Hello, world!

January 8th, 2013 by Chad Dorsey

For nearly 18 years, our logo has been a beautiful and complex sunflower, created by Senior Web Developer Noah Paessel. (He was Noah Fields back in 1994 when he worked at the Concord Consortium during his first stint with us, but that’s another blog post!)

With the former logo, our founder, Bob Tinker, wanted to showcase the Fibonacci sequence in nature, which represents a fascinating link between the sublime and the natural world and invites scientific inquiry and mathematical investigation. (Sunflower seeds exhibit many different Fibonacci spirals in their close-packed patterns, as do many other things in the natural world . Bob also thought Concord as a place evoked important concepts of revolution and free thinking and that the etymology of the name “Concord” linked with the sunflower expressed the ideas of “sharing one’s heart” and being “of the same mind,” both of which resonated with his pacifist and gentle nature.

We are now proud to announce our new logo, created by Derek Yesman of Daydream Design.

Concord Consortium Logo

This logo both simplifies and augments our original logo. It morphs the original sunflower while also referencing both technology and our core mission of generating, experimenting with and spreading important ideas.

The central star represents the initial spark of an idea, that “a-ha moment” of inspiration that can so quickly turn into extended experimentation – or possibly into a whole new research project. The light bulb surrounding it represents how we work to build these inspirational flashes into complete ideas and products and determine their potential to improve teaching and learning. The petals and radiating elements in the background represent our mission to spread the best of these ideas outward to transform learning for millions around the world.

We’ve recently modified our tag line to make this mission (and our ties to Concord’s location and history) even more explicit: Revolutionary digital learning for science, math and engineering.

By the way, for all you font geeks (don’t hide – we know you’re out there!) our logotype is rendered in Museo 500, part of Jos Buivenga’s excellent Museo family. We discovered this font when we worked with ISITE Design during our last website redesign – thanks Patrick! – and fell in love. Since then, we’ve explored the many weights of this font as well as its sans serif and slab variants. We’ve also had some early-adopter fun watching this font gain status and uptake in many print and Web locations on its way to becoming a modern classic.

We’re excited about this new logo and about how it represents an evolution we’re in the midst of as well. As we evolve toward a new phase as an organization while still embracing our legacy as pioneers in educational technology, we’re more committed every day to creating a bright future for STEM teaching and learning.

9 Highlights of 2012

January 3rd, 2013 by Cynthia McIntyre

It was a great year for the Concord Consortium!

  1. We won a Smaller Business Association of New England (SBANE) Innovation Award!
  2. Next-Generation Molecular Workbench interactives starred in the MIT MOOC (Massive Open Online Course) “Introduction to Solid State Chemistry” through a new collaboration with edX.
  3. Chad Dorsey described our vision of deeply digital education at the national Cyberlearning Research Summit.
  4. Six new projects were funded by the National Science Foundation: InquirySpace, Understanding Sub-Microscopic Interactions, High-Adventure Science: Earth’s Systems and Sustainability, GeniVille, Graph Literacy, and Sensing Science.
  5. The What Works Clearinghouse (WWC), a federally funded organization that scans educational research for high-quality studies, recognized our Technology Enhanced Elementary and Middle School Science (TEEMSS) software and materials.
  6. The Concord Consortium Collection was accessioned into the National Science Digital Library (NSDL).
  7. Our debut webcast featured Chad Dorsey, speaking about the scientific and engineering practices of the Next Generation Science Standards and our free, technology-based activities.
  8. We had two fabulous Google Summer of Code students.
  9. Our staff population increased by 10%, thanks to our new Software Portfolio and Project Manager Jen Goree, Web Developer Parker Morse, and Software Developer Tom Dyer, who just started (technically in 2013, but we’re so excited, we’ve included him on this 2012 list)!

2013 promises to be another great year! Follow us on Facebook, Twitter, Google+, and subscribe to our mailing list to receive print or email news updates.

Hitting the Wall

December 6th, 2012 by Amy Pallant

Gas laws are generally taught in high school chemistry. Students learn that Boyle’s law, for instance, can be expressed as P1V1=P2V2, where P is pressure and V is volume.

From the equation, it’s clear that there is an inverse relationship between the gas pressure and volume, but do students understand the molecular mechanism behind this relationship?

Since students are programmed to plug and chug, if you give them, say, P1, V1, and P2, they can find the numeric value of V2. Although students can get the correct answer, teachers have told us that their students don’t really understand the gas laws because they don’t have a mental model of what’s happening. Gases are, after all, invisible! Nor can students see volume or pressure.

Molecular Workbench makes the gases, volume, and pressure visible. With a new set of Next-Generation Molecular Workbench interactives, students can experiment with increasing the pressure on a gas to see why the gas volume decreases.

The “What is Pressure?” interactive (above) shows the inside (yellow atoms) and outside (pink atoms) of a balloon. (Even the velocities of the individual atoms are visible with vectors!) The green barrier represents the wall of the balloon.

Students learn that pressure is nothing more than molecular collisions with a barrier. In the beginning, atoms hitting the balloon wall on either side move it just a tiny bit—transferring some of their kinetic energy to the barrier. At equilibrium, the balloon wall remains (relatively) stationary. (Go ahead and run it to see!)

But if you add atoms to the balloon, the balloon wall moves out; more atoms means that there is increased pressure pushing outwards on the barrier. Since the number of atoms on the outside of the balloon hasn’t changed, the pressure pushing inwards is the same as it was before. With unbalanced forces, you get net movement.

With barriers, we can also measure the pressure caused by those molecular collisions.

In the “Volume-Pressure Relationship” interactive (above), students see a visual representation of Boyle’s law.

Other models allow students to investigate all the relationships of Charles’s law (V1T2=V2T1), Gay-Lussac’s law (P1/T1=P2/T2), and Avogadro’s law (V1/n1=V2/n2).

And, of course, all of these relationships together make up the Ideal Gas Law (PV=nRT). Explore gas laws today with some HTML5 molecular models!

Optimizing short-range and long-range atomic interactions

November 29th, 2012 by Piotr Janik

[Editor's note: Piotr Janik (janikpiotrek@gmail.com) was a Google Summer of Code 2012 student at the Concord Consortium and is now a consultant working on our Next-Generation Molecular Workbench.]

Some time ago we described the core engine used in Molecular Workbench and our attempts to speed it up. At that time we focused mainly on the low-level optimization connected with reducing the number of necessary multiplications. This promising early work encouraged us to think even more about performance.

We next reviewed existing algorithms in the core of the molecular dynamics engine. To make a long story short, atoms interact with each other using two kinds of forces:

  • Lennard-Jones forces (repulsion and short-range attraction)
  • Coulomb forces (electrostatic and long-range attraction)

Atomic interactions are pairwise, meaning that we have to calculate forces between each pair of atoms while using the basic, naive algorithm. Having n atoms, we must perform about n^2/2 calculations. “The Big O” notation can be used and the computational complexity can be described as O(N^2), which means that the execution time of calculations grows very fast as the number of atoms used in the simulation increases. This is definitely an unwanted effect, but fortunately there are ways to reduce the complexity.

Solutions are different for short-range and long-range forces, so let’s start with short-range. “Short-range” means that atoms interact only while they are quite close to each other. Let’s use rCut as a symbol for the interaction maximum distance. So, one obvious optimization would be to limit calculations to pairs of atoms that are closer to each other than rCut. How? There are two popular approaches—cell lists and Verlet (neighbor) list algorithms.

The cell lists algorithm is based on the concept that we can divide the simulation area into smaller boxes or cells. Each cell dimension is equal to the maximum range of interaction between atoms—rCut. So, while calculating interactions for a given atom, it’s enough to take into account only atoms from the same box and its closest neighbors. Atoms in other boxes are too far to interact with this atom. This is both simple and effective, reducing computational complexity to O(N)! Note that it’s C * O(N) with a pretty significant C, unfortunately.

However, while calculating interactions between atoms in neighboring cells, still only 16% of atoms that we take into account are interacting! This is a waste of resources and where we find room for further optimizations. So, what about creating a list for each atom, which contains only atoms actually interacting with it? This Verlet or neighbor list algorithm as it’s called works well. The only problem is that we have to be smart about updating these lists, as atoms constantly change their position and, thus, their “neighborhood.” We can slightly extend these lists to also include some atoms outside the area of interaction. So each list should include atoms closer than rCut + d from the given atom, where d defines a buffer area size. Because of that, lists need to be updated only when the maximum displacement of some atom, measured since the moment of the previous lists update, is bigger than d. If it’s smaller, neighbor lists are still valid. Lists can be updated using the normal, naive algorithm (which still leaves the complexity O(N^2)), or even better, using the cell lists algorithm presented above! This ensures complexity O(N) and greatly reduces inefficiencies of the cell lists approach.

We’re also working on long-range forces optimization. Since we can no longer use the assumption that atoms interact only when they are close to each other, we can’t rely on the optimization strategies above. The algorithms are now more complicated. The problem of the electrostatic interaction is akin to a problem of gravitational interactions (called N-body problem), popular in astrophysics. One of the most common algorithms for speed-up of such calculations is the Barnes-Hut algorithm. We tried to implement it, but the overhead connected with creating additional data structures was bigger than potential performance gains. The reason is that the number of charged atoms we use in our models is too small to see the advantage of such an approach. As a result, we left our naive algorithms for long-range interactions, which perform better due to their simplicity.

However, we successfully implemented both short-range optimizations in Next-Generation Molecular Workbench and the results are spectacular. The speed-up varies from 20% for really small models (where the number of atoms is less than 50) to 700% for bigger ones (where the number of atoms is about 250). This is the really significant improvement and made complex models usable. As you can see, conceptual, algorithmic optimizations really matter!

We’re still thinking about further optimizations, both low level and algorithmic. Stay tuned as the Next-Generation MW is getting more and more computational power!

Better than an Apple, a Gift for Teachers

November 27th, 2012 by Ethan McElroy

Thanks to everyone who entered our Suggest-a-Model contest. We always enjoy hearing from teachers and love to help with hard-to-teach science concepts. If you haven’t already, please vote for the model you’d most like us to build.

To Vote

1) Go to our Facebook page (you like us on Facebook already, right?)

2) Look for the poll pinned to the top left of the page’s wall

3) Click on the idea you like most to cast your vote

Our goal is to build a custom computer model to help teach a complex, science, math or engineering concept suggested by real teachers, like YOU! We know all too well the awkwardness of jumping up and down and waving your hands to model the behavior of molecules or dancing around the classroom to model photosynthesis.

We received a lot of great ideas and whittled the list down to three concepts.

One finalist told us that her students “are always making fun of me looking like I am doing a swim stroke in front of the class” when she tries to model convection! She’d love a new set of heat transfer models!

Another finalist is looking for a model of nutrient runoff into coastal waters and how that stimulates harmful algal bloom production. Concerned about the environment? Show your support for this model!

A model of meiosis and genetic recombination (known as crossing over, when exchanges of chromosome portions occurs) also made it to the top three. If you teach biology or know a student who’s taking Bio, this may be the one for you.

Voting ends on November 30th, so please go to our Facebook page and vote now.

After voting is over, we’ll announce the winner and get started on building the model. And once it’s done, it’ll be available for free to everybody. Win-win all around! If you want to know when it’s available, be sure to like us on Facebook, follow us on Twitter and subscribe to our mailing list and RSS feed. We’ll be posting about it through all those channels.

But don’t wait to use our models. Check out our Activity Finder and Classic MW. These free resources contain lots of great examples of the models we already have available for science, math and engineering teachers at all grades. You’re sure to find an activity (or two or three!) that covers other difficult-to-teach concepts. Enjoy!

Google Summer of Code Development: Single Sign-On

October 18th, 2012 by Vaibhav Ahlawat

[Editor's note:  Vaibhav Ahlawat was a Google Summer of Code 2012 student at the Concord Consortium.]

At any time, the Concord Consortium runs a number of small research projects and large scale-up projects, but in the past we built each system separately and each required a separate login. Want to teach your fourth graders about evolution? Great. Log in at the Evolution Readiness portal. Wait, you also teach your students about the cloud cycle? That requires logging in at the Universal Design for Learning (UDL) portal.

Clearly, some students and educators find value across different projects, and my goal is to make it a little easier for them to sign in just once and get access to the myriad great resources at the Concord Consortium for teaching science, math and engineering. As a Google Summer of Code student, I’m working under the guidance of Scott Cytacki, Senior Software Developer, to bring different projects under a single authentication system or, in the language of software development, a Single Sign-On.

Single Sign-On will allow both students and teachers to login across different projects with a single username and password, doing away with the need to remember multiple usernames and passwords. They’ll be able to move seamlessly among projects and find the resources they need to teach and learn. I’m also working on code that will allow students and teachers to sign up and login to Concord Consortium’s portals with their existing Google+ or Facebook accounts.

For those who want technical details, read on.

I’m working on moving from Restful Authentication to Devise, both of which are authentication solutions for Rails. These days, Devise is the preferred one among the Rails community and it makes things like password resetting and confirmation email pretty easy. Once we are done with this conversion, adding the support for signup and login using Facebook and Google+ accounts should be simple. For example, to add support for Google Oauth2 authorization protocol, all we have to do is add a gem named omniauth with Oauth2 strategy, which works brilliantly with Devise, then write a couple of functions.

Here’s a snippet of my code, which adds google oauth2 support to Devise

class Users::OmniauthCallbacksController < Devise::OmniauthCallbacksController
    def google_oauth2
 
    # The User.find_for_google_oauth2 method also needs to be implemented.
    # It looks for an existing user by e-mail, or creates one with a random password
    @user = User.find_for_google_oauth2(request.env["omniauth.auth"], current_user)
 
    if @user.persisted?
      flash[:notice] = I18n.t "devise.omniauth_callbacks.success", :kind => "Google"
      sign_in_and_redirect @user, :event => :authentication
    else
      session["devise.google_data"] = request.env["omniauth.auth"]
      redirect_to new_user_registration_url
    end
  end
end
Including support for authentication using the Facebook API can be done simply. Support for Oauth, which is used by many learning management systems, is provided, making integration far more easier than it was before.

I’m happy to help make it easier for Concord Consortium’s resources to be used by many more people.

– By Vaibhav Ahlawat

Improving Speed at the Heart of Molecular Workbench

September 27th, 2012 by Dan Barstow

At the heart of Molecular Workbench’s modeling of atomic interactions is a profoundly important but fundamentally simple concept:

At close distances, atoms attract each other until they get so close that they repel.

Here’s a demo of that concept: two atoms interacting. Drag the green atom to various locations near and far from the purple atom and watch what happens as the two atoms approach each other and move apart. (If you’re wondering why the atom slows down and stops, the answer is that we apply an artificial damping force to the green atom in order to make it easier for you to “grab” it and play with it.)

This concept is called intermolecular attraction. Molecular Workbench (MW) uses an approximate formula for calculating the intermolecular potential that was originally proposed by John Lennard-Jones (in 1924!) and is now called the “Lennard-Jones potential” or L-J for short.

Lennard-Jones potential

Here you see the L-J potential as a graph. The horizontal axis shows distance between two atoms, the vertical axis is the net intermolecular energy, with regions of negative slope indicating that the resulting force is repulsive, and regions of positive slope indicating attraction. This graph shows that these atoms will attract to each other if they are more than 2.3 radii apart, but begin to repel sharply at distances less than that value as shown by the steep rise in the curve.

This interaction is not just a fundamental concept in physics and chemistry. It also is quite central to the MW simulation engine. We typically do this calculation tens or hundreds of thousands times per second, especially when we have many atoms interacting, such as here:

If you study our code, going deeper and deeper, you’ll peel away layers of the coding onion, until you get to the very center of this model, which calculates the L-J force between just one pair of atoms. (Did we mention that the reason the L-J approximation is used is that it’s considered relatively fast to calculate?) It does this for each of the many pairs of interacting atoms, repeating over and over, with each time-tick of our simulation.

As it turns out, the L-J formula is still computationally demanding, requiring calculating 6th and 12th powers. (That’s the theory. In practice, the form that is most convenient for our code happens to use the 8th and 14th powers. That also speeds things up–but we’ll save that for another blog post.) So when we’re looking for ways to make our code run faster and our model run better, the L-J calculation is a prime place to look!

In our first pass at improving speed (in MW Classic), we converted the 6th and 12th power calculations to simpler repetitive multiplications:

X2 = X * X

X3 = X2 * X

X6 = X3 * X3

X12 = X6 * X6

That gave us just 4 multiplication tasks, instead of 16 (X6 has 5 multiplications, X12 has 11).  This reduced the calculation demand to 25%. The model ran faster and smoother.

Recently, we tried another method to improve speed: using a look-up table, with pre-calculated values. We computed the L-J values for each element type for dozens of typical interatomic distances and put them into a table. We thought this would save computational time because the software could simply look up the values in the table without any multiplication.

We tested the two methods (multiplication vs. look-up table) over many iterations and found that the look-up table was slower! As we investigated further, we saw that accessing the look-up table had its own overhead in terms of use of cache memory and data transfer. This was evidence of a trend in improving computer speeds over the past few years: computation speeds improve at a faster rate than memory access speeds. They both are faster, but computation (i.e., all that multiplying we have to do) has gotten the improvement edge.

So, after this testing, we went back to the efficient multiplication approach. This illustrates our basic approach: creative thinking validated by empirical data.

We will continue to develop and test creative ways to speed the software and improve the user experience, especially as we move to support a greater variety of learning activities and computer platforms.

Exhibit Booth at BCCE Conference and free MW buttons

August 10th, 2012 by Dan Damelin

Just got back from the Biennial Conference on Chemical Education (BCCE 2012), where I participated in a symposium titled “Web-Based Resources for Chemical Education.” About 60 people attended to learn about Molecular Workbench and other online tools and resources. One of the audience questions was about future availability of Molecular Workbench on the iPad and other tablets. Our latest work on the HTML5/JavaScript next-generation MW project, generously funded by Google.org, will address exactly this. We’ll be bringing much of the Java-based Classic MW to the browser, so that any device running a modern Web browser will be able to run our newest interactives and activities.

I didn’t get to attend many of the other sessions at BCCE because much of my time was spent staffing Concord Consortium’s exhibit booth to disseminate our free software. Jeanne Hurtz and I spoke with hundreds of people who stopped by our booth to hear about the current MW capabilities and see a next-generation MW model running on a tablet. We gave away about 350 MW buttons, but have a few left. If you’d like one of your own, please stop by our office at 25 Love Lane in Concord, MA, to pick one up!

It was great to share the excitement of MW’s potential and versatility with so many new people. We heard from many (surprised) guests at our booth: “This is free?”  Yes! And so is the button.

Bungee Physics

August 7th, 2012 by Bob Tinker

Last week, Paul, Ed, and I did physics. This is such a rare event that it deserves note. We actually developed a theory, collected data, compared theory to data, came up with new ideas and tested them. We only wish kids everywhere could have the same experience.

This investigation was prompted by Ewa Kedzierska’s presentation at the World Conference on Physics Education in Istanbul in early July*. She presented a student activity on bungee jumping that claimed that the jumper falls faster than a free-falling object. This seems difficult to believe, in spite of video data she presented—collected and graphed by the wonderful COACH software—that clearly showed this to be true. We immediately thought of many reasons why this should be impossible. Imagine jumping without a tethered Bungee cord—jumper and cord would fall in free-fall just as Galileo proved in his famous Tower of Pisa experiment (never mind the fatal consequences—this is physics!). Attaching the far end of the Bungee rope would seem to apply an upward force that could only slow the jumper, not speed her up!

As typical science skeptics, we had to do it ourselves and understand the mechanism, if the effect was true. Following the maxim that was current when CERN supposedly found neutrinos travelling faster than light—“Extraordinary results require extraordinary evidence”—we needed to do the experiment ourselves and get a feel for the situation. So Ed  gathered a stepladder, chain (substitute Bungee), tennis ball (for the jumper), and a camera that takes 240 frames per second, and we collected data.

Paul, ever the theoretician, showed that the far end of a horizontal chain link held steady at the near end would fall faster than a free body, and hence, could impart some force to the falling chain. Thus, each chain link, on reaching the bottom of the “U” formed by the falling links, could impart a bit of force on the falling side and make it fall faster than free-fall. Another way of saying this is that each link, when brought to a halt, rotates 180 degrees and can exert some torque on the falling side.

We collected the data, and clearly saw the effect. It is real! And it is huge when the falling mass is small. We photographed side-by-side tennis balls, one attached to a chain and one in free fall. The one with the chain fell faster! Every time. The picture shows a frame from a movie of the experiment, clearly showing Paul about to fall (he didn’t), and the free-falling ball going slower.

Don’t believe us? Do it yourself. We attached a force sensor to the end of the chain and could detect the force from individual links. The force increased non-linearly and dramatically. Stopping the last link required 50 N even though the entire chain weighed only 4 N (see graph). We are still arguing about why the force increases so much for the last few links.

I noticed that sometimes if the falling part of the chain is close to the tethered part, the links at the bottom of the “U” do not rotate, but slide. When they slide, they do not rotate and, hence, should not accelerate the falling chain. We could hear the difference, but our results were inconclusive, because near the end of the fall, the chain doesn’t fall evenly and this causes it to revert to the link-rotation mode.

In our next blog, we’ll present the data and our analysis. Stay tuned.



Reconnecting with Ton Ellermeijer at WCPE

July 30th, 2012 by Bob Tinker

I recently attended the modestly named World Conference on Physics Education in Istanbul. One of the highlights of the meeting was connecting with my old friend Ton Ellermeijer and meeting his colleague, André Heck.

Some of the most innovative developments in educational technology have been made during the last 25 years at the AMSTEL Institute at the University of Amsterdam, The Netherlands, under the direction of Ton Ellermeijer. At this university, Ph.D. students in physics and other sciences could specialize in education at the Institute, which was on a par with more traditional areas of physics research. Sadly, a new dean eliminated AMSTEL in 2010. Ton soldiers on from a nonprofit he founded in 1987 (Foundation CMA), but with a reduced staff.

AMSTEL developed extensive probeware for real-time data acquisition, as well as several generations of COACH, software for analyzing these data, modeling, control, video data capture and animations. This technology has been integrated into STEM instruction using well-designed and tested materials. One area in which they have done particularly interesting work is sports physics using video analysis. Widely used in Europe, this material is unknown in the U.S., which is a great loss.

André Heck worked with Ton for a decade and published nearly 60 scholarly articles on various aspects of this research. This wealth of material has recently been collected in André’s Ph.D. thesis.The print version of the thesis comes with a CD ROM that includes all these articles as well as considerable student materials.