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.

UMass Amherst students contribute to dragon genome project

Can dragons get cancer? Students in Dr. Ludmila Tyler’s Biochemistry Molecular Genetics and Genomics course at the University of Massachusetts, Amherst asked this question last semester. As part of their course work, they used our Geniverse software to study dragon genetics and develop new genes, mutant alleles, and phenotypes based on investigations of scientific literature. They imagined the genotypic and phenotypic possibilities for the fictional drake, the model species in Geniverse. Drakes are essentially miniature dragons, so students can take what they learn about drakes and apply it to dragons just as scientists study model species like mice to learn about human genetic disease.

We recently revealed the science behind the genes of Geniverse. Thanks to Dr. Tyler’s students, the dragon genome has the potential to expand in exciting ways.

  • Some drakes now have a high-frequency acoustic sensitivity, which gives them the ability to navigate and forage using sound waves—thanks to research conducted by Nicholas Fordham and Thomas Riley Potter. They focused on the SLC26A5 gene, which encodes Prestin, a protein that functions in the membrane of cochlear outer hair cells and is involved in auditory function. In bats and dolphins, a change in one amino acid in the Prestin protein allows for echolocation.
  • A form of dwarfism called achondroplosia was introduced to the drake genome by Brian Kim, Danny McSweeney, and Jared Stone. The group identified research showing a connection between short-limbed dwarfism and one altered amino acid in the FGFR3 transmembrane protein receptor expressed in bone-building cells. They created a drake with short stature due to a heterozygous genotype, containing a single mutated allele; the wild-type homozygous recessive genotype would result in an average-sized drake while a homozygous dominant genotype would result in the death of the drake offspring.
  • The MaSp1 gene now enables drakes to secrete and shoot silk from their mouths (for example, to capture prey or build a home). Brandon Hancock and Mitch Kimber researched the MaSp1 fibroin protein across several spider species to look for areas of gene conservation.
  • Drakes may now be resistant to cancerous tumors, thanks to research by Evan Smith and Kaitlyn Barrack, who added the TP53 tumor-suppressor gene. The gene encodes the p53 protein, which acts as a major tumor suppressant in many different organisms.

We’re excited that these students and other members of the class have extended the database of drake genes, and we’d love to be able to incorporate them in Geniverse software in the future.

Try Geniverse now. What additions to the dragon genome would you like to see?

Dashboard helps teachers understand student progress and performance in genetics game

Our dragon genetics games have engaged thousands of students for many years. In that time, teachers have asked for an easy way to track their students’ progress and performance. Until now, teacher reports have been difficult to pull out of our system and impossible to parse in real time. The GeniGUIDE project, in partnership with North Carolina State University, is developing a teacher dashboard to accompany our new Geniventure software. We are currently piloting the beta version of this dashboard in multiple classrooms in Maine, North Carolina, New Jersey, and Massachusetts.

“A dashboard is a visual display of the most important information needed to achieve one or more objectives that has been consolidated on a single computer screen so it can be monitored at a glance.” – Stephen Few

Our dashboard displays information processed by an Intelligent Tutoring System (ITS) integrated into Geniventure. As students complete challenges in the game, they are rewarded with different color crystals for their accomplishments (Figure 1). Students who complete a challenge efficiently and without mistakes receive a blue-green crystal. Those who make a small number of missteps receive a yellow crystal while those with more mistakes receive a red. A black “try again” crystal is given to a student with too many mistakes to move on. As students level up through the missions, the ITS builds a model of conceptual understanding of specific learning goals. As student performance on these concepts improves over time, evidence that they have a solid understanding grows stronger.

Figure 1. Student view within Geniventure of the colored crystals (bottom of screen).

Our preliminary teacher dashboard design (Figure 2) was guided by three factors. First, we looked back at our many years of classroom observations of teachers who implemented our suite of dragon genetics games—from our most recent Geniverse to GeniGames and BioLogica—and asked: What information could have helped teachers better facilitate student use of the game? Second, we examined recent dashboard designs implemented in prior Concord Consortium projects to help us distinguish between in-class and after-class use. Finally, we looked at other teacher dashboards that are currently available on the market.

Figure 2. Beta version of Geniventure teacher dashboard.

During the pilot testing, we’re closely observing how teachers use the primary view of the dashboard, which provides information on both student progress and performance during class time. We hope to answer the following questions:

  • Can the teacher adequately track student progress through the game?
  • When do teachers intervene and when do they allow students to struggle? (Do teachers first help those students with black or red crystals?)
  • Do teachers look at how many attempts a student made at a challenge?
  • If teachers notice that particular students are ahead of the class, what actions do they take?

The dashboard also displays a graphical representation of student understanding of genetics concepts highlighted in the game. Some concepts are directly related to specific student actions (e.g., two recessive alleles are required to produce a recessive trait) while others are calculated based on performance across certain challenge types (e.g., genotype to phenotype mapping). The teacher can delve deeper into these secondary reports to view not only individual student data (Figure 3), but also aggregated class data (Figure 4). Through classroom observations and interviews with teachers, we hope to determine:

  • Do teachers have the time and bandwidth to make sense of the concept understanding graphs during class?
  • To what extent do the concept graphs help teachers understand where individual students, or the entire class, are having trouble?
  • What action, if any, do teachers take based on the concept graphs?

Figure 3. Display of individual student’s conceptual understanding.

Figure 4. Representation of class average conceptual understanding.

As our ITS becomes more sophisticated, we plan to widen the concepts we track and make better use of student data to inform teachers.

How do you make use of dashboards? Let us know what features you’d like to see as we improve our ITS-enhanced dashboard.

Energy2D used as a simulation tool in astrobiology research

Fig. 1: Frasassi Caves, Italy (credit: Astrobiology)
Deposition of minerals in caves may be affected by microbes. Geochemical analysis of these minerals can reveal biosignatures of subsurface life on a planet such as the Mars. Research in this area can help NASA build subsurface life probes for future planetary missions.

Fig. 2: Energy2D simulations (credit: Astrobiology)
Astrobiology, a peer-reviewed scientific journal covering research on the origin, evolution, distribution and future of life across the universe, just published a research paper titled "Transport-Induced Spatial Patterns of Sulfur Isotopes (δ34S) as Biosignatures" by a group of researchers at Pennsylvania State University, the University of Texas at El Paso, and Rice University. The lead author is Dr. Muammar Mansor. The researchers analyzed sample sites in the Frasassi Caves, Italy (Figure 1) and used Energy2D to simulate the effects of convection and diffusion on the chemical deposition processes (Figure 2). According to the paper, the results of the deposition simulated using Energy2D are consistent with the data collected from the cave sites, suggesting the importance of the effect of natural convection.

This is the second paper that uses Energy2D in astrobiology research (and the 16th published paper that used Energy2D in scientific research to simulate a natural or man-made system). In the first paper, Energy2D was used to simulate the thermal conditions for the origin of life. Once again, the publication of this paper provides fresh evidence for the broader impacts of our work.

Lights, camera, action: A video that introduces the NGSS practice of scientific argumentation

Following the recommendation to incorporate the Next Generation Science Standards (NGSS) science and engineering practices in their classrooms, schools across the country are looking for ways to integrate scientific argumentation into their curriculum. Since 2012 the High-Adventure Science project in collaboration with National Geographic Education has offered free online modules for Earth and space science topics—including climate change, freshwater availability, the future of energy sources, air quality, land management, and the search for life in the universe—that include multiple opportunities for students to engage in argument from evidence.

Over 67,000 teachers and students across the globe have used High-Adventure Science modules. Based on teacher feedback, classroom observations, and analysis of student data, we have learned that when students engage in argumentation from data and model-based evidence, they need a lot of support on how to write a convincing argument.

Last year, we added an introductory activity to each module where students learn about the component parts of a scientific argument before they are asked to write one. In this highly scaffolded task, students see written examples of a claim and explanation and learn about uncertainty in scientific data and how to express this uncertainty. In High-Adventure Science, argumentation takes a special form, including a multiple-choice structured claim, open-ended explanation, five-point Likert scale uncertainty rating, and uncertainty rationale.

In this introductory activity, students learn about the components of a good explanation.

Even with this new activity, some students still struggled, so we recently created an animated video to introduce the scientific practice of developing an argument. We start by helping students identify the difference between a scientific argument and so-called “arguments” they may have with their friends (e.g., arguing about favorite ice cream flavors!), and making the distinction between claims backed by evidence and opinion. The goal is to introduce students to scientific arguments in a fun and relatable way and to make the terminology and process of scientific argumentation less daunting.

We’re piloting the video in our Will there be enough fresh water? module for select students. We’re looking forward to student and teacher feedback and may revise the video based on their comments. We want everyone to be able to engage in the critical practice of arguing from evidence.

We welcome your comments about our video, as well as your challenges and successes with incorporating the NGSS practice of engaging in argument from evidence.

Energy3D uses intelligent agents to create adaptive feedback based on analyzing the "DNA of design"

Fig. 1: A simple case of teaching thermal insulation.
Energy3D is a "smart" CAD tool because it can monitor the designer's behavior in real time, based on which it can generate feedback to the designer to regulate the design behavior. This capacity has tremendous implications to learning and teaching scientific inquiry and engineering design with open-ended nature that requires, ideally, one-to-one tutoring so intense that no teacher can easily provide in real classrooms.

The computational mechanism for generating feedback in Energy3D is based on intelligent agents, which consist of sensors and actuators (in very generic terms). In Energy3D, all the events are logged behind the scenes. The events provide the raw data stream from which various sensors produce signals based on subsets of the raw data. For instance, a sensor can be created to monitor any event related to solar panels of a house. An agent then uses a decision tree model to determine which actuators should be called to provide feedback to the user or direct Energy3D to change its state. For instance, if a solar panel is detected to be placed on the north-facing roof, the agent can remind the designer to rethink about the decision. Just like what a teacher may do, the agent can even suggest a comparative analysis between a solar panel on the north-facing roof and a solar panel on the south-facing or west-facing roof. Although this type of inquiry and design can be also taught using directly scaffolded instruction that guides students to explore step by step, in practice we have found the effect of this approach often diminishes because many students do not read instruction carefully enough and remember them long enough. It is also challenging for teachers to guide the whole class through this kind of long learning process as students often pace differently. Adaptive feedback provides a way to help students only when they need or just when a need is detected, thus providing a better chance to deliver effective instruction.

Let's look at a very simple example. Figure 1 shows a learning activity, the goal of which is to teach how the thermal property of a wall, called the U-value, affects the energy use of a house. Many students may walk away with a shallow understanding that the higher the U-value is, the more energy a house uses. The challenge is to help them deepen their understanding. For example, how can we make sure that students will collect enough data points to discover that the energy a house uses is proportional to the U-value? How can we support them to find out that the relationship is independent of seasonal change, wall orientation, and solar radiation (e.g., a lower U-value is good in both summer and winter, irrespective of whether or not the wall faces the sun). Helping students accomplish this level of understanding through inquiry-based activities is by no means a trivial task, even in this seemingly simple example. Let's explore what we may do in Energy3D now that we have a way to monitor students' interactions with it.

Fig. 2: An event sequence coded like a DNA sequence.
In nearly all software that support learning and teaching, the events during a process can be coded as a string with characters representing the events and ordered by their timestamps, such as Figure 2. In this case, A represents an analysis event in the Energy3D CAD tool, U represents an event of changing the U-value of a wall, C represents an event of changing the date for the energy simulation, a questionmark (?) represents an event of requesting help from the software, an underscore (_) represents an inactive time period longer than a certain threshold, and * is a wildcard that represents any other event "silenced" in this expression in order to reduce the dimensionality of the problem. For those who know a bit about bioinformatics, this resembles a DNA sequence. In the context of Energy3D, we may also call it as the DNA of a design, if that helps your imagination.

Now that we have converted the sequence of events into a string, we can use all sorts of techniques that have been developed to analyze strings to analyze these events, including those developed in bioinformatics such as sequence alignment or those developed in natural language processing. In this article, I am going to show how the widely-supported regular expressions (regex) can be used as a technique to detect whether a certain type of event or a certain combination of events occurred or how many times it occurred. I feel that regex, in our case, may be more accurate than edit distances such as the Levenshtein distance in matching the pattern. For example, a single substitution of event may represent a very different process despite the short edit distance.

Fig. 3: A sequence that shows high usage of feedback
We know that, a fundamental skill of inquiry is to keep everything else fixed but change only one variable at a time and then test how the system's output depends on that variable. Through this process of inquiry, we learn the meaning of that variable, as explained by Bruce Alberts, former president of the National Academy of Sciences and former Editor-in-Chief of the Science Magazine. In the example discussed here, that variable is the U-value of a selected wall of the house and the test is the simulation-based analysis. A pattern that has alternating U and A characters in the event string suggests a high probability of inquiry, which can be captured using a simple regex such as (U[_\\*\\?]*A)+. Between U and A, however, there may be other types of events that may or may not exist to weaken the probability or compromise the rigor. For example, changing the color of the wall between U and A may also result in an additional difference in energy use of the house that originates from the absorption of solar radiation by the external surface of the wall and has nothing to do with its U-value. In this case, changing multiple variables at a time appears to be a violation of the aforementioned inquiry principle that should be called out by the agent using another regex to analyze the substring between U and A.

An interesting feature in Energy3D is that feedback itself is also logged. Figure 3 shows a sequence that has an alternation pattern similar to that of Figure 2, but it records a type of behavior showing that the user may rely overly on feedback from the system to learn (the questionmarks in the string stand for feedback requests made by the user) and avoid deep thinking on their own. This may be a common problem in many intelligent tutors (sometimes this behavior is called "gaming the system").

The development of data mining and intelligent agents in Energy3D is opening interesting opportunities of research that will only grow more important in the era of artificial intelligence (AI). We are excited to be part of this wave of AI innovation.

General Motors funds engineering education based on Energy3D

Designing a parking lot solar canopy at Detroit Airport
General Motors (GM), along with other RE100 companies, has committed to powering its worldwide factories and offices with 100% renewable energy by 2050. Last month, the company furthered its commitment by giving the Engineering Computation Team at 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.

Modeling GM's rooftop solar arrays in Baltimore, MD
Modeling GM's solar arrays in Warren, MI
The project will use our signature Energy3D software, which is a one-stop-shop CAD tool for designing and simulating all kinds of solar power systems including photovoltaic (PV) and concentrated solar power (CSP), both of which have reached a very competitive cost of merely 5¢ per kWh or below in the world market. A unique feature of Energy3D is its ability to collect and analyze "atomically" fine-grained process data while users are designing with it. This capability makes it possible for us to develop machine learning algorithms to understand users' design behaviors, based on which we can develop intelligent agents to help users design better products and even unleash their creativity.

The generous grant from GM will allow us to bring this incredible engineering learning tool and the curriculum materials it supports to more science teachers across New England. It will also help extend our fruitful collaboration with the Virtual High School (VHS) to convert our Solarize Your World curriculum into an online course for sustainable engineering. VHS currently offers more than 200 titles to over 600 member schools. Through their large network, we hope to inspire and support more students and teachers to join the crucial mission that GM and other RE100 companies are already undertaking.

By supporting today's students to learn critical engineering design skills needed to meet the energy and environmental challenges, GM is setting an example of preparing tomorrow's workforce to realize its renewable energy vision.