge stereotypes that continue to exist in relation to subjects such as computer science, in order for all students to feel included.
... This masculine culture helps men maintain their power by occupying the top positions in a society that, in recent decades, has come to value CS competence highly and to equate it with a nation's success in the international arena [67]. In fact, CS is so strongly associated with masculinity and an obsession with computers that it has become a norm and identity of CS shared by both children and adults, excluding any potential student who cannot reconcile with this stereotypical view [21,43,68]. ...
... Previous studies have highlighted a lack of understanding or a misunderstanding of what CS entails and stereotypical views of computer scientists as barriers to attracting students from diverse backgrounds to the ield [20,21,23,43,64,94,95]. A study by Master et al. [61] found that stereotypical cues in a CS classroom can deter underrepresented students from studying CS. ...
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The aim of this conceptual article is to provide a framework and a lens for educators in diversifying and making CS education more inclusive. In this paper, we conceptualize the notion of computer science capital (CSC), which extends Bourdieu’s sociological theory of capital and Archer et al.’s work on ‘science capital’. The CSC concept was developed by contrasting the concept of science capital with a literature review on key factors affecting students’ aspirations in CS. We argue that there is a need to distinguish between science capital and CSC, because the types of capital that are considered legitimate vary between the field of natural science and computer science. The CSC concept uses a sociocultural perspective on learning and can be understood as a form of symbolic capital that is influential in facilitating students’ possibility to fully participate in, engage with, and form aspirations in CS. The CSC concept consists of three main components, each with associated subcomponents. We believe our CSC framework, along with the self-reflection prompts included in this article, will offer support for reflections for educators in their daily pedagogical work. By taking students’ various levels of social and cultural capital into consideration, educators can plan didactic activities with a focus to strengthen students’ various types of capital. This includes reflection on how implicit and explicit norms, beliefs, thoughts, expectations, values, and ideas can affect the pedagogical practices and ultimately the students. Only when we are reflective about our teaching practices can we be better positioned to construct a more inclusive teaching and learning environment.
... Bollin et al. (2020) also report that interest in computer science tends to decline among young women. Hansen et al. (2017) developed the so-called Draw A Computer Scientist Test to understand how young children perceive computer scientists finding more female students drew female computer scientists that after their curriculum than before, whereas male computer scientists were mostly drawn working alone. ...
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Companies regularly report difficulties in recruiting ICT specialists. The shortage of skilled women in this domain is especially prominent. Research shows that early exposure to STEM may spark children's interest and influence their future choice of careers. Children's understanding and conceptualization of their physical environment strongly influence their ability to grasp STEM concepts and learning outcomes in related subjects. The goal of our work is to provide a better picture of children's conceptions before they are confronted with computer science as a subject at school. To investigate students' preconceptions of computer science, a study of 188 fifth-grade students was conducted before they first experienced computer science lessons at school. We asked them about their perceptions and experiences of computer science. Our results show that both students who identify as female and those who identify as male have a narrow view of computer science and associate the field mainly with working with computers. Despite the narrow view, many students show an interest in computer science but few want to work in this field in the future. Students who identify as male have a significantly higher interest in the field than those who identify as female.
... A second implication of the over-representativeness of the US is that the reporting is also distinctly US flavored. For example, when discussing SES, a paper stated that a school has ''eligibility for Title I funding'' (Ibe et al., 2018) or that children received ''free or reduced lunch' ' (Buffum et al., 2016;Hansen et al., 2017) referring to distinct US policies that people outside the US might not be familiar with. Similarly, a concept such as an ''urban'' area (Grover, Rutstein & Snow, 2016;Tsan et al., 2018) does not have the same connotation in all countries; it can refer to a school in a poor inner city area or a well-educated high-income neighborhood. ...
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Computer science education (CSEd) research within K-12 makes extensive use of empirical studies in which children participate. Insight in the demographics of these children is important for the purpose of understanding the representativeness of the populations included. This literature review studies the demographics of subjects included in K-12 CSEd studies. We have manually inspected the proceedings of three of the main international CSEd conferences: SIGCSE, ITiCSE and ICER, of five years (2014–2018), and selected all papers pertaining to K-12 CSEd experiments. This led to a sample of 134 papers describing 143 studies. We manually read these papers to determine the demographic information that was reported on, investigating the following categories: age/grade, gender, race/ethnic background, location, prior computer science experience, socio-economic status (SES), and disability. Our findings show that children from the United States, boys and children without computer science experience are included most frequently. Race and SES are frequently not reported on, and for race as well as for disabilities there appears a tendency to report these categories only when they deviate from the majority. Further, for several demographic categories different criteria are used to determine them. Finally, most studies take place within schools. These insights can be valuable to correctly interpret current knowledge from K-12 CSEd research, and furthermore can be helpful in developing standards for consistent collection and reporting of demographic information in this community.
... As a result, there is a wealth of published literature that uses the DAST paradigm to code and interpret student images to ascertain perceptions of science and scientists. Tasking children and adolescents to draw their thoughts and feelings (perceptions) of various topics in STEM has been used to gauge a range of perceptions including those of computer scientists (Hansen et al., 2017) and engineers (Knight and Cunningham, 2004) as examples. This method continues to be an effective means of researching perceptions among children or those with limited written abilities, as drawing images lack rigorous cognitive demand, allowing even the youngest of children to participate (Buldu, 2006;Chambers, 1983;Goodenough, 1926;Ring, 2006), and yields rich information of one's "symbolic relationship with [lived] experience" (Rawson, 1969, p. 1). ...
States across the United States are enacting policies aimed at increasing computer science (CS) courses and content in K–12 schools. We explore the relationship between such policy with the capacity of high schools to teach CS courses and student access to and participation in CS courses. To do this, we focus on Georgia, a state that has had a robust CS education movement over the past two decades. This paper investigates two research questions: 1) What factors at the school or district level are related to whether a high school teaches CS in Georgia? 2) What factors are related to CS enrollment rates in Georgia high schools? We examine these questions within the context of national and state policy factors that increase capacity of schools to teach CS. We describe the results of correlation and regression analyses of publicly available data for each school and district from 2016 and school CS enrollment from 2012 to 2016 in answering these questions. Results indicate that the odds of a school offering CS in 2016 were greatest for schools that taught CS the prior year, although median income and school size were also significant factors when prior CS was not considered. For CS enrollment, the model that included prior CS enrollment rates explained the most variance, although school size, median income, and the percentage of students who identify as Asian were also significant when prior CS enrollment was not included in the model. CS-specific policy has the potential to mitigate the effects of school size and income by offering capacity supports to schools that do not currently offer CS and can contribute to the sustainability and growth of CS offerings. These results have implications for policy efforts beyond the state of Georgia and provide direction for future research examining the causes of sustained CS offerings and enrollment patterns.
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As advertised in the October 2013 edition of the Bulletin, this year's CS Ed Week encouraged everyone to host an "Hour of Code" event, in which educators hosted (at least) an hour of CS instruction to any audience they chose. SIGCSE members responded to the call enthusiastically. Here is just a sampling of the many great Hour of Code events that were put on.