Using Artificial Intelligence to Design a Solar Farm

Everyone loves to maximize the return of investment (ROI). If you can effortlessly find a solution that pays a higher profit -- even only a few dollars more handsomely, why not? The problem is that, in many complicated engineering cases in the real world, such as designing a solar farm, we often don't know exactly what the optimal solutions are. We may know how to get some good solutions based on what textbooks or experts say, but no one in the world can be 100% sure that there aren't any better ones waiting to be discovered beyond the solution space that we have explored. As humans, we can easily get complacent and settled with the solutions that we feel good about, leaving the job (and the reward) of finding better solutions to another time or someone else.

Artificial intelligence (AI) is about to change all that. As design is essentially an evolution of solutions, AI techniques such as genetic algorithms (GA) are an excellent fit to the nature of many design problems and can generate a rich variety of competitive designs in the same way genetics does for biology (no two leaves are the same but they both work). These powerful tools have the potential to help people learn, design, and discover new things. In this article, I demonstrate how GA can be used to design a photovoltaic (PV) solar farm. As always, I first provide a short screencast video in which I used the daily output or profit as the objective function to speed up the animation so that you can see the evolution driven by GA. The actual assessments are based on using the annual output or profit as the objective function, presented in the text that follows the video. Note that the design process is still geared towards a single objective (i.e., the total output in kWh or the total profit in dollars over a given period of time). Design problems with multiple objectives will be covered later.


In GA, the solution depends largely on the choice of the objective function (or the fitness function), which specifies the main goal. For example, if the main goal is to generate as much electricity as possible on a given piece of land without the concern of the cost of solar panels, a design in which solar panels are closely packed may be a good choice. On the other hand, if the main goal is to generate as much electricity as possible from each solar panel because of their high price, a design in which rows of solar panels are far away from one another would be a good choice (in the case shown in the video, a single rack of solar panels was unsurprisingly found as the best solution). The real-world problems always lie between these two extremes, which is why they must be solved using the principles of engineering design. The video above clearly illustrates the design evolution driven by GA in the three cases (the two extremes and an intermediate).

Figure 1. An Energy3D model of an existing solar farm in Massachusetts.
To test the usefulness of the GA implementation in Energy3D for solving real-world problems, I picked an existing solar farm in Massachusetts (Figure 1) to see if GA could find better solutions. A 3D model of the solar farm had been created in the Virtual Solar Grid based on the information shown on Google Maps and its annual output calculated using Energy3D. Because I couldn't be exactly sure about the tilt angle, I also tweaked it a bit manually and ensured that an optimal tilt angle for the array be chosen (I found it to be around 32° in this case). The existing solar farm has 4,542 solar panels, capable of generating 2,255 MWh of electricity each year, based on the analysis result of Energy3D. [I must declare here that the selection of this site was purely for the purpose of scientific research and any opinion expressed as a result of this research should be viewed as exploratory and not be considered as any kind of evaluation of the existing solar farm and its designer(s). There might be other factors beyond my comprehension that caused a designer to choose a particular trade-off. The purpose of this article is to show that, if we know all the factors needed to be considered in such a design task, we can use AI to augment our intelligence, patience, and diligence.]

Figure 2. The results of 10 iterations.
Energy3D has a tool that allows the user to draw a polygon within which the solar farm should be designed. This polygon is marked by white lines. Using this tool, we can ensure that our solutions will always be confined to the specified area. I used this tool to set the boundary of the solar farm under design. This took care of an important spatial constraint and guaranteed that GA would always generate solutions on approximately the same land parcel as is situated by the existing solar farm.

For the objective function, we can select the total annual output, the average annual output of a solar panel, or the annual profit. I chose the annual profit and assumed that the generated electricity would sell for 22.5 cents per kWh (the 2018 average retail price in Massachusetts) and the daily cost of a solar panel (summing up the cost of maintenance, financing, etc.) would be 20 cents. I don't know how accurate these ROI numbers are. But let's just go with them for now. The annual profit is the total sale income minus the total operational cost. Qualitatively, we know that a higher electricity price and a lower operational cost would both favor using more solar panels and a lower electricity price and a higher operational cost would both favor using less solar panels. Finding the sweet spots in the middle requires quantitative analyses and comparisons of many different cases, which can be outsourced to AI.

Figure 3: The best design from 2,000 solutions
Figure 4: The second best design from 2,000 solutions.
In Energy3D, GA always starts with the current design as part of the first generation (so if you already have a good design, it will converge quickly). In order for GA not to inherit anything from the existing solar farm, I created an initial model that had only a rack with a few solar panels on it and a zero tilt angle. The size of the population was set to be 20. So at the beginning, this initial model would compete with 19 randomly generated solutions and was almost guaranteed to lose the chance to enter the next generation. In order to stop and check the results, I let GA run for only 10 generations. For convenience, let's call every 10 generations of GA evolution an iteration. Figure 2 shows that GA generated solutions below the supposed human performance in the first two iterations but quickly surpassed it after that. The solution kept improving but got stuck in iterations 5-7 and then it advanced again and stagnated again in iterations 8-10. This process could continue indefinitely, but I decided to terminate it after 10 iterations, or 100 generations. By this time, the software had generated and evaluated 2,000 solutions, which took a few hours as it had to run 2,000 annual simulations for thousands of solar panels.

The best solution (Figure 3) that emerged from these 2,000 generated solutions used 5,420 solar panels fixed at a tilt angle of 28.3° to generate 2,667 MWh per year and was about 16% better than the existing one based on the ROI model described above. The second best solution (Figure 4) used 4,670 solar panels fixed at a tilt angle of 38.6° to generate 2,340 MWh per year and was about 5.5% better than the existing one based on the ROI model. Note that if we use the average annual output per solar panel as the criterion, the second best solution would actually be better than the best one, but we know that the average panel output is not a good choice for the fitness function as it can result in an optimal solution with very few solar panels.

In conclusion, the generative design tools in Energy3D powered by AI can be used to search a large volume of the solution space and find a number of different solutions for the designer to pick and choose. The ability of AI to transcend human limitations in complex design is a significant application of AI and cannot be more exciting! We predict that future work will rely more and more on this power and today's students should be ready for the big time.

Using Artificial Intelligence to Design Energy-Efficient Buildings

The National Science Foundation issued a statement on May 10, 2018 in which the agency envisions that "The effects of AI will be profound. To stay competitive, all companies will, to some extent, have to become AI companies. We are striving to create AI that works for them, and for all Americans." This is probably the strongest message and the clearest matching order from a top science agency in the world about a particular area of research thus far. The application of AI to the field of design, and more broadly, creativity, is considered by many as the moonshot of the ongoing AI revolution, which is why I have chosen to dedicate a considerable portion of my time and effort to this strategically important area.

I have added two more application categories of using genetic algorithms (GAs) to assist engineering design in Energy3D, the main platform based on which I am striving to create a "designerly brain." One example is to find the optimal position to add a new building with glass curtain walls to an open space in an existing urban block so that the new building would use the least amount of energy. The other example is to find the optimal sizes of the windows on different sides of a building so that the building would use the least amount of energy. To give you a quick idea about how GAs work in these cases, I recorded the following two screencast videos from Energy3D. To speed up the search processes visualized in the videos, I chose the daily energy use as the objective function and only optimized for the winter condition. The solutions optimized for the annual energy use are shown later in this article.



Figure 1: A location of the building recommended by GA if it is in Boston.
Figure 2: A location of the building recommended by GA if it is in Phoenix.
For the first example, the energy use of a building in an urban block depends on how much solar energy it receives. In the winter, solar energy is good for the building as it warms up the building and saves the heating energy. In the summer, excessive heating caused by solar energy must be removed through air conditioning, increasing the energy use. The exact amount of energy use per year depends on a lot of other factors such as the fenestration of the building, its insulation, and its size. In this demo, we only focus on searching a good location for a building with everything else fixed. I chose a population with 32 individuals and let GA run for only five generations. Figures 1 and 2 show the final solutions for Boston (a heating-dominant area) and Phoenix (a cooling-dominant area), respectively. Not surprisingly, the GA results suggest that the new building be placed in a location that has more solar access for the Boston case and in location that has less solar access for the Phoenix case.

Figure 3: Window sizes of a building recommended by GA for Chicago.
Figure 4: Window sizes of a building recommended by GA for Phoenix.
For the second example, the energy use of a building depends on how much solar energy it receives through the windows and how much thermal energy transfers through the windows (since windows typically have less thermal resistance than walls). In the winter, while a larger window allows more solar energy to shine into the building and warm it up during the day, it also allows more thermal energy to escape through the larger area, especially at night. In the summer, both solar radiation and heat transfer through a larger window will contribute to the increase of the energy needed to cool the building. And this complicated relationship changes when the solution is designed for a different climate. Figures 3 and 4 show the final solutions for Chicago and Phoenix as suggested by the GA results, respectively. Note that not all GA results are acceptable solutions, but they can play advisory roles during a design process, especially for novice designers who do not have anyone to consult with.

In conclusion, artificial intelligence such as GA provides automated procedures that can help designers find optimal solutions more efficiently and thereby free them up from tedious, repetitive tasks if an exhaustive search of the solution space is necessary. Energy3D provides an accessible platform that integrates design, visualization, and simulation seamlessly to demonstrate these potential and capabilities. Our next step is to figure out how to translate this power into instructional intelligence that can help students and designers develop their abilities of creative thinking.

3 Reasons to Vote in STEM For All Video Showcase

We’re thrilled to present three videos in the National Science Foundation STEM for All Video Showcase from May 14 to 21! We invite you to view the videos and join the conversation about research projects that are transforming the STEM educational landscape. Please vote for our videos through Facebook, Twitter, or email!

Geniventure

Geniventure

Geniventure is a free online game with an Intelligent Tutoring System that engages students from middle school through higher education in genetics and heredity by saving virtual dragons from extinction. Through scaffolded virtual investigations, students explore the physical traits that result from allele combinations, then zoom into cells and manipulate the proteins that ultimately give rise to those traits.

Watch & Vote


InSPECT

Integrating Computational Thinking and Experimental Science

InSPECT supports the integration of computational thinking (CT) in experimental science with a novel technology-enhanced curriculum, and examines how students engage in CT using these tools for inquiry. InSPECT is designing a series of open-ended high school biology experiments using inexpensive DIY lab instruments developed in partnership with Manylabs, including Dataflow—a digital tool for experimental control and data acquisition using Internet-of-Things sensors.

Watch & Vote


Teaching Environmental Sustainability with Model My Watershed

With our collaborators at  and Stroud Water Research Center, we’re developing interdisciplinary, place-based, problem-based, hands-on resources and models aligned to NGSS to promote watershed stewardship, geospatial literacy, and systems thinking. We’re introducing middle and high school students to environmental and geospatial science that engenders critical incidents and encourage students to pursue environmental and geoscience careers.

Watch & Vote

Exploring Hawai’i (and the rest of Earth) with Seismic Explorer

Kilauea, Hawai’i’s youngest and most active volcano, has been continuously erupting since 1983. But it made news again recently with large earthquakes and lava fountains erupting in residential areas.

Have you ever wondered what’s going on with Kilauea? Can scientists predict when and where a volcano will next erupt?

You can use Seismic Explorer to explore the locations of volcanoes and earthquakes in the Hawaiian Islands. In this zoomed-in view from Seismic Explorer, you can see the locations of Hawai’i’s active volcanoes.

Seismic Explorer, zoomed in to show the Hawaiian Islands. Triangles mark the locations of Hawai’i’s five active volcanoes. Triangles are color-coded by most recent eruption date.

Launch Seismic Explorer and click the Play button to show the earthquakes that have occurred in the Hawaiian Islands since January 2018. Using just the earthquake data, can you tell when and where the volcano erupted?

Why is Kilauea erupting? How did the Hawaiian Islands form?

The Hawaiian Islands are the result of a geological hotspot. At hotspots, magma rises to the surface and breaks through Earth’s crust, resulting in volcanoes.

If you choose the ocean basemap map type in Seismic Explorer, you’ll be able to see that the Hawaiian Islands are on one end of a long chain of underwater mountains. (The lighter colors represent higher elevations.)

Seismic Explorer view of the North Pacific Ocean basin, ocean basemap view. Hawai’i is at one end of a long chain of underwater mountains. Use the zoom tools to zoom out to a larger view.

The Hawaiian Islands are the youngest mountains in this chain. The current volcanic activity shows that the Hawaiian Islands are still being formed.

So, why is there a chain of islands instead of one big island? Has the hotspot moved? You can use Seismic Explorer to get some clues to answer this question as well.

Hawai’i is located in the middle of the Pacific Plate, one of Earth’s many tectonic plates. Tectonic plates are composed of the crust (the part of Earth you can see) and the upper part of the mantle. Using the plate boundaries and plate movement data, you can explore the motion of the Pacific Plate.

Seismic Explorer, zoomed out to show the Pacific Ocean. Showing plate boundaries and plate movement. Movement arrows show that the Pacific Plate is moving to the northwest.

The detailed plate movement arrows show that the Pacific Plate has been moving to the northwest. The hotspot has remained stationary, and as the Pacific Plate has moved, the island chain has grown. Older islands in the chain were moved away from the hotspot, and over millions of years, they were eroded so that they’re no longer above sea level.

Even though Seismic Explorer shows only the current activity, you can use the data to make inferences about the past and predictions about the future.

Using data to figure out the past
You may have noticed that there is a bend in the underwater island chain. Can you explain what happened there? How must the Pacific Plate have been moving at that time?

Using data to predict the future
Can you use the plate motion data to predict the location of the next active volcano in this chain?

Using Seismic Explorer to explore other areas on Earth

Geological hotspots are the least common places for volcanoes. Most volcanoes on Earth are the result of convergent plate boundaries, where two plates move towards each other, like the volcanoes of Japan and the Andes Mountains of South America. Some volcanoes form along divergent boundaries, like the volcanoes of Iceland.

You can use Seismic Explorer to explore all of Earth’s volcanoes and earthquakes. Try using the cross section tool to get a 3D underground view of earthquakes.

Seismic Explorer, showing the area of the cross section.

Seismic Explorer, showing a 3D view of the earthquakes under Hawai’i.

How are the patterns of earthquakes different at different types of volcanoes? Compare the Hawaiian volcanoes to volcanoes in the Andes to volcanoes in Iceland. (Spoiler alert – the views are very different!) Along the plate boundaries, make sure to draw your cross section perpendicular to the lines of earthquakes – that way, you’ll be able to see the patterns of earthquakes along each boundary.

If you’re interested in exploring more about plate tectonics, earthquakes, and volcanoes, check out the GEODE activities in the STEM Resource Finder. You’ll find links to models, like Seismic Explorer, and classroom activities. You’ll also find links to sign up to be a field test teacher and help us test the latest plate tectonics models and curricula.

The GEODE project, funded by the National Science Foundation, is developing computational models of plate tectonics and associated curricula for the middle and high school level.

 

 

Sharing Research Results and Special Poster Session to Commemorate Robert Tinker at AERA 2018

Several researchers and senior scientists from the Concord Consortium traveled to New York City in April for the annual meeting of the American Educational Research Association (AERA). A record 17,148 educators and researchers around the world attended AERA 2018, which offered 900 sessions in eight hotels centered in bustling Times Square.

A poster with research from Hee-Sun Lee and Amy Pallant focused on the design of formative science assessments in which a system interprets students’ constructed scientific arguments via natural language processing and scores them automatically using machine learning technologies to provide tailored feedback and facilitate revision and improvement. Paul Horwitz and colleagues described digital learning environments involving collaborative problem-solving, using an evidence-centered design framework.

Carolyn Staudt, together with collaborators from Millersville University of Pennsylvania and Stroud Water Research Center, shared promising results for teaching environmental sustainability using their Model My Watershed software. They found that place-based watershed modeling is an effective tool for increasing students’ understanding of watersheds, encouraging personal environmental action, and serving as a critical incident for watershed engagement.

Angela Kolonich from the CREATE for STEM Institute at Michigan State University and Dan Damelin from the Concord Consortium presented results from the Interactions project, a collaboration between the two institutions. They shared findings from a study pairing an educative, project-based, 3D science curriculum with professional learning of inclusive 3D instruction. Findings indicate that providing teachers with sustained, research-based curricular and instructional supports assists them in making instructional decisions that bridge 3D learning with the unique needs of their students.

Sherry Hsi and Hee-Sun Lee participated in a structured poster session on the theme of knowledge integration in science. The session, chaired by Professor Marcia Linn and joined by colleagues and alumni from the Technology-Enhanced Learning Collaborative and WISE Group at the University of California, Berkeley, demonstrated multiple examples of how knowledge integration as a curricular design framework builds toward a more coherent understanding of the many ideas students have about science. Discussants Bat-Sheva Eylon and Esther Bagno attended the session from the Weizmann Institute of Science in Israel to reflect upon the influence the knowledge integration framework has had on the design of learning in middle and high school science instruction, and on the professional development of teachers internationally.

On the final morning of AERA, a special poster session commemorated the Concord Consortium’s founder, Robert Tinker. Posters celebrated his immeasurable impact in educational technology over the past 40 years and highlighted his continued influence on the field. As the international group of 14 presenters provided brief overviews of each poster, the sharing quickly gave way to tributes and personal stories about how Bob had inspired, nurtured, and fueled such a diverse variety of personal research trajectories and programs. (Read more stories and share your own at https://rememberingbob.concord.org)

AERA Special Poster Session Presenters

The posters included examples of probeware, model building, online professional development, mixed-reality applications, video-based data for inquiry, tools for learning about solar energy, geosciences, biology, thermodynamics, and more. As a whole, the session featured crosscutting themes of simulation and modeling, pedagogical content models, innovative assessment, learner analytics, collaborative learning, and inquiry-based laboratories. Chris Hoadley from NYU and Marcia Linn from UC Berkeley closed the session with heartfelt remarks about Bob’s passion for building powerful tools, his knack for initiating productive and collaborative partnerships, and his persistent belief that students were capable of doing real science with authentic tools if given the opportunity to play, be curious, and ask questions.

To continue to honor Bob’s legacy at future AERA conferences, Chad Dorsey and Sherry Hsi announced the Robert F. Tinker Scholarship for emerging scholars during the AERA 2018 Joint SIG Business meeting of the Learning Sciences and Advanced Technologies for Learning. This award will be presented annually to a graduate student or postdoc member of the Learning Sciences and/or Advanced Technologies for Learning SIG to support travel to deliver an accepted AERA poster or presentation. We look forward to this ongoing opportunity to build the field along the themes that were important to Bob: tools for inquiry, learning and collaboration, data explorations, sustainability and the environment, tinkering with models, playful experimentation, online learning, and learning everywhere.

Janice Gobert and Paul Horwitz

Janice Gobert, Rutgers University and Paul Horwitz

Sherry Hsi, Chris Hoadley, and Marcia Linn

Sherry Hsi, chair of the Robert Tinker poster session, with discussants Chris Hoadley and Marcia Linn

AERA Special Poster Session

AERA Special Poster Session

National Teacher Appreciation Day & High-Adventure Science: Preparing students for real-world problems

“Thinking is hard work,” laughs Stephanie Harmon, who teaches physics, Earth science, and physical science at Rockcastle County High School in Kentucky. One of her primary goals is teaching students to think.“So much happens to us on a daily basis that we take for granted as long as everything is going okay,” she says. “What happens when something goes wrong? How do we make sense of that? What do we do about it? Science helps us foster critical thinking and problem-solving capacity . . . But you have to build that capacity in students. Science does that.”

In 2013, when she was looking for some robust Earth science materials, and wasn’t finding any, Stephanie discovered High-Adventure Science (HAS) and became a field-test teacher. “It was a relief,” she says. “There isn’t anything I could do in a traditional fashion that would even begin to mimic the experience that the students have using this.”

Water issues are real in her Kentucky community where there’s a serious problem with algae growth in the city water supply. Her class used the HAS fresh water module to guide collection of samples from a creek that runs through the high school property. “The module fits in with the big picture. It wasn’t just reading a bunch of articles about water quality, or looking to see what the book said. It became real.”

Photo by Amy Wallot/Kentucky Department of Education

Stephanie is consciously preparing students for issues they will face as adults: energy issues, water issues, clean air issues. “It can’t just be a school thing,” she insists. “The purpose of taking science classes is to be scientifically literate. What that means is you understand those common experiences that we all have: if you’re called to jury duty, you know how to make sense of forensic data; if you’re in the doctor’s office and you get a series of lab results, you know how to make sense of that information; when your neighbors detect radon gas in their basement, what does that mean you need to do? Those are the types of things we need to know, and that’s what scientifically literate means to me.”

From 2010-2013 Stephanie worked on the team that reviewed the Kentucky Next Generation Science Standards. In 2014 she won the Kentucky Science Teacher Association’s Outstanding High School Science Teacher award. And since 2016 she has been part of the team that developed a 3D NGSS state science assessment. During the 2018 NSTA national conference in Atlanta, she talked to a packed room about scientific argumentation and modeling using High-Adventure Science. We’re gratified that such an accomplished teacher uses our STEM resources.

Happy Teacher Appreciation Day to Stephanie and all teachers!

* * *

High-Adventure Science addresses big issues in Earth and space science: climate change, fresh water, land management, clean air, and more. It emphasizes the excitement of scientific discovery using the same methods that practicing scientists use so students can see science as a dynamic, evolving process. HAS lessons and interactives are available free online, including some in Spanish. They are also on the National Geographic Education website.

¡El módulo de clima está disponible en español! (The climate module is available in Spanish!)

We’re thrilled to announce that the popular High-Adventure Science (HAS) climate module is now available in Spanish. Many thanks (muchas gracias) to Penny Rowe (University of Santiago of Chile) and Cristián Rizzi (Universidad de San Andrés, Argentina) for taking this on! The Spanish-language version directly parallels the existing English-language version.

Spanish-language version of the HAS climate module

English-language version of the HAS climate module

 

 

 

 

 

 

 

 

 

The HAS climate module poses the question, What is the future of Earth’s climate? This is a question to which climate scientists do not (yet) know the answer; while there is ample evidence that Earth is warming, there is uncertainty about how much the temperature will increase. There is continued active research to learn about all of the factors that affect Earth’s climate and their interactions. And it’s an interesting question, one with an answer that affects everyone on the planet.

These are types of questions that are posed by High-Adventure Science modules – big, interesting, unanswered questions about Earth and environmental science topics, coupled with real-world data and computational models. High-Adventure Science was funded by grants from the National Science Foundation.

While cutting-edge science is interesting, it can be challenging for non-scientists (students and adults alike) to understand. That’s why we scaffolded the data and models. Text and a series of guided questions help learners to figure out how factors such as carbon dioxide and water vapor affect temperature and each other (through positive feedback loops). Students can use the models to run experiments – what might happen if greenhouse gas emissions decreased by 50%, for example?

Model in High-Adventure Science climate module. What might happen to the temperature if greenhouse gas emissions decrease by 50%?

 

Additional scaffolding comes in the form of uncertainty-infused scientific argumentation items. Climate science, like all science, has uncertainties. Just because some of the scientific understandings are uncertain does not mean that no conclusions can be drawn, however. We don’t shy away from the complexity, but instead help students to consider some of the reasons for uncertainty with the data. For example, the real-world temperature data include error bars. Students are asked to consider the year-to-year variations, as well as the longer, multiyear trends. Additionally, students are asked to consider why the size of the error bars is different across different time periods, including methods of data collection, and how that affects the strength of conclusions that can be reached from the data.

Real-world data embedded in the High-Adventure Science climate module. Average temperature change, compared to 1950-1980 baseline, from 1880 to 2010. NASA Goddard Institute for Space Studies.

In each of the embedded four-part argumentation items, students (1) make claims based on the data, (2) explain their claims in light of that data, (3) rate their level of certainty with their explanations, and (4) explain what affected their certainty levels. Rather than turn students into “climate deniers,” this process has helped students to more deeply learn the underlying science. In our research, students who used the High-Adventure Science climate module improved their abilities to formulate good, data- and evidence-supported scientific arguments, even with an uncertain science.

You can find both the English- and Spanish-language High-Adventure Science climate modules, as well as other High-Adventure Science modules and models, in the STEM Resource Finder at learn.concord.org/has.

High-Adventure Science project makes significant impact

With renewed attention to global environmental challenges, understanding how Earth’s systems work is essential to both thinking about those challenges and finding potential solutions. Teaching about human interactions with Earth systems requires that students apply relevant science concepts to these challenges. For example, students should understand the water cycle when exploring freshwater distribution, the atmospheric greenhouse effect when studying climate change, and nutrient cycling when investigating soil quality and food production. In the High-Adventure Science project, students have the opportunity to explore these and other Earth systems and discover how system components interact to produce emergent behaviors.

One promising way to engage students is to have them consider important unanswered questions that scientists around the world are actively exploring. In High-Adventure Science modules, students learn about the human impact on Earth’s systems. Students explore science that is relevant to their lives and engage in authentic science practices, such as making predictions and considering the variability and uncertainty associated with data and predictions based on the data.

High-Adventure Science, funded through a series of grants from the National Science Foundation, developed a plan for incorporating contemporary science into classrooms. The resulting curricula and dynamic computer models enable students to become thoughtful, scientifically literate citizens.

We developed six online curricular modules for middle and high school Earth and environmental science classes. The modules cover freshwater availability, land resource management, air quality, climate change, energy choices, and the search for exoplanets.

Five design principles guided the development of the modules:

  • Engage students in real-world frontier science
  • Use open-ended questions to frame each module
  • Have students interpret data collected by scientists
  • Immerse students in experimentation with dynamic computer models depicting complex Earth systems
  • Support students’ evidence-based scientific argumentation while considering sources of uncertainty

Our research focused on scientific argumentation with uncertainty and system dynamics thinking. Our analysis of several thousand students showed that students significantly improved their scientific argumentation ability after engaging with High-Adventure Science modules.

As part of the scientific argumentation research, we developed a taxonomy of students’ uncertainty attributions. This taxonomy is the first such attempt to characterize the developmental trajectory of secondary school students’ uncertainty attribution. The taxonomy represents the degree to which students understand the role of uncertainty in science, in particular the strengths and limitations of the evidence used in a scientific argument.

We also studied students’ system dynamics thinking to assess their understanding of complex systems and developed rubrics to categorize students’ written explanations into qualitatively different levels. This framework tracked students’ uses of stocks and flows when they explained causal mechanisms associated with complex systems.

We’re delighted that the six web-based modules are available at the National Geographic Society website as well as through the High-Adventure Science website.

Join the nearly 100,000 users of these research-based modules and bring the excitement of frontier science to your secondary Earth science or environmental science classroom!

14 Chances at NSTA 2018 to Learn about Our Work

Are you attending the 2018 NSTA annual conference in Atlanta March 15-18? We’re leading 10 presentations at the Georgia World Congress Center (GWCC) and the Omni Atlanta Hotel at the CNN Center and one short course at the Westin Peachtree Plaza Hotel. Something for everyone, from modeling science in kindergarten to data science education. Join us for one or more sessions. We’re giving out free STEM resources for K-14! Schedule is below.

Calling all teachers! We want to talk with you at #NSTA18. Tell us what you like about our STEM resources and what could be improved. Don’t miss this chance to give us a piece of your mind! Please complete this short survey to register your interest in connecting with us. We’ll contact you to arrange a short meeting in Atlanta.

You can also tweet your thoughts to @ConcordDotOrg or email projects@concord.org.

THURSDAY, March 15

8:00-9:00 AM, GWCC, A401
“Sensing Science Through Modeling Matter for Kindergarten Students”
Discover models, probes, and online interactive stories.

12:30-1:00 PM, GWCC, A410
“Argumentation and Modeling in Earth Science Using Free Online Modules”
Free Earth system and environmental science simulations and curricula.

3:00-6:00 PM, Westin Peachtree Plaza Hotel, Chastain C
SHORT COURSE SC-1: If You Can Think It, You Can Model It
Use our popular SageModeler for modeling complexity and examining behavior.
You can purchase tickets online for this course.

5:00–6:00 PM, GWCC, A408
“Using Models to Support STEM Learning in Grades K–5: Examples and Insights from NSF’s DRK–12 Program”
Discussion centers on research-based examples of how students can engage in modeling in the elementary grades.

FRIDAY, March 16

8:00 AM, GWCC, A301
“Precipitating Change: Embedding Weather into the Middle School Science Classroom”
Everybody has weather! Make meteorology part of STEM learning.

8:00 AM, GWCC, A402
“Using Models to Support STEM Learning in Grades 6-12: Examples and Insights from NSF DRK-12 Program”
What does the research say about modeling practice?

9:30 AM, GWCC, C213
“Powerful Free Simulations for 3-D NGSS Teaching”
Free tips and resources for molecular simulations and curricula.

9:30 AM, GWCC, A301
“Teaching Environmental Sustainability Using a Free Place-Based Watershed Model”
Explore your local watershed with a web-based application.

2:00 PM, GWCC, B102
“NGSS@NSTA Forum Session: Interactions – A Free 3-D Science Curriculum for 9th Grade Physical Science
Atoms and molecules are the foundation to explaining scientific and everyday phenomena.

4:00 PM, Dantanna’s Downtown, One CNN Center, Suite 269
Join our informal Data Science Education Meetup. Get a bite to eat and talk with others about how to empower students with data science skills. And don’t miss tomorrow’s 9:30 AM presentation on data science and CODAP. RSVP dset@concord.org

5:00–6:00 PM, GWCC A301
“Model My Watershed: Using Real Data to Make Watershed Decisions”
Learn about an exciting free online modeling application that gives anyone the ability to use STEM practices to explore their local watershed.

SATURDAY, March 17

9:30 AM, Omni Atlanta Hotel at the CNN Center, Dogwood A
“Introducing Students to Data Science with Simulations & Interactive Graphing”
No coding required! Learn about CODAP (Common Online Data Analysis Platform), a free online tool for data analysis.

12:30 PM, GWCC, A313
“Systems Thinking, Modeling and Climate Change”
Explore a free, open-source modeling tool for climate change. Free e-book, too!

2:00 PM, GWCC, C206
“Liven Up Your Labs with Free 3-D Learning Tools and Resources”
Learn science by doing science. Adapt your labs using new tools.