Computational Thinking in K–12 A Review of the State of the Field

Abstract

Jeannette Wing’s influential article on computational thinking 6 years ago argued for adding this new competency to every child’s analytical ability as a vital ingredient of science, technology, engineering, and mathematics (STEM) learning. What is computational thinking? Why did this article resonate with so many and serve as a rallying cry for educators, education researchers, and policy makers? How have they interpreted Wing’s definition, and what advances have been made since Wing’s article was published? This article frames the current state of discourse on computational thinking in K–12 education by examining mostly recently published academic literature that uses Wing’s article as a springboard, identifies gaps in research, and articulates priorities for future inquiries.

Full text

Educational Researcher, Vol. XX, No. XX, pp. 1–6
DOI: 10.3102/0013189X12463051
© 2012 AERA. http://er.aera.net month XXXX 1
Jeannette Wing’s influential article on computational thinking 6
years ago argued for adding this new competency to every child’s
analytical ability as a vital ingredient of STEM learning. What is com-
putational thinking? Why did this article resonate with so many and
serve as a rallying cry for educators, education researchers, and
policy makers? How have they interpreted Wing’s definition, and
what advances have been made since Wing’s article was published ?
This article frames the current state of discourse on computational
thinking in K–12 education by examining mostly recently published
academic literature that uses Wing’s article as a springboard, identi-
fies gaps in research, and articulates priorities for future inquiries.
Keywords : computers and learning; learning environments;
literacy; mixed methods; multisite studies; obser-
vational research; problem solving; qualitative
research; student cognition; technology
Introduction
Six years ago, Jeannette Wing’s succinct and influential article,
“Computational Thinking,” appeared in the Viewpoint section
of the March 2006 edition of the Communications of the ACM
with the pronouncement: “It represents a universally applicable
attitude and skill set everyone, not just computer scientists,
would be eager to learn and use” (p. 33).
Wing’s arguments caught the attention of a broad academic
community. Prompted by her article, and a growing community of
researchers, educators, and policymakers, computational thinking
(or CT) as a concept and associated research agenda has witnessed
increasing attention and research. The tailwinds in the larger envi-
ronment have fanned this growing interest. “Computer Science
(CS) Education in K–12” took center-stage following a stark report
titled Running on Empty: The Failure to Teach K–12 Computer
Science in the Digital Age (Wilson, Sudol, Stephenson, & Stehlik,
2010) that revealed precipitously low numbers for women in com-
puting, and that more than two thirds of the country had few
computer science standards at the secondary school level. Concerns
about these statistics deepen given projections from the Bureau of
Labor Statistics (http://www.bls.gov/ooh/) that computing is one
of the fastest-growing job markets through 2018. This CS impera-
tive has dovetailed with the science policy attention to STEM
learning in the United States since the turn of the 21st century.
With CT being viewed as at the core of all modern science,
technology, engineering, and mathematics (STEM) disciplines
(Henderson, Cortina, Hazzan, & Wing, 2007) it appears that
computing in K–12 is an idea whose time has come.
Of course, the idea of CT is not new. Back in the 1960s, Alan
Perlis argued for the need for college students of all disciplines to
learn programming and the “theory of computation” (Guzdial,
2008). However in the context of K–12 education, computing
first gained popular traction around Seymour Papert’s MIT work
in the 1980s, as he pioneered the idea of children developing
procedural thinking through LOGO programming (Papert,
1980, 1991). This recent resurgence takes a fresh, “21st century”
perspective on the topic, and Wing’s 2006 article forms a logical
starting point for our critical examination of the current state of
the field of CT in K–12 education. The following sections exam-
ine mostly recently published, salient, academic literature that
has used Wing’s article as a springboard. The article will also
report on key efforts around computing education in K–12.
Given the definitional confusion that has plagued CT as a
phrase and how imperative it is for school education, the next
section looks deeply at the varied perspectives and evolving defi-
nitions of CT, the rationale for building CT among school chil-
dren, and common criticisms against CT in schools. The article
then surveys recent research investigating CT (including some
that do not use the phrase computational thinking per se but
nonetheless examine computational competencies in children),
the various environments and tools that are believed to foster CT
development, and studies attempting to assess CT are appraised.
Finally, the article lays out priorities for broadening the K–12 CT
discourse on the basis of the gaps in current research.
The What and Why of Computational Thinking
According to Wing (2006), “computational thinking involves
solving problems, designing systems, and understanding human
behavior, by drawing on the concepts fundamental to computer
science” (p. 33). CT’s essence is thinking like a computer scientist
when confronted with a problem.
Computational Thinking in K–12:
A Review of the State of the Field
Shuchi Grover1 and Roy Pea2
1Stanford University School of Education, Stanford, CA, USA
2School of Education and the H-STAR Institute, Stanford, CA, USA
Reviews/Essays
EDR463051.indd 1 25/09/2012 7:17:15 PM

educational ReseaRcheR
2
Wing’s call to action for CT in school education served as the
starting point for two National Academy of Science workshops
convening leading researchers from education, learning sciences
and computer science departments, and leaders from the com-
puting industry, to explore “the nature of computational think-
ing and its cognitive and educational implications” (National
Research Council [NRC], 2010, p. viii) and the pedagogical
aspects of computational thinking (NRC, 2011). In the first
workshop, early notions of CT that focused on procedural think-
ing and programming (Papert, 1980, 1991), though still consid-
ered valid, were revisited and broadened to encompass several
core concepts of computer science that take it beyond “just pro-
gramming.” The workshop, however, threw into sharp relief the
lack of consensus that seems to have bedeviled this space. Some
of the central questions left unanswered by the workshop
included the following: How can CT be recognized? What is the
best pedagogy for promoting CT among children? Can program-
ming, computers, and CT be legitimately separated? (NRC,
2010). Some of these questions were reexamined in the follow-up
workshop that focused on better defining the space by gathering
and synthesizing insights from educators addressing CT in their
work with K–12 teachers and learners. The aim of the workshop
was to share examples and best practices of pedagogies and envi-
ronments for teaching CT and revealed a plethora of perspectives
that reflected several tools and pedagogies that are legitimate can-
didates for use in developing these competencies.
In a much-needed revisit of the topic, Wing (2011) described
CT as the “thought processes involved in formulating problems
and their solutions so that the solutions are represented in a form
that can be effectively carried out by an information-processing
agent.” Aho (2012) simplified this further by defining CT as the
thought processes involved in formulating problems so “their
solutions can be represented as computational steps and algo-
rithms” (p. 832).
Recently, the Royal Society (2012) also offered a succinct
and tractable definition that captures the essence of CT—
“Computational thinking is the process of recognising aspects
of computation in the world that surrounds us, and applying
tools and techniques from Computer Science to understand and
reason about both natural and artificial systems and processes”
(p. 29).
A valuable perspective that breaks down the meaning of CT,
especially for high school curricula, comes from the CS Principles
course being piloted by the College Board and NSF (http://www.
csprinciples.org/). The course focuses on the practices of computa-
tional thinking and is based on the seven “big ideas” of computing:
1. Computing is a creative human activity
2. Abstraction reduces information and detail to focus on
concepts relevant to understanding and solving problems
3. Data and information facilitate the creation of knowledge
4. Algorithms are tools for developing and expressing solu-
tions to computational problems
5. Programming is a creative process that produces computa-
tional artifacts
6. Digital devices, systems, and the networks that intercon-
nect them enable and foster computational approaches to
solving problems
7. Computing enables innovation in other fields, including
science, social science, humanities, arts, medicine, engi-
neering, and business.
Following workshops organized by the Computer Science
Teachers Association (CSTA) and the International Society for
Technology in Education (ISTE), Barr and Stephenson (2011)
provided a similar “operational definition of CT” aimed at K–12
teachers that comprised an explanatory checklist for what CT
means along with an enumeration of core CT concepts and capa-
bilities, and examples of how they might be embedded in activi-
ties across multiple disciplines.
It is worth noting here that the potent idea of “computational
literacy” (diSessa, 2000) pre-dates Wing’s charter for CT for all.
Although the essence of both concepts targets this new digital age
competency, diSessa separates the “material” tools such as pro-
gramming environments, from the “cognitive” and the “social”
aspects of computational literacy. Furthermore, diSessa under-
scores the use of “computing as a medium” for exploring other
domains such as Math and Science, much like Kay and Goldberg
(1977) explored math, science, and art via programming in
Smalltalk. This notion is often neglected in popular definitions
of CT. The term computational literacy is perhaps susceptible to
confusion with earlier ones like computer literacy, information lit-
eracy, and digital literacy that have assumed various meanings over
the years and fall well short of what diSessa demands of compu-
tational literacy. Although the phrase and notion of computa-
tional thinking now seems to be preferred over computational
literacy, in research and practice today the two phrases are often
used interchangeably.
Procedural literacy is another avatar of CT that was first pro-
posed in 1980 by B. A. Sheil at Xerox PARC. In our reading,
there is little to distinguish between procedural literacy and CT
applied mostly to creating video games and other computational
media artifacts or, more broadly, the practice of CT in the context
of new media art and design.
To sum up, researchers and CS educators for the most part
now work broadly with the aforementioned recent descriptions
of CT. The value of abstraction as CT’s keystone (distinguishing
it from other types of thinking) is undisputed. Abstraction is
“defining patterns, generalizing from specific instances,” and a
key to dealing with complexity (Wing, 2011). The following ele-
ments are now widely accepted as comprising CT and form the
basis of curricula that aim to support its learning as well as assess
its development:
· Abstractions and pattern generalizations (including models
and simulations)
· Systematic processing of information
· Symbol systems and representations
· Algorithmic notions of flow of control
· Structured problem decomposition (modularizing)
· Iterative, recursive, and parallel thinking
· Conditional logic
· Efficiency and performance constraints
· Debugging and systematic error detection
Programming is not only a fundamental skill of CS and a
key tool for supporting the cognitive tasks involved in CT but a
EDR463051.indd 2 25/09/2012 7:17:15 PM

month XXXX 3
demonstration of computational competencies as well.
Noteworthy efforts like CS Unplugged (http://csunplugged.
org/) that introduce computing concepts without the use of a
computer, while providing valuable introductory activities for
exposing children to the nature of CS, may be keeping learners
from the crucial computational experiences involved in CT’s
common practice.
Finally, although there is broad acknowledgement that com-
puting pervades all aspects of the global economy, its place as a
mandatory part of the school curriculum is far from secure.
Many criticisms have revolved around these multiple interpreta-
tions of CT and a lack of clarity among educators on CS as a
discipline. Another valid concern is whether there is a compel-
ling rationale for all children, including those who allege no
interest in pursuing CS and STEM careers, to develop compu-
tational competencies in school. In the zero-sum school curricu-
lum map, how should curriculum policymakers make room in
already packed school curricula? There is also lack of agreement
on whether CT should ultimately be incorporated into educa-
tion as a general subject, a discipline-specific topic, or a multi-
disciplinary topic (NRC, 2011). Lastly, there is some question
whether CT is distinct enough from other forms of thinking
that children are developing. Advocates of CT concede that
although it shares elements with mathematical, engineering, and
even design thinking, and draws on a rich legacy of related
frameworks, it also extends each of those thinking skills in a
unique way (Lee et al., 2011). Denning and Freeman (2009)
observes that although the computing paradigm “contains
echoes of engineering, science, and mathematics, it is distinc-
tively different because of its central focus on information pro-
cesses” (p. 30) and that Wing’s CT interpretation embeds well
into this system of practice.
We claim that the approach to problem solving generally
described as CT is a recognizable and crucial omission from the
expertise that children are expected to develop through routine
K–12 Science and Math education (although CT has finally
been mentioned, albeit briefly, in the 2012 NRC K–12 Science
Education framework). If basic literacy in Math and Science can
be considered essential for all children to understand how our
world works, why should school education not lift the hood
on all-pervasive computing devices as well? We believe that those
in possession of computational competencies will be better
positioned to take advantage of a world with ubiquitous com-
puting. Early experiences with this way of problem solving
will not only alleviate problems in introductory CS courses
undergraduates have been known to face but also generate inter-
est and prime students for success in this growing field rife with
opportunity.
Recent news from media and industry suggest that the move
to make programming a more commonplace skill for everyone
and introducing ’rithms (short for algorithms) as the fourth “r”
for 21st-century literacy is gaining momentum globally. Israel has
long boasted an exemplary mandatory high school CS curricu-
lum. Countries such as Russia, South Africa, New Zealand, and
Australia have already made room for CS in the K–12 curricu-
lum. More recently, the United Kingdom has piloted programs
to teach computing to all schoolchildren following a bold 2012
policy charter from the Royal Society.
Summary of Pertinent Research on CT in K–12
With broadly agreed on definitions of CT in K–12 education,
focus has recently shifted to tackling the more practical questions
of how to promote and assess the development of CT. There is
extensive literature from the last three decades tackling issues of
teaching and learning programming and CS. The bulk of CS
education research, however, is set in the context of undergradu-
ate classrooms. Although there is much to learn about CT in
K–12 both from studies of kids and programming in the 1980s
(using languages such as LOGO and BASIC) as well as early
programming and CS experiences of college students, the space
constraints imposed by the essay as well as a focus on the recent
resurgence of CT force the review to be limited to recent research
involving 21st-century tools and school-age children.
Environments and Tools That Foster CT
The idea of “low floor, high ceiling” as one of the guiding prin-
ciples for the creation of programming environments for children
has been around since the days of LOGO. It essentially means
that though it should be easy for a beginner to cross the threshold
to create working programs (low floor), the tool should also be
powerful and extensive enough to satisfy the needs of advanced
programmers (high ceiling). Computationally rich environments
and effective CT tools for school children must have low thresh-
old and high ceiling, scaffold, enable transfer, support equity, and
be systemic and sustainable (Repenning, Webb, & Ioannidou,
2010). Several programming tools fit these criteria to varying
degrees. Popular among these are graphical programming envi-
ronments such as Scratch, Alice, Game Maker, Kodu, and
Greenfoot; Web-based simulation authoring tools such as
Agentsheets and Agentcubes; and robotics kits and tangible
media such as Arduino and Gogo Boards. Graphical program-
ming environments are relatively easy to use and allow early expe-
riences to focus on designing and creating, avoiding issues of
programming syntax. By allowing novices to build programs by
snapping together graphical blocks that control the actions of
different dynamic actors on a screen, environments like Scratch,
MIT’s popular offering, quite literally make programming a
snap.
Several of these introductory computational experiences use
the three-stage “use–modify–create” progression to help the
learner go from user to modifier to creator of computational arti-
facts (Lee et al., 2011), a progression first broadly used in Apple’s
Hypercard application in the mid-1980s to early 1990s.
Curricular activities such as game design and robotics have typi-
cally served well as a means for the iterative exploration of CT,
making them ideal not only for motivating and engaging school
children but for introducing them to computer science. Visual
and tangible programming experiences are often followed by
exposure to high-level programming languages such as Python,
Java, and Scheme.
Recommendations for engaging girls through computing in
context (Margolis & Fisher, 2002; also see Cooper &
Cunningham, 2010) provide a compelling rationale for tools that
strive to bridge the gender gap in the computing field. Emerging
computational environments poised to provide more opportuni-
ties for engagement in CT in formal and informal settings while
EDR463051.indd 3 25/09/2012 7:17:15 PM

educational ReseaRcheR
4
also engaging girls include App Inventor for building Android
mobile apps, and “computational construction kits” that use
small, powerful hardware, such as the Lilypad Arduino, and allow
children to combine traditional arts and crafts such as sewing and
sketching with computation and electronics. App Inventor, a
visual programming environment that uses blocks like Scratch, is
more gender neutral and complete than most tools. It sets a low
floor for allowing creative app building (something all teens,
including girls, are eager to do) while still engaging with complex
CT concepts including procedural and data abstraction, iterative
and recursive thinking, structured task breakdown, conditional
and logical thinking, and debugging.
Despite its growing popularity for promoting many 21st-cen-
tury competencies in K–12 (NRC, 2012), video gaming as a plat-
form for examining CT among children has been underutilized
in recent research. Holbert and Wilensky (2011) successfully
developed and tested a prototype video game, FormulaT, which
aimed to serve as a platform for learning principles of kinematics
as well as “systematic computational strategies.” FormulaT used
NetLogo, a computational environment for agent-based model-
ing. The activities of abstracting pertinent behaviors into agents,
applying rules, and evaluating the results via modeling and simu-
lation are key ways of engaging in CT. Blikstein (2010) demon-
strates leveraging Netlogo computational models for science
learning in secondary-level classrooms. Agent-based modeling,
however, remains relatively underused in CT research.
Not surprisingly, current computational tools vary in their
effectiveness in allowing for engagement with the various compo-
nent elements of CT. Maloney, Peppler, Kafai, Resnick, and Rusk
(2008) reported demonstration of several CT elements such as
conditional logic, iterative and parallel thinking, and data
abstraction in Scratch programs created by urban youth in after-
school settings. However Scratch lacks the means to abstract
functionality into functions and procedures, prompting a version
called Snap! from Berkeley that seeks to address this. Perhaps an
imperative for CS in K–12 will fuel the development of new tools
built expressly for fostering CT among school-age children.
These should not only embody all the characteristics of effective
CT tools and promote the development of all the competencies
now identified as elements of CT but also be guided by recent
research on commonsense human understanding of computing,
and how children explain their approaches to problem solving
(Pane, Ratanamahatana, & Myers, 2001; Simon, Chen,
Lewandowski, McCartney, & Sanders, 2007).
Lastly, despite the variety of environments in which current
CT research is situated, many promising spaces are still untapped;
Fab Labs, Makerspaces, and DIY movements, such as Maker
Faire and Instructables, that promote construction of tangible
computational artifacts, informal “hacker” events for kids, as well
as ubiquitous and powerful smartphones, all present exciting pos-
sibilities.
Assessment of CT
Without attention to assessment, CT can have little hope of mak-
ing its way successfully into any K–12 curriculum. Furthermore,
to judge the effectiveness of any curriculum incorporating CT,
measures that would enable educators to assess what the child has
learned need to be validated.
Most recent research addressing questions of CT assessment,
such as Werner, Denner, Campe, and Kawamoto’s (2012) Fairy
Assessment in Alice, has used either student-created, or prede-
signed programming artifacts to evaluate students’ understand-
ing and use of abstraction, conditional logic, algorithmic
thinking, and other CT concepts to solve problems. Ideas of
deconstruction, reverse engineering, and debugging to assess
children’s understanding in computational contexts have long
enjoyed educational appeal. Fields, Searle, Kafai, and Min (2012)
evaluated students’ engineering and programming skills as they
debugged prebuilt faulty e-textile projects. Han Koh,
Basawapatna, Bennett, and Repenning (2010) attempted with
some success to assess the thorny issue of transfer to answer ques-
tions like “Now that the student can program Space Invaders, can
the student program a science simulation?”
In the past two decades, “academic talk” has been leveraged
for promoting and assessing math and science literacy. The devel-
opment in the student use of the vocabulary and language of CS
over the course of engaging in computationally rich activities
provides an additional instrument for measuring the growth of
CT (Grover, 2011).
Computing Education in K–12
Wilson and Guzdial (2010) maintain that although the national
urgency for strengthening STEM in K–12 has translated into
billions of dollars in funding, research explicitly in computing
education remains underfunded. NSF initiatives such as CPATH,
BPC, and most recently, CE21 have gone a long way in energiz-
ing projects aimed at bringing CT/CS concepts to the secondary
level. Additional boost for guiding interested middle and high
school students into CS careers comes from DARPA’s initiatives,
such as CS-STEM, and FIRE (Fostering Innovation through
Robotics Exploration) at CMU.
Although ongoing research in development of CT will help
inform computing curricula throughout K–12, preparing teach-
ers for computing education and ensuring gender equity remain
huge challenges. The NSF’s CS10K initiative aims to add 10,000
new CS teachers in U.S. high schools by 2015. The Georgia
Computes! alliance is at the forefront of nationwide efforts for
teacher preparation, development of CT/CS K–12 curricula as
well as motivating female students in CS. Georgia Tech’s Guzdial
argues in his blog (http://computinged.wordpress.com/) that
challenges to meeting the CS10K deadline include answering
questions like the following: What do teachers need in order to
develop into successful computer science teachers? What kind of
pedagogy will fit into the lives of in-service high school teachers?
What is Computer Science Pedagogical Content Knowledge?
In terms of curriculum, besides CS Principles for AP CS, the
Exploring CS curriculum (http://www.exploringcs.org) is
intended to be a 1-year college preparatory curriculum for high
school students. Other initiatives aimed at introducing CS into
schools include CS4HS (http://www.cs4hs.com/) and
Computing in the Core (http://www.computinginthecore.
org/)—both of which represent collaborations between aca-
demia, national bodies, and organizations such as Microsoft and
Google. CSTA’s Model Curriculum for K–12 Computer Science
provides curricular suggestions to help build interest, engage, and
motivate students in CS. In addition, Google’s Exploring
EDR463051.indd 4 25/09/2012 7:17:15 PM

month XXXX 5
Computational Thinking website (www.google.com/edu/com-
putational-thinking) has a wealth of links to CT resources on the
web. ACM has also recently introduced a new thread, EduBits,
in its ACM Inroads quarterly that highlights principal educa-
tional activities within ACM and affiliated organizations.
Broadening the Scope of the Discourse and
Priorities for Empirical Inquiry
It is thus quite evident that much of the recent work on CT has
focused mostly on definitional issues, and tools that foster CT
development. Some strides have been made in the realm of defin-
ing curricula for nurturing computational competencies, and
assessing their development. Large gaps, however, still exist that
call out for empirical inquiries.
In a view that was echoed by Alfred Aho, Wing argued, “an
application of the science of learning research in designing grade-
and age-appropriate curricula for computational thinking is nec-
essary to maximize its impact on and significance for K–12
students” (NRC, 2011, p. 4). Barring some recent studies, such
as Fadjo, Lu, and Black (2009) and Berland and Lee (2011), few
others have taken into account contemporary research in the
learning sciences in socio-cultural and situated learning, distrib-
uted and embodied cognition, as well as activity, interaction and
discourse analyses. Cognitive aspects of children and novices
learning computational concepts were studied extensively in the
1980s—issues such as development of thinking skills (Kurland,
Pea, Clement, & Mawby, 1986); debugging (Pea, Soloway, &
Spohrer, 1987); problems with transfer (Clements & Gullo,
1984; Pea & Kurland, 1984); use of appropriate scaffolds for
successful transfer (Klahr & Carver, 1988), to name a few. That
body of literature should be brought to bear on 21st-century CT
research.
Also underinvestigated is the idea of computing as a medium
for teaching other subjects—dovetailing the introduction of CT
at K–12 with transfer of problem-solving skills in other domains.
Past work includes demonstrations of children successfully
designing LOGO software to teach fractions (Harel & Papert,
1990) and science (Kafai, Ching, & Marshall, 1997), and using
modeling software in science (Metcalf, Krajcik, & Soloway,
2000).
Empirical studies on CT in schoolchildren could leverage
extensive research on the types of problems beginner CS under-
graduates face in their early programming experiences that go
beyond syntactical issues: Are there well-defined hurdles or tar-
gets of difficulty that exist in the path of developing some ele-
ments of CT in children (e.g., recursion)? If so, what are these
and how can they be addressed?
Also largely untapped is the territory of dispositions for, atti-
tudes toward, and stereotypes concerning CT and CS, and how
they relate to the development of learner identity. How crucial
are these as we strive to provide both girls and boys with learning
experiences that aim to nurture CT competencies? Recent incip-
ient work on surveys of student attitudes toward computing rep-
resents a start in gaining a better understanding of this.
Clearly, much remains to be done to help develop a more
lucid theoretical and practical understanding of computational
competencies in children. What, for example, can we expect chil-
dren to know or do better once they’ve been participating in a
curriculum designed to develop CT and how can this be evalu-
ated? These are perhaps among the most important questions
that need answering before any serious attempt can be made to
introduce curricula for CT development in schools at scale. It is
time to redress the gaps and broaden the 21st-century academic
discourse on computational thinking.
ACKNOWLEDGMENT
We gratefully acknowledge grant support of the LIFE Center
from the National Science Foundation for this work (NSF-
0835854).
REFERENCES
Aho, A. V. (2012). Computation and computational thinking. Computer
Journal, 55, 832–835.
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to
K-12: What is involved and what is the role of the computer science
education community? ACM Inroads, 2, 48–54.
Berland, M., & Lee, V. (2011). Collaborative strategic board games as a
site for distributed computational thinking. International Journal of
Game-Based Learning, 1(2), 65–81.
Blikstein, P. (2010). Connecting the science classroom and tangible
interfaces: the bifocal modeling framework. In Proceedings of the 9th
International Conference of the Learning Sciences, Chicago, IL, 128–
130.
Clements, D. H., & Gullo, D. F. (1984). Effects of computer program-
ming on young children’s cognitions. Journal of Educational Psychology,
76, 1051–1058.
Cooper, S., & Cunningham, S. (2010). Teaching computer science in
context. ACM Inroads, 1, 5–8.
Denning, P., & Freeman, P. (2009). Computing’s paradigm.
Communications of the ACM, 52(12), 28–30.
diSessa, A. A. (2000). Changing minds: Computers, learning, and literacy.
Cambridge: MIT Press.
Fadjo, C. L., Lu, M., & Black, J. B. (2009, June). Instructional embodi-
ment and video game programming in an after school program. Paper
presented at the World Conference on Educational Multimedia,
Hypermedia & Telecommunications, Chesapeake, VA.
Fields, D. A., Searle, K. A., Kafai, Y. B., & Min, H. S. (2012). Debuggems
to assess student learning in e-textiles. In Proceedings of the 43rd
SIGCSE Technical Symposium on Computer Science Education. New
York, NY: ACM Press.
Grover, S. (2011, April). Robotics and engineering for middle and high
school students to develop computational thinking. Paper presented at
the annual meeting of the American Educational Research Association,
New Orleans, LA.
Guzdial, M. (2008). Paving the way for computational thinking.
Communications of the ACM, 51(8), 25–27.
Han Koh, K., Basawapatna, A., Bennett V., & Repenning, A. (2010).
Towards the automatic recognition of computational thinking for
adaptive visual language learning. In Proceedings of the 2010 Conference
on Visual Languages and Human Centric Computing (VL/HCC 2010)
(pp. 59–66). Madrid, Spain: IEEE Computer.
Harel, I., & Papert, S. (1990). Software design as a learning environ-
ment. Interactive Learning Environments, 1, 1–32.
Henderson, P. B., Cortina, T. J., Hazzan, O., and Wing, J. M. (2007)
Computational thinking. In Proceedings of the 38th ACM SIGCSE
Technical Symposium on Computer Science Education (SIGCSE ’07),
195–196. New York, NY: ACM Press.
Holbert, N. R., & Wilensky, U. (2011, April). Racing games for exploring
kinematics: a computational thinking approach. Paper presented at the
EDR463051.indd 5 25/09/2012 7:17:16 PM

educational ReseaRcheR
6
annual meeting of the American Educational Research Association,
New Orleans, LA.
Kafai, Y. B., Ching, C. C., & Marshall, S. (1997). Children as designers
of educational multimedia software. Computers & Education, 29,
117–126.
Kay, A., & Goldberg, A. (1977). Personal dynamic media. IEEE
Computer, 10, 31–41.
Klahr, D., & Carver, S. M. (1988). Cognitive objectives in a LOGO
debugging curriculum: Instruction, learning, and transfer. Cognitive
Psychology, 20, 362–404.
Kurland, D. M., Pea, R. D., Clement, C., & Mawby, R. (1986). A study
of the development of programming ability and thinking skills in high
school students. Journal of Educational Computing Research, 2, 429–
458.
Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J.,
. . .Werner, L. (2011). Computational thinking for youth in practice.
ACM Inroads, 2, 32–37.
Maloney, J., Peppler, K., Kafai, Y. B., Resnick, M., & Rusk, N. (2008).
Programming by choice: Urban youth learning programming with
Scratch. In Proceedings of SIGCSE ’08. New York, NY: ACM Press.
Margolis, J., & Fisher, A. (2002). Unlocking the clubhouse: Women in
computing. Cambridge: MIT Press.
Metcalf, J. S., Krajcik, J., & Soloway, E. (2000). Model-It: A design
retrospective. In M. J. Jacobson & R. B. Kozma (Eds.), Innovations in
science and mathematics education (pp. 77–115). Mahwah, NJ:
Lawrence Erlbaum.
National Research Council. (2010). Committee for the Workshops on
Computational Thinking: Report of a workshop on the scope and nature
of computational thinking. Washington, DC: National Academies
Press.
National Research Council. (2011). Committee for the Workshops on
Computational Thinking: Report of a workshop of pedagogical aspects of
computational thinking. Washington, DC: National Academies Press.
National Research Council. (2012). A framework for K–12 science educa-
tion: Practices, crosscutting concepts, and core ideas. Washington, DC:
National Academies Press.
Pane, J. F., Ratanamahatana, C. A., & Myers, B. A. (2001). Studying the
language and structure in non-programmers’ solutions to program-
ming problems. International Journal of Human-Computer Studies, 54,
237–264.
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas.
New York, NY: Basic Books.
Papert, S. (1991). Situating constructionism. In S. Papert & I. Harel
(Eds.), Constructionism. Cambridge: MIT Press.
Pea, R. D., & Kurland, D. M. (1984). On the cognitive effects of learn-
ing computer programming. New Ideas in Psychology, 2, 137–168.
Pea, R. D., Soloway, E., & Spohrer, J. C. (1987). The buggy path to the
development of programming expertise. Focus on Learning Problems
in Mathematics, 9, 5–30.
Repenning, A., Webb, D., & Ioannidou, A. (2010). Scalable game
design and the development of a checklist for getting computational
thinking into public schools. In Proceedings of the 41st ACM Technical
Symposium on Computer Science Education (SIGCSE ’10), 265–269.
New York, NY: ACM Press.
Royal Society. (2012). Shut down or restart: The way forward for
computing in UK schools. Retrieved from http://royalsociety.org/
education/policy/computing-in-schools/report/
Simon, B., Chen, T., Lewandowski, G., McCartney, R., & Sanders, K.
(2007, March). Commonsense computing: What students know before we
teach (Episode 1: Sorting). Paper presented at the Second International
Workshop on Computing Education Research, Canterbury, UK.
Werner, L., Denner, J., Campe, S., & Kawamoto, D. C. (2012). The
Fairy performance assessment: Measuring computational thinking in
middle school. In Proceedings of the 43rd ACM Technical Symposium
on Computer Science Education (SIGCSE ’12), 215-220. New York,
NY: ACM.
Wilson, C., & Guzdial, M. (2010). How to make progress in computing
education. Communications of the ACM, 53(5), 35–37.
Wilson, C., Sudol, L. A., Stephenson, C., & Stehlik, M. (2010). Running
on empty: The failure to teach K-12 computer science in the digital age.
New York, NY: The Association for Computing Machinery and the
Computer Science Teachers Association.
Wing, J. (2006). Computational thinking. Communications of the ACM,
49(3), 33–36.
Wing, J. (2011). Research notebook: Computational thinking—What
and why? The Link Magazine, Spring. Carnegie Mellon University,
Pittsburgh. Retrieved from http://link.cs.cmu.edu/article.php?a=600
AUTHORS
SHUCHI GROVER is a doctoral candidate at Stanford University School
of Education, 485 Lasuen Mall, Stanford, CA 94305-3096; shuchig@
stanford.edu. Her research focuses on helping children become compu-
tationally literate—studying social, cultural, and cognitive processes that
help in developing computational competencies—and on tools and
environments that nurture such development.
ROY PEA is David Jacks Professor of Education & Learning Sciences at
Stanford University, School of Education, and Computer Science
(Courtesy), and Director of the H-STAR Institute, Wallenberg Hall,
450 Serra Mall, Bldg. 160, Stanford, CA 94305; roypea@stanford.edu.
His work in the learning sciences focuses on advancing theories, find-
ings, tools, and practices of technology-enhanced learning of complex
domains.
Manuscript received April 14, 2012
Revision received June 13, 2012, and July 19, 2012
Accepted September 6, 2012
EDR463051.indd 6 25/09/2012 7:17:16 PM