Computational Thinking in the Workforce and Next Generation Science Standards at NSTA 2014 (April 4)
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Presentation from NSTA Boston 2014, with Joyce Malyn-Smith, and with great thanks to Irene Lee, for whom I stood in.
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Computational Thinking in the Workforce and Next Generation Science Standards at NSTA 2014 (April 4)
- 1. Preparing Today’s Youth to Become Tomorrow’s Computational Thinking-Enabled Scientists and Engineers Joyce Malyn-Smith, EDC Josh Sheldon, MIT NSTA - April 4, 2014
- 2. 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.
- 3. New Skills
- 4. Dancing with Robots - Human Skills for Computerized Work, Levy and Murnane, 2013
- 5. “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)
- 6. 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”
- 7. Technical Committee • Larry Snyder, U Washington • Duane Bailey, Amherst College • Mitch Resnick, MIT Media Lab • Irene Lee, Santa Fe Institute • Joseph Wong, Raytheon
- 9. 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
- 10. 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
- 12. 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)
- 14. 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.
- 15. Job Functions • Engages in a creative process • Collaborates • Documents
- 16. 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
- 18. CT in Action A 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.)
- 19. Computational Thinking in America’s Workplaces ACTIVITIES ACTIVITIES REFINES DEFINES MODELS QUALIFIES JOB FUNCTIONS Projects 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.
- 20. 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.
- 21. What is Computational Science?
- 22. 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?
- 23. 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
- 24. 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
- 25. 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
- 26. Computational Thinking (Wing 2010) IS IS NOT Conceptualizing (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
- 27. 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.
- 28. Computational Thinking Computer Modeling and Simulation: ▫ Agent based modeling ▫ Stochastic modeling ▫ Monte Carlo simulation ▫ Systems dynamics modeling Computational Thinking: ▫ Abstraction ▫ Automation ▫ Analysis
- 29. NGSS Structure: NRC Framework: “Vision” document Standards: Every standard has three dimensions: 1. Disciplinary core ideas (DCIs) = content 2. Science/Engineering practices (SEPs) = practice 3. Cross-cutting concepts (CCs) = themes
- 30. 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.)
- 31. NGSS: 8 Scientific practices 1. Asking questions and defining problems 2. Developing and using models 3. Planning and carrying out investigations 4. Analyzing and interpreting data 5. Using math and computational thinking 6. Constructing explanations / solutions 7. Engaging in argument from evidence 8. Obtaining, evaluating, and communicating info.
- 32. 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.
- 33. 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.
- 34. The Computational Science Cycle
- 35. 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
- 36. 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
- 37. 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 explanations 7. Engaging in argument from evidence 8. Obtaining, evaluating, and communicating info. The Computational Science Cycle & NGSS Scientific Practices
- 38. 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)
- 39. NGSS: 8 Scientific practices 1. Asking questions and defining problems 2. Developing and using models 3. Planning and carrying out investigations 4. Analyzing and interpreting data 5. Using math and computational thinking 6. Constructing explanations / solutions 7. Engaging in argument from evidence 8. Obtaining, evaluating, and communicating info.
- 40. Rich Computational Tools StarLogo Nova…. Allows exploration of emergent and complex systems Users create simulations by writing simple rules for individual “agents” No sophisticated mathematics or advanced programming http://imaginationtoolbox.org/ * skills are required Learn more & register for summer PD
- 41. • 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
- 42. Use-Modify-Create progression USE MODIFY CREATE
- 43. App Inventor – Democratizing App Creation http://appinventor.mit.edu/
- 44. For more information: Joyce Malyn-Smith, EDC jmsmith@edc.org Josh Sheldon, MIT jsheldon@mit.edu Irene Lee, Santa Fe Institute Lee@santafe.edu With support from the National Science Foundation
Editor's Notes
- Get some background on the audience: How many are Science Teachers? (of those how many are middle school and how many are high school) How many are STEM professionals? How many are education researchers? How many are school administrators? How many are technology integration specialists?Level of familiarity with NGSS?Level of familiarity with Computer Modeling and Simulation?
- This presentation will focus on using computational modeling and simulation toelucidate and align with the NGSS Scientific Practice Dimension, and specifically toComputational Thinking. The New Mexico Computer Science for All curriculum willbe featured as an example program that seeks to prepare teachers to meet the NGSS.During the session, the presenters will introduce the NGSS Framework anddemonstrate the alignment between the New Mexico Computer Science for Allprogram and the Scientific Practice Dimension, specifically Computational Thinking.Specific examples of models that address content areas in STEM will bedemonstrated and discussed. Pedagogy and best practices for using, modifyingand creating models in the STEM classroom will be shared.
- New Division of Labor – How computers are creating the next job market. 2005
- These observations lie at the core of the idea for 21st century skills. It's not than unstructured problem solving or working with new information are new skills for the 21st century, it's that they are newly important in the 21st century as computers replace routine-based work. In economic terms, humans have a comparative advantage over computers in these domains. In the past three decades, jobs requiring routine manual or routine cognitive skills have disappeared from the labor market, and jobs requiring solving unstructured problems, communication, and non-routine manual work have grown as a proportion of the labor market. The best chance of preparing young people for decent paying jobs in the decades ahead is preparing them with the skills to solve these kinds of complex tasks.
- At the frontiers of science and engineering, advanced computation has become a major element of the third leg of discovery tools – the other two legs being theory and experimentation. Computer modeling and simulation dramatically accelerate the pace of innovation and enable new-to-the world knowledge and insights.Accelerating design & engineering of new productsReducing time to market through virtual prototypingIncreased supply chain efficiency and flexibilityAmerica’s innovation advantage rests not just on having the most advanced tools and technologies in the world, but the people to use them.“America’s innovation advantage means continuous innovation in scientific talent as well as well as technology and creating the competitive difference that will concentrate cutting-edge investments in this country”WHAT DOES IT LOOK LIKE WHEN SCIENTISTS/ENGINEERS DEVELOP EXPERTISE IN USING THOSE TEHCNOLOGY TOOLS FOR DISCOVERY AND INNOVATION? HOW DO THEY DEFINE THEMSELVES? WHAT DO THEY NEED TO KNOW AND BE ABLE TO DO?NSF FUNDED COMPUTATIONAL THINKING IN AMERICA’S WORKPLACES HELPS TO MAKE THIS MORE CONCRETE.
- JMSTechnical committee represents the people engaged in the national dialogue.Understand computationally thinkingRepresent education and industry
- We invite you to participate in a National Science Foundation funded focus group activity for the Computational Thinking in America’s Workplaces project. We are seeking up to 12 expert computational thinkers currently working in a STEM field to participate in a 3-day working meeting that will be held at the Santa Fe Institute on January 24-26, 2011. Participants will engage in an intensive focus group activity and will also be asked to contribute examples of what computational thinking looks like in their own work to solve problems.Ideally we are seeking individuals who are considered by their peers to be expert Computational Thinking Enabled Professional/ Technical STEM Workers – those scientists, engineers, technologists and technicians who represent, model, abstract and validate processes -- often through the use of computers and often in collaboration with other people -- in order to solve problems, design products, automate systems, or improve understanding. We would like to have as balanced a representation possible with the focus group including: scientists, engineers, technicians representing pure science and applied science, a balance of gender, age and race. In terms of criteria for individual panelists, we are seeking the following:- Considered by peers as people who are expert computational thinkers.- Experts in their field (Energy) and highly regarded in their organizations,- Experienced (2+ years) and engaged in computational thinking / computational science,- Experienced (3+ years) in field of expertise and currently involved in energy projects.
- IALThe DACUM panel represents expert workers in a diverse set of STEM occupations and fieldsRecognized by their peers as expert computational thinkers. Definition usedEmployed in the field as computationally enabled STEM professionalsExperts in their fieldThey were chosen to represent different ethnic groups, age range, and areas of specialization.National laboratories, and industry
- JMSIntroduce DACUM chart
- JMSreview the learning occupation
- Three cross cutting job functions
- JMS8 Specific job functions - 68 work activities
- JMS
- IALHAND OUT EXAMPLESWe asked the expert panel to select activities that they did on the job and give concrete examples.Here’s one
- IAL
- JMSDiscuss the model.
- The International Society for Technology in Education (ISTE) and the Computer Science Teacher Association (CSTA) have collaborated with leaders from higher education, industry and K-12 education to develop an operational definition of computational thinking. The operational definition provides a framework and vocabulary for computational thinking that will resonate with all K-12 educators. ISTE and CSTA gathered feedback by survey from nearly 700 computer science teachers, researchers, and practitioners who indicated overwhelming support for the operational definition.
- See Cuny, Snyder, Wing article.
- Framework – visioning what new Science education should look likeStandards – standards are based on the Framework. They describe “How to put the vision into Practice” Every standard has three dimensions:Disciplinary core ideas (DCIs) = contentScience and engineering practices (SEPs) = practiceCross cutting concepts (CCs) = themesThe standards are performance expectations for students. They are goals that reflect what students should know.They are dictate teaching methods.
- What is new?Content and application (practice) are intertwined. (More learn and do, learn by doing, rather than memorization)Practice include use of computational models and simulationPractice includes computational thinkingSTUDENTS NEED TO ACT AS SCIENTISTS, not simply learn science facts!
- Dimension #1 in the NGSS Framework is Scientific PracticesPractices that scientists employ as they investigate and build models and theories of the world. “OUR expectation is that students will themselves engage in the practices and not merely learn about them secondhand.” p. xv of NGSS.8 Science /Engineering Practices (NGSS Framework)
- SPECIFIC GOALS described in the NGSS Framework and addressed in NM-CSforAllGoals relating to developing and using models (p. 50)By grade 12, students should be able to:Represent and explain phenomena with multiple types of models—for example, represent molecules with 3-D models or with bond diagrams—and move flexibly between model types when different ones are most useful for different purposes.Discuss the limitations and precision of a model as the representation of a system, process, or design and suggest ways in which the model might be improved to better fit available evidence or better reflect a design’s specifications. Refine a model in light of empirical evidence or criticism to improve its quality and explanatory power.Use (provided) computer simulations or simulations developed with simple simulation tools as a tool for understanding and investigating aspects of a system, particularly those not readily visible to the naked eye.Make and use a model to test a design, or aspects of a design, and to compare the effectiveness of different design solutions.THIS slide could be broken out further to include 1 slide per bullet point with an example.
- Diffusion model as exampleGoals related to using computational and mathematical tools for data analysis (p. 56)By grade 12, students should be able to:Recognize dimensional quantities and use appropriate units in scientific applications of mathematical formulas and graphs.Express relationships and quantities in appropriate mathematical or algorithmic forms for scientific modeling and investigations.Recognize that computer simulations are built on mathematical models that incorporate underlying assumptions about the phenomena or systems being studied.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.a design, and to compare the effectiveness of different design solutions.THIS slide could be broken out further to include 1 slide per bullet point with an example.
- So, as you can see, all 8 of the scientific practices are used in the Computational Science Cycle.