Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 1
1
Introduction
1.1 SCOPE AND APPROACH OF THIS REPORT
This report summarizes the second of two workshops on computa-
tional thinking, which was held February 4-5, 2010, in Washington, D.C.,
under the auspices of the National Research Council’s (NRC’s) Committee
for the Workshops on Computational Thinking.1 This second workshop
was structured to gather pedagogical inputs and insights from educators
who have addressed computational thinking in their work with K-12
teachers and students.
Questions posed to participants in the second workshop included the
following:
• What are the relevant lessons learned and best practices for improv-
ing computational thinking in K-12 education?
• What are some examples of computational thinking and how, if at
all, does computational thinking vary by discipline at the K-12 level?
• What exposures and experiences contribute to developing compu-
tational thinking in the disciplines? What are some innovative environ -
ments for teaching computational thinking?
1 The first workshop, held February 19-20, 2009, is summarized in National Research
Council, 2010, Report of a Workshop on the Scope and Nature of Computational Thinking , Wash-
ington, D.C.: The National Academies Press. Available at http://www.nap.edu/catalog.
php?record_id=12840. Last accessed February 7, 2011.
1
OCR for page 2
2 PEDAGOGICAL ASPECTS OF COMPUTATIONAL THINKING
• Is there a progression of computational thinking concepts in K-12
education? What are some criteria by which to order such a progression?
• How should professional development efforts and classroom sup-
port be adapted to the varying experience levels of teachers such as
pre-service, inducted, and in-service levels? What tools are available to
support teachers as they teach computational thinking?
• How does computational thinking education connect with other
subjects? Should computational thinking be integrated in other subjects
taught in the classroom?
• How can learning of computational thinking be assessed? How
should we measure the success of efforts to teach computational thinking?
This workshop was structured to illuminate different approaches to
the teaching of computational thinking. Participants often clarified their
own interpretations of computational thinking in relation to the discus-
sion in the first workshop report.
To improve readability and to promote understanding, background
material on some of the topics and ideas raised is interspersed in this
workshop report. This workshop report also includes some of the material
discussed in the first workshop that related to pedagogy and how best to
expose students to the ideas of computational thinking but that was not
addressed in the first workshop report.
The second workshop was deliberately organized to include indi-
viduals with a broad range of perspectives. For this reason and because
some of the discussion amounted to brainstorming, this workshop sum -
mary may contain internal inconsistencies that reflect the wide range of
views offered by workshop participants. In keeping with its purpose of
exploring the topic, this workshop summary does not contain findings or
recommendations.
The reader is cautioned that the workshop was not intended to result
in a consensus regarding the scope and nature of computational thinking.
As was true in the first workshop, participants in the second workshop
expressed a host of different views about the scope and nature of compu-
tational thinking. As stated in the first report:
Even though workshop participants generally did not explicitly disagree
with views of computational thinking that were not identical to their
own, almost every participant held his or her own perspective on com-
putational thinking that placed greater emphasis on particular aspects or
characteristics of importance to that individual.2
2
National Research Council, 2010, Report of a Workshop on the Scope and Nature of Computa-
tional Thinking, Washington, D.C.: The National Academies Press, p. 59. Available at http://
www.nap.edu/catalog.php?record_id=12840. Last accessed February 7, 2011.
OCR for page 3
3
INTRODUCTION
The first report raised the possibility that given this variation in indi-
vidual perspectives, one possibility concerning the structure of computa -
tional thinking is that computational thinking should be regarded simply
as “the union of these different views—a laundry list of different char-
acteristics” (p. 59). Noting that most participants in the first workshop
would have found this view deeply unsatisfying, the first report pointed
out the value of addressing a number of questions that emerged from the
first workshop, including the following:
• What is the core of computational thinking?
• What are the elements of computational thinking?
• hat is the sequence or trajectory of development of computa-
W
tional thinking?
• Does computational thinking vary by discipline?
Similar questions regarding the structure and content of computa-
tional thinking were raised in the second workshop as well. For exam-
ple, Joyce Malyn-Smith of the Education Development Center, Inc., said
that adopting a consistent definition of computational thinking is nec -
essary because people see computational thinking through only their
own lenses—and efforts to advocate for computational thinking in the
curriculum will not be credible in the absence of a consensus about its
structure and content. Al Aho from Columbia University acknowledged
the community’s need for a common definition of computational think -
ing, which was inherently difficult given the rapidly changing world to
which computational thinking is often applied. Any static definition of
computational thinking likely would be obsolete 10 or 20 years from now,
he argued, and thus, “The real challenge for the entire community is to
define computational thinking and also to keep it current.”
Recognizing that there is no easy-to-summarize definition of com-
putational thinking, the first report noted the view of many computer
scientists that computational thinking is a fundamental analytical skill
that “everyone, not just computer scientists, can use to help solve prob -
lems, design systems, and understand human behavior. [As such,] com-
putational thinking is comparable . . . to the mathematical, linguistic, and
logical reasoning . . . taught to all children” (p. 3).
The first report also noted that as usually construed, computational
thinking includes “a broad range of mental tools and concepts from com-
puter science that help people solve problems, design systems, under-
stand human behavior, and engage computers to assist in automating a
wide range of intellectual processes” (p. 3). The report went on to say that
Computational thinking might include reformulation of difficult prob-
lems by reduction and transformation; approximate solutions; paral-
OCR for page 4
4 PEDAGOGICAL ASPECTS OF COMPUTATIONAL THINKING
lel processing; type checking and model checking as generalizations of
dimensional analysis; problem abstraction and decomposition; problem
representation; modularization; error prevention, testing, debugging,
recovery, and correction; damage containment; simulation; heuristic
reasoning; planning, learning, and scheduling in the presence of un-
certainty; search strategies; analysis of the computational complexity of
algorithms and processes; and balancing computational costs against
other design criteria. Concepts from computer science such as algorithm,
process, state machine, task specification, formal correctness of solu-
tions, machine learning, recursion, pipelining, and optimization also find
broad applicability. (p. 3)
Participants in the first workshop discussed computational thinking
as a range of concepts, applications, tools, and skill sets; as a language
for expression; as the automation of abstractions; and as a cognitive tool.
They further commented on how it is related to thinking skills and habits
of mind associated with mathematics and engineering, and how various
aspects of computational thinking (problem solving/debugging, testing,
data mining and information retrieval, concurrency and parallelism, and
modeling) are applicable to various disciplines. Many of these ideas were
reflected in the second workshop as well.
1.2 MOTIVATING AN EXAMINATION OF PEDAGOGY
Participants in the first workshop offered a number of reasons for pro-
mulgating computational thinking skills broadly in the K-12 curriculum.
These included succeeding in a technological society, increasing interest in
the information technology professions, maintaining and enhancing U.S.
economic competitiveness, supporting inquiry in other disciplines, and
enabling personal empowerment.
To launch the second workshop, Jeannette Wing, then assistant direc-
tor of NSF’s Computer and Information Science and Engineering Direc -
torate, discussed her goal for a workshop on pedagogy. She argued that
an application of the science of learning research in designing grade-
and age-appropriate curricula for computational thinking is necessary to
maximize its impact on and significance for K-12 students.
Wing pointed to mathematics as a field that has been successful in
developing learning progressions that have a solid foundation in research
on the human brain and how it learns mathematical concepts. She noted
that humans have an innate understanding of relative quantities—they
have the ability in many situations to distinguish between larger and
smaller quantities at a very early age. This level of recognition suggests
that mathematical activities involving concepts of “greater than” and “less
than” might be appropriate for very young students. Symbolic representa-
OCR for page 5
5
INTRODUCTION
tions require different kinds of mental processing, and such processing is
usually not possible until later in development, suggesting that activities
involving symbolic representation would better be undertaken later in
the K-12 sequence.
As a point of contrast, Wing pointed to the way that computer science
is typically taught. What is often taught in computer science to middle
school and high school students, said Wing, reflects a relatively casual
approach and is a minimally modified version of what is taught to under-
graduates. What makes the approach casual is that it is done without a
deep appreciation for how students learn at different ages. She noted that
there is not a body of grounded and research-based knowledge about
how the various aspects of computational thinking or computing map
on to brain development. She went on to point out that despite this lack
of knowledge, many people believe that some of the abstract concepts of
computational thinking cannot be taught before students enter the eighth
grade, because of a common assumption that only at that age are students
able to learn abstract concepts. There are many such assumptions, she
said, that must be evaluated in light of serious research about learning,
research that has not yet been done with reference to computing or com-
putational thinking.
1.3 ORGANIZATION OF THIS REPORT
Most of the workshop was devoted to describing and discussing vari-
ous approaches to the teaching of computational thinking.
This report is organized as follows. Chapter 2 provides what the com-
mittee believes to be the key points raised by workshop participants—that
is, the committee extracted from the various presentations and discussion
sessions a number of key points that in its judgment speak most closely
to the teaching of computational thinking. Chapter 3 presents individual
committee members’ personal synthesis of points made in the respective
panel sessions that they moderated. Chapter 4 contains summaries of
individual presentations by workshop participants, which often elabo -
rate in more detail examples described in Chapter 2. Depending on the
depth and degree of context in which the reader is interested, the com -
mittee encourages reading back and forth between these different levels
of summary.
Although workshop participants did not agree explicitly on a defini-
tion of computational thinking, the examples they provided during this
workshop are valuable as indicators of ways that people see the inter-
section of computation, disciplinary knowledge, and algorithms. Other
examples identify what the participants saw as issues and problems when
trying to introduce computational thinking into school and non-school
pedagogical contexts.
