Hi and welcome. In this video I'm going to describe how computer modeling and
simulation
and computational thinking align with the National Research Council's
A framework for K-12 science education. Called the NRC framework,
and Achieve's Next Generation science standards. I'll introduce the NRC
framework
and point out the alignment between the practice of Computational Science
and the scientific practice to mention, specifically computational thinking.
Computational science is a combination of computer science,
science and mathematics made possible
with the advent of powerful computers. It is seen as the third leg of science in
addition to theoretical and lab science.
Examples of new computational fields include
computational biology computational physics and computational social science
computer modeling and simulation
is one of the many tools in the computational scientist's tool kit.
The tools include types of modeling packages and techniques.
Computational thinking is the key thinking skill used when developing
models and simulations.
Computational thinking is the human ability to harness the power of
computers to address real-world problems.
It is a human thought process involved in formulating problems so that their
solutions can be represented
as computational steps or algorithms to be carried out by a computer.
There are three main pillars of computational thinking:
abstraction, automation and analysis.
abstraction is stripping down a problem to its bare essentials
and/or capturing common characteristics or actions into one set
that can be used to represent all other instances. Automation is achieved by
using a computer as a labor-saving device
to execute repetitive tasks quickly and efficiently.
Analysis in the context of modeling and simulation
is the verification and validation
of the model. Verification occurs when it is determined that the model was
implemented correctly.
Validation occurs when it can be determined that the abstractions were
correct.
Specific questions can help the scientists verify and validate a model.
Now, let's look at the NRC framework's description of scientific practice.
The NRC framework presented a new vision of what science education should look
like in the twenty first century.
Achieve's NGSS standards are based on the NRC framework.
The standards describe how to put the vision into practice.
So every standard has three dimensions.
The first would be disciplinary core ideas
or content. The second be science and engineering practices
or practice. And third would be cross-cutting concepts
or themes. The standards are performance expectations for students.
They are goals that reflect what students should know. So what's new in the NRC
framework?
Well, content and application are intertwined
so there's now more learning by doing and less memorization.
Practice includes the use of computer models and simulation
and practice also includes computational thinking.
The overall message is that students need act as scientists not simply learn
science fact. Additionally,
the NRC framework advocates students develop and use models including computer
models.
Dimension number one in the NRC framework
is scientific and engineering practices. These are practices that scientists
employ as they investigate and build models and theories of the world
let's see how these compare with the practice of Computational Science
In Project GUTS we introduce students to the computational science process
starting in middle school
and we often use this diagram. We start with a real world phenomenon or
problem
and a question. We simplify the real world into just the parts we think we
need
and note the assumptions we've made. We come up with an idea for a model using
abstraction
it's not an attempt at exact duplication in the real world.
From this we develop a computational model. Then we use the computational
model as an experimental testbed
complete with variables and parameters. We run experiments
changing these inputs, collect and analyze data
and interpret our results. Our primary goal is to understand the model
and see how it can help us understand the real world.
So here's an example of what a student scientific practice might look like.
I'm interested in the spread of a new disease how an impact my community.
Rather look at the whole world I'm only going to model my school
and my school community. I think it will be important to have kids
and teachers and movement from classroom to classroom.
I use what is currently known about how contagious this disease is.
I'll include all these components in my computer model
and I'll use algorithms to make the people move and pass the disease from
person to person.
I'll use my computer model as an experimental testbed
and run experiments that show what might happen based on how contagious the
disease is.
Then I'll compare model generated data to real-world
absentee data during an outbreak of the disease at school.
Now this cycle closely aligns with the NRC framework scientific practices.
After analyzing and interpreting data we compare
the model generated data to real-world data to see if it makes sense.
If it is comparable then this model may be suitable to be used as a test bed for
running predictive simulations.
If it is not comparable, ask could there be bugs in your code?
and rethink your model. Did I choose the right abstractions in the model?
Throughout the cycle we engage in constructing explanations or solutions,
engaging in argument from evidence and obtaining evaluating and communicating
information.
The key takeaway point is that the NRC framework's description of
8 scientific practices is totally in alignment with with what we call
the computational science cycle. Additionally
growing the capacity in our students to understand, create and use computer
models
is on the leading edge of the change in science education that the NRC is
advancing.