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32 acm Inroads 2011 March • Vol. 2 • No. 1
Irene Lee  Fred Martin  Jill Denner  Bob Coulter  Walter Allan
Jeri Erickson  Joyce Malyn-Smith  Linda Werner
1INTRODUCTIONComputational thinking (CT) is a term coined by Jeannette
Wing [11] to describe a set of thinking skills, habits and approaches
that are integral to solving complex problems using a computer and
widely applicable in the information society. Thinking computation-
ally draws on the concepts that are fundamental to computer science,
and involves systematically and effi ciently processing information and
tasks. CT involves defi ning, understanding, and solving problems,
reasoning at multiple levels of abstraction, understanding and apply-
ing automation, and analyzing the appropriateness of the abstractions
made. CT shares elements with various other types of thinking such
as algorithmic thinking, engineering thinking, design thinking, and
mathematical thinking. As such, CT draws on a rich legacy of related
frameworks as it extends previous thinking skills.
This paper aims to help computing and STEM (science, technol-
ogy, engineering and mathematics) educators understand computa-
tional thinking (what it looks like “in practice”, how it connects with
their existing curriculum, and how to nurture computational think-
ing in today’s youth) by sharing rich examples from National Science
Foundation funded Innovative Technology Experiences for Students
and Teachers (ITEST), Academies for Young Scientists (AYS) and
Research and Evaluation on Education in Science and Engineering
(REESE) programs. The examples provide a lens through which one
can consider the implications for learning and teaching computational
thinking in grades K through 12.
Key questions include:
 What does computational thinking for youth look like in practice?
 How can we support growth in computational thinking, both in
and out of school?
The examples and recommendations presented within this pa-
per were collected by the ITEST working group on Computational
Thinking. All of the authors are members of this community by virtue
of their involvement with current or previous ITEST programs. This
work is intended to complement The National Academies “Compu-
tational Thinking for Everyone” workshop series and the work cur-
rently being carried out by the Compuer Science Teachers Association
(CSTA) and the International Society for Technology in Education
(ISTE) as part of the Computational Thinking Thought Leaders
project, and to further the discussion by presenting examples of com-
putational thinking in action within programs for youth in both for-
mal and informal settings.
2COMPUTATION THINKING FOR YOUTH IN PRACTICE
In this paper, we respond to several recent calls to describe CT
among youth and to identify strategies for integrating CT into
K-12 settings [4][5][7]. We apply and build on existing descrip-
tions of CT, which have been based on thinking like a computer
scientist in college and beyond. Specifi cally, we offer examples of
what computational thinking looks like among youth from a range
of cultural and socioeconomic backgrounds, both in and out of
school. Examples are drawn from three domains: modeling and
simulation, robotics, and game design and development. Across
these domains, we have identifi ed commonalities in the nature of
youth’s computational thinking.
Computational thinking (CT) has
been described as the use of abstraction, automation,
and analysis in problem-solving [3]. We examine
how these ways of thinking take shape for middle
and high school youth in a set of NSF-supported
programs. We discuss opportunities and challenges
in both in-school and after-school contexts. Based on
these observations, we present a “use-modify-create”
framework, representing three phases of students’
cognitive and practical activity in computational
thinking. We recommend continued investment in
the development of CT-rich learning environments, in
educators who can facilitate their use, and in research
on the broader value of computational thinking.
Computational
Thinking for
Youth in Practice

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2011 March • Vol. 2 • No. 1 acm Inroads 33
Irene Lee  Fred Martin  Jill Denner  Bob Coulter  Walter Allan
Jeri Erickson  Joyce Malyn-Smith  Linda Werner
We found the terms of abstraction, automation, and analysis [3]
to be useful for understanding how youth can use CT to approach
novel problems. Abstraction is “the process of generalizing from spe-
cifi c instances.” In problem solving, abstraction may take the form of
stripping down a problem to what is believed to be its bare essentials.
Abstraction is also commonly defi ned as the capturing of common
characteristics or actions into one set that can be used to represent
all other instances. Automation is a labor saving process in which a
computer is instructed to execute a set of repetitive tasks quickly and
effi ciently compared to the processing power of a human. In this light,
computer programs are “automations of abstractions.” Analysis is a re-
fl ective practice that refers to the validation of whether the abstractions
made were correct. One might ask “Were the right assumptions made
when narrowing the problem to its bare essentials?”, “Were important
factors left out?” or “Was the implementation of the abstraction or
automation faulty?” Table 1 provides a summary of these domains
In the next sections, we use examples from three out-of-school
time (OST) youth programs to illustrate what the three aspects of
CT look like in practice, in each of the three domains. Each of these
programs offers opportunities for middle and high school students
to engage in computational thinking. The students come with a
range of computer experience and confi dence, including students
with limited English and no computer at home, as well as students
who have grown up tinkering with technology. The hands-on and
student-driven nature of the programs is designed to allow students
at all levels to engage in CT.
2.1 Modeling and Simulation
The fi rst domain we consider is modeling and simulation. Dave
Moursund [6] suggests “the underlying idea in computational think-
ing is developing models and simulations of problems that one is
trying to study and solve.” In Project GUTS (Growing up Think-
ing Scientifi cally) middle school students actively engage in com-
putational thinking as they design and implement models of local
relevance and then use the models to run simulations. Students used
the process of abstraction to narrow the problem down to something
that could be implemented on the computer using StarLogo TNG,
an agent based modeling tool. Restrictions imposed by the model-
ing environment include an upper bound on the number of agents
(4076) and a limit on the size of the environment (101 by 101 cells).
Within these parameters students designed and created models as
testbeds to answer questions about real-world concerns. For exam-
ple, as part of the Project GUTS unit on Epidemiology, a group of
students wanted to know if a disease would spread throughout their
school population given the layout of the school, the number of stu-
dents, the movement of the students, the virulence of the disease,
and the number of students initially infected. See Figure 1.
Mapping this question and scenario onto an agent based model,
agents were used as abstractions or simplifi ed representations of stu-
dents and the number of agents matched the number of students
in their school. Agents were given movement behaviors that were
abstractions of moving from classroom to classroom, and decisions
were made about which features of the school were important to take
into consideration before a 3-D virtual model of the school building
was created. For instance, students decided that recreating the num-
ber and location of passages and doors at the school was important.
Additionally students modeled the characteristics of the contagion
being spread: how often contact between students spread the disease
from one to the other and how many students were initially infected.
To make the model a testbed capable of running experiments, it was
equipped with interface sliders to control individual variables. One
slider controlled the number of initially infected agents and another
controlled the virulence of the contagious element. See Figure 2.
Automation was used in a number of ways. The “program” itself
automated “stepping through” or advancing the simulation through
the use of a run loop that updated each agent’s state, location, and
color (representing sick or healthy) at each time step. Because
agent-based models involve randomness, for example, the initial
location of infected individuals is chosen randomly, they tell us the
probabilities of certain outcomes rather than predictions. Automa-
tion was used to execute multiple “runs” of the experiment with
Figure 1: GUTS club members creating ecosystem models in Chicago.
TABLE1: EXAMPLES OF CT IN THREE DOMAINS
Abstraction Automation Analysis
Modeling &
Simulation
Selecting
features of
real-world to
incorporate in a
model
Time stepping
using a
model as an
experimental
testbed
Were the
correct
abstractions
made?
Does the model
refl ect reality?
Robotics Design robot to
react to a set of
conditions
Program
checks sensors
to monitor
conditions
Are there
situations that
were not taken
into account?
Game
Design &
Development
Games are
abstracted into
a set of scenes
containing
characters
Game responds
to user actions
Do the
elements
incorporated
make the game
fun to play?