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Cracking The Code: The Impact
of Computer Coding on the
Interactions of a Child with Autism
Abstract
This paper reports on the communication patterns of
two students in two settings: the elementary school
classroom and the computer lab. One child was
diagnosed with autism and the other was neurotypical.
These students participated in a computer science
curriculum designed for upper elementary school
children (grades 4-5; ages 9-10), featuring block-based
coding. The computer science instruction occurred in an
inclusive general education setting. Analysis of video
data revealed the child with autism communicated
more (in terms of both total time speaking and
interactions initiated) in the computer lab than was
observed in the traditional classroom setting. Opposite
trends were observed for the neurotypical child.
Author Keywords
Computer Science Education; Autism; Communication;
Elementary School; Scratch; Coding; Logo; Papert
ACM Classification Keywords
K.3.1 [Computer Uses in Education]: Collaborative
learning, K.3.2 [Computer and Information Science
Education]: Computer science education
Permission to make digital or hard copies of part or all of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. Copyrights
for third-party components of this work must be honored. For all other
uses, contact the Owner/Author.
IDC '17, June 27-30, 2017, Stanford, CA, USA
© 2017 Copyright is held by the owner/author(s).
ACM ISBN 978-1-4503-4921-5/17/06.
http://dx.doi.org/10.1145/3078072.3084307
Jim Gribble
UC-Santa Barbara
Goleta,CA, 93117, USA
jgribble@ucsb.edu
Alexandria Hansen
UC-Santa Barbara
Goleta,CA, 93117, USA
ahansen@ucsb.edu
Danielle Harlow
UC-Santa Barbara
Goleta,CA, 93117, USA
dharlow@education.ucsb.edu
Diana Franklin
University of Chicago
Chicago, IL, 60637, USA
dmfranklin@uchicago.edu
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Introduction
In 2016, the White House announced the Computer
Science for All initiative, calling for computer science
instruction in K-12 schools, “offering every student the
hands-on computer science and math classes that
make them job-ready” [7]. There has been an increase
in novice-friendly programming environments (e.g.,
Scratch, Alice), many of which occur in after-school.
As computer science is integrated into K-12 inclusive
schools, research-based findings should inform the
development of curriculum and teaching practices to
ensure all students can access the content, including
children with exceptionalities (formerly special needs).
Despite children with exceptionalities making up 13%
of the school-age population [12], little research has
focused attention on understanding how these children
understand computer science. A significant portion of
this 13% consists of children diagnosed with autism or
who exhibit autism-like characteristics, such as
difficulty communicating with others.
Research on children with autism has largely focused
on differentiating instruction or on supporting these
children to succeed with existing curricula. To our
knowledge, no research has focused on the impact
computer science has on the communication of children
with autism, as we do here. We focus on whether
computer science instruction can serve as a tool for
helping a child with autism communicate more with his
peers, a skill he struggled with in other school contexts.
Related Works
Scratch is a programming environment for children
[10]. Like its predecessor, Logo [8], Scratch was
developed to encourage children to be makers, rather
than consumers, of their worlds [9]. This requires
children learn more than just academic skills. Social
and communication skills are essential as well,
including the ability to learn from and with others. This
is difficult for children diagnosed with autism, who may
easily acquire deep specialized knowledge about a topic
or subject of study and struggle to collaborate with
peers. Below we describe research on computer science
instruction for children with exceptionalities.
The Individuals with Disabilities Act (IDEA) and the
Americans with Disabilities Act (ADA), legally requires
educators to provide all students access to the general
education curriculum. In the K-12 school level they
should be placed in the LRE (Least Restrictive
Environment) in a classroom with their peers, rather
than receiving instruction in separate classrooms.
Further, in many states, computer science is being
taught as part of the general education curriculum;
therefore, it is important for educators to consider how
to meaningfully include students with exceptionalities.
The term “exceptionality” encapsulates a wide range of
disorders impacting learning. This paper focuses on one
student diagnosed with autism, “a neurodevelopmental
syndrome defined by deficits in social reciprocity and
communication” [6]. Individuals diagnosed with autism
typically struggle to effectively communicate with peers
and adults. Limited research exists investigating how
elementary school children with autism engage with
and learn computer coding in elementary school.
While some research has focused attention on students
with exceptionalities studying computer science at the
secondary [3] and post-secondary levels [2], less
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studies have looked at elementary school students
(aged 5-11) with exceptionalities. Israel et al. [5]
conducted a cross-case analysis of elementary school
teachers implementing computer science lessons in the
general education curriculum at one school. They found
implementation varied based on teacher and context
(e.g. classroom vs. library); successful teachers more
often integrated coding into other content areas (e.g.,
math). This signals a need to prepare elementary
school teachers to ensure all students are able to
participate in computer science instruction. Further,
Snodgrass, Israel and Reese [11] conducted a
comparative case study of three elementary school
students diagnosed with learning exceptionalities, one
of which was autism. This student struggled to initiate
and maintain interactions with others while generally
engaging in programming exercises on the computer.
Adults working with this student frequently took control
of the computer. When he remained in control of the
computer, he engaged for longer periods.
Research Design
This study is part of a larger research project
investigating how students in grades 4-6 (ages 9-11)
learn computer science using a block-based
programming environment, modified from Scratch [10].
For more information about our modifications and
curriculum, see Hill et al [4]. Our study followed
design-based research methods [1] which, depended
on the collaboration of multiple stakeholders.
We tested our curriculum with over 1,500 students
(aged 9-12) at 10 schools ranging from 2013-2015.
4%-100% of students qualified for free or reduced
lunch (a proxy for socioeconomic status), and 2%-82%
designated English language learners.
While many teachers reported difficulties engaging
students with exceptionalities in the computer science
curriculum, one exception emerged – a student we call
Alex, who was diagnosed with autism.
Alex participated in roughly 30 hours of coding and
performed lower than his typical peers on programming
tasks. By the end of 5th grade, he surpassed those
same peers. Further, teachers noted Alex emerged as a
leader; his peers frequently asked him for help or
observed his work. Based on these observations, a new
research question was formulated to investigate the
connection between Alex’s programming abilities and
communication patterns: How does engagement in a
computer science curriculum impact the communication
patterns of a student identified as having autism?
To answer this question, we narrowed our focus to Alex
and another student—Nick. Nick did not have a learning
exceptionality and was selected for contrastive
purposes to better understand Alex’s communication
patterns. We chose Nick because he was the only child
who appeared in the classroom as well as the computer
lab video records when Alex was in the video frame.
Some of the computer science lessons occurred in a
normal classroom without a computer, while other
lessons occurred in the school’s designated computer
lab. This created an interesting opportunity to
investigate how lesson type – on or off a computer –
impacted student communication, generating a more
specific research question: How did Alex’s
communication patterns change based on lesson type –
on computer or off computer?
We identified all segments in which Alex was visible
both in a typical classroom setting (working off
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computers) and in a computer lab setting. Table 1
shows the amount of video records analyzed.
In the classroom context, students prepared for their
final project—to program a game. In this lesson, the
teacher led the class through a group brainstorm,
recording aspects of common games students enjoyed.
Students then completed a worksheet and recorded
ideas they would later use to program a game in the
computer lab. Students were encouraged to talk with
nearby partners and to move around the room. No
computers were used.
In the computer lab context, two lessons were
analyzed. In one, students completed guided
programming exercises. They were reminded about
sprites (programmable objects or characters) and how
to start a script (code) in the computer lab. They were
challenged to use 3 sprites and 15 coding blocks for
this exercise. In the second lesson, students completed
guided programming exercises on variables where they
programmed sprites’ scores to increase or decrease
during a game. In both cases, if a student finished
early, they were encouraged to tinker in an open-ended
play area to practice their newly learned skills. All
instruction occurred in the computer lab.
For all 120 minutes of video, we coded both Alex and
Nick’s interactions with other peers, the teacher and
each other. We defined an interaction as an instance of
conversation initiation, such as when a child turned to
speak to a peer or ask the teacher a question. We used
time stamps in the video to calculate the amount of
time each child was speaking for each interaction. We
then compared the number of interactions and
speaking time in the computer lab to the classroom.
In this section, we first discuss Alex’s interactions in the
two contexts. We then compare Alex’s interactions to
Nick’s, a neurotypical peer. All lessons focused on
computer science content; however, computers were
only used in the computer lab.
Comparing Contexts: Alex in Classroom to Alex
in the Lab
Alex’s Interactions in Classroom
Alex’s participation in the traditional (non-computer
based) classroom was characterized by physically
disengaging himself from the conversation with few
initiated interactions. In the classroom, Alex rarely
interacted with his peers (2 times) or with a teacher (4
times). For example, the teacher approached Alex to
re-direct him when he leaned back in his chair and
asked: “Got everything down?” Alex was non-
responsive so the teacher read what he wrote aloud
saying: “Computer Skills. Okay.” Alex replied: “Good”
and gave a thumbs up. Further, Alex frequently got out
himself from any potential discussion.
Alex’s Interactions in Computer Lab
In contrast to his participation in the typical classroom,
Alex’s participation in the computer lab was
characterized by increased interactions with his peers
(24 times) and his teacher (10 times). For example,
Alex frequently offered to share his work with the
teacher, or help his nearby peers when he finished a
programming task early. As shown in Figure 2, Alex
was often seen leaning in and pointing to his neighbor’s
computer (in this case, Nick). This action occurred
multiple times throughout the lesson.
Lessons
(N)
Video
Length
(s)
Classroom
1
2160
Lab
2
5100
Total
3
7260
Table 1: Summary of video data
analyzed
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Comparison to Nick, the Neurotypical Peer
We found Alex spoke for less time in the classroom
setting when no computers were used. As shown in
Table 2, Alex spoke for a total of 300 seconds (out of
2160 total seconds), or roughly 14% of the time.
Further, Alex initiated interactions 6 times throughout
the class period, or approximately once every 6
minutes. In contrast, in the same classroom setting,
Nick spoke for 480 seconds, or 22% of the time and
initiated interactions 23 times, or approximately once
every 90 seconds.
Alex’s interactions in the classroom were very limited
compared to Nick.
Time (s)
Initiations
With Whom?
Student
Context
Speaking
%
Teacher
Peer
Alex
Classroom
300
14%
4
2
Nick
Classroom
480
22%
1
22
Alex
Lab
955
19%
10
24
Nick
Lab
235
5%
3
10
Table 2. Initiated interactions of Alex and Nick, by context.
See Table 1 for total time observed in each context.
The opposite trend was observed when Alex worked in
the computer lab. As shown in Table 2, Alex verbally
communicated for 955 seconds (out of 5100 total
seconds), or roughly 19% of the time. Further, Alex
initiated 34 interactions. In contrast, Nick spoke for
only 235 total seconds, or roughly 5% of the time, and
initiated only 13 interactions.
Nick’s limited interactions over the course of the two
lessons in the computer lab were not surprising either.
Other students were observed to be more solitary in
their work and less likely to interact with nearby peers.
Recall, Nick interacted 23 times in the classroom
setting and only 13 times in the computer lab whereas
the reverse was true for Alex. Alex interacted only 6
times in the classroom setting and 34 times in the
computer lab. We recognize these anecdotal findings
are not generalizable to the general population.
Discussion
We reported on the communication patterns of a child
with autism learning how to code in two contexts—the
classroom and computer lab. When working in the
computer lab, Alex spoke for longer amounts of time
and initiated more interactions with peers and the
teacher than in the classroom. Alex’s teachers said he
was more engaged socially during computer coding
class. Jim Gribble saw similar communication when he
taught children with autism how to code in the past.
Alex also used twice as many blocks when compared to
the average number used by his peers in class. Alex’s
communication assisted peers in their understanding of
coding. We delve deeper into this in another paper we
are working on: “Understanding the Potential of
Computer Science for Elementary School Students with
Disabilities.” Additionally, Alex led one of the summer
school coding classes at the end of the sessions.
Further similar individual case studies, as well as
studies with multiple students, are encouraged.
Figure 2: Alex and Nick in the
classroom. This shows a typical
scene in which most of the
children were engaged in small
group discussions at their tables,
while Alex was leaning back in his
chair. In these instances, a
teacher typically attempted to
help Alex re-focus on the task.
Figure 2: Alex helping Nick with
coding on a desktop computer.
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Similar to Papert, we advocate for using the computer
as a powerful tool to think with and act through. In our
case, the computer served as a tool for a student to
communicate and connect with both peers and adults.
We, as researchers, must attend to the social benefits
coding provides students with autism if we want
elementary school teachers to embrace coding through
mediums born out of Logo to teach computational
thinking or other disciplinary content. This will help
ensure computer science for all children is achieved.
Acknowledgements
This work is supported by the National Science
Foundation (USA) 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 NSF.
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