Computational thinking for youth in practice

Abstract

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.

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32 acm In ro ad s 2011 Marc h • Vol. 2 • No. 1
Irene Lee ■ Fred Martin ■ Jill Denner ■ Bob Coulter ■ Walter Allan
Jeri Erickson ■ Joyce Malyn-Smith ■ Linda Werner
➧1INTRODUCTION
Computational 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 Ma rch • Vo l. 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?

Computational Thinking for Youth in Practice
continued
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34 ac m In ro ad s 2011 Marc h • Vo l. 2 • N o. 1
the same parameter settings in order to attain the probabilities of
certain outcomes. Once the simulations were run and data on the
number of infected individuals after a fi xed number of time steps
were collected, students refl ected on the outcomes. In some cases,
the students were able to analyze their models, the assumptions
and abstractions made, by comparing the model generated data
with data collected within their schools. For instance, one group
compared the data generated using their model that simulated the
spread of swine fl u with attendance/absenteeism records collected
during a period of time when swine fl u was known to be circulating
within their school. Analysis of this sort may lead to reconsidera-
tion of what factors to include in a model and cycle back to the
beginning of the process described above.
2.2 Robotics
A second domain that promotes computational thinking with pre-
college students is robotics. In a robotics project, student programmers
design and program robots and other physical devices with embedded
code. They need to think about how the robotic agent will interact
within its world, based on factors such as its sensor values and the ef-
fects of its actuators. As
they do this, the student
makes choices of how
their programming will
connect these processes
together to achieve the
desired results.
In the iCODE
project (Internet Com-
munity of Design En-
gineers), middle and
high school students
complete a variety of
microcontroller-based
projects, beginning with
a simple project with
programmable fl ash-
ing lamps, to a musical
memory game, to fully
autonomous (self-con-
trolled) robots that enter
a contest. See Figure 3.
Abstraction takes place as students design robots to react to a
limited set of conditions that may be encountered in the real world.
Students think about how to sense the world, and how those stim-
uli will be abstracted as numerical or true-false values inside the
control program. Automation occurs as the students’ programs are
executed by the embedded computing device. Students perform
analysis when they decide whether or not the robot operated as
expected in the real-world environment. If the robot “misbehaves,”
it may either mean that their implementation of their control idea
is faulty, or that conditions were encountered that were not taken
into account during the abstraction phase.
2.3 Game Design and Development
A third domain in which computational thinking takes place is
computer game design and development. In the iGame after-
school program, middle school students engage in computational
thinking by designing, programming, and testing computer games
of their choosing using Storytelling Alice (SA). SA, as with many
other programming languages, allows students to create their own
abstractions. Because SA is a programming environment that al-
lows the creation of 3D animations, students can then test highly
complex abstractions quickly and precisely. To create their game,
students build a group of scenes, where each scene contains charac-
ters, and each character has behaviors. Students choose from an ar-
ray of character attributes and behaviors selecting only those details
appropriate for the virtual world they are creating.
Students can defi ne new methods representing behaviors not
built into SA. Into these methods, students can place a combina-
tion of existing behaviors and changes to character attributes. In
iGame, many student-created methods were simple combinations
of sequential commands, but creating methods often requires an
understanding of conditionals, iteration, and sequential and paral-
lel execution. For example, in the Labyrinth of the Turtle game, a
student programmed a character to dance using a series of parallel
movements and vocalizations.
Games often require multiple similar characters to perform the
same action. Students can “scale up” their abstraction creating a
list data structure containing these similar characters. Then they
can program similar behaviors for these characters by using special
instructions that iterate through a list data structure. For example,
in the Zombie Invasion game, a student programmed a group of
zombies to wait and then start moving at the same time. As the
player clicks on each one, it is programmed to disappear. However,
most game programmers in iGame choose to repeat commands
they understand, rather than learn to use lists.
Students in iGame engage in analysis when they judge whether
or not their abstractions were correct and effi cient. Analysis of cor-
rectness focuses on whether they produced the game they intended,
i.e., whether the game plays the way they want it to or whether the
game that was designed to be fun was, in fact, fun. Analysis of ef-
fi ciency involves creating the simplest code to achieve the desired
behavior. Analysis takes place during the process of testing and
debugging their game, and students often need an external motiva-
tor to focus on effi ciency because when their game is working, they
see little reason to edit the code. Students also play-test their peers’
Figure 2: Students’ customization of contagion model to refl ect school layout.
Figure 3: Two iCODE students display their
Sumo robot.

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2011 Ma rch • Vo l. 2 • No. 1 acm Inroads 35
games, and analyze them in terms of playability, and whether they
have created the best (most effi cient, most believable, or most fun)
abstractions.
2.4 Other Domains
The domains mentioned are by no means a comprehensive list.
There are many other domains such as designing and program-
ming webpages, cell phone apps, etc. that have potential to develop
CT in youth. Common among these examples is the active use
of key computational thinking concepts: abstraction, automation
and, to various extents, analysis, by youth within middle school
programs. Through these examples, we posit that not only is CT
possible at the middle school level, it can easily be embedded
within activities that encourage youth to be creators, innovators,
and problem-solvers. Computational thinking projects like these
support an iterative cycle of refi nement that enables increasing a
sense of agency, where learners are empowered to imagine, create,
play, share, and refl ect on what they are learning [9]. In all of these
projects, the end result is a unique product created by the students.
➧3SUPPORTING GROWTH IN
COMPUTATIONAL THINKING
Based on our experiences with youth learning CT both during the
school day and out- of- school contexts, we suggest concrete steps that
can be taken to support the development of computational thinking.
3.1 Rich Computational Environments
The fi rst is the use of rich computational environments. Rich com-
putational environments are ones in which the underlying abstrac-
tions and mechanisms can be inspected, manipulated and custom-
ized. For example, consider the differences between SimCity and a
model in StarLogo TNG. In SimCity, a user may add buildings to
a city and see correlations between adding buildings and CO2 pro-
duction but the underlying formulae and model are hidden from
view. Contrast that with a StarLogo TNG environment in which
the user can “look under the hood” and inspect the causal rela-
tionships and abstractions that are embedded in a model. The rich
computational environment is one in which the user can develop
CT skills and transform from user to creator. See Figure 4.
3.2 Three-stage Progression “Use-Modify-Create”
Second, we propose using a three-stage progression for engaging
youth in CT within these rich computational environments. This
progression, called Use-Modify-Create, describes a pattern of en-
gagement (see Figure 5) that was seen to support and deepen youth’s
acquisition of CT in the authors’ NSF projects. It is based on the
premise that scaffolding increasingly deep interactions will promote
the acquisition and development of CT. In the use stage, students
are consumers of someone else’s creation. For example, they run ex-
periments using pre-existing computer models, run a program that
controls a robot, or play a ready-made computer game. Over time
they begin to modify the model, game or program with increasing
levels of sophistication. For example, a student may initially want to
change the color of a character or some other purely visual attribute.
Later the student may want to change the character’s behavior in a
way that entails developing new pieces of code. Modifi cation of this
kind necessitates an understanding of at least a subset of the abstrac-
tion and automation contained within a program, model or game.
Through a series of modifi cations and iterative refi nements, new
skills and understandings are developed as what was once someone
else’s becomes one’s own. As youth gain skills and confi dence, they
can be encouraged to develop ideas for new computational projects
of their own design that address issues of their choosing. Within
this “create” stage, all three key aspects of computational thinking:
abstraction, automation and analysis, come into play.
Moving through this progression, it is important to maintain
a level of challenge that supports growth while limiting anxi-
ety. As Repenning [8] notes, students can maintain their sense
of cognitive fl ow [1] as they progress iteratively through a series
of projects. In this work, students tackle progressively higher de-
sign challenges as their skills and capacities increase. Activities
that were once “too hard” and were anxiety-inducing become
possible with appropriate, incrementally challenging experiences.
Conversely, boredom will set in if challenges don’t keep pace with
growing skills [8]. While we are advocating use of this three-stage
progression to foster growth over time and with increasing capac-
ity, we also raise a caution about taking it too literally. Just as an
early teenage youth is moving from childhood to adolescence in
fi ts and starts, there are no clean break points from using to modi-
fying to creating. Youth may transition back and forth from users
to modifi ers to creators.
Figure 4: Inspecting the mechanism for infection in a basic contagion
model in StarLogo TNG.
Figure 5: Use-Modify-Create
Learning Progression

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36 ac m In ro ad s 2011 Ma rc h • Vo l. 2 • N o. 1
3.3 Other Domains
The examples of CT in youth programs described thus far took
place after school, either during weekends, holiday breaks, and/or
over the summer. EcoScienceWorks (ESW) is a program in Maine
that leverages the State’s one-to-one laptop initiative to engage
students with environmental simulations as part of the school day
science curriculum. This project also exposes students to simple
programming challenges as a way of introducing them to the com-
putational thinking that underlies the simulations. Through guided
experimentation, EcoScienceWorks deepens students’ understand-
ing of both ecology and computer modeling.
The success of the project has been partly a result of address-
ing some of the challenges in introducing computational think-
ing into the classroom head on. For instance, because CT is not
evaluated by standardized testing, it is diffi cult in the current
educational climate for teachers to teach CT concepts directly.
The ESW staff addressed this constraint by designing a simula-
tion-based ecology curriculum in which the CT portions of the
curriculum were what students had to do in order to fulfi ll ex-
plicit content learning goals. That is, an ecology curriculum that
arguably required CT was designed to replace the existing cur-
riculum that focused on content transfer. With this pedagogical
design, the required core ecology concepts could be covered in
much greater depth, and CT was fostered through the use and
understanding of computational models.
While this work offers a promising example, it is important to rec-
ognize the resources that were necessary in order for it to be success-
ful. Infrastructure was not a signifi cant obstacle as each student had
access to a laptop, district support had been established, and intensive
support was provided by the project staff in the form of professional
development and ongoing assistance. Transformative applications of
CT can work in schools with all of these ingredients in place.
Implementing CT during the school day is a compelling vision,
but there are substantial challenges to this, including existing cur-
riculum standards, lack of opportunities for teachers to learn CT as
part of their professional development, and lack of access to neces-
sary infrastructure. Consequently, much of the work in CT with
youth remains in out-of-school environments. As shown in this
paper, new opportunities for fostering computational thinking are
emerging, and NSF-funded programs are actively exploring ways
in which computational thinking works in both in-school and out-
of-school environments.
➧4CONCLUSIONS
The call for integrating CT into K-12 settings has
been growing increasingly louder, despite the lack of descrip-
tions of what learning to think computationally actually looks like
among youth. In this paper, we have contributed to efforts to defi ne
and support CT for youth by using examples from several youth
projects to make two key points.
The fi rst key point is that existing defi nitions of CT can be ap-
plied to K-12 settings. The examples show that youth can engage
in key aspects of computational thinking within programs focus-
ing on modeling and simulation, robotics, and
game design and development. Students from
a range of backgrounds are able to use abstrac-
tion, automation, and analysis to create original
products when given access to rich learning en-
vironments that include skilled teachers, devel-
opmental considerations, and usually include
new technology. However, the fi eld requires
systematic assessment procedures that build on
existing research from the learning sciences in
order to describe the developmental progres-
sion of these three CT constructs. Some of the
authors are currently testing a variety of assess-
ment approaches.
The second key point is that CT takes place
on a continuum. The use-modify-create pro-
gression is offered as a framework for educa-
tors and researchers that are looking at how
CT develops, and how that development can
be supported. But research is needed to understand why students
are thinking at different levels of abstraction, automation, and
analysis. These differences may be a function of students working
in different phases of the use-modify-create learning progression.
For example, we suggest that moving from modifying to creating
an original project requires increasing levels of abstract representa-
tion and understanding. Similarly, simple analysis includes testing
and debugging a program, while a deeper level of analysis would
involve trying to determine if a model can be validated against real-
world data. As a foundation moving forward, the use-modify-cre-
ate framework offers a helpful progression for developing CT over
time. Its greatest benefi t is in illustrating the benefi ts arising from
engaging youth with progressively more complex tasks and giving
them increasing ownership of their learning.
This paper aims to inform efforts to engage K-12 students in
CT, and to assess the value of these efforts. We recommend that
future efforts get more specifi c about the type and level of CT that
will be addressed. The CS Principles project has moved this effort
Implementing CT during the school
day is a compelling vision, but
there are substantial challenges to
this, including existing curriculum
standards, lack of opportunities for
teachers to learn CT as part of their
professional development, and lack
of access to necessary infrastructure.

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2011 Ma rch • Vo l. 2 • No. 1 acm Inroads 37
forward in the context of high school CS classes, and a recent post-
ing by Snyder [10] describes specifi c computational thinking prac-
tices. We are working on contributing to a similar effort in OST.
This paper builds on existing efforts to describe the scope and
nature of CT [7] as well as the concepts involved in CT and how
youth should be able to use those concepts [2]. We hope this paper
will contribute to a national dialogue about the most important di-
mensions of CT in K-12, how different aspects of CT develop, the
role of context and motivation in this development, and effective
strategies for engaging youth in computational thinking. Ir
Acknowledgments
Portions of this paper were adapted from
Computational Thinking for Youth
, ITEST Working Group
on Computational Thinking (2010) with permission from Education Development Center, Inc.,
Newton, Massachusetts.
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Communications of the ACM 49
(3), 33-35.
IRENE LEE
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87505 USA
lee@santafe.edu
FRED MARTIN
University of Massachusetts Lowell, Olsen Hall Rm 208, Lowell, Massachusetts 01854 USA
fredm@cs.uml.edu
JILL DENNER
ETR Associates, 4 Carbonero Way, Scotts Valley, California 95066 USA
jilld@etr.org
BOB COULTER
Missouri Botanical Garden, PO Box 299, St. Louis, Missouri 63166-0299 USA
bob.coulter@mobot.org
WALTER ALLAN
Foundation for Blood Research
ScienceWorks for ME
8 Science Park Road, Scarborough, Maine 04070 USA
allan@fbr.org
JERI ERICKSON
Foundation for Blood Research, P.O. Box 190
8 Science Park Road, Scarborough, Maine 04070-0190 USA
jerickso@maine.rr.com
JOYCE MALYN-SMITH
ITEST Learning Resource Center, Education Development Center
55 Chapel Street, Newton Massachusetts 02458-1060 USA
jmsmith@edc.org
LINDA WERNER
University of California, Santa Cruz, Baskin Engineering
1156 High Street, Santa Cruz, California 95064 USA
linda@soe.ucsc.edu
Categories and Subject Descriptors: K.3.2 [Computers and Education]: Computer and
Information Science Education –
Computer science education, curricula, literacy
.
General Terms: Human Factors, Performance, Design, Experimentation.
Keywords: Computer science education, computational thinking, abstraction, automation, analysis.
DOI: 10.1145/1929887.1929902 © 2011 ACM 2153-2184/11/0300 $10.00
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