Preparing Today’s Youth to Become Tomorrow’s Computational Thinking-Enabled Scientists and Engineers

Joyce Malyn-Smith, EDCJosh Sheldon, MITNSTA - April 4, 2014

Outline of today’s presentation

Part I• Computational Thinking in the 21st Century Workplace• Profile of the Computational Thinking-Enabled STEM

Professional

Part II• Why Computational Thinking in Science?• What is Computational Thinking?• NGSS’ Scientific Practices• Computational science cycle & NGSS’ practice• Examples of computational tools addressing NGSS’ Scientific

Practice dimension.• Why “USING models & apps” is not enough.

New Skills

Dancing with Robots - Human Skills for Computerized Work, Levy and Murnane, 2013

“To Outcompute is to Outcompete”

•At the frontiers of science and engineering, advanced computation has become a major element of the third leg of discovery tools

•Computer modeling and simulation dramatically accelerate the pace of innovation

•American needs more computational scientists

◦ (Thrive Report , Council on Competitiveness, 2008)

Occupational Analysis of the CT-Enabled Professional•Technical Committee•Learning Occupation•Expert CT Panel•DACUM Analysis (Developing a

Curriculum)•Validation•Examples of CT “in action”

Technical Committee

•Larry Snyder, U Washington•Duane Bailey, Amherst College•Mitch Resnick, MIT Media Lab• Irene Lee, Santa Fe Institute• Joseph Wong, Raytheon

CT Expert Panel

Expert CT Panel

•Mark Galassi, Theoretical Physicist, Astrophysicist, Los Alamos National Lab

•Neil Henson, Material Scientist, Chemist, Los Alamos National Lab

•Nadine Miner, Computer Engineer, Sandia National Labs

•Melanie Moses, Biologist/Computer Scientist, University of New Mexico

•Bela Nagy, Computer Science & Statistics (Statistician), Santa Fe Institute

Expert CT Panel• Doug Roberts, Chemical & Industrial Engineer,

RTI• Chris Rose, Electromagnetic Physicist, Los

Alamos National Lab• Amy Sun, Chemical Engineer, Sandia National

Lab• Joshua Thorp, Computational Modeler, Santa

Fe Institute• Eleanor Walther, Operations Research Analyst,

Sandia National Lab• Chris Wood, Neuroscientist, Santa Fe Institute

DACUM chart• Front side

▫Definition of the computational thinking enabled STEM professional.

▫ Job functions ▫Activities

• Back side▫Knowledge▫Skills▫Tools▫Behaviors / Dispositions▫ Industry trends (coming soon)

Computational Thinking Enabled STEM Professional:

Engages in a creative process to solve problems, design products, automate systems, or improve understanding by defining, modeling, qualifying and refining systems, processes or mechanisms generally through the use of computers. Computational thinking often occurs in collaboration with others.

Job Functions

•Engages in a creative process•Collaborates•Documents

Job Functions•Defines

▫ Identifies the Problem▫ Specifies Constraints

•Models▫ Designs the model/system▫ Builds the model▫ Develops experimental design

•Qualifies▫ Verifies the model

•Refines▫ Optimizes the user interface and model▫ Facilitates knowledge/discovery

CT in ActionA nuclear engineer validates a coupled thermo-mechanical computer model, by comparing the model predictions with existing thermal stress experimental data, to assess the performance of a nuclear fuel element for the purpose of extending the operational lifetime of the fuel in the reactor.

Qualifies: Validates the model(F6/F3 Validates the model/by comparing the behavior of the model to a known solution.)

Computational Thinking in America’s Workplaces

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A COMPUTATIONAL THINKING ENABLED STEM WORKER:

•engages in a creative process to solve problems, design products, automates systems, or improve understanding by defining, modeling, qualifying and refining systems, processes or mechanisms generally through the use of computers. Computational thinking often occurs in collaboration with others.

Why Computing in Science?Urgent need to understand large complex systems to address the problems of the 21st century that affect us all such as climate change, loss of biodiversity, energy consumption and virulent disease.

What is Computational Science?

Increases in computational power enable us to:

• design and conduct experiments on models of systems too big, too expensive or too dangerous to experiment with in the real-world.

• run multiple “what-if” scenarios quickly.

• collect and analyze large amounts of data.

What does Computational Science Allow?

New Fields Need New Tools

Computer Modeling and Simulation:▫Agent based modeling▫Stochastic modeling▫Monte Carlo simulation▫Systems dynamics modeling

New Fields:▫Computational Biology▫Computational Physics▫Computational Social Science▫Computational Chemistry

Computational Thinking(Wing 2006, 2008)

• Skills, habits and approaches integral to solving problems using a computer

• Thinking patterns that involve systematically and efficiently processing information and tasks.

• Reasoning at multiple levels of abstraction; Understanding and applying automation; Understanding dimensions of scale

ISTE K-12 Computational Thinking

CT is a problem-solving process that includes (but is not limited to) the following characteristics: • Formulating problems in a way that enables us to use a computer and other

tools to help solve them.

• Logically organizing and analyzing data• Representing data through abstractions such as models and simulations• Automating solutions through algorithmic thinking (a series of ordered

steps)• Identifying, analyzing, and implementing possible solutions with the goal of

achieving the most efficient and effective combination of steps and resources

• Generalizing and transferring this problem solving process to a wide variety of problems

These skills are supported and enhanced by a number of dispositions or attitudes that are essential dimensions of CT. These dispositions or attitudes include:• Confidence in dealing with complexity• Persistence in working with difficult problems• Tolerance for ambiguity• The ability to deal with open ended problems• The ability to communicate and work with others to achieve a common goal

or solution

Computational Thinking (Wing 2010)

IS IS NOTConceptualizing (Only) Computer

programming

Fundamental Rote skills

A way humans think A way that computers think

Complements and combines mathematical and engineering thinking

Only mathematical and engineering thinking

Ideas Artifacts

For everyone, everywhere Only programming, computer science jobs

28

Three Pillars of CT(Cuny, Snyder, Wing 2010)

• Abstraction 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 using a computer as a labor saving device that executes repetitive tasks quickly and efficiently.

• Analysis validating if the abstractions made were correct. 

Computational Thinking

Computer Modeling and Simulation:▫Agent based modeling▫Stochastic modeling▫Monte Carlo simulation▫Systems dynamics modeling

Computational Thinking:▫Abstraction▫Automation▫Analysis

NGSS Structure:

NRC Framework: “Vision” document

Standards:Every standard has three dimensions:1. Disciplinary core ideas (DCIs) = content2. Science/Engineering practices (SEPs) =

practice3. Cross-cutting concepts (CCs) = themes

What’s new?

A)Content and practice are intertwined. B)Practices include the use, creation and

analysis of computer models and simulations in STEM inquiry and engineering design cycle.

C)Practices include computational thinking The NGSS encourage students to “learn and do”

science, rather than engaging in rote memorization, or learning about science.)

NGSS: 8 Scientific practices1. Asking questions and defining problems2. Developing and using models3. Planning and carrying out investigations4. Analyzing and interpreting data5. Using math and computational thinking6. Constructing explanations / solutions7. Engaging in argument from evidence8. Obtaining, evaluating, and communicating

info.

Goals relating to developing and using models (NRC Framework, p. 50)

By grade 12, students should be able to:•Represent and explain phenomena with

multiple types of models.•Discuss the limitations and precision of a

model …•Refine a model ….•Use computer simulations as a tool for

understanding aspects of a system….•Make and use a model to test a design and to

compare the effectiveness of different design solutions.

Goals related to using computational and mathematical tools for data analysis (NRC Framework, p. 56)

By grade 12, students should be able to:• Recognize that computer simulations are built

on mathematical models that incorporate underlying assumptions …

• Use simple test cases of mathematical expressions, computer programs, or simulations—that is, compare their outcomes with what is known about the real world—to see if they “make sense.”

• Use grade-level appropriate understanding of mathematics and statistics in analyzing data.

The Computational Science Cycle

The Computational Science Cycle & NGSS Scientific Practices

1. Asking questions /defining problems

2. Developing and using models

3. Planning and carrying out investigations

4. Analyzing and interpreting data

5. Using math and computational thinking

1. Asking questions /defining problems

2. Developing and using models

3. Planning and carrying out investigations

4. Analyzing and interpreting data

5. Using math and computational thinking

Compare outcomes with what is known about the real world—to see if they “make sense.”

The Computational Science Cycle & NGSS Scientific Practices

1. Asking questions /defining problems

2. Developing and using models

3. Planning and carrying out investigations

4. Analyzing and interpreting data

5. Using math and computational thinking

Compare outcomes with what is known about the real world—to see if they “make sense.”

6. Constructing explanations7. Engaging in argument from evidence8. Obtaining, evaluating, and communicating info.

The Computational Science Cycle & NGSS Scientific Practices

Computational Thinking and the Computational Science Cycle

(Abstraction)

(Abstraction)

(Automation)

(Automation)(Automation)

(Analysis)

(Analysis)

Compare outcomes with what is known about the real world

(Automation)

NGSS: 8 Scientific practices1. Asking questions and defining problems2. Developing and using models3. Planning and carrying out investigations4. Analyzing and interpreting data5. Using math and computational thinking6. Constructing explanations / solutions7. Engaging in argument from evidence8. Obtaining, evaluating, and communicating

info.

Rich Computational Tools

StarLogo Nova…. Allows exploration of emergent and complex systems

Users create simulations by writing simple rules for individual “agents”

No sophisticated mathematicsor advanced programming http://imaginationtoolbox.org/ *skills are required

Learn more & register for summer PD

• Computational Thinking about Complex Systems in Biology using StarLogo Nova

• Students observe complex systems by watching starling flocking video

• Students modify a model of a pond ecosystem & use that model to test hypotheses

• Students then construct their own models of biological phenomena

Use-Modify-Create progression

USE MODIFY CREATE

App Inventor – Democratizing App Creation

http://appinventor.mit.edu/

For more information:

Joyce Malyn-Smith, EDCjmsmith@edc.org

Josh Sheldon, MITjsheldon@mit.edu

Irene Lee, Santa Fe InstituteLee@santafe.edu

With support from the National Science Foundation