Computational Thinking for All: Pedagogical Approaches to Embedding 21st Century Problem Solving in K-12 Classrooms

Abstract

The recent focus on computational thinking as a key 21st century skill for all students has led to a number of curriculum initiatives to embed it in K-12 classrooms. In this paper, we discuss the key computational thinking constructs, including algorithms, abstraction, and automation. We further discuss how these ideas are related to current educational re- forms, such as Common Core and Next Generation Science Standards and provide specific means that would allow teachers to embed these ideas in their K-12 classrooms, in- cluding recommendations for instructional technologists and professional development experts for infusing computational thinking into other subjects. In conclusion, we suggest that computational thinking ideas outlined in this paper are key to moving students from merely being technology-literate to using computational tools to solve problems.

Full text

ORIGINAL PAPER
Computational Thinking for All: Pedagogical Approaches
to Embedding 21st Century Problem Solving in K-12
Classrooms
Aman Yadav
1
&Hai Hong
2
&Chris Stephenson
2
#The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract The recent focus on computational thinking as a
key 21st century skill for all students has led to a number of
curriculum initiatives to embed it in K-12 classrooms. In this
paper, we discuss the key computational thinking constructs,
including algorithms, abstraction, and automation. We further
discuss how these ideas are related to current educational re-
forms, such as Common Core and Next Generation Science
Standards and provide specific means that would allow
teachers to embed these ideas in their K-12 classrooms, in-
cluding recommendations for instructional technologists and
professional development experts for infusing computational
thinking into other subjects. In conclusion, we suggest that
computational thinking ideas outlined in this paper are key
to moving students from merely being technology-literate to
using computational tools to solve problems.
Keywords Computational thinking .K-12 .
Computer science education .Teac her s
Introduction
Computer science plays a vital role in today’stechnologyand
globally connected world, which means that we need to intro-
duce computing ideas to students early during their schooling
years. Google (2015) found that while students, parents,
teachers, and administrators value computer science educa-
tion, school administrators significantly underestimate paren-
tal demand for access to computer science learning for all
students and instead focus attention and resources on subject
areas that require mandatory testing. Given the recent focus of
educational reform movement on standardized testing of core
subject areas (such as, mathematics, reading) and that com-
puter science is not a required subject area, this is not a sur-
prising finding. Furthermore, administrators have also report-
ed that computer science is not always prioritized by their
school boards and the lack of qualified teachers and resources
to train or hire teachers makes it difficult to offer computer
science (CSTA, 2013;Google,2015). The challenges of of-
fering computer science courses are even more stark for rural
and small school districts where administrators are less likely
to agree that their school board thinks computer science is
important, and less likely to say it is a top priority. The ques-
tion then is how do we address the need to introduce K-12
students to computer science ideas while operating within the
constraints of available resources and focus on standardized
testing? How do we enable teachers in the core subject areas to
embed computing in the curriculum?
Computational thinking (CT) offers an encompassing ap-
proach that exposes students to computing ideas and princi-
ples in the context of the subject areas they are already
learning. Wing popularized the term in her 2006
Communications for the ACM article arguing, BTo reading,
writing, and arithmetic, we should add computational thinking
to every child’sanalyticalability^(Wing 2006,p.33).Whatis
computational thinking? The essence of computational think-
ing involves breaking down complex problems into more
familiar/manageable sub-problems (problem decomposition),
using a sequence of steps (algorithms) to solve problems,
reviewing how the solution transfers to similar problems (ab-
straction), and finally determining if a computer can help us
*Aman Yadav
ayadav@msu.edu
1
Michigan State University, 620 Farm Lane, East Lansing, MI 48824,
USA
2
Google Inc, 1600 Amphitheatre Parkway, Mountain
View, CA 94043, USA
TechTrends
DOI 10.1007/s11528-016-0087-7

more efficiently solve those problems (automation). These
computational thinking steps are foundational to computer
science, but their power and utility extend far beyond any
single discipline. Mishra and Yadav (2013), for example, ar-
gued that Bcomputational thinking can foster creativity by
allowing students to not only be consumers of technology,
but also build tools that can have significant impact on
society^(p. 11). Furthermore, computational thinking aligns
with the need for students to become media information liter-
ate, which includes understanding how information and data
can be represented to convey different meaning (Wilson et al.,
2013).
The following sections elaborate on computational think-
ing constructs and how they relate to K-12 learning, and pres-
ent resources and examples that can help educators easily and
effectively embed CT in their classrooms.
Computational Thinking and Schooling
Wing (2006) argued that computational thinking involves
three key constructs: Algorithms, Abstraction, and
Automation - the three A’sofCT.Analgorithm (much like
a recipe) is a step-by-step series of instructions. Abstraction
involves generalizing and transferring the problem solving
process to similar problems (Barr and Stephenson 2011).
Finally, automation involves using digital and simulation
tools to mechanize problem solutions. In their effort to make
computational thinking more applicable to K-12, the
Computer Science Teachers Association (CSTA) and
International Society for Technology in Education (ISTE) de-
veloped an operational definition of computational thinking
that includes nine core CT concepts and capabilities (Barr and
Stephenson 2011). These core computational thinking ideas
include: data collection, data analysis, data representation,
problem decomposition, abstraction, algorithms & proce-
dures, automation, parallelization, and simulation.
There have been a number of efforts to embed computa-
tional thinking in K-12 classrooms based on these computa-
tional thinking competencies. The majority of these efforts
have focused primarily on computer science curricula such
as the proposed Advanced Placement Computer Science
Principles (CSP) course, the Exploring Computer Science
course, and programming tool use (such as, Code.org’s
block-based programming environment). The CSP course is
the largest of these efforts with the new course set to launch in
Fall 2016. The CSP framework proposes six computational
thinking practices to Bhelp students coordinate and make
sense of knowledge to accomplish a goal or task^(College
Board 2014, p. 2). The six practices are designed to allow
students to: better understand the influence of computing
and its implications on individuals and society (connecting
computing); engage in computing by designing and
developing computational artifacts (creating computational
artifacts); apply abstraction to develop models and simula-
tions of natural and artificial phenomena (abstracting); devel-
op solutions, models, and artifacts for problems (analyzing
problems and artifacts); describe the influence of technolo-
gy and computation supported by data visualizations
(communicating); and learn to work together effectively
to solve ill-structured problems using computation
(collaborating). In sum, the idea of CT is to allow students
to develop a foundational understanding of computing and
develop competencies that move them from being users of
technology to producers of information technology (Yadav
et al. 2014).
While it is valuable for students to learn computational
thinking within the context of computer science curricula
and programming environments, the constraints of a K-12
classroom might not make it feasible for all schools to have
access to standalone computing courses. However, computa-
tional thinking ideas are cross-disciplinary and can be embed-
ded across the elementary and secondary subject areas.
Furthermore, current educational reforms, such as Next
Generation Science Standards (NGSS) and Common Core
also highlight the need for students to be exposed to compu-
tational thinking in the K-12 curriculum. The nine core con-
cepts and capabilities from CSTA/ISTE framework provide a
good place to begin to embed CT in the K-12 core content
areas. For example, one of the key components of computa-
tional thinking is using an algorithm, which involves using a
sequence of steps in an orderly manner to solve problems or
complete tasks. We use algorithms everyday in our lives from
following a cooking recipe to giving directions from point A
to point B. Algorithms are a key skill in order to develop
students’Bability to interpret the world as algorithmically con-
trolled conversions of inputs to outputs^(Denning 2009,p.
29). For example, elementary students can learn about algo-
rithms by breaking down a simple daily task, such as brushing
teeth, into a sequence of steps (Yadav et al. 2014). Similarly,
students in second language classes could use cooking recipes
to learn about algorithms (Deschryver and Yadav 2015).
Another set of CTconcepts that provides an opportunity to
introduce computational thinking concepts across core con-
tent areas is data collection/data analysis/data representation.
For example, a social studies teacher can use data from most-
used words from presidential inaugural speeches from 1789 to
2009 (Source: http://nyti.ms/1XLe26x) and have students
analyze differences between the speeches across an era or
between Democratic and Republican presidents. Similarly,
science teachers can have students explore international and
United States greenhouse emission data sets from Google
Public Data Explorer (http://www.google.com/publicdata)to
compare emission rates across states, countries, and even
economic sectors (agriculture, energy, industrial processes,
and waste). The ability to make sense of data to identify
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solutions to problems is a key computational thinking skill. It
can also provide access to significant career opportunities
given that jobs requiring computing skills are expected to
grow from 1.9 million to 4.4 million by 2017 alone (Big
Data Jobs Index, 2016).
Abstraction is another key computational thinking idea that
can be embedded in the classroom. Abstraction involves the
ability to generalize and transfer a solution from one problem
to other similar problems. Abstraction also involves develop-
ing and representing models of the real world (such as using a
physical model that represents our solar system or a simula-
tion that shows how populations respond to situations such as
disease outbreaks). Building on our prior example of data
analysis and representation, abstraction could be practiced in
science classrooms by teachers engaging students in analyzing
data to draw conclusions and develop general principles.
While the CT concepts and practices discussed above can
be implemented in a regular classroom, one of the key CT
components is automation, which requires access to comput-
ing tools. One way to engage students in automation is
through modeling and simulation. A number of tools and cur-
ricula are available to teachers who want to address computa-
tional thinking concepts through simulation. NetLogo (https://
ccl.northwestern.edu/netlogo/), for example, provides a
plethora of simulations and models that could be embedded
in content areas such as earth science, biology, chemistry,
social studies, physics, and social science to help students
see how patterns emerge. For example, a social studies
teacher could use the model to examine how individual
preferences to live in proximity to populations similar to
themselves can have a ripple effect leading to a large-scale
segregation pattern. Barr and Stephenson (2011) provide spe-
cific examples of how the CT concepts and capabilities from
the CSTA/ISTE framework could be implemented in specific
subject areas in K-12, such as language arts, social studies,
science, mathematics, and computer science.
It would be unreasonable to expect teachers to incorporate
computational thinking concepts into their practice without
support and opportunities to apply these ideas to authentic
tasks (Yadav et al. 2013,2014). We believe that these support
mechanisms need to be continuous rather than one- or two-
time professional development events. One way to provide
these professional development opportunities is via online
learning reinforced with communities of practice. Google, for
example, provides a free online course called Computational
Thinking for Educators (https://computationalthinkingcourse.
withgoogle.com/course). This course is structured around
algorithms and patterns for four groups of teachers - humani-
ties, mathematics, science, and computer science teachers. In
addition to online materials, it offers a community of practice
that provides a place for educators to share ideas, challenges,
solutions, and resources learned from their classroom practice.
Michigan State University also offers a computational thinking
course for educators as a part of its Masters in Educational
Technology (MAET) program. The course is designed to en-
able teachers to explore ideas that support the development of
their CT skills and pedagogical approaches to incorporating
CT in their own classrooms. The teachers work collaboratively
on a semester-long project to develop meaningful activities
relevant to their own contexts (such as, subject area, grade
level, and school resources) that allow them to integrate com-
putational thinking at their schools.
Conclusion
Recent national efforts have emphasized the importance of
computational thinking to prepare students to succeed and
thrive in our increasingly technological society, to maintain
U.S. economic competitiveness, to support inquiry in other
disciplines, and to enable personal empowerment to tackle
complex problems (National Research Council 2011). Given
that computational thinking focuses on problem solving and
engages students in designing processes that can be automat-
ed, it is essential that K-12 educators and administrators ex-
plore ways to embed CT ideas into their curricula and practice.
Given the challenges of meeting today’s curricular demands,
we believe that connecting computational thinking ideas to
what teachers already do in their classrooms is the best ap-
proach. The CSTA/ISTE framework offers one approach to
integrating computational thinking constructs and capabilities
within the context of the existing content areas. While re-
search in this area is relatively new, there are promising results
that highlight the positive impact of CT ideas on student out-
comes in traditional K-12 subject areas. For example, a recent
study found that when computational thinking ideas were em-
bedded in a sixth grade mathematics classroom, students’un-
derstanding of mathematical processes increased significantly
when compared to students in the control group (Calao et al.
2015). These findings highlight that computational thinking
can not only impact students’problem solving skills in gen-
eral, but also have a significant influence on their academics
(Calao, Moreno-Le ́on, Correa, & Robles).
In order to achieve the goal of computational thinking for
all, it is important to provide professional development oppor-
tunities that are tied to teachers’curricular needs in their sub-
ject areas. Specifically, computer science educators, teacher
educators, and educational technology faculty need to collab-
orate with teachers to develop activities that make visible the
inherent overlap of computational thinking idea and practices
with subject area concepts. As discussed previously, the
CSTA/ISTE framework provides a starting point for profes-
sional development experts to work closely with teachers and
instructional technologists at the K-12 level. In summary, pro-
fessional development for teachers must go hand-in-hand with
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the curriculum and lesson development plans that work within
the local context of the classroom.
While working with inservice teachers to embed CT
in elementary and secondary classrooms is important,
we also need to introduce computational thinking for
preservice teachers in their teacher education programs.
Within teacher education curricula, preservice teachers
could learn about computational thinking in core
courses (such as, learning theory and educational tech-
nology courses) and then expand their understanding
about how computational thinking applies in a particular
subject through teaching methods courses in their licen-
sure areas.
The kinds of changes that we are advocating require
vision and leadership from the instructional technology
community and professional development experts.
Members of this community can play a pivotal role by
helping to articulate the connection between computa-
tional thinking and all academic disciplines, developing
content to support integration into curricula, and taking
the lead in designing and facilitating both preservice
and inservice opportunities for learning. They can also
help drive critical conversations with and between
leaders in all academic subject areas.
In conclusion, we believe that the computational thinking
ideas outlined in this paper are key to moving students from
merely being technology-literate to using computational tools
to solve problems and represent knowledge. Developing
teachers’understanding of computational thinking and
highlighting connections to their curricular context is key to
successfully embedding CT in K-12 classrooms. Our work
with preservice teachers has shown that they see the relevance
of these ideas in the classroom and can learn to adopt them in
their own curriculum (Yadav et al. 2014). This effort also
requires buy-in from the K-12 administrators, including
superintendents, principals, and school board members
to provide necessary resources and tools for teachers.
These resources might include, release time for profes-
sional development, access to computing tools in the
classrooms, and engaging with computational thinking
leaders to discuss approaches to CT in the classrooms.
For teachers interested in learning more about these ideas,
Google’s Computational Thinking for Educators course
serves as a good place to start and connect with a com-
munity of teachers with similar teaching interests.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give ap-
propriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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