Data Science Education Meetup at Cyberlearning 2017

Thank you to the fantastic crowd at Cyberlearning 2017 who attended our Data Science Education Meetup at Mussel Bar and Grill on Tuesday night. The Meetup provided an opportunity for members of the Cyberlearning community to discuss innovative ways that we could implement data science education into research and curriculum development, to address NSF’s 10 Big Ideas for Future Investments strand in data science [PDF], and to enjoy some great food!

Our attendees at the Data Science Education Meetup at Cyberlearning 2017

Attendees at the Data Science Education Meetup at Cyberlearning 2017

There were several strands of activity toward data science education at Cyberlearning 2017, and the Meetup provided an opportunity for us to share and reflect on the following:

  • A roundtable session on Teaching Data Science, in which our own William Finzer facilitated a discussion on our Data Science Education Technology conference in Berkeley in February.
  • Hearing about some of the challenges researchers face in implementing curriculum at the middle school and high school level, as well as some of the Cyberlearning projects (such as Data Science Games, CODAP, Impact Studio, Learning and Youth, and STEM Literacy Through Infographics), which are hoping to address these challenges.
  • Participating in the Data Science Education for 21st Century Learning CL17 Working Sessions Strand. It was especially great to work with Sayamindu Dasgupta and Jesse Bemley to identify the important aspects necessary to move data science education forward.
  • Identifying data science as part of computational thinking, as put forward by NSF Program Officer Arlene M. de Strulle on Wednesday morning.

Our Meetup was packed (we had to move to a larger set of tables, as a matter of fact!), and the evening was full of lively discussion about the impact and need for data science education. We thank everyone who attended and are especially grateful to our community for bringing in so much passion and energy for data science education.

Please check out our future Data Science Education Meetup dates and locations at concord.org/meetup. Or join us online through a series of monthly Data Science Education Webinars, starting in May. We have a great lineup of speakers, including Cliff Konold, Amelia McNamara, and Rob Gould. We’ll post dates and additional information at concord.org/meetup.

As always, please leave a comment or suggestion for future Meetups, community-building activities, or future innovations in data science education you’d like to see. We love hearing from you.

A demo of Infrared Street View

An infrared street view
The award-winning Infrared Street View program is an ambitious project that aims to create something similar to Google's Street View, but in infrared light. The ultimate goal is to develop the world's first thermographic information system (TIS) that allows the positioning of thermal elements and the tracking of thermal processes on a massive scale. The applications include building energy efficiency, real estate inspection, and public security monitoring, to name a few.
An infrared image sphere


The Infrared Street View project is based on infrared cameras that work with now ubiquitous smartphones. It takes advantages of the orientation and location sensors of smartphones to store information necessary to knit an array of infrared thermal images taken at different angles and positions into a 3D image that, when rendered on a dome, creates an illusion of immersive 3D effects for the viewer.

The project was launched in 2016 and later joined by three brilliant computer science undergraduate students, Seth Kahn, Feiyu Lu, and Gabriel Terrell, from Tufts University, who developed a primitive system consisting of 1) an iOS frontend app to collect infrared image spheres, 2) a backend cloud app to process the images, and 3) a Web interface for users to view the stitched infrared images anchored at selected locations on a Google Maps application.

The following YouTube video demonstrates an early concept played out on an iPhone:



Data Science Education Meetup at NSTA 2017

Thanks to all the great folks who attended our NSTA 2017 Data Science Education Meetup at BottleRock LA last night. We had a great crowd attend, complete with representatives from Magnitude.io, the education arm of the International Space Station (ISS), CASIS, the folks from MiniPCR, LAUSD, Lodi USD, the CREATE for STEM Institute, Educational Passages and more! We’re still processing the yummy food and hours of great conversation about all things data.

Happy attendees at the Data Science Education Meetup at NSTA 2017

A few highlights of the evening included:

  • Hearing about streaming atmospheric data from Magnitude.io’s successful rocket and balloon launches at Lodi schools and others all across California, and their ExoLab experiments on the ISS
  • Debating the grand challenges and barriers to data science education with teachers and district staff
  • Hearing about how using data, modeling and evidence has transformed teaching for those using the Interactions curriculum from the CREATE for STEM Institute and the Concord Consortium
  • Seeing Liam Kennedy’s amazing ISS-ABOVE device that changes your TV into a portal to the International Space Station, complete with live alerts when the ISS passes overhead
  • Learning about how MiniPCR’s Genes in Space competition is bringing high school students’ experiments to the space station and how a new USB-stick-sized gene sequencing device is speeding up microbe research on the ISS by orders of magnitude
  • Hearing stories of how data from GPS-tracked drifter boats from Educational Passages has connected students across continents
The place was packed and the evening was full of great geekery, all the more evidence that the time has come for data science education. We thank everyone who attended and we look forward to building networks, collaborations and new modes of teaching and learning together.
Missed us last night? No problem. Join us for the next one, at an upcoming conference near you! See all our meetups and RSVP at concord.org/meetup.

High school students to solarize the city of Lowell — virtually


In April, high school students in Lowell, Massachusetts will start exploring various solarization possibilities in the city of Lowell -- famously known as the Cradle of American Industrial Revolution. Many municipal properties and apartment buildings in Lowell have large roofs that are ideal for rooftop solar installations. Public parking facilities also provide space for installing solar canopies, which serve the dual purpose of generating clean energy and providing shade for parked cars. Students will discover the solar potential of their city and calculate the amount of electricity that can generated based on it.

This project is made possible by our Energy3D software, which supports engineering-grade solar design, simulation, and analysis. The Lowell High School, local business owners, and town officials have been very supportive about this initiative. They provided a number of public and private sites for students to pick and choose. Some of them have even agreed to serve as the "clients" for students to provide specifications, inputs, and feedback to students while they are carrying out this engineering project.

Among the available sites, five public parking garages managed by the municipal authority, which have not installed solar canopies, will be investigated by students through feasibility studies that include 3D modeling, solar energy simulation, and financial planning. Through the project work, students will author reports addressed to the property owners, in which they will recommend appropriate solar solutions and financial options.

Solving real-world problems like these creates a meaningful and compelling context and pathway for students to learn science and engineering knowledge and skills. Hopefully, their work will also help inform the general public about the solar potential of their city and the possibility of transitioning it to 100% renewable energy in the foreseeable future, which is a goal recently set by Massachusetts lawmakers.

Global Researchers and Developers Connect at 2017 Data Science Education Technology Conference

Over 100 thought leaders from organizations around the U.S. and four continents gathered from February 15 to 17 to generate important innovations needed in technology and teaching and learning at the Concord Consortium’s first Data Science Education Technology conference. Senior researchers, educators, and scientists from as far as Nigeria and New Zealand convened at the David Brower Center in Berkeley, California, to share a comprehensive overview of this rapidly growing area of data science education.

“This is a thrilling milestone,” notes Chad Dorsey, president and CEO of the Concord Consortium. “Never before have experts across mathematics, science, and technology come together to focus their thinking on the emerging field of data science education.”  

Dan Damelin, William Finzer, and Natalya St. Clair of the Concord Consortium.

Some of the 100 DSET Conference attendees.

Over the next few decades, data science education is expected to undergo a profound change. According to opening panelist Deb Nolan, UC Berkeley is offering an Intro to Data Science Class with enrollment of over 700 students in it, and there is currently a campus-wide initiative to plan for a data science major. In addition, the National Science Foundation has included Harnessing Data for 21st Century Science and Engineering as a priority in a “10 Big Ideas for Future Investments” report.

To reflect the dual-sided nature of this new territory in education, the conference topics comprised two strands. The Teaching and Learning strand, designed for those considering data science-related curriculum development, brought together researchers and educators, and generated a wealth of new understandings and patterns for educational research—educators in particular left with fresh ideas on how to apply cutting-edge curriculum practices in their classroom. Researchers left with important ideas about how to define success for educational results in this fast-growing and essential research field.

“Data science is evolving. And data science education is a science on its own, which can reach out to all branches of science and similar social sciences. Already I see people here who are trying to brainstorm ways we can move ahead on data science and how we can move forward to educate people on data science,” says Kenechukwu Okeke of Nnamdi Azikiwe University, Nigeria.

In the Technology strand, participants worked with data analysis platforms such as Tuva, Desmos, and CODAP (the Common Online Data Analysis Platform). Participants with software experience created their own CODAP plug-ins and also built skills to make their work more efficient. In the closing session, DSET participants watched as Ph.D. student Takahiko Tsuchiya (Georgia Institute of Technology) made data in CODAP audible as sound using a sonification plug-in he had programmed during the conference.

Register now for CADRE Webinar on DSET conference highlights

Join William Finzer, Daniel Damelin, and Natalya St. Clair of the Concord Consortium in a CADRE webinar Friday, March 3, from 1-2 pm EST for highlights from the DSET conference. Register now!

Join us at future DSET Meetups

We’ll continue to gather thought leaders in data science education technology through informal meetups in Austin, San Antonio, Los Angeles, Portland, Baltimore, and New Zealand. Going to SXSW Edu, NCTM, or NSTA? Contact us for more information about upcoming meetups. We look forward to seeing you!

 

Designing building-integrated photovoltaics with Energy3D

Fig. 1: An example of solar facade.
Building-integrated photovoltaics (BIPV) represents an innovative way to think and design buildings as both human dwellings and power plants. In BIPV, solar panels or photovoltaic thin films are used to replace conventional constructional materials in parts of the building envelope such as roofs, walls, and even windows. Designing new buildings nowadays increasingly includes BIPV elements to offset operational costs. Existing buildings can also be retrofitted with BIPV (e.g., replacing glass curtain walls with solar panels). BIPV is expected to grow more important in architectural design and building engineering.

Fig. 2: An example of solar curtain walls
We are developing modeling capabilities in Energy3D to support the design, simulation, and analysis of BIPV. Figures 1 and 2 in this article show a few cases that demonstrate these capabilities in their primitive forms. Considering BIPV is relatively new and a lot of research is still under way to develop and test new ideas and technologies, we expect the development of these capabilities in Energy3D will be a long-term effort that will be integrated with latest research and development in the industry.
Fig.3: Power balancing throughout the day.

As the first step towards that long-term vision, the current version of Energy3D has already allowed you to add solar panel racks to any planar surface, being it horizontal, vertical, or slanted. Running a simulation for any day, you will be able to predict the daily output of all the solar panels. You can also compare the outputs of selected arrays. For example, if you want to track down on which side solar panels produce the most at a given time during the day, you can compare them in a graph. Figure 3 shows a comparison of the solar arrays in the model shown in Figure 1. As you can see, the east-facing array produces peak energy in the morning whereas the west-facing array produces peak energy in the afternoon. In this case, the BIPV solution ensures that the photovoltaic system generates some electricity at different times of the day.

Video tutorial: Solarize an imported building in Energy3D

We are pleased to announce that the solar panel and analysis tools in Energy3D (version 6.5.6 or higher) are now fully applicable to arbitrary imported structures. We hope that the new capabilities can help engineers who design rooftop solar systems and building solar facades to get their jobs done more efficiently and students who are interested in engineering to learn the theory and practice in an inquiry-based fashion. The six-minute video in this article demonstrates how easy it is to perform solar panel design and analysis in Energy3D. (Note: Unfortunately, the annotations in the video do not show if you are watching this on a smartphone.)



One of the handiest features is the automatic, real-time detection of the angle of the surface under a solar panel while the user is moving it. This feature basically allows the user to drag and drop a solar panel or a solar panel rack anywhere (on top of roofs, walls, or other surfaces) without having to set its tilt angle manually.

Solar heat map of a house with solar panels
Copy and paste a house with solar panels
Solar panels are "first-class citizens" in Energy3D as they are readily recognized by the built-in simulation engines. Energy3D provides a comprehensive list of properties that you can choose for each solar panel or solar panel array. For example, even the temperature coefficient of Pmax, a parameter that specifies the change of solar cell efficiency with regard to ambient temperature change, is supported. The software also has a variety of analytic tools for predicting the hourly, daily, and annual outputs of each solar panel and their sums. Interactive graphs are available to intuitively show the trends and allow the user to compare the outputs of different solar panels, of different arrays, on different days, or with different environmental settings (e.g., with or without a tree nearby).

These "native" solar panels are now completely blended into the "alien" meshes of structures imported into Energy3D from other CAD software or Google Earth. For example, once you drop a solar panel on a surface of the structure, it will stick to it. In other words, if you move or rotate the structure, the solar panel will go with it as if it were part of the original design. When you copy and paste the entire building, the solar panels will be copied and pasted as well (by the way, it takes only four clicks to copy and paste a building in Energy3D through the pop-up menu: one click to pick which one to act upon, one click to select the "Copy" action, one click to pick where to paste, and one last click to select the "Paste" action). That is to say, the native and alien meshes are completely meshed.

Automatic remeshing in Energy3D for solar analysis

A headache in the practice of simulation-based engineering or computer-aided engineering is the incompatibility of the meshes used to create and render structures (let's call them the drawing meshes) and the meshes needed to simulate and analyze certain functions (let's call them the analysis meshes). This incompatibility stems deeply from the fundamental differences in computer graphics for visual rendering and computer simulation for physics modeling.

When importing a model from a CAD tool into a simulation tool, engineering analysts often have to recreate new analysis meshes for their computation, which requires fine-grained discretization such is in the case of finite element analysis and other methods. It becomes a nightmare when the conversion from the drawing meshes to the analysis meshes can only be done manually. Even if only a few drawing meshes need to be taken care by hand while the majority of the meshes can be converted automatically, the analyst still would have to check all the meshes to make sure that every mesh is good for simulation. So we really need a tool that can deal with all sorts of scenarios. And this kind of tool is by no mean easy to develop.


Mesh incompatibility was (and may remain for a long time) a problem for Energy3D when it imports structure models created by other tools such as SketchUp. For example, in most architectural CAD models, a structural element such as a wall or a roof (or a part of them) is represented by two planar meshes that have exactly opposite normal vectors, representing the interior and exterior surfaces, respectively. These two meshes have identical coordinates for their vertices, though the orders of their definition are different (one goes clockwise and the other anticlockwise in order for their normal vectors to be exactly opposite). Whatever they represent has therefore no thickness, but with the two faces, we can apply two different textures to them so that the viewer can tell if they are looking at its interior or exterior surface.


While this sounds good to people who use such models in 3D games, it spells troubles to Energy3D. The first problem is that it increases the number of vertices that Energy3D has to load and, therefore, the memory footprint of such models. In a physics simulation, a structural element without thickness is meaningless. If we don't really need the two faces in most cases (we do in some other cases, but I will skip that line of discussion in the article), why bother to import them in the first place? Despite spending days on researching on the Internet and checking the Collada specification, I couldn't figure out how to force SketchUp to export only the exterior meshes (SketchUp's Colloda export function does provide the option of exporting two-sided faces or not, but it sometime exports only the interior sides for some meshes, mixed with exterior sides for others). So it occurred to me that I had to deal with them in the Energy3D code.

If we don't treat them and use the meshes as they are, we then have the second problem: which mesh should we pick to be the side that receives solar radiation? While it is self-evident which mesh of the two identical ones faces outside when we look at the 3D model after it is rendered on the computer screen, we absolutely have no way to tell from their coordinates alone in our code. Somehow, we have to invent an algorithm that simulates how people discern it when they look at the 3D view of the model on a computer screen.

Picking and choosing the right meshes, of course, is crucial to the correctness of the simulation results. In order to test the new algorithms in Energy3D, I selected the lower Manhattan island and the US Capitol Building as two test cases. The results of the lower Manhattan island have been reported in an earlier article. But the Capitol Building has turned out to be a harder case as -- with over 15,000 meshes -- it is geometrically more complicated and it has so many details that really put our code to test. After more than a week of my work on solving a myriad of problems, it seems that Energy3D has passed the test (or has it?). The screenshots of the solar irradiance heat maps of the Capitol Building from different angles show that severe anomalies are practically non-existent across the heat maps.


The heat maps occasionally suffer from a flickering effect called "Z-fighting" when two planar surfaces are visually close, especially when being looked at from a distance sufficiently far away. For the Z-fighting between identical meshes, this can be mitigated by increasing the offset of their distance. But, as this doesn't affect the simulation result, it is less a concern for the time being.

Automatic remeshing for solar analysis may also need to include automatic repair of models that contain errors. For example, some models may contain surfaces that have only one mesh with the normal vector pointing inward (this could happen if the original designer accidentally used the "Reverse Faces" feature in SketchUp without realizing that the normal vectors would be flipped). This kind of mesh is tolerated in SketchUp because it does not affect rendering. But they cannot be tolerated in Energy3D because such a single-face mesh with a north-pointing normal vector, which appears to the viewer as south-facing, will show little to zero solar radiation when the sun shines on it. Luckily, due to the visualization in Energy3D, we can quickly spot those incorrect surfaces and fix them by reversing their faces manually. But we will need to find a way to automate this process (not easy, but not impossible).

Data Science Education Technology Conference ready to welcome 100 thought leaders

We are proud to announce the Data Science Education Technology (DSET) Conference to be held February 15-17, 2017, at the David Brower Center in Berkeley, CA. Over 100 thought leaders from a range of organizations, including UC Berkeley, TERC, EDC, Desmos, SRI, Exploratorium, New York Hall of Science, Harvard’s Institute for Applied Computational Science, Lawrence Hall of Science, and Tuva will be in attendance at this groundbreaking event in the emerging field of data science education.

Conference attendees come from 15 states and 6 countries around the world. Map created with CODAP. View the data table and more information about attendees in this CODAP document.

“Data and analytics hold promise for revolutionizing all aspects of learning,” Chad Dorsey, president and CEO of the Concord Consortium, explains. “As we enter a world where practically every decision and moment of the day will connect to data in some way, preparing learners to explore, understand, and communicate with data must become a key national priority.”

The DSET conference addresses these new opportunities by focusing on two strands. The Teaching and Learning strand is designed for those thinking about curriculum development. This strand addresses the pedagogical challenges and opportunities associated with making use of data technologies in educational settings. Sessions will include data-driven learning experiences and discussion of lessons learned by curriculum designers. “It’s an exciting time to design activities to help students be ready for data science in the future,” notes Tim Erickson of Epistemological Engineering.

The Technology strand is designed especially for those with programming experience and focuses on software development. Attendees will build relevant, timely skills and learn to create a web app and increase efficiency. William Finzer, developer of the Common Online Data Analysis Platform (CODAP) at the Concord Consortium, will lead a session on data technology integration with online curricula and moderate a discussion on leveraging open-source software to enhance learners’ experience working with data.

You’re invited to attend the conference as a virtual participant. Registration is free! Join the virtual conference!

Register for the Virtual Conference

Where else can I connect with #dsetonline?

Virtual attendees are encouraged to join the conference backchannel social media conversation that will run concurrently with the face-to-face conference.

Evo-Ed Integrative Cases will be enriched to address NGSS

A new collaborative research project at the Concord Consortium and Michigan State University will develop and research learning materials on the molecular and cellular basis for genetics and the process of evolution by natural selection. These two areas are both difficult to teach and learn, and although they have been historically taught separately, they are interlinked and span multiple levels of organization. The goal of Connected Biology: Three-dimensional learning from molecules to populations is to design, develop, and examine the learning outcomes of a new connected curriculum unit for biology that embodies the conceptual framework of the Next Generation Science Standards.

Peter White, science education researcher and entomologist at Michigan State University (MSU); Louise Mead, Education Director at the BEACON Center for the Study of Evolution in Action at MSU; and postdoctoral fellow Alexa Warwick at the BEACON Center visited the Concord Consortium recently to plan our joint work together. Frieda Reichsman and Paul Horwitz will serve as the Principal Investigator and Co-PI at the Concord Consortium.

The new units will leverage the contextually rich Evo-Ed Integrative Cases, which build directly on the interlinked nature of evolution and genetics and connect the science ideas with meaningful real-world examples. The Evo-Ed case studies track the evolution of traits from their origination in DNA mutation, to the production of different proteins, to the fixation of alternate macroscopic phenotypes in reproductively isolated populations. For example, the Evolution of Lactase Persistence case study examines the genetics, cell biology, anthropology, and biogeography of this system.

The human lactase gene (LCT) is a 55 kilo-base pair segment of the second chromosome.

The curriculum will integrate the three dimensions of science—the core ideas of biology, the science and engineering practices, and the crosscutting concepts—to support all students in building toward deep understandings of biological phenomena. The project will be guided by two main research questions:

  1. How does learning progress when students experience a set of coherent biology learning materials that employ the principles of three-dimensional learning?
  2. How do students’ abilities to transfer understanding about the relationships between molecules, cells, organisms, and evolution change over time and from one biological phenomenon to another?

Note: If you’ve used the Evo-Ed cases in your classroom, we’d love to get your feedback! Please respond to this short survey to be entered into a drawing to receive a $50 Amazon gift card.