Category Archives: Main Blog

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

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

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.

Uncertainty: Real-world examples

When you live in New England in the winter, you pay attention to the forecast. Large snowstorms can make travel near impossible. Heavy snow and blowing winds can cause coastal flooding, power outages, and roof collapses.

The National Weather Service (NWS) exists to “provide weather, water, and climate data, forecasts and warnings for the protection of life and property and enhancement of the national economy.” They’re my favorite source for weather forecasts. And yesterday morning (February 26), they gave me one more reason to appreciate them.

You see, there’s a big storm that may (or may not) be coming later this week. Last week, some forecasters (not from the NWS, it should be noted) were calling for blizzard conditions – seven to eight days from any potential storm! That’s lots of planning time, but is it valid to make plans based on seven-day forecasts?

Yesterday morning’s post from NWS Boston included this graphic and description:


Note the words “POTENTIALLY” and “LOW CONFIDENCE FORECAST”. Clicking through to look at the details, you can learn a bit about the model information on which they’re basing their forecast. If you don’t know a lot about meteorology, you can get lost in the abbreviations and details of the models. But the meteorologists have made it easy to understand their shifting confidence by explaining how model runs have shifted as they compile more information. They’ve put a bit of this information into their graphic, illustrating that the model error decreases as more information is known closer to the event.

On a much more novice level, this is what students do when they use High-Adventure Science (HAS) activities. (High-Adventure Science, a National Science Foundation-funded project, produced six NGSS-aligned curricular modules on cutting-edge Earth and environmental science topics. These free, online curricula incorporate real-world data and computational models and are appropriate for middle and high school classrooms.) In HAS activities, students run models and make claims based on data from the model runs. They rate their confidence with their answers and explain the factors that led them to that confidence level.

In our research, we found that when students were asked to write about uncertainty in the context of scientific arguments, they improved their overall argumentation ability. That suggests that teaching about uncertainty in science enables students to better understand real-world science – including weather forecasts.

Will we experience a big snowstorm later this week? I’m confident that the staff at NWS Boston will keep an eye on the model runs, updating me (and the rest of the Boston area) with their forecasts and levels of certainty with the data. In the meantime, check out a High-Adventure Science activity to enhance your students’ scientific thinking skills!




Virtual Solar Grid adds Crescent Dunes Solar Tower

The Crescent Dues Solar Tower as modeled in Energy3D
A light field visualization in Energy3D
A top view
The Crescent Dunes Solar Power Tower is a 110 MW utility-scale concentrated solar power (CSP) plant with 1.1 GWh of molten salt energy storage, located about 190 miles northwest of Las Vegas in the United States (watch a video about it). The plant includes a whopping number of 10,347 large heliostats that collect and focus sunlight onto a central receiver at the top of a 195-meter tall tower to heat 32,000 tons of molten salt. The molten salt circulates from the tower to some storage tanks, where it is then used to produce steam and generate electricity. Excess thermal energy is stored in the molten salt and can be used to generate power for up to ten hours, providing electricity in the evening or during cloudy hours. Unlike other CSP plants, Crescent Dunes' advanced storage technology eliminates the need for any backup fossil fuels to melt the salt and jumpstart the plant in the morning. Each heliostat is made up of 35 6×6 feet (1.8 m) mirror facets, adding up to a total aperture of 115.7 square meters. The total solar field aperture sums to an area of 1,196,778 square meters, or more than one square kilometer, in a land area of 1,670 acres (6.8 square kilometers). That is, the plant is capable of potentially collecting one seventh of all the solar energy that shines onto the field. Costing about $1 billion to construct, it was commissioned in September 2015.

A close-up view of accurate modeling of heliostat tracking
Since its inception in January 2018, our Virtual Solar Grid has included the Energy3D models of nearly all the existing large CSP power plants in the world. That covers more than 80 large CSP plants capable of generating more than 11 TWh per year. The ultimate goal of the Virtual Solar Grid is to mirror every solar energy system in the world in the computing cloud through crowdsourcing involving a large number of students interested in engineering, creating an unprecedentedly detailed computational model for learning how to design a reliable and resilient power grid based completely on renewable energy (solar energy in this phase). The modeling of the Crescent Dunes plant has put our Energy3D software to a stress test. Can it handle such a complex project with so many heliostats in such a large field?
A side view

Near the base of the tower
Over the shoulder of the tower
The solar field
This became my President's Day project. To make this happen, I had to first increase the resolution of Google Maps images supported in Energy3D. A free developer account of Google Maps can only get images of 640 × 640 pixels. When you are looking at an area that is as big as a few square kilometers, that resolution gets you very blurry images. To fetch high-resolution images from Google without paying them, I had to basically make Energy3D download many more images and then knit them together to create a large image that forms an Earth canvas in Energy3D (hence you see a lot of Google logos and copyrights in the ground image that I could not get rid of from each patch). Once I had the Earth canvas, I then drew heliostats on top of it (that is, one by one for more than 10,000 times!) and compared their orientations and shadows rendered by Energy3D with those shown in the Google Maps images. Now, the problem is that Google doesn't tell you when the satellite image was taken. But based on the shadows of the tower and other structures, I could easily figure out an approximate time and date. I then set that time and date in Energy3D and confirmed that the shadow of the tower in the Energy3D model overlaps with that in the satellite image. After this calibration, every single virtual heliostat that I copied and pasted then automatically aligned with those in the satellite image (as long as the original copy specifies the tower that it points to), visually testifying that the tracking algorithm for the virtual heliostats in Energy3D is just as good as the one used by the computers that control the motions of the real-world heliostats. Matching the computer model with the satellite image is essential as the procedure ensures the accuracy of our numerical simulation.

The solar field
After making numerous other improvements for Energy3D, the latest version (V7.8.4) was finally capable of modeling this colossal power plant. This includes the capability of being able to divide the whole project into nine smaller projects and then allow Energy3D to stitch the smaller 3D models together to create the full model using the Import Tool. This divide-and-conquer method makes the user interface a lot faster as neither you nor Energy3D need to deal with 9,000 existing heliostats while you are adding the last 1,000. The predicted annual output of the plant by Energy3D is 462 GWh, as opposed to the official projection of 500 GWh, assuming 90% of mirror reflectance and 25% of thermal-to-electric conversion.

One thing I had to do, though, was to double the memory requirement for the software from the default 256 MB to 512 MB for the Windows version (the Mac version is fine), which would make the software fail on really old computers that have only 256 MB of total memory (but I don't think such old computers would still work properly today anyways). The implication of this change is that, if you are a Windows user and have installed Energy3D before, you will need to re-install it using the latest installer from our website in order to take advantage of this update. If you are not sure, there is a way to know how much memory your Energy3D is allocated by checking the System Information and Preferences under the File Menu. If that number is about 250 MB, then you have to re-install the software -- if you really want to see the spectacular Crescent Dunes model in Energy3D without crashing it.

With basically only the three Ivanpah Solar Towers left to be modeled and uploaded, the Virtual Solar Grid has nearly incorporated all the operational solar thermal power plants in the world. We will continue to add new CSP plants as they come online and show up in Google Maps. In our next phase, we will move to add more photovoltaic (PV) solar power plants to the Virtual Solar Grid. At this point, the proportion of the modeled capacity from PV stands at only 8% in the Virtual Solar Grid, compared with 92% from CSP. Adding PV power plants will really require crowdsourcing as there are many more PV projects in the world -- there are potentially millions of small rooftop systems in existence. On a separate avenue, the National Renewable Energy Laboratory (NREL) has estimated that, if we add solar panels to every square feet of usable roof area in the U.S., we could meet 40% of our total electricity need. Is their statement realistic? Perhaps only time can tell, but by adding more and more virtual solar power systems to the Virtual Solar Grid, we might be able to tell sooner.

Everyday Inquiry with R: Is Yogurt-X Expensive?

To kick off this Everyday Inquiry with R series, I’d like to recount a conversation between my friend Eric and me about one of Americans’ favorite foods, yogurt.

R is a free programming language for statistical computing and graphics, which we’re using in our new National Science Foundation-funded CodeR4MATH project to research the development of students’ computational thinking and mathematical modeling competencies.

The other day I showed Eric my yogurt collection. He was amazed that I had tried so many different brands and flavors.

Eric: Which one is your favorite?
Jie: Currently yogurt-X (a pseudonym of my favorite brand).
Eric: How much is it?
Jie: $1.59
Eric: That’s expensive.
Jie: No, it’s not.
Eric: It is. Take a look at the prices of all the products you collected.

I had previously stored the prices in a vector called ‘yogurt_price’. Below is the simple R code to do that.

# create a vector 'yogurt_price' consisting of yogurt prices
yogurt_price = c(1.13, 2.00, 1.69, 1.79, 2.09, 1.00, 1.00, 0.60, 1.00, 1.11, 1.79, 3.19, 1.79, 1.99, 3.69, 2.79, 0.60, 1.79, 1.99, 4.09, 4.49, 4.49, 0.89, 0.89, 1.99, 2.09, 2.09, 2.09, 2.09, 0.69, 1.59, 0.69, 0.69, 0.69, 1.00, 1.19, 7.69)

Jie: Here they are (typing yogurt_price in R console to view the data).

# view a vector

[1] 1.13 2.00 1.69 1.79 2.09 1.00 1.00 0.60 1.00 1.11 1.79 3.19 1.79 1.99

[15] 3.69 2.79 0.60 1.79 1.99 4.09 4.49 4.49 0.89 0.89 1.99 2.09 2.09 2.09

[29] 2.09 0.69 1.59 0.69 0.69 0.69 1.00 1.19 7.69

Eric: Nice. How many products did you collect?
Jie: There are…(calling the length() function)

# count the number of elements in a vector

[1] 37

Jie: 37.
Eric: Oh, that’s a lot. Hmmm, which one is the most expensive (trying to eyeball the greatest number)?
Jie: Well, let me show you…(calling the sort() function)

# sort the elements in a vector

[1] 0.60 0.60 0.69 0.69 0.69 0.69 0.89 0.89 1.00 1.00 1.00 1.00 1.11 1.13

[15] 1.19 1.59 1.69 1.79 1.79 1.79 1.79 1.99 1.99 1.99 2.00 2.09 2.09 2.09

[29] 2.09 2.09 2.79 3.19 3.69 4.09 4.49 4.49 7.69

Eric: Wow, $7.69? And the least expensive is only $0.60. What’s the normal price then?
Jie: Normal? Well, there are a number of $0.69s, $1.00s, $1.79s, and $2.09s. Let me show a frequency count (calling the table() function).

# generate a table of counts for each element in a vector


0.6 0.69 0.89   1 1.11 1.13 1.19 1.59 1.69 1.79 1.99   2 2.09 2.79 3.19

   2   4   2   4   1   1   1   1   1   4   3   1   5   1   1

3.69 4.09 4.49 7.69

   1   1   2   1

Jie: There are 5 yogurts priced at $2.09. Is that normal?
Eric: Hmmm…there are four $0.69, four $1.00, and four $1.79. $2.09 seems to be on the expensive end.
Jie: Let’s plot the data points on a number line and see where most prices fall (calling the stripchart() function).

# draw a strip chart for a vector

Eric: What is this?
Jie: The x axis is price. Each little square stands for a data point. Some of them are overlapping because the default method is ‘overplot’. Let me make a few changes. We’ll use the ‘stack’ method to stack up data points of the same value. Also, let’s use solid dots instead of hollow squares and set some distance between the points.

  method = "stack",   # stack up data points of the same value
  pch = 16,           # use solid round label for the points
  offset = 0.5,       # set the distance between points at 0.5
  at = 0              # set the location of points to be near the x axis

Eric: Beautiful! Looks like there are one, two, three…(counting dots between 0 and 1) 12 products priced at $1.00 or below. And there are…
Jie: You want to see how many products fall in each dollar bracket? Let’s pull out the histogram (calling the hist() function).

# draw a histogram for a vector

Eric: That saves me a lot of time counting. There are 12 products between $1.00 and $2.00. Your yogurt-X is $1.59. I am sure there are a lot below $1.50. Can you narrow the bins so I can see how many cost less than $1.50?
Jie: Sure! (specifying the breaks argument for the hist() function)

  breaks = 16   # set the number of breaks or bins at 16

Eric: See? There are 12 (between $0.50 and $1.00) and 3 (between $1.00 and $1.50), a total of 15 under $1.50. Your yogurt-X is the 16th and there are 37 yogurts in total…
Jie: So yogurt-X is on the cheap side.
Eric: Wait a second, these are over $3.00 (pointing to the middle part of the histogram)? I have never seen yogurt that expensive. What are they?
Jie: Oh, oops… (checking yogurt collection table), they are family-sized products. My bad.
Eric: Yeah, that’s not even a fair comparison.
Jie: Right, we need to look at products of the same size.
Eric: Also, perhaps the same yogurt type, flavor, organic or not, etc.
Jie: Exactly.

Obviously, our inquiry did not start out to be very scientific. But that’s okay. It is the process of seeking knowledge. We explore just because we are curious. We argue just because it is stimulating and fun.

And there is nothing exclusive about R. You don’t need a degree or a job to use it. As long as you have some questions and data that may answer those questions, R is amazingly empowering.

I plan to write on this Everyday Inquiry with R theme for K-12 teachers and students. If you have suggestions, please leave a comment. In the meantime, I encourage you to try R and discover its power on your own.

Note: I’m excited that this blog post will also be published on, which aggregates content contributed by bloggers who write about R. Learn more about all things R.

This material is based on work supported by the National Science Foundation under Grant No. 1742083. 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.

Students Learn Genetics with Geniverse  

How well do students learn genetics concepts using Geniverse in their high school biology class?

Scarlett, the Geniverse female avatar, and Arrow the dragon journey to the remote Drake Breeder’s Guild.

With funding from the National Science Foundation, we sought to understand the contributions and challenges of teacher implementation of digital games by studying Geniverse, an immersive, game-like learning environment that infuses virtual experimentation in genetics with narrative elements. Our research, published in the Journal of Science Education and Technology, used a quasi-experimental design to study the replacement of existing high school biology genetics lessons with Geniverse over a three- to six-week period.

Students determine patterns of inheritance by breeding drakes, the model organism for dragons.

Results indicate that overall, students’ genetics learning increased significantly and was commensurate with genetics learning in comparison classrooms. In addition, when Geniverse was implemented as intended, student learning of genetics content was significantly greater than in the comparison, “business-as-usual” group. However, only 25% of students in the study progressed through the game to the minimum required level. Further, a wide range of levels of Geniverse implementation resulted in no significant difference between the groups as a whole.

We discovered multiple reasons for low levels of student progression, including teacher difficulties managing students moving through the game at different speeds, challenges with teaching science via student-led inquiry and exploration rather than through teacher-led, whole-class instruction, as well as multiple issues around teaching with technology. To address these issues, two new NSF-funded projects, GeniGUIDE and GeniConnect, are investigating the use of real-time student tracking, data analytics, and scaffolding with an intelligent tutoring system to assist teachers with implementation.

Needed Math for the 21st Century STEM Workplace

There are three kinds of mathematics: the math that’s taught, the math that’s learned, and the math that’s needed in the 21st century STEM workplace. With support from the Advanced Technological Education Program at the National Science Foundation, Michael Hacker, Co-Director of the Center for STEM Research at Hofstra University, and I organized a conference to study why those three “maths” are not the same.

Held in Baltimore from January 12th through the 15th, the conference attracted 46 attendees drawn from three groups: math educators, STEM content instructors, and STEM employers. Three fields of STEM employment were represented: Information and Communication Technology, Biotechnology, and Advanced Manufacturing.

There is ample evidence (see, for example, “Still Searching: Job Vacancies and STEM Skills”) that companies in these and other STEM-related fields are finding it difficult to find qualified employees for entry-level jobs. This is due in part to the poor math skills of prospective candidates, and – perhaps even more telling – their lack of confidence in their ability to “do” math. In this context, the objective of the meeting was to solicit from employers examples of problems that prospective recruits often could not solve. The meeting would then collectively examine those problems, identify the underlying relevant mathematical concepts and skills, and explore possible explanations for why high school and even two- and four-year college graduates find the problems so challenging.

A complete reporting of the findings of the conference must await our analysis of the data we collected over the course of two days of intense discussion. However, it is already evident that real-world challenges, such as those described by the employers, differ from the math problems that most students encounter in formal school settings.

An example – one of many – may illuminate these differences.

An employee in a communications technology firm is tasked with providing a commercial space, consisting of several offices as well as other rooms, with wireless Internet access. The tools available consist mainly of access points and routers, the former connecting multiple devices using radio frequency communication, the latter directing information between those devices and an Internet service provider (ISP). Access points have a limited range and their locations must be selected so that those ranges overlap, providing connectivity to every device on the network as well as to one or more routers. Routers, in turn, require connectivity to the ISP.

At first glance, the problem seems simple enough: just place the access points close enough to one another so that their ranges overlap. But real-world complications soon arise.

To save money, the number of access points should be minimized. Further, they require power so installing them in some locations may result in wiring expenses. The range of each access point may be affected by the materials used for interior walls or by metallic structures such as elevators or vaults. Privacy and security concerns dictate that access to the network be restricted, as much as possible, to the premises of the customer. Some locations within those premises – e.g., conference rooms – may require greater bandwidth than others.

These and other real-world considerations are not, strictly speaking, mathematical in nature, but insofar as they constrain the set of acceptable solutions, they require mathematical skills – e.g., modeling – that may be foreign to many would-be network technicians. Moreover, although the calculations required consist primarily of arithmetic operations on numbers (signed integers, decimals, and fractions), the semantics behind these calculations – unit conversions, use of the Pythagorean Theorem to compute point-to-point distances, algorithms for computing overall costs – are not explicitly called out in the statement of the problem.

Thus, even though the problem appears to require no more than middle school math and Algebra 1, it differs from the problems commonly encountered in traditional classes in those subjects.

  • The statement of the problem does not contain all the information required to solve it and may in fact contain irrelevant information.
  • The mathematical concepts and skills required are not spelled out (in contrast to the problems found at the end of the chapter in a math textbook, all of which involve the specific concept covered in that chapter).
  • The problem is multi-step and involves multiple variables.
  • The problem may have many solutions of varying utility, rather than a single “right” one.

A major finding of the conference was that the kind of mathematics encountered in each of the three domains represented (ICT, biotech, and manufacturing) involved contextualized problems similar to the one described above. Thus, an important barrier to success in these fields may arise from the features of such problems that we have identified.

Are there ways in which educational technologies such as those pioneered, deployed, and investigated by the Concord Consortium could help students to acquire the relevant, contextualized problem-solving skills? A major outcome of the conference may turn out to be a number of proposals aimed at answering that question.

Computational Thinking in Biology: What is an InSPECT Dataflow Diagram?

Integrating computational thinking into core science content and practices is a major goal of our InSPECT project, which is developing hands-on high school biology investigations using simple electronic sensors with Internet of Things (IoT) connectivity—a far cry from the simple germination experiments students usually encounter.

An article in the Fall 2017 Concord Consortium newsletter (“Science Thinking for Tomorrow Today”) describes the overall InSPECT project. Let’s take a closer look at a unique and powerful component of the project: virtual programming using a dataflow diagram.

The Dataflow interface enables students to do virtual programming within the browser-based interface. 

Dataflow diagrams have been around since the 1970s. They’re a visual model of the “flow” of data through a system. InSPECT has created a diagramming environment called Dataflow, the first version of which was developed in partnership with Peter Sand at Manylabs, that is much more than boxes and arrows on a page; components come alive when wirelessly synced to sensors, whose numerical data are displayed on the screen in real time as it’s recorded.

An eco-column activity includes sensors plugged into a Raspberry Pi computer. Dataflow automates data collection and initiates actions, such as raising or lowering temperature.

InSPECT is piloting an eco-column activity that includes electronic sensors plugged into a low-cost Raspberry Pi computer the size of a credit card. The sensors collect data about conditions inside the eco-column chambers, such as humidity and oxygen levels, sending it wirelessly over the Internet to Dataflow, which can automate data collection 24 hours a day, uninterrupted and unattended.

With the addition of actuators, Dataflow also can be programmed to create actions based on the sensor data. Students control the real-world actuators by defining variables and setting up conditionals that are entered directly into Dataflow’s simple, visual interface, which can run in any browser on a Mac or PC platform. For example, students can program a Dataflow diagram to adjust the current to a Peltier cooler in order to tweak the temperature of an eco-chamber when the sensor reading becomes too low or too high.

Finally, students can visualize, analyze, and interpret their data by exporting it to one of Concord Consortium’s most popular tools, CODAP (Common Online Data Analysis Platform), a free web-based environment for data analysis. Dataflow can be embedded directly into CODAP.

The use of sensor technology is still new to many biology classes, but our early research is based on the idea that when students collect data in real time, they make a powerful connection to the concepts they are studying. Using Dataflow, students not only learn to design the experiment and control the variables, they come to understand dynamic ecological systems.

The InSPECT project is currently recruiting biology teachers for our fall 2018 and spring 2019 studies. Our goal is to explore how to support integrated science practices and computational thinking in biology. We have several labs to select from involving photosynthesis, respiration, seed germination, and/or plant growth in chambers that can be from three classes to four weeks long. If you’re interested, contact us at

An Edited Google Doodle and a Genetics Mini-Mystery

Google’s Doodle on January 9 honored Har Gobind Khorana, a Nobel laureate whose work with DNA, RNA, and protein synthesis was seminal to deciphering the genetic code. Did anyone besides us (shout out to our own Eli Kosminsky!) notice that, midway through the day, the cartoon changed?

Google Doodle in the morning…


The same Doodle at night!

A comparison shows that the letters vanished from the paired strands draped across the doodle, leaving the flag-like bases letterless. A look at the letters depicted in the original doodle’s strands shows the letter “U,” for the base uracil, on both sides, making the drawing look like two paired strands of RNA. It’s the paired RNA strands that was the problem, we surmise. RNA, unlike DNA, comprises a single strand of nucleotide bases or “letters,” not a double strand (which, in the case of DNA, twists into the classic double-helix shape). By labeling both strands of the molecule with RNA letters, the doodle effectively depicted RNA as an extended double-stranded molecule, which is incorrect.

Removing the letters allowed the doodle to be interpreted correctly as showing the transcription of RNA from DNA, which is not only biologically relevant, it’s also a critical component of Khorana’s work. In fact, part of Khorana’s approach involved assiduously avoiding the now-classic behavior of some RNA sequences that might have been unintentionally represented in the original doodle—a strand folding back on itself, base-pairing to form obstinate structures that can actually prevent the reading and translation of the code by cellular enzymes. In addition, synthesizing custom-coded RNA strands was much more difficult than synthesizing DNA strands, so part of the time, Khorana cleverly synthesized DNA strands and allowed the cellular enzymes to make the RNA strands for him.

You can explore how to decode the genetic code yourself using our DNA/RNA simulator!* How would you determine the number of bases (letters) in each DNA word? You can design DNA and RNA sequences that clearly answer this question, and then move on to figuring out how to use your own sequences to reveal the code.


The process of transcription. Note that only the red RNA strand includes “U” for Uracil, so the original Doodle’s labels didn’t make sense.


The process of translation. Here, the top base pairs are passed in by tRNA, and don’t form a strand at all, so this doesn’t match the original Doodle either.

* This Next-Generation Molecular Workbench model was developed thanks to a generous grant from

Concord Consortium Publishes Important Research in Educational Technology

Nine publications illuminate our research in educational technology in 2017. Learn about engineering design tools that may help bridge the design-science gap (#5), a systems modeling tool that supports students in the NGSS practice of developing and using models and the crosscutting concept of systems (#1), an Earth science curriculum that increases student scientific argumentation abilities (#6), the relative ease of creating hierarchical data structures (#9), automated analysis of collaborative problem solving in electronics (#8), and more.

1. New systems modeling tool supports students

The NGSS identify systems and system models as one of the crosscutting concepts, and developing and using models as one of the science and engineering practices. However, students do not naturally engage in systems thinking or in building models to make sense of phenomena. The Concord Consortium and Michigan State University developed a free, web-based, open-source systems modeling tool called SageModeler and a curricular approach designed to support students and teachers in engaging in systems modeling.

Damelin, D., Krajcik, J., McIntyre, C., & Bielik, T. (2017). Students making system models: An accessible approach. Science Scope, 40(5), 78-82.

2. Students should face the unknown and engage in frontier science questions

Students should see science as an ongoing process rather than as a collection of facts. Six High-Adventure Science curriculum modules provide an opportunity to bring contemporary science and the process of doing science into the classroom. Interactive, dynamic models help students make sense of complex Earth systems. Embedded assessments prompt students to interpret data to make scientific arguments and evaluate claims while considering the uncertainty inherent in frontier science.

Pallant, A. (2017). High-Adventure Science: Exploring evidence, models, and uncertainty related to questions facing scientists today. The Earth Scientist, 33, 23-28.

3. Automated feedback helps students write scientific arguments

Automated scoring and feedback support students’ construction of written scientific arguments while learning about factors that affect climate change. Results showed that 77% of students made revisions to their open-ended argumentation responses after receiving feedback. Students who revised had significantly higher final scores than those who did not, and each revision was associated with an increase on the final scores.

Zhu, M., Lee, H.-S., Wang, T., Liu, O. L., Belur, V., & Pallant, A. (2017). Investigating the impact of automated feedback on students’ scientific argumentation. International Journal of Science Education, 1–21.

4. Review of research on women’s underrepresentation in computing fields

This literature review synthesizes research on women’s underrepresentation in computing fields across four life stages: 1) pre-high school; 2) high school; 3) college major choice and persistence; and 4) postbaccalaureate employment. Access to and use of computing resources at the pre-high school and high school levels are associated with gender differences in interest and attitudes toward computing. In college, environmental context contributes to whether students will major in computing, while a sense of belonging and self-efficacy as well as departmental culture play a role in persistence in computing fields. Work-life conflict, occupational culture, and mentoring/networking opportunities play a role in women’s participation in the computing workforce.

Main, J. B., & Schimpf, C. (2017). The underrepresentation of women in computing fields: A synthesis of literature using a life course perspective. IEEE Transactions on Education, 60(4), 296-304.

5. Students improve knowledge by designing with robust engineering tools

Eighty-three 9th grade students completed an energy-efficient home design challenge using our Energy3D software. Students substantially improved their knowledge. Their learning gains were positively associated with three types of design actions—representation, analysis, and reflection—measured by the cumulative counts of computer logs. These findings suggest that tools are not passive components in a learning environment, but shape design processes and learning paths, and offer possibilities to help bridge the design-science gap.

Chao, J., Xie, C., Nourian, S., Chen, G., Bailey, S., Goldstein, M. H., Purzer, S., Adams, R. S., & Tutwiler, M. S. (2017). Bridging the design-science gap with tools: Science learning and design behaviors in a simulated environment for engineering design. Journal of Research in Science Teaching, 54(8), 1049-1096.

6. Students improve their scientific argumentation skills

Making energy choices means considering multiple factors, exploring competing ideas, and reaching conclusions based on the best available evidence. Our High-Adventure Science project created a free online energy module in which students compare the effects of energy sources on land use, air quality, and water quality using interactive models, real-world data on energy production and consumption, and scaffolded argumentation tasks. We analyzed pre- and post-test responses to argumentation items for 1,573 students from three middle schools and seven high schools. Students significantly improved their scientific argumentation abilities after using the energy module.

Pallant, A., Pryputniewicz, S. & Lee, H-S. (2017). The future of energy. The Science Teacher, 84(3), 61-68.

7. Students learn about sustainability

Educators must figure out how to prepare students to think about complex systems and sustainability. We elucidate a set of design principles used to create online curriculum modules related to Earth’s systems and sustainability and give examples from the High-Adventure Science module “Can we feed the growing population?” The module includes interactive, computer-based, dynamic Earth systems models that enable students to track changes over time. Embedded prompts help students focus on stocks and flows within the system, and identify important resources in the models, explain the processes that change the availability of the stock, and explore real-world examples.

Pallant, A., & Lee, H. S. (2017). Teaching sustainability through systems dynamics: Exploring stocks and flows embedded in dynamic computer models of an agricultural system. Journal of Geoscience Education, 65(2), 146-157.

8. Automated analysis sheds light on collaborative problem solving

The Teaching Teamwork project created an online simulated electronic circuit, running on multiple computers, to assess students’ abilities to work together as a team. Modifications to the circuit made by any team member, insofar as they alter the behavior of the circuit, can affect measurements made by the others. We log all relevant student actions, including calculations, measurements, online student communications, and alterations made by the students to the circuit itself. Automated analysis of the resulting data sheds light on the problem-solving strategy of each team.

Horwitz, P., von Davier, A., Chamberlain, J., Koon, A., Andrews, J., & McIntyre, C. (2017). Teaching Teamwork: Electronics instruction in a collaborative environment. Community College Journal of Research and Practice, 41(6), 341-343.

9. Students understand how to structure data

In this study participants were presented with diagrams of traffic on two roads with information about eight attributes (e.g., type of vehicle, its speed and direction) and asked to record and organize the data to assist city planners in its analysis. Overall, 79% of their data sheets successfully encoded the data. Even 62% of the middle school students created a structure that could hold the critical information from the diagrams. Students were more likely to create nested data structures than they were to produce one flat table, suggesting that hierarchical structures might be more intuitive and easier to interpret than flat tables.

Konold, C., Finzer, W., & Kreetong, K. (2017). Modeling as a core component of structuring data. Statistics Education Research Journal, 16(2), 191-212.

The Concord Consortium’s Top 10 News Stories from 2017

The year 2017 was a significant one for the Concord Consortium. Even though we lost our founder—and an amazing friend, colleague, mentor, and collaborator—our memories of Robert Tinker and his work resonate in an enduring way. Not many people can say they’ve worked with a legend. But anyone who knew our beloved founder recognized they were in the presence of a brilliant mind and a person with genuine compassion. While Bob’s passing on June 21, 2017, is a source of sadness for us all, we honor his legacy every day through our work. Share your memory of how Bob inspired you (and read stories of the many people Bob inspired).

Here, we share our year’s top 10 news stories.

1. Data Science Education Leaps into the Future

We jump-started the new field of data science education to bring about effective learning with and about data. In February 2017 we convened the Data Science Education Technology conference in Berkeley, California—right next to our West Coast office—with over 100 thought leaders from organizations around the U.S. and six continents. We’ve also hosted over a dozen meetups and webinars since that seminal event. We’re planning our schedule for 2018 and invite you to help us bring about the data science education revolution.

2. We Publish Influential Research and Analysis

We published authoritative articles in the Earth Scientist, the International Journal of Science EducationIEEE Transactions on Education, the Journal of Research in Science Teaching, the Science Teacher, the Journal of Geoscience Education, the Community College Journal of Research and Practice, the Statistics Education Research Journal, and Science Scope. We’re looking forward to 2018, too, with several papers scheduled to be published in the New Year.

3. We Embraced Our Creative Side and Reached Out to You

We embraced our creative side, and collaborated with Blenderbox to create a website that invites users to explore our work and use our free digital resources. Two jam-packed newsletters offered visionary commentary as well as practical instruction. We expanded our blog, and reached out to many more of you through Twitter and Facebook. Keep your shares and comments coming.

Energy3D can be used to design four types of concentrated solar power plants: solar power towers, linear Fresnel reflectors, parabolic troughs, and parabolic dishes.

4. General Motors Awards $200,000 Grant

General Motors is committed to powering its worldwide factories and offices with 100% renewable energy by 2050. The company furthered its commitment by awarding the Concord Consortium a $200,000 grant to promote engineering education using renewable energy as a learning context and artificial intelligence as a teaching assistant. The project will use our signature Energy3D software, an easy-to-use CAD tool for designing and simulating solar power systems.

5. What a Busy Year Presenting on the Road

We presented our free resources and research at over 25 sessions at NSTA, NARST, AERA, ISTE, BLC, American Society for Engineering Education, NSTA STEM Forum & Expo, MAST, EdSurge, International Dialogue on STEM 2017, and ISDDE2017, plus the Global Education & Skills Forum in Dubai and the International Conference on Tangible, Embedded, and Embodied Interactions in Japan. Phew! At AERA 2018 we’ll host a special session to honor the work and legacy of Bob Tinker called “Deeply Digital Learning: The Influence of Robert Tinker on STEM Education and the Learning Sciences.”

Students can explore and evaluate the condition of their local watershed using the free, web-based Model My Watershed application.

6. We Won!

Congratulations to the WikiWatershed online toolkit, which includes the Model My Watershed app developed in collaboration with the Stroud Water Research Center. It was awarded the 2017 Governor’s Award for Environmental Excellence by the Pennsylvania Department of Environmental Protection. And our Water SCIENCE project won a facilitators’ choice award in the National Science Foundation’s STEM for All Video Showcase.

7. We Partnered with Publishers to Bring STEM Inquiry Activities to More Students

  • We continued our partnership with McGraw-Hill Education to create engaging simulations for their Inspire Science elementary science curriculum. These simulations allow students to explore questions in ways that scientists and engineers do, and cover a variety of topic areas in K-5 science.
  • We incorporated our Next-Generation Molecular Workbench into PASCO’s Essential Chemistry textbook as fully interactive simulations that challenge students to explore topics in chemistry such as chemical reactions and particle motion.

If you’re interested in creating a groundbreaking STEM curriculum or pursuing an innovative new idea together, we’re excited to explore the possibilities with you.


8. Twenty-four Hours of Pandemonium and Prototypes

Our East and West Coast offices got together in July for a “FedEx day,” so called because the goal is to develop a blizzard of new prototypes and innovations in 24 hours and deliver them overnight! We developed prototypes for blocks-based programming in augmented reality (imagine Scratch/StarLogo, but with printable blocks that connect like puzzle pieces); a collaborative ecology game based on a tangible user interface; an internal project dashboard (think Intranet on steroids); an agent-based convection model; a way to connect real-time sensor data from our offices directly into our data exploration tool CODAP; and an open-source editor for activity transcripts. Plus President Chad Dorsey got out his power tools and built a picnic table that turns into a bench — almost Transformer-worthy.



9. Six New Employees Sign On

We welcomed six fabulous new employees in our Concord, MA, and Emeryville, CA, offices: Tom Farmer, Lisa Hardy, Eli Kosminsky, Andrea Krehbiel, Joyce Massicotte, and Judi Raiff. Want to join our growing family? We’re hiring!

10. Thirty-One Projects Research and Develop Educational Technology and Curriculum

Through 31 research projects with countless amazing collaborators, we’re extending our pioneering work in the field of probeware and other tools for inquiry and continuing to develop award-winning STEM models and simulations. We’re taking the lead in new areas, including data science education, analytics and feedback, and engineering and science connections. And we’re exploring and creating cutting-edge new tools and technologies for tomorrow’s learners in our innovation lab.