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NetLogo is a multi-agent programmable modeling environment. It is used by many hundreds of thousands of students, teachers, and researchers worldwide. It also powers HubNet participatory simulations. It is authored by Uri Wilensky and developed at the CCL. You can download it free of charge. You can also try it online through NetLogo Web.

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Home Download Help Resources Extensions FAQ NetLogo Publications Contact Us Donate Models: Library Community Modeling Commons Beginners Interactive NetLogo Dictionary (BIND) NetLogo Dictionary User Manuals: Web Printable Chinese Czech Farsi / Persian Japanese Spanish NetLogo is a multi-agent programmable modeling environment. It is used by many hundreds of thousands of students, teachers, and researchers worldwide.

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Science communication is part of a scientist's everyday life. Scientists must give talks, write papers and proposals, communicate with a variety of audiences, and educate others. Thus to be successful, regardless of field or career path, scientists must learn how to communicate. Moreover, scientists must learn how to communicate effectively. In other words, to be a successful scientist, you must be an effective communicator.

Before I go on, I should note that for the purpose of this post, I am defining science communication broadly, meaning any activity that involves one person transmitting science-related information to another, from peer-reviewed articles to tweets.

Effective communication means transmitting your message clearly and concisely so that it is understood. It's about engaging your audience - it's about the 'So what?' and 'Why does it matter?' of your message.

When scientists communicate more effectively, science thrives. Science is increasingly interdisciplinary and the ability to communicate more effectively across disciplines fosters collaboration and innovation. Being able to communicate the relevance and impact of their ideas and discoveries can enhance scientists' ability to secure funding or find a job. It allows them to write better and more comprehensible research papers. It also allows them to be better teachers and mentors for next-generation scientists.

When scientists are able to communicate effectively beyond their peers to broader, non-scientist audiences, it builds support for science, promotes understanding of its wider relevance to society, and encourages more informed decision-making at all levels, from government to communities to individuals. It can also make science accessible to audiences that traditionally have been excluded from the process of science. It can help make science more diverse and inclusive.

Although having more scientists who are effective communicators benefits science and society greatly, there are still relatively few training opportunities for science students and professionals to develop these skills.

Fortunately, effective communication skills are no longer perceived as soft skills. Increasingly, they are becoming part of the core professional skills every science student and professional should have.

Many science communication training programs and courses for scientists use the public communication of science as a tool to develop effective communication skills. See, for example, this list of training opportunities compiled by COMPASS, an organization dedicated to improving science communication. Here are a number of other resources:

Public communication of science is not for everyone, of course. We can't expect all scientists to use Twitter, participate in their local school's career day or blog, but a little bit of effort goes a long way.

Public communication encourages scientists to think about the big picture. For instance, scientists can get bogged down with the specifics of a research question or use too much jargon to explain a concept. Public communication encourages scientists to find simple, more succinct ways to get the essentials of their message across. Why does/should it matter to your audience? Why is it important?

Sure, no one can argue that writing a peer-reviewed research article is the same as writing a science blog for high school students, or that giving a talk to your peers at a scientific conference is the same as standing in front of a group of middle schoolers to teach them about chemistry. Although public communication may seem very different from scholarly communication of science, the principles and strategies that make messaging effective in each arena are very similar.

For example, know your audience. Who are they? You always need to know who you are trying to reach, as it affects everything else you do. Are you trying to reach peers in your field or are you communicating across fields? Are you talking to a potential funder or to a local reporter? Regardless of your message and your goal, you always need to know your audience.

"If you can't explain it simply, you don't understand it well enough," Albert Einstein said. As experts, scientists have a deep knowledge of particular subjects. To communicate something effectively, one needs a similarly deep knowledge of the associated skills. Public communication offers scientists ways to learn and practice the basics of effective communication. By teaching scientists how to explain their work simply-and more effectively-public communication increases the impact of science in multiple dimensions.

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Science communication is part of a scientist's everyday life. Scientists must give talks, write papers and proposals, communicate with a variety of audiences, and educate others.

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The Science and Engineering Practices (SEPs) outlined in the Next Generation Science Standards (NGSS) provide us with a clear picture of what inquiry instruction should look like in the science classroom:

1. Asking Questions (Science) and Defining Problems (Engineering)

2. Developing and Using Models

3. Planning and Carrying Out Investigations

4. Analyzing and Interpreting Data

5. Using Mathematical and Computational Thinking

6. Constructing Explanations (Science) and Designing Solutions (Engineering)

7. Engaging in Argument from Evidence

8. Obtaining, Evaluating, and Communicating Information

Not only do these practices help us keep our students active, but they can also help students better learn the content, pique their curiosity, and give them an understanding of how science knowledge actually develops. Focusing solely on covering the vast amount of content included in textbooks leaves students with a skewed view of what science is and how science advances. NGSS advocates a 3-dimensional approach to learning in which the SEPs are used to help students learn the Disciplinary Core Ideas or DCIs (i.e, the content: physical sciences, life sciences, earth and space sciences, and engineering and technology) and the Crosscutting Concepts or XCCs (i.e., the big ideas that span all branches of science).

Teaching students how to participate in productive science talk in the classroom is a key way to help them learn about and enact the 8 SEPs, from asking questions (SEP #1) to engaging in argument from evidence (SEP #7).
This blog briefly explains the differences between traditional classroom talk and "productive talk" and includes tools and strategies for generating and facilitating the latter in your classroom. Traditional versus Productive Talk: What's the difference?
As thinking is often done simultaneously with speech, all students need regular opportunities to think out loud. Making student thinking visible through talk reveals key information for the teacher about their initial and developing understandings. While learning is the result of thinking, not all forms of classroom talk lead to the same levels of thinking. Research shows that most classroom talk follows the Initiation-Response-Evaluation (I-R-E) pattern [e.g., Lemke, J. L. (1990). Talking science: Language, learning, and values. Norwood, NJ: Ablex]:

Initiation: The teacher poses a question, typically one that is closed and requires students to recall a "right" answer or perform a quick calculation.

Response: Often the first student to volunteer or call out provides a one word or sentence response.

Evaluation: The teacher reacts with a comment indicating whether the student's response is correct.
The Problem with I-R-E:

Answers are valued over thinking

Few students, and only certain types of students, participate

Encourages only the lowest levels of thinking

What Should Classroom Talk Look Like?
Through a variety of grouping formats (i.e., pairs, small groups, and whole class), students should spend the majority of their class time discussing, grappling with, and reasoning and asking question about how and why science phenomena occur.
How Can I Generate and Facilitate Productive Science Talk? Below are tools and strategies for supporting productive discourse in your classroom: 1) Develop and Reinforce Talk Norms:
It takes time for students to learn new patterns of communicating and engaging in scientific discussions may feel uncomfortable at first. For students who have grown up with traditional I-R-E style instruction, opening the floor for them to compare different science explanations, to weigh evidence, to critique models and explanations, and to revise ideas over time will be unfamiliar. Students need time carved out for them to try new forms of discussion and they need explicit rules for participation. Here are some example discussion norms:
1. Everyone participates 2. Support claims with evidence 3. Challenge ideas, but respect the person 4. Revise and rethink often
Penuel, W.R., Moorthy, S., DeBerger, A., Beauvineau, Y., & Allison, K. (2012). Tools for Orchestrating Productive Talk in Science Classrooms. The Future of Learning: Proceedings of the 10th International Conference of the Learning Sciences (ICLS 2012). Sydney, Australia: International Society of the Learning Sciences.
2) Implement Teacher Talk Moves:
Productive discussions do not materialize on their own-the teacher plays an important role! Students tend to need help in the following areas:
(a) Sharing, expanding, and clarifying their thoughts (b) Listening carefully to their peers (c) Deepening their reasoning (d) Considering and responding to others' ideas
Michaels, S., & O'Connor, C. (2012). Talk science primer. Cambridge, MA: TERC.

Check out these resources:

Handout containing a list of teacher prompts/talk moves you can use to support students with each of these four goals.

Videos of these moves in action with a class of 4th grade students (if they can do it, our middle and high school students should have no problem!)

3) Introduce Student Talk Stems:

To support students in responding to one another without the need for prompting from the teacher, below are some resources containing productive talk stems for students.

(a) Engage Students to Communicate in Scientific Ways (BSCS, 2014): contains a chart outlining what scientists do and sentence stems for what scientists say (also indicates which category of stems supports each of the NGSS SEPs).

(b) Science Sense-Making Role Cards (University of Washington): contains stems for students taking on the following roles: observation specialist, inference specialist, connections specialist, and model specialist.

(c) More Role Cards (University of Washington): the cards contain stems for the following roles: questioner, skeptic, idea exchanger, interpreter
Share stories of productive science talk from your classroom below! Let us know how it went with trying out any of these strategies/tools or post other effective examples of how you get your students talking like scientists.

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Talking the Talk: Tips for Engaging Your Students in Scientific Discourse

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The Science and Engineering Practices (SEPs) outlined in the Next Generation Science Standards (NGSS) provide us with a clear picture of what inquiry instruction should look like in the science classroom: Asking Questions (Science) and Defining Problems (Engineering) Developing and Using Models Planning and Carrying Out Investigations Analyzing and Interpreting

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Computational Thinking (CT) is a problem solving process that includes a number of characteristics and dispositions. CT is essential to the development of computer applications, but it can also be used to support problem solving across all disciplines, including math, science, and the humanities. Students who learn CT across the curriculum can begin to see a relationship between subjects as well as between school and life outside of the classroom.

CT involves a number of skills, including:

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 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

CT concepts are the mental processes (e.g. abstraction, algorithm design, decomposition, pattern recognition, etc) and tangible outcomes (e.g. automation, data representation, pattern generalization, etc) associated with solving problems in computing. These include and are defined as follows:

Abstraction: Identifying and extracting relevant information to define main idea(s)

Algorithm Design: Creating an ordered series of instructions for solving similar problems or for doing a task

Automation: Having computers or machines do repetitive tasks

Data Analysis: Making sense of data by finding patterns or developing insights

Data Collection: Gathering information

Data Representation: Depicting and organizing data in appropriate graphs, charts, words, or images

Decomposition: Breaking down data, processes, or problems into smaller, manageable parts

Parallelization: Simultaneous processing of smaller tasks from a larger task to more efficiently reach a common goal

Pattern Generalization: Creating models, rules, principles, or theories of observed patterns to test predicted outcomes

Pattern Recognition: Observing patterns, trends, and regularities in data

Simulation: Developing a model to imitate real-world processes

See our Computational Thinking Concepts Guide for a printable version of this list, along with teaching tips for each concept.

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Google for Education: Computational Thinking

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Computational thinking (CT) involves a set of problem-solving skills and techniques that software engineers use to write programs that underlie the computer applications you use such as search, email, and maps

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NASA's Practical Uses of Math And Science (PUMAS). A collection of one-page examples of how math and science topics taught in K-12 classes can be used in interesting settings, including every day life.

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Understanding Science:  How Science Really Works has several great pages about planning and carrying out investigations.

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What are experiments? An experiment is a test that involves manipulating some factor in a system in order to see how that affects the outcome. Ideally, experiments also involve controlling as many other factors as possible in order to isolate the cause of the experimental results.

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Almost 400 Engineering Activities for junior and high school!

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Almost 400 Engineering Activities for junior and high school!

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All of the content on this site is free!

Registering for the site helps us tailor future activities, webinars, and events so that we can serve you better.

Thank you for your interest and for helping us in our mission to transform lives as we put the E in STEM!

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Explore STEM Activities that will further your understanding of the different disciplines and topics found within each category.

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University of Michigan Risk Science Center

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University of Michigan School of Public Health

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The Naked Truth about (scientific) models Part 2 - Three Rules of Thumb

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http://youtube.com/riskbites How do you make sense of scientific models without falling into common traps? Here are three rules of thumb, before Risk Bites gets back to dose-response models. See Part 1 of the Naked Truth about (scientific) models here: http://youtu.be/PXNEqrX_Sxw This week's Risk Bites team: David Faulkner (post-production) Andrew Maynard (all the other stuff) Risk Bites is supported by: University of Michigan School of Public Health.

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How Science Changes and Models as Tools in Science

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University of Michigan School of Public Health

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University of Michigan Risk Science Center

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The Naked Truth about (scientific) models - Part 1

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http://youtube.com/riskbites What is a scientific model and what is it good for? As a prelude to talking about dose-response models in a couple of weeks, this week's Risk Bites is a primer on models -- what they are, and why they are useful.

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Some resources I find are just THAT good. This is one of them!

The Concord Consortium offers an online tool Find Your Path through the NGSS that lets you find lesson ideas based on Core Idea, Crosscutting Concept, and Science and Engineering Practices. Geared towards middle and high school

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Find your path through the Next Generation Science Standards with help from the Concord Consortium.

1. Start in the center with a core idea. What do you teach?

2. Add a science and engineering practice-or two!

3. Add a crosscutting concept.

4. You've created one path through the NGSS. Use the Concord Consortium resources available for that path below.

Practices

Asking Questions
Defining Problems

Using Models

Conducting
Investigations

Analyzing
Data

Using Mathematics &
Computational Thinking

Constructing Explanations
Designing Solutions

Arguing from
Evidence

Communicating
Information

Core Ideas
What do you teach?

Life
Sciences

Engineering &
Technology

Earth & Space
Sciences

Physical
Sciences

Crosscutting Concepts

Patterns

Causation

Scale

Systems

Energy

Structure & Function

Stability & Change

Start by selecting a Core Idea.

About the Next Generation Science Standards

You can also download our
NGSS "fortune tellers" (PDF) for another fun way to find resources available for different paths.

The Next Generation Science Standards provide a framework and examples for STEM learning. Grounded in the National Academy of Science's thoughtful Framework for K-12 Science Education, these new K-12 science standards have been developed to provide students an internationally benchmarked science education, and signify a new direction for STEM education. They elevate the importance of Earth science, present engineering education as coequal with science education for the first time and emphasize a key set of Scientific and Engineering Practices and Crosscutting Concepts that should buttress all learning in these disciplines.

The NGSS hold the potential for helping focus the current national concern for improving STEM education. They will undoubtedly help bring clarity and unity to the patchwork of state standards developed throughout the standards movement in the past decades. As this occurs, innovative educational technology will be a critical component in this STEM education revolution.

The Concord Consortium and the NGSS

Our NGSS Pathfinder provides numerous examples of how the NGSS-and especially its Practices and Crosscutting Concepts-are central to our work in STEM education. Over nearly two decades, we've been demonstrating how technology can make complex concepts more approachable, underscore important crosscutting ideas and engage students in the practices of science and engineering.

Using computational models and probe-based activities, elementary students can watch biological evolution, middle school students can analyze and interpret data to understand the genetic basis of inheritance and high school and college students can argue from evidence in discussing interactions between molecules. With these technology-supported activities, students can engage in doing real science as they plan and carry out investigations, use models, analyze data and design solutions. Students also gain wide experience with crosscutting concepts-from scales in space and time to energy and systems-across domains in science, math and engineering.

More about the NGSS

© Copyright 2020 The Concord Consortium. All rights reserved.

This material is based upon work supported by the National Science Foundation under Grant No. DUE-1044263. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Find your path through the Next Generation Science Standards with help from the Concord Consortium. Start in the center with a core idea. What do you teach? Add a science and engineering practice-or two! Add a crosscutting concept. You've created one path through the NGSS. Use the Concord Consortium resources available for that path below.

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...for Constructing Explanations and Designing Solutions

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A Boeing and Teaching Channel Partnership

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A Boeing and Teaching Channel Partnership

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...for Developing and Using Models

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This  continuum  is  intended  for  teachers  to  use  in  guiding  and  evaluating  student  performance in  the  science  practices.  

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This  continuum  is  intended  for  teachers  to  use  in  guiding  and  evaluating  student  performance in  the  science  practices.  

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This continuum is intended forteachers to use in guiding and	evaluating science practice-based instruction.

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This continuum is intended forteachers to use in guiding and	evaluating science practice-based instruction.

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...for Asking Questions and Defining Problems

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Practice 8 - Obtaining, Evaluating and Communicating Information

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Practice 7 - Engaging in Argument from Evidence

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Practice 6 - Constructing Explanations and Designing Solutions

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Practice 5 - Using Mathematics and Computational Thinking

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Practice 4 - Analyzing and Interpreting Data

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Practice 3 - Planning and Carrying Out Investigations

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Practice 2 - Developing and Using Models

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Practice 1 - Asking Questions and Defining Problems

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8. Obtaining, Evaluating, and Communicating Information

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8. Obtaining, Evaluating, and Communicating Information

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Any education in science and engineering needs to develop students’ ability to read and produce domain-specific text. As such, every science or engineering lesson is in part a language lesson, particularly reading and producing the genres of texts that are intrinsic to science and engineering. (NRC Framework, 2012, p. 76) Being able to read, interpret, and produce scientific and technical text are fundamental practices of science and engineering, as is the ability to communicate clearly and persuasively. Being a critical consumer of information about science and engineering requires the ability to read or view reports of scientific or technological advances or applications (whether found in the press, the Internet, or in a town meeting) and to recognize the salient ideas, identify sources of error and methodological flaws, distinguish observations from inferences, arguments from explanations, and claims from evidence. Scientists and engineers employ multiple sources to obtain information used to evaluate the merit and validity of claims, methods, and designs. Communicating information, evidence, and ideas can be done in multiple ways: using tables, diagrams, graphs, models, interactive displays, and equations as well as orally, in writing, and through extended discussions.

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The study of science and engineering should produce a sense of the process of argument necessary for advancing and defending a new idea or an explanation of a phenomenon and the norms for conducting such arguments. In that spirit, students should argue for the explanations they construct, defend their interpretations of the associated data, and advocate for the designs they propose. (NRC Framework, 2012, p. 73) Argumentation is a process for reaching agreements about explanations and design solutions. In science, reasoning and argument based on evidence are essential in identifying the best explanation for a natural phenomenon. In engineering, reasoning and argument are needed to identify the best solution to a design problem. Student engagement in scientific argumentation is critical if students are to understand the culture in which scientists live, and how to apply science and engineering for the benefit of society. As such, argument is a process based on evidence and reasoning that leads to explanations acceptable by the scientific community and design solutions acceptable by the engineering community. Argument in science goes beyond reaching agreements in explanations and design solutions. Whether investigating a phenomenon, testing a design, or constructing a model to provide a mechanism for an explanation, students are expected to use argumentation to listen to, compare, and evaluate competing ideas and methods based on their merits. Scientists and engineers engage in argumentation when investigating a phenomenon, testing a design solution, resolving questions about measurements, building data models, and using evidence to evaluate claims.

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6. Constructing Explanations and Designing Solutions

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6. Constructing Explanations and Designing Solutions

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The goal of science is to construct explanations for the causes of phenomena. Students are expected to construct their own explanations, as well as apply standard explanations they learn about from their teachers or reading. The Framework states the following about explanation: “The goal of science is the construction of theories that provide explanatory accounts of the world. A theory becomes accepted when it has multiple lines of empirical evidence and greater explanatory power of phenomena than previous theories.”(NRC Framework, 2012, p. 52) An explanation includes a claim that relates how a variable or variables relate to another variable or a set of variables. A claim is often made in response to a question and in the process of answering the question, scientists often design investigations to generate data. The goal of engineering is to solve problems. Designing solutions to problems is a systematic process that involves defining the problem, then generating, testing, and improving solutions. This practice is described in the Framework as follows. Asking students to demonstrate their own understanding of the implications of a scientific idea by developing their own explanations of phenomena, whether based on observations they have made or models they have developed, engages them in an essential part of the process by which conceptual change can occur. In engineering, the goal is a design rather than an explanation. The process of developing a design is iterative and systematic, as is the process of developing an explanation or a theory in science. Engineers’ activities, however, have elements that are distinct from those of scientists. These elements include specifying constraints and criteria for desired qualities of the solution, developing a design plan, producing and testing models or prototypes, selecting among alternative design features to optimize the achievement of design criteria, and refining design ideas based on the performance of a prototype or simulation. (NRC Framework, 2012, p. 68-69)

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5. Using Mathematics and Computational Thinking

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5. Using Mathematics and Computational Thinking

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Although there are differences in how mathematics and computational thinking are applied in science and in engineering, mathematics often brings these two fields together by enabling engineers to apply the mathematical form of scientific theories and by enabling scientists to use powerful information technologies designed by engineers. Both kinds of professionals can thereby accomplish investigations and analyses and build complex models, which might otherwise be out of the question. (NRC Framework, 2012, p. 65) Students are expected to use mathematics to represent physical variables and their relationships, and to make quantitative predictions. Other applications of mathematics in science and engineering include logic, geometry, and at the highest levels, calculus. Computers and digital tools can enhance the power of mathematics by automating calculations, approximating solutions to problems that cannot be calculated precisely, and analyzing large data sets available to identify meaningful patterns. Students are expected to use laboratory tools connected to computers for observing, measuring, recording, and processing data. Students are also expected to engage in computational thinking, which involves strategies for organizing and searching data, creating sequences of steps called algorithms, and using and developing new simulations of natural and designed systems. Mathematics is a tool that is key to understanding science. As such, classroom instruction must include critical skills of mathematics. The NGSS displays many of those skills through the performance expectations, but classroom instruction should enhance all of science through the use of quality mathematical and computational thinking.

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Once collected, data must be presented in a form that can reveal any patterns and relationships and that allows results to be communicated to others. Because raw data as such have little meaning, a major practice of scientists is to organize and interpret data through tabulating, graphing, or statistical analysis. Such analysis can bring out the meaning of data—and their relevance—so that they may be used as evidence. Engineers, too, make decisions based on evidence that a given design will work; they rarely rely on trial and error. Engineers often analyze a design by creating a model or prototype and collecting extensive data on how it performs, including under extreme conditions. Analysis of this kind of data not only informs design decisions and enables the prediction or assessment of performance but also helps define or clarify problems, determine economic feasibility, evaluate alternatives, and investigate failures. (NRC Framework, 2012, p. 61-62) As students mature, they are expected to expand their capabilities to use a range of tools for tabulation, graphical representation, visualization, and statistical analysis. Students are also expected to improve their abilities to interpret data by identifying significant features and patterns, use mathematics to represent relationships between variables, and take into account sources of error. When possible and feasible, students should use digital tools to analyze and interpret data. Whether analyzing data for the purpose of science or engineering, it is important students present data as evidence to support their conclusions.

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3. Planning and Carrying Out Investigations

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3. Planning and Carrying Out Investigations

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Students should have opportunities to plan and carry out several different kinds of investigations during their K-12 years. At all levels, they should engage in investigations that range from those structured by the teacher—in order to expose an issue or question that they would be unlikely to explore on their own (e.g., measuring specific properties of materials)— to those that emerge from students’ own questions. (NRC Framework, 2012, p. 61) Scientific investigations may be undertaken to describe a phenomenon, or to test a theory or model for how the world works. The purpose of engineering investigations might be to find out how to fix or improve the functioning of a technological system or to compare different solutions to see which best solves a problem. Whether students are doing science or engineering, it is always important for them to state the goal of an investigation, predict outcomes, and plan a course of action that will provide the best evidence to support their conclusions. Students should design investigations that generate data to provide evidence to support claims they make about phenomena. Data aren’t evidence until used in the process of supporting a claim. Students should use reasoning and scientific ideas, principles, and theories to show why data can be considered evidence. Over time, students are expected to become more systematic and careful in their methods. In laboratory experiments, students are expected to decide which variables should be treated as results or outputs, which should be treated as inputs and intentionally varied from trial to trial, and which should be controlled, or kept the same across trials. In the case of field observations, planning involves deciding how to collect different samples of data under different conditions, even though not all conditions are under the direct control of the investigator. Planning and carrying out investigations may include elements of all of the other practices.

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Modeling can begin in the earliest grades, with students’ models progressing from concrete “pictures” and/or physical scale models (e.g., a toy car) to more abstract representations of relevant relationships in later grades, such as a diagram representing forces on a particular object in a system. (NRC Framework, 2012, p. 58) Models include diagrams, physical replicas, mathematical representations, analogies, and computer simulations. Although models do not correspond exactly to the real world, they bring certain features into focus while obscuring others. All models contain approximations and assumptions that limit the range of validity and predictive power, so it is important for students to recognize their limitations. In science, models are used to represent a system (or parts of a system) under study, to aid in the development of questions and explanations, to generate data that can be used to make predictions, and to communicate ideas to others. Students can be expected to evaluate and refine models through an iterative cycle of comparing their predictions with the real world and then adjusting them to gain insights into the phenomenon being modeled. As such, models are based upon evidence. When new evidence is uncovered that the models can’t explain, models are modified. In engineering, models may be used to analyze a system to see where or under what conditions flaws might develop, or to test possible solutions to a problem. Models can also be used to visualize and refine a design, to communicate a design’s features to others, and as prototypes for testing design performance.

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1. Asking Questions and Defining Problems 

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1. Asking Questions and Defining Problems 

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Students at any grade level should be able to ask questions of each other about the texts they read, the features of the phenomena they observe, and the conclusions they draw from their models or scientific investigations. For engineering, they should ask questions to define the problem to be solved and to elicit ideas that lead to the constraints and specifications for its solution. (NRC Framework 2012, p. 56) Scientific questions arise in a variety of ways. They can be driven by curiosity about the world, inspired by the predictions of a model, theory, or findings from previous investigations, or they can be stimulated by the need to solve a problem. Scientific questions are distinguished from other types of questions in that the answers lie in explanations supported by empirical evidence, including evidence gathered by others or through investigation. While science begins with questions, engineering begins with defining a problem to solve. However, engineering may also involve asking questions to define a problem, such as: What is the need or desire that underlies the problem? What are the criteria for a successful solution? Other questions arise when generating ideas, or testing possible solutions, such as: What are the possible trade-offs? What evidence is necessary to determine which solution is best? Asking questions and defining problems also involves asking questions about data, claims that are made, and proposed designs. It is important to realize that asking a question also leads to involvement in another practice. A student can ask a question about data that will lead to further analysis and interpretation. Or a student might ask a question that leads to planning and design, an investigation, or the refinement of a design. Whether engaged in science or engineering, the ability to ask good questions and clearly define problems is essential for everyone. The following progression of Practice 1 summarizes what students should be able to do by the end of each grade band. Each of the examples of asking questions below leads to students engaging in other scientific practices.

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What are the Science and Engineering Practices?

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What are the Science and Engineering Practices?

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They are the DO of science. Remember, the Disciplinary Core Ideas (DCI's) are what students are supposed to know about science, and the Crosscutting Concepts (CCC's) are how students are supposed to think about science. The Science and Engineering Practices (SEP's) are what they do in science!