Guest Editorial: Exploring the Science Framework and the NGSS: Computational Thinking in Elementary School Classrooms

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TEACHER’S TOOLKIT
“Computational thinking is a fundamental skill for ev-
eryone, not just for computer scientists. To reading,
writing, and arithmetic, we should add computational
thinking to every child’s analytical ability” (Wing 2006,
p. 33).
A
Framework for K–12 Science Education identi-
fies eight practices as “essential elements of
the K–12 science and engineering curriculum”
(NRC 2012, p. 49). These practices are embedded in
the Next Generation Science Standards (NGSS) (NGSS
Lead States 2013), where they are wedded closely to
core ideas in the science disciplines.
Most of the practices, such as Developing and Us-
ing Models, Planning and Carr ying Out Investigations,
and Analyzing and Interpreting Data, are well known
among science educators. In contrast, Using Mathe-
matics and Computational Thinking, specifically “com-
putational thinking,” may be less familiar. The vision of
computational thinking as a powerful intellectual tool
is described in the Framework as follows:
Since the mid-20th century, computational theo-
ries, information and computer technologies,
and algorithms have revolutionized virtually all
scientific and engineering fields. These tools
and strategies allow scientists and engineers to
collect and analyze large data sets, search for
distinctive patterns, and identify relationships
and significant features in ways that were previ-
ously impossible. They also provide powerful
new techniques for employing mathematics to
model complex phenomena—for example, the
circulation of carbon dioxide in the atmosphere
and ocean (NRC 2012, p. 64).
Exploring the science Framework and
NGSS: Computational thinking in the
science classroom
by Cary Sneider, Chris Stephenson, Bruce Schafer, and Larry Flick
When considering the different forms of computa-
tional thinking suggested in this description, we should
also reflect on how computational thinking differs from
mathematical thinking. Students develop mathemati-
cal thinking when they approach a new situation with
mathematical skills at their disposal. Similarly, they
develop computational thinking when they approach a
new situation with an awareness of the many ways that
computers can help them visualize systems and solve
problems. Many students have already begun their
journey into computational thinking through informal
use of computers. Teachers aware of the features of
computational thinking can leverage students’ informal
understanding and take students to the next level of
knowledge and skill.
One way to understand computational thinking is
to look at capabilities that can be considered mathe-
matical thinking, those that can be considered com-
putational thinking, and those capabilities that are
both. The Venn diagram (itself a mathematical tool)
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in Figure 1 shows how we (the authors) see the re-
lationship between mathematical and computational
thinking.
As illustrated in Figure 1, analyzing and interpret-
ing data is just one of several capabilities common to
both mathematical and computational thinking. Others
are problem solving, modeling, and statistics and prob-
ability.
The NGSS describes the practice of mathematics and
computational thinking for middle school as follows:
“Mathematical and computational thinking in
[grades] 6–8 builds on K–5 experiences and pro-
gresses to identify patterns in large data sets and
using mathematical concepts to support explana-
tions and arguments.
• Use digital tools (e.g., computers) to analyze
very large data sets for patterns and trends.
• Use mathematical representations to describe
and/or support scientific conclusions and
design solutions.
• Create algorithms (a series of ordered steps)
to solve a problem.
• Apply mathematical concepts and/or
processes (e.g., ratio, rate, percent, basic
operations, simple algebra) to scientific and
engineering questions and problems.
• Use digital tools and/or mathematical
concepts and arguments to test and
compare proposed solutions to an
engineering design problem.” (NGSS Lead
States 2013, Appendices, p. 59)
In the examples that follow, these practices are com-
bined with others to support student learning from a
number of performance expectations in the NGSS.
Simulation
Simulations that allow students to use computational
thinking are not simply animations, they are dynamic
computer models that involve students in changing
conditions and observing new outcomes. In most cas-
es, simulations are best used after students experience
a physical phenomenon. However, some phenomena,
such as natural selection, are difficult to experience
directly. In these cases, simulations can be even more
valuable, as they enable students to elaborate their own
thinking by exploring “what if…” scenarios. Figure 2
illustrates a simulation that can provide students with
experiences to help them meet a performance expecta-
tion from the middle school physical-science section of
the Next Generation Science Standards, MS-PS3-2 (see
sidebar, p. 14).
In the energy simulation in Figure 2, students are
introduced to the conversion of kinetic energy to po-
tential energy and vice versa through watching a skate-
boarder move up and down a ramp As he moves down
the ramp, potential energy is converted to kinetic en-
ergy as he picks up speed. As he moves up the ramp,
kinetic energy is converted to potential energy as he
slows down. Students can choose how much friction
the ramp has and whether the skate park is on Earth,
the Moon, or Jupiter, or in space, which affects the
gravitational force and thus both the potential energy
and the rate that the skateboarder’s speed changes.
To help your students understand energy, ask them to
observe how the bar graphs of kinetic and potential en-
ergy on the right side of the simulation screen change
as the skateboarder rides up and down. To promote
computational thinking, ask them how the simulation
helps them visualize energy changes and whether the
results of the simulation match what they might see at
a skate park. Students might also reflect on how the
simulation could be misleading if the friction is set to
an unrealistic value. They can use computational think-
ing for a deeper understanding of how the simulation
operates.
It is even possible for middle school students to cre-
ate their own simulations using simulation software
Venn diagram of mathematical
and computational thinking
FIGURE 1
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(e.g., StarLogo TNG). An article by
Irene Lee and colleagues describes a
three-step process in which students
are (1) invited to use a simulation; (2)
shown how to modify the software;
and (3) challenged to apply what
they learned to design an entirely
new simulation from the ground up
(Lee et al. 2011).
Data mining
Data mining is different from data ac-
quisition and analysis in three ways:
(1) Someone else has already ac-
quired the data, (2) the data sets are
very large, and (3) the primary focus
is on obtaining insights from the ex-
isting data. When students analyze
data from an experiment that they
have performed, the number of data
points is small and the meaning is usually straightfor-
ward. In contrast, the amount and variety of data avail-
able through today’s computer networks is vast. Conse-
quently, the process for using these data for scientific
investigations is different.
A number of programs (see Resources) help stu-
dents mine the mother lode of information available
through the internet today. For example, the Earth
Exploration Toolkit provides Earth-system science
data and scientific tools to manipulate data. Software
developed for the Hands On Universe project, SalsaJ,
enables students to mine astronomical databases to
search for previously undiscovered asteroids. The My
NASA Data website is a free data-mining resource that
has several well-developed activities for elementary,
middle, and high school. Two of the activities on this
website support NGSS performance expectations on
weather and climate, MS-ESS2-5 (see sidebar).
In the lesson plan Great Lakes Snow Analysis Col-
laborative Network on the My NASA Data website (see
Resources), students learn about lake-ef fect snowfall
that occurs when cold, dry arctic air passes over warm-
er Great Lakes water. Students develop hypotheses
based on lake-effect theor y about how temperature
and precipitation will vary in the Great Lakes Basin.
They then mine large snow and ice data sets to gather
evidence for or against their hypotheses.
Gathering the evidence and discussing its signifi-
cance with respect to weather theory is an excellent
means of meeting this performance expectation. Stu-
dents can also extend the activity to examine weather
data sets in their local region. To meet the performance
expectation, students will need experience with other
weather phenomena, such as the conditions that give
rise to tornadoes in the Midwest; they may mine the
vast bank of NOAA data to see where such phenomena
usually occur.
The “evidence” called for in this next performance
expectation, MS-ESS3-5 (see sidebar), cannot be gath-
ered from a classroom activity. It can, however, be
gathered from the internet in a number of ways.
One lesson plan that can be used to help students
achieve this performance expectation is called Is
Grandpa Right? Were Winters Colder When He Was
a Boy? (see Resources). In this lesson, students mine
data from the NOAA paleoclimatology website. They
use the provided tools to graph changes in tempera-
ture, precipitation, and cloud cover over the past 100
years and discuss possible reasons for the changes
they observe. Students can also find historic images
of extreme weather from NOAA’s image galleries, as
shown in Figure 3. Although this activity does not ad-
dress causes, it provides evidence of century-long cli-
mate change and stimulates questions that students
can answer through further data mining, for example
the following: Were winters colder ever ywhere? Did
cold winters last longer, as well? If so, what might
cause such differences?
Energy simulation
FIGURE 2
COURTESY OF PHET INTERACTIVE SIMULATIONS, UNIVERSITY OF COLORADO: HTTP://PHET.COLORADO.EDU
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Data mining illustrates that computational thinking
does not always mean using a computer to do complex
calculations or to write computer programs. In this
case, the power of computers allows scientists and stu-
dents to investigate the natural or designed world in
ways that were impossible before. The data available
via computer networks include not only numbers but
also images, sound recordings, and video. Using these
data to answer scientific questions, or to solve engi-
neering problems, means developing new skills, such
as formulating search strategies, using the structure
of the data to better understand the data’s meaning,
evaluating data sources, synthesizing and reporting
findings, providing appropriate citations and credit for
others’ intellectual contributions, and in some cases
making ethical judgments about the use of certain
types of data.
Automated data collection and analysis
This last example involves the use of computers to
automate data collection and analysis through a les-
son called Photosynthesis (see Resources). This les-
son gives two choices for data collection: a calorim-
eter or a spectrometer; at the middle school level we
recommend you use the calorimeter. The lesson has
several goals, including studying the effect of light on
photosynthesis and comparing the rates of photosyn-
thesis in different light conditions. To align the les-
son with the performance expectation MS-LS1-6 (see
sidebar), focus on developing students’ explanations
of conversion of water and carbon dioxide to glucose
and oxygen and how the combination of glucose and
oxygen has more chemical energy than water and car-
bon dioxide.
While the main focus of the lesson is photosynthe-
sis, you can also help students develop explanations of
matter and energy cycles and encourage discussion
about how glucose and oxygen are inputs to cell respi-
ration (producing water and carbon dioxide and releas-
ing energy to be used by the cell).
The lesson Photosynthesis uses data from a calo-
rimeter to measure energy transfer over 20 minutes
and then uses a computer application to perform a
linear regression to estimate the rates of photosyn-
thesis in several different situations. Students con-
nect the calorimeter sensor to a computer, structure
the data so they can be used by the computer, and
manipulate the software to produce the regression.
This process engages students in computational
thinking as a path to developing their understanding
of photosynthesis.
From computer use to computational thinking
As students learn more about what computers can do,
and about their limitations, they will have an increased
understanding of scientific issues (such as predicted
effects of climate change) and engineering projects
(such as construction of bridges and skateboard ramps
and restoration of wetlands) that depend on comput-
ers. In some cases, students’ increased computational-
thinking abilities will encourage them to pursue sci-
ence, technology, engineering, and mathematics in
college and possibly as a career.
We still have much to learn about how best to en-
gage students in computational thinking. Our goal is to
enable a growing number of students and teachers to
use computational thinking for a wide variety of prob-
lems—including many that we cannot even begin to
imagine today.
Additional resources
The examples in this article are a small sample of the
resources available on the web for teachers in all disci-
plines. Here are four websites to get you started:
• The Computer Science Teachers Association
website (csta.acm.org/Curriculum/sub/
CompThinking.html) provides articles, brochures,
teachers manuals, PowerPoint documents, and
even a Camtasia presentation.
• Google has an excellent website on computational
Deep snow
FIGURE 3
COURTESY OF NOAA: WWW.PHOTOLIB.NOAA.GOV/700S/WEA00963.JPG
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Addressing the Next Generation Science Standards (NGSS Lead States 2013)
Performance expectation: MS-PS3-2. Develop a model
to describe that when the arrangement of objects
interacting at a distance changes, different amounts
of potential energy are stored in the system.
Science and engineering practice: Developing and
Using Models: Modeling in 6–8 builds on K–5 and
progresses to developing, using, and revising models
to describe, test, and predict more abstract phenomena
and design systems.
• Develop a model to describe unobservable
mechanisms.
(Each NGSS performance expectation incorporates
a science and engineering practice. The one quoted
here incorporates the practice of developing and using
models. Computer simulations enable students to
manipulate models in various ways.)
Performance expectation: MS-ESS2-5. Collect data to
provide evidence for how the motions and complex
interactions of air masses results in changes in
weather conditions.
Science and engineering practice: Planning and
Carrying Out Investigations in 6–8 builds on K–5
experiences and progresses to include investigations
that use multiple variables and provide evidence to
support explanations or solutions.
• Collect data to . . . serve as the basis for evidence
to answer scientic questions or test design
solutions under a range of conditions.
Performance expectation: MS-ESS3-5. Ask questions
to clarify evidence of the factors that have caused
the rise in global temperatures over the past century.
Science and engineering practice: Asking Questions and
Dening Problems in grades 6–8 builds on grades K–5
experiences and progresses to specifying relationships
between variables, clarify arguments and models.
• Ask questions to identify and clarify evidence of an
argument.
Performance expectation: MS-LS1-6. Construct a
scientic explanation based on evidence for the role
of photosynthesis in the cycling of matter and ow
of energy into and out of organisms.
Science and engineering practice: Constructing
Explanations and Designing Solutions in 6–8 builds on
K–5 experiences and progresses to include constructing
explanations and designing solutions supported by
multiple sources of evidence consistent with scientic
knowledge, principles, and theories.
• Construct a scientic explanation based on valid
and reliable evidence obtained from sources
(including students’ own experiments) and the
assumption that theories and laws that describe the
natural world operate today as they did in the past
and will continue to do so in the future.
thinking (www.google.com/edu/computational-
thinking), which includes a link to an excellent
TED Talk about computational thinking and a
variety of other resources for educators.
• The Center for Computational Thinking at
Carnegie Mellon (www.cs.cmu.edu/~CompThink)
includes links to education resources for all grades.
• The University of Colorado’s PhET Interactive
Simulations (http://phet.colorado.edu) offers a
large number of excellent, free simulations. ■
Acknowledgments
We wish to thank the PhET Interactive Simulations
group at the University of Colorado for providing a wide
array of free simulations; NASA and NOAA for provid-
ing access to scientic databases in a form that stu-
dents and teachers can use; researchers at TERC for
their pioneering efforts in developing ways to engage
students in computational thinking; and Irene Lee and
colleagues for their ideas on how to help students cre-
ate their own simulations.
This article was developed with partial suppor t
of the National Science Foundation (IIS-1041322
Supporting Continued Improvements to K–12 Com-
puter Science Education). Any opinions, findings,
and conclusions or recommendations expressed in
this material are those of the authors and do not
necessarily reflect the views of the National Sci-
ence Foundation.
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TEACHER’S TOOLKIT
Cary Sneider (csneider@pdx.edu) is an associate
research professor in the Center for Science
Education at Portland State University in Portland,
Oregon. Chris Stephenson is computer science
education program lead at Google in Mountain
View, California. Bruce Schafer recently retired from
the Oregon University System in Portland, Oregon.
Larry Flick is dean of education at Oregon State
University in Corvallis, Oregon.
References
Lee, I., F. Martin, J. Denner, B. Coulter, W. Allan, J. Erickson,
J. Malyn-Smith, and L. Werner. 2011. Computational
thinking for youth in practice. ACM Inroads 2 (1): 32–37.
National Research Council (NRC). 2012. A framework for K–12
science education: Practices, crosscutting concepts, and
core ideas. Washington, DC: National Academies Press.
NGSS Lead States. 2013. Next Generation Science
Standards: For states, by states. Washington, DC:
National Academies Press. www.nextgenscience.org/
next-generation-science-standards.
Wing, J. 2006. Computational thinking. Communications of
the ACM 49 (3): 33–35.
Resources
Earth Exploration Toolkit—http://serc.carleton.edu/eet
Great Lakes Snow Analysis Collaborative Network—http://
tinyurl.com/ne3botc
The Hands On Universe project—www.handsonuniverse.org/
software
Is Grandpa Right? Were Winters Colder When He Was a
Boy?—http://tinyurl.com/oojrh32
My NASA Data—http://mynasadata.larc.nasa.gov
Photosynthesis—http://tinyurl.com/m8o3e5w
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