In order to scale up the models from microscopic to macroscopic, we employ specific unit-scaling conventions. The Next-Generation Molecular Workbench (MW) engine simulates molecular behavior by treating atoms as particles that obey Newton’s laws. For example, the bond between two atoms is treated as a spring that obeys Hooke’s law, and electrostatic interactions between charged ions follow Coulomb’s Law.

Dipole-dipole interactions simulated using Coulomb’s Law.

At the microscale, the Next-Generation MW engine calculates the forces between molecules or atoms using atomic mass units (amu), nanometers (10⁻⁹ meters) and femtoseconds (10^-15 seconds), and depicts their motion. To simulate macroscopic particles that follow the same laws, we can imagine them as microscopic particles with masses in amu, distance in nanometers, and timescales measured in femtoseconds. Once the Next-Generation MW engine calculates the movement of these atomic-scale particles, we simply multiply the length, mass and time units by the correct scaling factors. This motion satisfies the same physical laws as the atomic motion but is now measured in meters, kilograms and seconds.

In the pendulum simulation below, the Next-Generation MW engine models the behavior of a pendulum by treating it as two atoms connected by a very stiff bond with a very long equilibrium length. The topmost atom is restrained to become a “pivot” while the bottom atom “swings” because of the stiff bond. Once the engine has calculated the force using the atomic-scale units, it converts the mass, velocity and acceleration to the appropriate units for large, physical objects like the pendulum.

Large-scale physical behavior simulated with a molecular dynamics engine.

In order to appropriately model the physical behavior of a pendulum or a spring, we use specific scaling constants. Independent scaling constants for mass, distance and time enable us to convert nanometers to meters, atomic mass units to kilograms and femtoseconds to model seconds. Using the same scaling constants, we can derive other physical conversions, such as elementary charge unit to Coulomb. In order to make one model second pass for every real second, we adjusted the amount of model time between each page refresh. We also chose to simulate a gravitation field—a feature usually absent in molecular dynamics simulators—because it is relevant to macroscopic phenomena.

From microscale to macroscale, the Next-Generation Molecular Workbench engine is a powerful modeling tool that we can use to simulate a wide variety of biological, chemical, and physical phenomena.  Find more simulations at mw.concord.org/nextgen/interactives.

MW was developed over a decade with funding from the National Science Foundation by senior scientist and software developer Charles Xie. It includes a powerful physics engine that calculates the forces acting at the atomic level, with rules for photons, chemical bonds and macromolecules, plus Newton’s laws to determine the resulting motion. With all these calculations, emergent behavior, um… simply emerges! And that means MW can simulate real scientific phenomena over a wide variety of domains—from microscopic to macroscopic—in chemistry, physics, biology and more.

With one product harnessing all that power and flexibility, we had tried to convey quite a lot in our original logo. The diverse history of MW’s development contributed as well to a logo that had become as internally diverse as MW itself. Based roughly on a methane molecule to show its roots in the molecular world, each “hydrogen” atom surrounding the central “carbon” workbench showed one of the many, many phenomena MW could model.

We’re now moving MW to the Web, thanks in no small part to generous funding from Google, and we’re revamping our logo for this brave new world. Our goal was to simplify MW’s logo while still conveying its diversity—its ability to demonstrate ideas across multiple scales and bring to life the dynamic nature of the molecular world all around us. We also wanted this, our “flagship” product, to connect to our recently redesigned Concord Consortium logo.

We’re pleased with the result, and think it accomplishes all of this and more. The new MW logo’s central star is the same as the star inside the Concord Consortium’s new logo. Here it represents the nucleus of inspiration surrounded by dynamic and colorful stylized atomic “orbits” that evoke MW’s dynamic nature. These shapes hearken back to classic representations of the atomic world, evoking the Bohr model so central to the history of atomic understanding, while at the same time hinting at electrons’ evanescent quantum nature—which MW can also demonstrate quite effectively.

Viewing with another eye, you may instead see something at a vastly different scale. Spheres exhibiting circular motion? A representation of a star and orbiting planets? Even another surprising new solution to the three-body problem? If so, you’re not wrong either—it turns out that MW can model just about anything.

This is the next generation of MW and we’re excited about expanding the use of this software. It’s been downloaded a million times already. Go ahead—make it a million and one.

Special thanks to Derek Yesman of Daydream Design, who created our new logo.

Basically FF versions after v16 are now almost unusable running NextGen models of any complexity for longer than 30s.

See: Firefox Performance Comparison 20131902 and Confirmation of FF slowdown over time.

There are several active Mozilla issues that people are working on. This is my original bug report:

• [Bug 791699] Slow setting of attributes on SVG elements due to time spent in region operations on this molecular dynamics simulation

And the other bug reports resolving this issue depends on:

• [Bug 827915] Get rid of SVG’s own invalidation mechanisms and rely on DLBI instead
• [Bug 839865] Stop calling nsSVGUtils::InvalidateBounds for SVG transform changes, and use DLBI instead

I looked around for a while to see if this has been documented anywhere, but I didn’t find anything. So I decided to try something and test it to see if it would work.

The Conclusion

Yes it is possible. At least the ‘%t’ option when added to the request headers is the time which the request is started. This time is before most of the POST data is uploaded, so it can be used to get an estimate of upload times. This estimate seems very good with my testing, but it should be verified in the real world of computers in schools before relying on it for something important.

The Test

The idea for this test came from Aaron Unger.

In summary it was tested with a simple Rack app running on a EC2 server that was identical to the servers we use to run our portals. Then on the client side I used curl and Charles (the personal proxy) to send it a large chunk of data and record the timing.

The server was running Apache 2.2.22 and it was configured with a Passenger web app. I won’t go into that setup here. Additionally I added this to the Apache configuration:

RequestHeader set X-Queue-Start "%t"

Then in the web app folder I added this config.ru file:

run lambda { |env| 
  start_time = Time.now
  if env['rack.input']
    env['rack.input'].read
  end
  sleep 5
  [200, {"Content-Type" => "text/plain"}, 
    ["Apache Start Time: #{env['HTTP_X_QUEUE_START']}\n" +
     "Start Time: #{start_time}\n" +
     "End Time: #{Time.now}\n"]]
}

Then on my local machine. I ran Charles the personal proxy. This starts a proxy on port 8888.

I made a large random data file with:

head -c 2000000 random_data

Then I sent that off to the server with curl:

% time curl -x localhost:8888 --data-urlencode something@random_data http://testserver-on-aws
Apache Start Time: t=1359399773413862
Start Time: 2013-01-28 19:02:55 +0000
End Time: 2013-01-28 19:03:00 +0000
.
real    0m8.229s
...

Converting the time stamp shows the apache start time is 3 seconds before the start time. The simple server always waits for 5 seconds so together this makes up the 8 seconds reported. Bingo!

I wasn’t convinced that the 3 seconds was actually the upload time. I thought perhaps it was some apache processing time that happened after the upload. So I used the throttle option in Charles to slow down the upload. Doing this gave the expected result: the apache start time was even earlier than before. And subtracting the end time from the apache start time was very close to the total request time reported on the command line.

Notes

This server side approach does not cover all the time that user is waiting for an upload to complete. I would guess there will be cases when it isn’t accurate. For example some proxy or other network device might delay POST requests in someway and in that case this approach would not record that time.

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.

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.

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.

Design a city block with Energy3D.

A sample design of the city block.

A sample timeline graph.

• The height of a spike measures the action intensity at that moment, i.e., how many actions the user has taken since the last recording;
• The density of spikes measures the continuity and persistence of actions over a time period;
• A gap indicates an off-task time window: A short idling window may be an effect of instruction or discussion;
• The trend of height and density may be related to loss of interest or improvement of proficiency in the CAD tool: If the intensity (the combination of height and density of spikes) drops consistently over time, the student's interest may be fading away; if the intensity increases consistently over time, the student might be improving on using the design tool to explore design options.

Timeline graphs from six students.

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!

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!

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!

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

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

– By Vaibhav Ahlawat

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.

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.

A hypothetical nano sorting machine.

• All-atom molecular dynamics
• Coarse-grained molecular dynamics
• Gay-Berne molecular dynamics
• Soft-body biomolecular dynamics
• Quantum dynamics (including real space and imaginary space)

A Gay-Berne model of molecular self-assembly.

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.

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 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.

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.

The growing role of digital textbooks is gaining momentum. Major publishers are not just converting their textbooks to digital format, they’re also reconceptualizing them, adding a more diverse array of embedded interactives and providing states and districts with the option to pick and choose sections to meet local educational goals.

Think about this for a moment.

We are used to the monolithic textbook package – a basal textbook, lab manuals, CDs and other ancillaries. Each major publisher offers its package. States and districts decide which publisher’s package to purchase. End of story.

But that world is changing. A district might choose several chapters from one publisher and other chapters from a second publisher. From a third publisher, they might select a lab manual that is especially engaging for their students. And they might select multiple online resources to extend student learning.

From the teacher’s perspective, this is potentially liberating. Instead of working through the standard textbook and its aligned support materials, teachers have a richer set of options. They can select resources based on personal expertise, knowledge of their students, teaching style and familiarity with the growing array of digital interactives.

How does Molecular Workbench fit in? MW helps students understand fundamental principles of physics, chemistry and biology, yet it hasn’t always been clear how to fit this into the classroom, as it might seem a diversion from the flow of the textbook.

With a more flexible approach to teaching and learning, science teachers will be able to easily integrate the power of atomic and molecular simulations into their classrooms. This will not be an aberration, but the new norm.

This change will take a few years to fully play out, but it is a welcome transition away from the dominance of the standard, one-size-fits-all textbook and towards freedom to use a robust set of resources – including Molecular Workbench.

I taught chemistry and one of the most difficult things I had to deal with was the fact that pretty much everything we explored had to do with atoms and molecules too small to see. Somehow chemistry students need to find a way to imagine a world full of uncountable, invisible particles flying around at blistering speeds, colliding, reacting, attracting and repelling. For the past 10 years we have been developing a piece of software to address this challenge–the Molecular Workbench. During that time I worked with the MW team to create simulation-based activities that give students a concrete handle for thinking about the atomic-level world. Using these activities, students do virtual experiments with atoms and molecules, push and prod them, change the parameters of various simulations and build their own mental model of the atomic foundation of the world around them.

Do you struggle with this issue or just want to “play” with some molecules? Below are a few of my favorites:

• Intermolecular Attractions
• Chemical Bonds
• Diffusion and Active Transport (This activity is a nice connection with biology. Did I mention we have models across physics, chemistry, biology and biotechnology?)

Check out the huge collections of models and activities found at http://mw.concord.org and be sure to take a look at our latest work in making the Molecular Workbench run in the browser without the need for Java.

Have a great summer. May it be both relaxing and productive.

Do you bookmark your favorite websites in categories or sequence the most important ones first? Have hundreds of favorites? Or a top ten list? However you manage them, your Web, music and other playlists are an essential part of keeping your technical life efficient and effective.

Such a “playlist mentality” is becoming increasingly clear in education. And the teachers we’ve interviewed have requested personal playlists of Molecular Workbench activities and models.

Needless to say, each teacher has his or her own style of teaching, manifested in how they use MW. Some use it mostly for classroom demos, others focus more on direct student use. Some use MW just a few times during the year, others almost weekly. Some use it during class time, others assign MW activities for homework.

Teachers also vary in how they sequence topics during the year. One chemistry teacher might teach organic chemistry late in the year as a synthesis while another might teach it earlier as a way to whet the appetite and spark questions.

The MW development team confronted this diversity challenge (and opportunity) early on. It was clear we could not create, for example, a single master flow of MW activities for high school chemistry. Instead, we need to support differences among teachers by providing a rich variety of activities. Teachers can select, adapt and integrate models and activities into their own style and classroom flow.

As we’ve been documenting in videos and articles, we are now creating a Web-based Molecular Workbench system, which will give teachers more flexibility in how they select and sequence the activities. At the simplest level, they’ll be able to use standard browser tools to bookmark their favorites, organizing them in whatever flow they want. And our teacher interviews have pushed us even further in thinking about design.

Our goal is to include the ability for teachers to store personal MW playlists – with even more information than you get in a bookmark, such as level of difficulty and duration of activity. Teachers will be able to provide playlists to students or share them with colleagues.

We still have to work out the details, but we’ve heard from teachers and high on their wish list is a personal playlist with lots of flexibility to meet their needs and style. That’s the best way to make sure MW is effective and widely used!

It takes a lot of computation to model the atomic and molecular world! Fortunately, modern Web browsers have 10 times the computational capacity and speed compared with just 18 months ago. (That’s even faster than Moore’s Law!) We’re now taking advantage of HTML5 plus JavaScript to rebuild Molecular Workbench models to run on anything with a modern Web browser, including tablets and smartphones.

Director of Technology Stephen Bannasch describes the complex algorithms that he’s been programming behind the scenes to get virtual atoms to behave like real atoms, forming gases, liquids and solids while you manipulate temperature and the attractive forces between atoms. See salt crystallize and explore how the intermolecular attractions affect melting and boiling points. Imagine what chemistry class would have been like (or could be like today) if the foundation of your chemical knowledge started here.

Technology and Curriculum Developer Dan Damelin goes on to describe how open source programming opens up possibilities. For instance, Jmol is a Java-based 3D viewer for chemical structures that we were able to incorporate into Molecular Workbench to allow people to easily build activities around manipulation of large and small molecules, and to make connections between static 3D representations and the dynamic models of how molecules interact. We’re planning to build a chemical structure viewer that won’t require Java and will extend another open source project based on JavaScript and WebGL to visualize molecules in a browser.

Interested in this innovative programming? Great! We’re looking for software developers.

The significance of this advance struck me at a recent conference on educational technology sponsored by the Software & Information Industry Association. Many creative people and companies attended, from large publishers to innovative startups. Throughout the presentations and conversations, I envisioned ways these potential partners might use MW to enhance their products and services.

Ron Dunn, CEO of Cengage, gave a keynote describing their new digital textbooks and aligned homework helpers and other digital resources. He pointed out that 35% of their sales are “digitally driven,” and that technology is essential to their future. Other major publishers echoed those messages. When publishers embed Molecular Workbench models and activities throughout their e-books as a consistent modeling environment, students will be able to investigate fundamental principles of chemistry, physics and biology more deeply than the simple animations and videos now so typical in e-books.

SmartScience is a startup, developing supplemental science education activities. Their idea to link videos of science phenomena with corresponding graphing tools is clever. For example, in a time-lapse video of rising and falling tides, students mark the ocean height and automatically see their data in a graph in order to understand both the scientific phenomena and the graph output. Augmenting reality is great, and we love the idea of integrating videos of physical, chemical and biological processes at the macroscopic scale with MW models to show what happens at the microscopic scale.

Karen Cator, Director of the Office of Educational Technology at the U.S. Department of Education, discussed a new framework for evaluating the effectiveness of educational technology projects. Software can monitor how students work their way through online problems, providing teachers with deeper insights on student learning, especially in terms of scientific thinking and problem-solving skills. Teachers can focus on students’ higher-level thinking skills, and provide useful, real-time feedback to identify strengths, progress and areas in need of help. We agree whole-heartedly and have been working on ways to capture student data in real time and provide feedback loops for teachers. Our next-generation Molecular Workbench will record what students do as they explore the models and make that information available to teachers and researchers.

Partnerships with creative teachers, publishers, and software developers will help us ignite large-scale improvements in teaching and learning through technology. That’s our mission and our goal for Molecular Workbench. Thanks to Google funding, we’re working to increase access to the incredibly powerful next-generation Molecular Workbench.

I was terrible at the first four weeks of organic chemistry. I just couldn’t get the right pictures into my head.

The depictions of the chemical reaction mechanisms I was supposed to memorize seemed like just so many Cs (and Hs and Os and, alarmingly, Fs) laid out randomly as if I were playing Scrabble. And I swear the letters rearranged themselves every time I looked away, like a scene out of a movie about an art student’s science-class nightmares (minus the extended fantasy sequence in which the letters grow fangs and leap off the page to menace the poor protagonist – unless I’ve blocked that part out).

Fortunately, I knew exactly what to do: I had to start picturing molecules in 3D, and in motion, as soon as possible. That ability seemed to take its own sweet time to develop. But once things “clicked” and I could visualize molecules in motion, the reactions finally made sense, as did all the associated talk of electronegativity, nucleophilic attack, and inside-out umbrellas. I aced the final.

Now, our Molecular Workbench software isn’t specifically designed to help undergraduates get through organic chemistry. It is designed to help students at many levels by letting them interact with simulations of the molecular world so they get the right pictures into their heads, sooner. It’s here to help that future art student and movie director beginning to nurse a complex about the 10th grade science class he’s stuck in right now.

The weight of history

But the “Classic” Molecular Workbench we have now was built for a different world. It runs in desktop Java, for one thing, meaning (among other things) that it’ll never run on iPads. More fundamentally, it was built to be “Microsoft Word for molecules” in a time when Microsoft Word was the dominant model for thinking about how to use a computer:

“Hello, blank page! Let’s see, today I’ll make a diffusion simulation. I should write something about it … Let’s make that 12-point Comic Sans. No, my graphic designer brother-in-law keeps telling me not use that so much, so Verdana it is, then. Now how do I add that model again? Oh yeah, Tools -> Insert -> Molecular Model…”

This model is constraining even though it’s always been possible to download and open Molecular Workbench via the Web, and even though MW saves simulation-containing activities to special URLs.

We have somewhat different expectations these days because of the Web, social media, mobile apps, and casual games. If I build a great in-class “activity” based on a series of molecular models, then I should be able to share that activity with the world with minimum difficulty. And if you find one of the simulations I created particularly illustrative, you should be able to put that model in a blog you control, or include the model as part of your answer to a question on http://physicsforums.com/.

Moreover you ought to be able to perturb the running simulation by reaching out and touching it with your fingers, or simply by shaking your tablet to see what effect that has on the simulation “inside” it. You shouldn’t be required to operate the simulation at one remove, via a mouse and keyboard, when it’s not necessary.

That’s why we’re excited about the Google-funded, next-generation Molecular Workbench we have started to build. The HTML5 + JavaScript technology we’re using to build the next generation of our MW software (hereafter called NextGen MW for short) will make it much more practical to enable these kinds of uses.

Boldly doing that thing you should never do

But designing NextGen MW to be a native of the real-time Web of 2012 rather than a visitor from the land of 1990s desktop computing means that we’re committed to rebuilding the capabilities of “Classic” Molecular Workbench from scratch. That is, we’re doing the very thing Joel Spolsky says you must never do! But ignoring platforms which run Java badly or not at all isn’t an option, and neither is trying to run Classic MW in a Google Web Toolkit-style compatibility layer that compiles Java to JavaScript. (With the latter option, we would almost surely be unable to optimize either the computational speed or the overall user experience well enough to make it practical to use NextGen MW on phones, inexpensive tablets, or even expensive tablets. But even that misses the point. We’re not a consumer products company trying to optimize the return on our past investment. We’re an R&D lab. We try new things.)

But writing things from scratch poses a challenge. We want the molecular dynamics simulations run by NextGen MW to run “the same” as the equivalent simulations run in Classic MW. But “the same” is a slippery concept. In traditional software development, asking two different implementations of a function or method to produce the “same” result often means simply that they return identical data given identical input, modulo a few unimportant differences.

It would be nice to extend this idea to the two-dimensional molecular dynamics simulations we are now implementing in NextGen MW. Classic MW doesn’t have a test suite that we can simply adapt and reuse. But, still, we might think to set up identical initial conditions in NextGen MW and Classic MW, let the simulations run for the same length of simulated time, and then check back to make sure that the atoms and molecules end up in approximately the same places, and the measurements (temperature, pressure, etc.) are sufficiently close. And, voilà, proof that at least this NextGen MW model works “the same” as the Classic MW model. (Or that it doesn’t, and NextGen MW needs to be fixed.)

Never the same thing twice?

Unfortunately, this won’t work. Not even a little bit, and the reason is kind of deep. The trajectories of the particles in a molecular dynamics simulation (and in reality) exhibit a phenomenon known as sensitive dependence on initial conditions. Think of two identical simulations with exactly the same initial conditions except a tiny difference. Now, pick a favorite particle and watch “the same” particle in each simulation as you let the simulations run. (And assume the simulations run in lockstep.) For a very short time, the particle will appear to follow the same trajectory in simulation 1 as in simulation 2. But as you let the simulation run a little longer, the trajectories of the two particles will grow farther and farther apart, until, very quickly, looking at simulation 1 tells you nothing about where to find the particle in simulation 2.

Very well, you say: maybe simulation 1 and simulation 2 started a little too far apart. So let’s make the difference in the initial conditions a little smaller. Sure enough, the trajectories stay correlated a little bit longer. But a very little bit. Here’s the rub: if you want to simulation 2 to match simulation 1 for twice as long, you need the initial conditions to be some number, let’s say 10, times closer. But if you need the simulations to match for 1 more “time” as long, that is, 3 times as long, you need the initial conditions to be 10 times closer still, or 100 times closer. And if you want simulation 1 to make a meaningful prediction about simulation 2 for ten times as long? Now you need the initial conditions to be a billion(10⁹) times closer. In practice, this means that if there’s any difference at all between the two initial conditions, no matter how seemingly insignificant, then outside of a short window of time the two simulations will predict very different particle locations and velocities.

Perhaps you think this is a contrived situation having nothing to do with comparing Classic MW and NextGen MW. Can’t we start them with, not just similar, but identical initial conditions? Unfortunately, this escape hatch is barred, too. The tiniest and most seemingly insignificant difference between the algorithms NextGen MW runs and the algorithms Classic MW runs right away result in a small difference in the trajectories, and after that point, sensitive dependence on initial conditions takes over: the subsequent trajectories soon become totally different. Trying to run precisely the same algorithms in NextGen MW as in Classic, down to the exact order of operations, would not only intolerably constrain our ability to develop new capabilities in NextGen MW, but would be futile: the differing numerical approximations made by Java and JavaScript would result in yet another small difference which would in short order become a big difference.

Science!

So, wait a minute: You can’t test NextGen MW against Classic MW because even the tiniest difference between them makes them behave … totally differently? How do we trust either program, then? And how is this science again?

Well, notice that I didn’t say quite say the two programs behave totally differently. Yes, the exact trajectories of the molecules will quickly diverge, but the properties we can actually measure in the real world — temperature, pressure, and the like — unfold according to laws we understand, and should be the same in each (not counting minor, and predictable, statistical fluctuations.) After all, we can do beautifully repeatable experiments on “molecules in a box” in the real world without knowing the location of the molecules exactly. Indeed, when van der Waals improved on the ideal gas law by introducing his equation of state, which includes corrections for molecular volume and intermolecular attraction, the notion that molecules actually existed was not yet universally accepted.

So what we need are molecular models whose temperature, pressure, diffusion coefficient, heat capacity, or the like depend in some way on the correctness of the underlying physics. Ideally, we would like to be able to run a Classic MW model and have it reliably produce a single number which (whatever property it actually measures) is demonstrably different when the physics have been calculated incorrectly. Then we could really compare NextGen MW and Classic MW — and perhaps even find a few lingering errors in Classic MW!

Unfortunately for this dream, our library of models created for Classic MW tend to be complex interactives which require user input and aim to get across the “gestalt” of molecular phenomena (e.g., one model encourages students to recognize that water molecules diffusing across a membrane aren’t actively “aiming for” the partition with a higher solute concentrations but move randomly). The models are not intended to be part of numerical experiments designed carefully to produce estimates otherwise-difficult-to-measure properties of the real world. They require substantial rework if they are to generate single numbers that are known to reliably test the physics calculations. For that matter, there aren’t many Classic models at all that conveniently limit themselves to just the features we have working right now in NextGen MW, and we can’t just wait until we develop all the features before we begin testing.

Charts and graphs that should finally make it clear

Therefore, we have turned to making new Classic MW models that demonstrate the physics we want NextGen MW to calculate, and comparing the numbers generated in Classic MW to the numbers generated when the equivalent model is run in NextGen MW. I’ve begun to think of this process as creating the “datasheet” for Classic and NextGen MW, after the datasheets which contain charts and graphs detailing the performance characteristics of an electronics part, and which an engineer using the part can expect it to obey.

So far, we’ve just gotten started creating the MW datasheet. I’ve written a few ugly scripts in half-remembered Python to create models and plot the results and so far, sure enough, it looks like an issue with the NextGen MW physics engine that I knew needed fixing, needs fixing! (The issue is an overly clever, ad hoc correction I introduced to smooth out some of the peculiar behavior of our pre-existing “Simple Atoms Model.” But that’s good fodder for a future blog post.)

But we have ambitions for these characterization tests. Using the applet form of Classic MW, we hope to make it possible to run each of these “characterization tests” by visiting a page with equivalent Classic and NextGen MW models side by side, with output going to an interactive graph. But with or without this interactive form of the test, once characterization tests have been done they will help us to find appropriate parameters for automated tests that will run whenever we update NextGen MW, so that we can be sure that the physics remain reliable.

I’ll update you as we make progress.

The Molecular Workbench has been downloaded over 800,000 times, making it Concord Consortium’s most popular single piece of software. We’re heading to a million and documenting in video both our history and our vision for the future.

Learn from Charles Xie, Senior Scientist and creator of the Molecular Workbench, about the computational engines that accurately simulate atomic motions, quantum waves, and atomic-scale interactions based on fundamental equations and laws in physics.

Amy Pallant, who researched student use of Molecular Workbench, describes the phone calls she made to students months after they’d used the software—and how impressed she was with their memory of the science of atoms and molecules.

Dan Damelin, Technology and Curriculum Developer, recalls his time as a classroom teacher and his frustration with trying to describe atoms and molecules to his students with words and pictures. He wanted more—and found it in Molecular Workbench!

Dan sums up the goal for Molecular Workbench: “It’s going to be just a given that this is a regular tool that will just be part of learning science.” We hope so.

We’re closing in on a million downloads and looking toward the next million.

We’re using The Lean Startup as a guide to optimize our software for the Web. It’s encouraging us to experiment to see which ideas are brilliant and which are crazy and get feedback from users early. We’re thinking about how not to assume we know what people want, but instead go and find out, and be prepared to shift our ideas. In short: Test. Iterate. Repeat.

So we held our first focus group with several Rhode Island teachers who have been loyal users of Molecular Workbench. Our goal was to get feedback on ways to make our new browser-based MW more valuable to them. We asked them to evaluate new designs (we invite you to take our survey, too). We also asked about tone and length of activities. And the teachers described ways they’d like to select and integrate MW models and activities into their classrooms.

Two major themes emerged: flexibility and student accountability. This confirmed what we knew about the classroom: teachers have limited time, a wide range of learners, a diversity of classes, and pressures around high-stakes tests. We’re now working on prototyping ways to incorporate teacher feedback into our Web-based MW models and activities. We’ll share our progress on our website.

And, of course, we’d love to hear your thoughts in the comments.

As we make our award-winning Molecular Workbench software more accessible and widely available, we’re documenting our story at the same time. Google’s grant to the Concord Consortium funds the conversion of MW from Java to HTML5 so it will run in modern Web browsers. This will reduce barriers for using the next generation MW in schools. Students will be able to access the software from a Web page on a school computer, iPad, or smartphone, giving them anywhere, anytime access to powerful science learning opportunities.

We’re creating videos to share our conversion story. We’ll describe Molecular Workbench, our technical development process, and the benefits of HTML5. We’ve teamed up with the excellent staff of Good Life Productions to produce these videos.

In the first video, Concord Consortium’s Director of Technology Stephen Bannasch describes the power of the modern Web browser to bring science to life. Enjoy.

Here at the Concord Consortium we are very interested in making sensors that are easy to use in the classroom or embedded directly into rich online curriculum. We’ve done some work in the past using applets as an intermediary to read data from commercial sensors and displaying them in lightweight graphs in the browser. When we think of fun, hackable, multi-probe sensors, though, we naturally think of Arduinos — we are open-source geeks after all.

In thinking of ways to display Arduino data in a browser with the minimum amount of fuss, we considered both our existing applet technique and using the new HID capabilities of the Arduino Unos. But while we will probably still find uses for both strategies, it occurred to Scott Cytacki, our Senior Developer, that we could simply use the common Ethernet Shields (or the new Arduino Ethernets) to send the data directly to the browser.

With this idea, it was quick work to hack the Arduino Server example to send JSON values of the analog pins and create a webpage that would rapidly poll the Arduino for data. So here is the first example that I wrote in about 70 lines of code (including the Arduino sketch) usable on any Ethernet-capable Arduino on any browser:

1. Upload the tiny server sketch to your Arduino
2. Plug in your ethernet shield, connect the Arduino to your computer with an ethernet cable and wait about 30 seconds for the Arduino server to boot up
3. Optionally connect a sensor to pin A0. (The demo below is scaled for a L35 temperature sensor, but you don’t need it — you might need to rescale the graph by dragging on the axis to see the plot, though)
4. Click the “Start Reading” button below

You should see your Arduino data filling up the graph. If not, wait another 20 seconds to ensure the server is fully booted and click the “play” button at the top right to start it again.

Wow, that was actually pretty easy!

I created the slightly more complicated example below reads data from all six analog pins, applies an optional conversion, and graphs any one of the data streams. If you were already reading data above, you don’t need to do anything new, just hit the button:

Direct link to stand-alone version

We think this is really cool, and we can’t wait to come up with new ways to integrate this into online content. Why not feed the temperature data into the HTML5 version of Molecular Workbench we’re developing under our new grant from Google.org, for instance, and see the atoms speed up as the temperature increases? Or set up an online classroom where students across the globe can take environmental readings and easily upload and pool their data?

Even by itself, the example above (perhaps expanded further by an interested reader) makes a great workbench for developing on an Arduino — much better than watching the raw Serial Out panel. And of course all the programming can happen in your friendly JavaScript environment, instead of needing to keep recompiling code and uploading it to your Arduino as you iterate.

Technical details:

• This Arduino Sketch creates a server running on http://169.254.1.1, which is a private local IP that will automatically try to not conflict with other servers, allowing for easier connection without a DHCP server. The sketch then returns JSON data using the JSON-P technique of calling back a function, which allows us to make cross-domain requests.
• Click on the tabs at the tops of the embedded jsFiddle examples to see the source code for streaming data to the webpage, or fork and edit any of the examples yourself.
• The graphs are creating using D3.js, and make use of the simple-graph library created by Stephen Bannasch.

Innovation? In many ways, the announcement was an example of the many things there are to be concerned about regarding shallow innovations in digital learning. The main features touted about digital textbooks were the obvious ones. They weigh less. They don’t fray at the edges. They can include images and videos. You can highlight. You can jump to individual sections, pages, or chapters. These are all good features of digital books, but do very little to move us past the transmissionist pedagogy that textbooks represent so strongly today.

Openness? A second large concern raised by many in the ensuing blogosphere echoes relates to the lack of openness that these textbooks permit. Creation occurs principally or solely (for now) on a Mac, via Apple’s iBooks Author application, and books created with this are for use on the iPad only, not even for use on Mac computers. Somewhat understandable, all, since Apple is all about ecosystems, and the iPad is certainly an imaginably good tool for use in the classroom. However, the strictures extend further in ways that seem relatively unpalatable in the long run. According to the iBooks Author EULA, as Dan Wineman identifies, the mere act of creating books via this application is supposed to legally restrict where they can be sold or distributed. This ranges from surprising to shocking, depending upon your views, and the viability of such a model will remain to be seen. Further, the standard used for iBooks, while a thin wrapper over ePub3, is apparently a closed standard, and the application is unlikely to output in formats that permit content to be used and distributed as widely as should be possible for educational materials.

However, there is a slight silver (gray?) lining involved, as the EULA does make it clear that textbooks created with iBooks Author can be distributed for free at will, seemingly across platforms as well. As long as you don’t ever want to attach a price to the materials, this may provide an out. May is the operative term, however, seeing as Apple has certainly been known to change its terms on various whims in the past.

Deeply Digital possibilities? This is where things get a bit interesting. Taking all the former concerns into stride (which may well be too difficult to do for many), the most intriguing and underreported innovation may be yet to be discovered within this. The possibility of creating custom widgets for iBooks using HTML5 and Javascript holds intriguing ramifications. Depending upon the potential and limitations of these widgets, it may be possible to begin opening up aspects of learning that transcend the mundane and push toward deeply digital learning. It’s yet unclear, and will require some cracks from programmers (in our camp as well as others) to try to stretch the possibilities of these Dashcode widgets for the iPad to see what they can enable. True computational models and simulations, rather than basic interactive images or animations? Access to probeware and sensors? Outside access to tools and data streams? Potential for real-time formative assessment and reporting on student progress?

It’s likely that some, but not all, of these will indeed be possible, and the iPad is a beautiful platform to create things for with creation tools that are usually equally elegant. Whether these push the possibilities of technology toward capabilities that can truly make a difference for teaching and learning or whether Apple’s format and strictures will limit these examples to another small stride or shallow cut at innovative educational technology remains to be seen.

Summary: I created a prototype of Learn.Ember.js, an interactive tutorial application for web developers who want to learn about Ember.js. Along the way I was reminded that one of the most useful things about HTML5 is that it helps us to blur the app vs. document distinction in useful ways.

Oh, and by the way, we’re hiring!

Here at the Concord Consortium we believe that interactive computational simulations are powerful tools for learning about the world in ways that were not previously practical, or even possible. Google seems to agree; their philanthropic arm Google.org recently gave us a substantial grant to make an HTML5 version of our Molecular Workbench molecular simulation environment.

Changing the world

But Google didn’t approach us just because they agree that simulations of molecular behavior are a great way to learn about science. They approached us because we have spent 10 years writing well-regarded content for Molecular Workbench. We don’t just make simulations. We embed them in documents that introduce topics gently, encourage you to play with the simulation in productive ways, and in general encourage you to think.

It turns out there are many other domains that can benefit from open-ended tools embedded in structured “learning activities” available via browser. In particular, web development itself can benefit.

Inspiration from learn.knockoutjs.com

Here at Concord I mostly do client-side web app development, and so recently I found myself surveying the new crop of client-side MVC libraries. I was looking for a lighter-weight alternative to SproutCore (which we have used for a few projects) while we waited to see what would come of on the greatly slimmed-down, SproutCore-inspired library then that was then supposed to become SproutCore 2.0, and is now a separate project called Ember.js.

But there a lot of “maybe” development tools out there — tools which might be useful someday, but which I don’t need urgently, and which aren’t such breakthroughs that they need to be understood for their own sake. One of the “maybe” libraries I came across was Steve Sanderson’s impressive Knockout.js.

Since I wasn’t doing this survey “for real”, there was a chance that I would have read through the Knockout documentation in detail, downloaded the library, and made sample pages to play with its features. A small chance. There are only so many hours in a day.

But Knockout.js has a secret weapon: its companion tutorial site, learn.knockoutjs.com. Without quite intending to, within a few minutes of stumbling onto the tutorials I built and ran working examples that felt like plausible components of a Knockout-powered app, right in the tutorial page itself. After I finished the first tutorial I had a much better idea of what kind of problems Knockout solves, and how it solves them, than I would have gotten from the usual desultory flip through the Knockout homepage. (You should try the tutorials yourself!)

Prototyping Learn.Ember.js

As it turns out, Ember.js (née SproutCore 2.0) is shaping up to be a cleanly designed and powerful library with a solid team behind it, and I am enthusiastic about its future.

And as Scott has previously blogged, we at Concord would like to create more value for the open source ecosystem. So I’ve begun work on a side project I call Learn.Ember.js. You can see the first public prototype here. (Warning: this does not work in some browsers, notably older versions of Firefox and — wait for it — IE.)

Once I had the most basic functionality working — 2 Ace editors for the Javascript code and the view template, and an embedded iframe for the results — I wanted to focus on establishing a clean visual design. That meant I had to stop writing code and stop dreaming up potential features long enough to focus on design. Fortunately I was saved from withdrawal symptoms by all the opportunities which that opened up for obsessive font fiddling and CSS tweaking.

The challenge here was not so much the design of the text content — though I tried to borrow the best from well-designed, readable sites I like, such as the new Boston Globe website, the Nieman Foundation’s Nieman Labs blog, arc90′s Readability tool, and Mark Pilgrim’s Dive Into HTML5. Rather, the challenge proved to be finding a way to keep all the buttons and assorted interactive knobby bits from interfering with the text.

My first attempts weren’t very promising. I couldn’t put my finger on why until I realized that the 4-box layout of learn.knockoutjs.com just wasn’t working for me. Somehow I got the idea that in order to make the tutorial readable, I would have to find a way to “unbox” the design and make it look something like a page of a good technical book that just happened to be able to run code. But that introduced its own problems. Where to put the results of the program the user writes (which is an interactive web app unto itself)? Put that in a box, and, together with the Javascript and Handlebars/HTML input, which seem to need to be in boxes — de facto, you have four little boxes again!

Gradually, it occurred to me that the program output could be in flow with the text, right below whichever paragraph prompted you to try running the updated program. Then, with just a little position: fixed and fluid-layout magic, it would be perfectly reasonable to have the whole page scroll, and the tutorial content with it. That is to say, I rediscovered the basic design of every web page ever.

You say app, I say document. Let’s call the whole thing off.

I mention this particular, uh, discovery because for some reason it seems to be common to design news and learning interactives to have little snippets of text written in large type and stuffed into little boxes. I confess to having cargo-culted this particular design idea not long ago; last year I even fired up an ancient Multimedia Beethoven CD-ROM made some time in the last century to confirm that, yup, instructional text is supposed to be really short and go into a little box on the left!

Microsoft Multimedia Beethoven, circa 1992. Via http://www.uah.edu/music/technology/cai/programs/msbeethoven.html

I wonder if this design habit is an artifact of the days of Flash and native applications built using layout manager APIs and visual UI builders. I get the impression that it’s both difficult and out of the ordinary to try to get text and interactive elements to flow together using those technologies. After all, the designer usually doesn’t know what the text is going to be in advance, and you, the developer, would have to come up with a way to keep track of where in the text the widgets go, then create the appropriate widget objects and break up the text string at the appropriate spots, so that you can feed it all to a layout manager that you would probably have to tweak and fiddle for your somewhat unusual use case. Which suggests a great idea — perhaps we could invent tokens that mean “a widget goes here” and have the author use those to mark up the text somehow…

I kid. But in a serious way, because one of the things I liked least about SproutCore is the way it seems to want to pretend that the web hasn’t been invented yet. It provides widgets that are really meant to be a particular size and at a particular, absolutely-positioned offset specified in Javascript. Until the oddly named StaticContentView was invented, the standard UI widget for displaying text was called a LabelView and wanted, again, to be a particular size (regardless of the size of its content) and at a particular location (regardless of the size of the content surrounding it).

The theory was that SproutCore is for designing “apps” rather than “documents”. But as you might guess, I don’t find that distinction very compelling in late 2011. Yes, clearly, there will always be some apps whose UI is legitimately just a box of buttons or a glorified data entry form. And “everything in its right place, and just where it was last time” is exactly the right motto for such apps.

But much of the interesting stuff in your life happens in some kind of stream of context. Facebook and Gmail (especially the new look) are containers for what are basically documents relevant to your life, yet their designers are not shy about inserting app widgets — stuff that does stuff — right into the middle of that “document”-like flow.

Educational apps likewise should include plenty of text that helps you understand the things they help you to do. At Concord, we’ve been calling for a “deeply digital” curriculum that weaves (among other interactive elements) sensors and simulations tightly into the fabric of textbooks and other media.

You occasionally hear “technology X is for app builders, and web technology Y is really for documents“–but that ignores an important category of innovation that is going on right now: apps that are documents. Or, wait, is that documents that are apps…?

What’s next for Learn.Ember.js

But, back to Learn.Ember.js and what’s next. The single page of tutorial text and the trivial example code I have so far are somewhat lazily inspired by the first page of the Knockout.js tutorial; I just needed some text that isn’t plain lorem ipsum. So I need to write more content. But it’s of equal importance to make it trivial for anyone to clone the Learn.Ember.js repo and submit pull requests with new content — or to simply host their own version, modified as they see fit.

For the time being the tutorial text itself is written as a Handlebars template with embedded expressions that tell where to put the buttons; and the initial example code is a string-valued property of a Javascript object. So far, it’s been pretty painless to edit the tutorial text in Handlebars form, but the need to include view class names into the text is an obvious mixing of unrelated concerns — and, worse, the tutorial text is transported to clients as a compiled Handlebars template that is completely invisible to search engines. (Until Javascript gets to work, the index.html file consists of a blank page.)

I think the solution is to put the actual tutorial content, written in clean, semantic HTML5, into the body of the index.html file. Then we can agree as a convention to identify the “run” buttons by applying a particular CSS class, and to represent the location of the output by inserting an empty div with a particular CSS class. The Learn application can then easily use jQuery to scan the DOM as needed, inserting Ember.js views into the right places using Ember.View’s appendTo method and a little bit of DOM manipulation magic.

A remaining question would be whether and how to specify the initial code and the working “help me” code inside the HTML document. Putting the code in script tags with a fake MIME type (text/x-example-javascript) would make it easy to insert the code without having to HTML-escape it and without it running on page load, but then the code isn’t visible to user agents — like search engines — that don’t execute Javascript. Perhaps that is enough, or perhaps the code should go, properly escaped, into hidden <div> elements.

If that were done, then anyone could write their own interactive Ember tutorial by writing an appropriately-marked up HTML file and inserting a few lines into the head of the document to include the Javascript code of the Learn application, which would take care of translating the tutorial document into a working app. And if they were to publish the HTML file to a server, it would be fully searchable.

Before I get that far, of course, I’ll have to tackle navigation between tutorials and pages of a tutorial — a bit of design I left for later. As fodder for a new blog post, of course!

Updated 1am Monday, December 19 with better information about browser compatibility after I made a quick fix to the Learn.Ember.js prototype itself to make it work in Safari, and with a link to all of our open positions rather than just the developer position.

A new 3-D model of early Earth suggests that the planet underwent significant changes–from very warm to very cold.  Past models were one-dimensional–holding constant the amount of cloud cover or sea ice–to make the calculations easier.  But with more advanced computing, researchers at the University of Colorado Boulder were able to make better models of the planet’s climate.

“The inclusion of dynamic sea ice makes it harder to keep the early Earth warm in our 3-D model,” Eric Wolf, doctoral student at CU-Boulder’s atmospheric and oceanic sciences department, said. “Stable, global mean temperatures below 55 degrees Fahrenheit are not possible, as the system will slowly succumb to expanding sea ice and cooling temperatures. As sea ice expands, the planet surface becomes highly reflective and less solar energy is absorbed, temperatures cool, and sea ice continues to expand.”

The scientists’ model shows that Earth was periodically covered by glaciers, but the geologic evidence suggests that it was much warmer than that.  The calculations show that an atmosphere that contained 6% carbon dioxide would have kept the temperature high enough for life to thrive, but the soil samples show that the carbon dioxide concentration was not that high. So what’s the warming mechanism?  Eric Wolf and Brain Toon are still searching for it.

Since the 3-D model takes so much computing time (up to three months for a single calculation), we’ll be waiting a while for the answer.

“The ultimate point of this study is to determine what Earth was like around the time that life arose and during the first half of the planet’s history,” said Toon. “It would have been shrouded by a reddish haze that would have been difficult to see through, and the ocean probably was a greenish color caused by dissolved iron in the oceans. It wasn’t a blue planet by any means.” By the end of the Archean Eon some 2.5 billion year ago, oxygen levels rose quickly, creating an explosion of new life on the planet, he said.

And along the way, better models of Earth’s climate will come out of this study, enhancing scientists’ ability to predict what Earth’s future might look like, and scientists will learn more about the conditions of early Earth, which could help in assessing the habitability potential of other planets.

Explore the interactions of greenhouse gases and ice sheets in the High-Adventure Science climate investigation, and explore the search for extraterrestrial life in the High-Adventure Science space investigation.

http://www.sciencedaily.com/releases/2011/12/111205140521.htm

According to a recent study by scientists at Yale and Purdue universities, the carbon dioxide level dropped. Carbon dioxide is a greenhouse gas that is contributing to the increased global temperatures on Earth today.

The scientists pinpointed the threshold for low levels of carbon dioxide below which an ice sheet forms at the South Pole. Matthew Huber, a professor of earth and atmospheric sciences at Purdue, said roughly a 40 percent decrease in carbon dioxide occurred prior to and during the rapid formation of a mile-thick ice sheet over the Antarctic approximately 34 million years ago.

“The evidence falls in line with what we would expect if carbon dioxide is the main dial that governs global climate; if we crank it up or down there are dramatic changes,” Huber said. “We went from a warm world without ice to a cooler world with an ice sheet overnight, in geologic terms, because of fluctuations in carbon dioxide levels.”

Having an ice-covered South Pole appears to be the tipping point for cooling the rest of the planet.  The team found that the threshold level of carbon dioxide necessary for ice formation is about 600 parts per million.  For reference, today’s carbon dioxide level is approximately 390 parts per million.  This is why ice sheets still remain on Earth today.

With carbon dioxide levels forecast to rise to 550-1,000 parts per million in the next 100 years, when will the ice sheets completely melt away?  Because the melting of an ice sheet is different than starting an ice sheet, and because the process is not linear, scientists can’t say for sure.  But it’s clear that once the carbon dioxide levels rise high enough, the Earth will have reached a tipping point in the warming direction and the ice sheets will melt away.

Huber next plans to investigate the impact of an ice sheet on climate.

“It seems that the polar ice sheet shaped our modern climate, but we don’t have much hard data on the specifics of how,” he said. “It is important to know by how much it cools the planet and how much warmer the planet would get without an ice sheet.”

So how warm will Earth be in the future?  What’s the cooling impact of the ice?  Will greenhouse gases continue to rise?  Will increased cloud cover compensate for the lack of ice?

Explore how greenhouse gases and ice affect Earth’s temperature and learn more about feedback and tipping points in the High-Adventure Science climate investigation.

http://www.sciencedaily.com/releases/2011/12/111201174225.htm

The map (above) shows the change in stored groundwater in the contiguous United States.  Texas, which experienced record heat and wildfires this summer, is experiencing a very severe drought.  The change in stored water should not be a surprise given the weather conditions of the past year.  (By contrast, New England has a surplus of water from a very wet summer and the remnants of Hurricane Irene.)

Drought maps offer farmers, ranchers, water resource managers and even individual homeowners a tool to monitor the health of critical groundwater resources. “People rely on groundwater for irrigation, for domestic water supply, and for industrial uses, but there’s little information available on regional to national scales on groundwater storage variability and how that has responded to a drought,” Matt Rodell, a hydrologist at NASA’s Goddard Space Flight Center, said. “Over a long-term dry period there will be an effect on groundwater storage and groundwater levels. It’s going to drop quite a bit, people’s wells could dry out, and it takes time to recover.”

The question is: how long will it take to replenish the water that has been removed from the aquifers in Texas? Matt Rodell estimates, “Texas groundwater will take months or longer to recharge.  Even if we have a major rainfall event, most of the water runs off. It takes a longer period of sustained greater-than-average precipitation to recharge aquifers significantly.”

Water is a resource that everyone needs.  In dry environments, such as southwestern Texas, water is especially precious.  Water is used for the usual personal purposes, for agricultural purposes, and in natural gas wells.  For example, accessing the natural gas in the Eagle Ford shale deposit, which runs from the Mexican border towards Houston and Austin, requires millions of gallons of water to fracture the shale and release the stored hydrocarbons.

The prolonged Texas drought is putting more pressure on local officials about how best to use the limited amount of groundwater.  What is the best way to use the water supply?  Who gets first dibs?  How much should different businesses pay for water?  These are highly-important questions that can only be answered with a full understanding of how groundwater works.

You can explore how groundwater flows and propose solutions to water-supply issues in the High-Adventure Science water investigation.

http://www.nasa.gov/topics/earth/features/tx-drought.html

Drought spurring fracking concerns

Oil’s Growing Thirst for Water

Texas

Second, one story that illustrates Apple’s genius in this arena and a second that questions it. If you missed All Things D’s story about the moment that Apple and Microsoft’s touch interface dreams diverged, chug it into your Instapaper queue right now – it’s a great reminder of how far we’ve come in such a short time, and about how Microsoft continued a strange fumble with their Surface platform while Apple managed this transition from practice with the iPhone to full-on victory with the iPad. (I touched on the consumer side of the success of practicing with the iPhone’s interface as readying the public for the concept of the iPad in my Perspective piece a year or so ago.)

Third, an interesting rant from Matt Honan at Gizmodo claims that Siri’s hands-off interface presents the nuanced user experience we have come to expect from Apple. Gruber agrees, and I have to say I do much of the time as well.

And finally, a group in Tokyo is turning everyday objects into interactive devices using projectors and cameras. I particularly like their turning a banana into a functioning telephone through the use of object detection and focused sound beams.

Happy snacking – maybe you can read this whole post without touching your computer. Just think “scroll up” really hard…

The scientists focused on stars more than 1.5 times more massive than our Sun.  To look for planets, they used the “wobble” method, which looks for shifts in the apparent wavelengths coming from the star.  The 18 planets that they found are all larger than Jupiter.

According to John Johnson, assistant professor of astronomy at Caltech, these discoveries support a theory of planet formation. There are two competing explanations for how planets form: a) tiny particles clump together to make a planet and b) large amounts of gas and dust spontaneously collapse into big dense clumps that become planets.

The discovery of these planets supports the first explanation.

If this is the true sequence of events, the characteristics of the resulting planetary system — such as the number and size of the planets, or their orbital shapes — will depend on the mass of the star. For instance, a more massive star would mean a bigger disk, which in turn would mean more material to produce a greater number of giant planets.

So far, as the number of discovered planets has grown, astronomers are finding that stellar mass does seem to be important in determining the prevalence of giant planets. The newly discovered planets further support this pattern — and are therefore consistent with the first theory, the one stating that planets are born from seed particles.

The larger the star, the larger the planets that orbit it.

“It’s nice to see all these converging lines of evidence pointing toward one class of formation mechanisms,” Johnson says.

But there’s another mystery that’s come out of this discovery.  The orbits of these 18 newly-discovered large planets are mainly circular.  Planets around other Sun-like stars have circular and elliptical orbits.  Is there something about the larger stars that make it more likely  that planets will have a circular orbit?  Or is it just a phenomenon noticed because of the small sample size? Johnson says he’s now trying to find an explanation.

Stay tuned–not only may we find a planet that could harbor life, we could also learn something about the origin of our own solar system!

Learn more about finding planets and the search for extraterrestrial life in the High-Adventure Science investigation, Is there life in space?

http://www.sciencedaily.com/releases/2011/12/111202155801.htm

For those who don’t recall, there was a time when clock radios were quite a novel invention. The ability to wake to the radio instead of some raucous bell was an entirely new concept. And to a budding radio-phile like me, it seemed like the newest of frontiers. I remember looking across the counter at our local Sterling Drug for many a visit, and piling up birthday money and allowance until the mound was enough to purchase this coolest of things. The red glow of the lights and the late-night sessions listening to AM talk radio or trying to pull the strains of Dr. Demento out of the static seem as close now as they did then.

This was a first – a multi-function gadget. And the mere concept of combining the functions was mesmerizing. Now, it’s entirely replaced by one entirely multi-function gadget. I use my iPhone both to listen to radio as I’m falling asleep and to wake me up. Of course, our family point-and-shoot camera and car GPS device are also starting to gather dust at a surprising rate.

This is no new revelation, of course, but the fundamental nature of my feeling at this loss was interesting to note. What other fundamental weirdness will we be in for as technology continues to contract our world of life and transform the world of education? The first time a teacher enters a classroom without a board he or she can write on? The first time a mom realizes she doesn’t need to buy any spiral-bound notebooks at the back-to-school sales? The first time a principal realizes that she can find out about the misconceptions all of her students hold on a given day about science concepts they are studying, even the students who transferred in to school that morning?

Time will tell with all of these. For now, I need a nap – Siri, can you set a timer to wake me up…?

About 56 million years ago, Earth’s temperature was a lot warmer than it is today–as much as 21°F higher than today (see the graph).  Earth’s temperature is rising today, likely because of human emissions of greenhouse gases.  But 56 million years ago, there were no human emissions; there were no humans.  What caused the big increase in Earth’s temperature?  And could it happen again today?

Researchers at Rice University suggest that the temperature increase could well be due to releases of stored methane from the oceans.

Methane is a powerful greenhouse gas and a natural product of bacterial decomposition.  In the oceans, methane sinks into the sediments and freezes into a slushy gas hydrate, stabilized in a narrow band under the seafloor.

According to calculations done by the Rice University scientists, the warmer oceans resulted in more methane hydrate being stored.  At warmer temperatures, bacteria decompose organic materials faster, resulting in more methane in a shorter period of time.  They estimate that, just before the PETM, there was as much methane hydrate stored as there is today, in a smaller band than exists today.

If this band is disturbed, as by a meteor impact or earthquake, the methane can be rapidly released into the atmosphere.  More greenhouse gases in the atmosphere result in increased warming.  But there’s no evidence of there having been an impact.  So what happened to release the methane 56 million years ago?

Nobody really knows, but the significance is clear.

“I’ve always thought of (the hydrate layer) as being like a capacitor in a circuit. It charges slowly and can release fast — and warming is the trigger. It’s possible that’s happening right now,” said Gerald Dickens, a Rice professor of Earth science and an author of the study.

That makes it important to understand what occurred in the PETM, he said. “The amount of carbon released then is on the magnitude of what humans will add to the cycle by the end of, say, 2500. Compared to the geological timescale, that’s almost instant.”

“We run the risk of reproducing that big carbon-discharge event, but faster, by burning fossil fuel, and it may be severe if hydrate dissociation is triggered again,” Guangsheng Gu, lead author of the study, said, adding that methane hydrate also offers the potential to become a valuable source of clean energy, as burning methane emits much less carbon dioxide than other fossil fuels.

Learn more about the feedback loops involved in climate change in the High-Adventure Science climate investigation.

http://www.sciencedaily.com/releases/2011/11/111109111542.htm

First, the simple fact that this had to be shortened is reflective of old/new media constraints. Clearly, the costs and space of paper itself drive this need at least partially. Electrons are cheap, and Joi has no problem posting something of any length he wants on his blog. Any new media does not live by these constraints. And the collision of the two is befuddling many in the industry right now. The NY Times online version of this story is the same as the print version as far as I can tell, though it does include a link to the MIT Media Lab. And, in fact, the Times’ forward-thinking hyperlink system is what enabled me to link directly to a highlighted sentence in Joi’s story online.

Now, I understand the necessity of editing things down. The vote-with-your-mouse Internet has made that all too clear. And I benefit significantly from the editing of others. But I think this piece suffered at the hands of old media constraints. Let’s look into it a bit.

First, the description of the creation of X.25 in the NYT article has it as a “standard that seemed to anticipate every possible problem and application.” When I first read that, I questioned why we ended up going with IP after all. Reading Joi’s phrasing, however, tells a different story. He states, “The X.25 people were trying to plan and anticipate every possible problem and application. They developed complex and extremely well-thought-out standards that the largest and most established research labs and companies would render into software and hardware.” The subtleties here describe quite a different proposition. “Trying to plan and anticipate every problem” and developing “complex” standards is not exactly the same thing as “seeming to anticipate every…problem.” From an Agile development point of view, one might in fact become increasingly skeptical of any solution that strives to anticipate every problem. This is part of the point I think Joi is trying to make here, and it is blurred in a subtle, but important, way by this edit.

A second edit that removes important concepts comes in the loss of the reference to the RFC process. To the NY Times’ credit, they have weighed in admirably on this in the past, and it is certainly a bit obscure, so I understand the change. However, for anyone familiar with the story, this nuance shows some of the depth behind how openness triumphed in this case. Not by pure magic, but as a product of carefully managed process and group dedication in equal measure.

Though the Times article captures many of the nuances of argument that the Maker movement parallels much about the early days of the Internet, a subtle change loses meaning here, too. Joi describes 3D printers and the related ecosystem as ” cheaper, standardized and connected via the Internet,” three essential elements to the core of the innovation happening here. While the Times’ description of this tightens this up nicely and gets the basics right, it is interesting to note the nuances that are missed.

New platforms and new media permit new messages and new opportunities. That is some of what the story of the Internet’ birth tells us. In the same way, it’s what we also see playing out in educational technology today. Let’s look closely as we go forward, and try not to miss the nuances.

One of the most exciting aspects of this all is something that Joi’s piece captures well – the freewheeling and wide-ranging freedom that this openness provides everyone who takes part. The fact that anyone with a good idea is in theory equally close to any other (though this apparently leaves many in this country without good network connections in the cold) is what makes the fabled guys-in-a-garage notion able to spring forth as the next Facebook or Google. It’s also what is fueling the burgeoning Maker movement (that the Economist captured somewhat well in a recent article highlighting the Maker Faire that our colleagues at the New York Hall of Science hold each year now).

Plying this sensibility for the development of educational technology is our stock in trade at the Concord Consortium. Capturing this sensibility in everything we do, on the other hand seems to be in the genes of most all of us. Listening to the table conversations over one of our staff potlucks, I’m always amazed at how one central emotion ties through such a diversity of discourse: a shared interest in creation and the discovery of new things. This overriding interest is probably what makes most of us here science geeks. The idea of applying it in order to make education better for students across the world is what draws us to the Concord Consortium.

It’s in the pursuit of this interest that we all do our work here, but it’s in the enactment of openness that the work is able to thrive. Our code is on Github, our content is shared, and our ideas are in the open air.

I understand intrinsically what Joi describes with his use of the wonderful word “neoteny” to describe the childlike wonder of discovery retained as an adult. It’s why our friends at the Media Lab’s Lifelong Kindergarten Group chose their moniker. And now that we have been introduced to the word, I’m fairly certain it’s one we’ll hear included in the vivacious conversations across the table at our next group potluck. Thanks, Joi, for helping the Times’ readers make the connection between this concept and the openness that is so important to innovation and the Internet.

Update: 12/6 at 10:10 AM: Corrected the idiom to the proper “stock in trade,” and (reluctantly!) corrected the typo “monkier” to read “moniker.”

That basic premise is supported by a recent report from Lawrence Livermore National Laboratory: Separating signal and noise in climate warming.  Earth’s overall temperature is affected by natural processes, such as La Niña and El Niño, as well as by human factors.

From 1999 to 2008, Earth’s temperature was fairly steady, coming after the steady rise in temperature that occurred from the late 1980s.  What happened in that 10 year period?  Probably noise from natural phenomena, conclude scientists at LLNL.

“Looking at a single, noisy 10-year period is cherry picking, and does not provide reliable information about the presence or absence of human effects on climate,” said Benjamin Santer, a climate scientist and lead author on an article in the Nov. 17 online edition of the Journal of Geophysical Research (Atmospheres).

The solution?  Look at longer time periods to see past the natural noisy fluctuations in Earth’s temperature data.  After looking at all of the data, scientists concluded that temperature records must be at least 17 years long to see the human-caused warming amidst the natural fluctuations.  More data leads to more accurate conclusions.

Explore the hows of climate change in the High-Adventure Science climate investigation.

https://www.llnl.gov/news/newsreleases/2011/Nov/NR-11-11-03.html

With the ongoing polarization of science in today’s political environment, it’s more important than ever to remember that science is filled with uncertainty.  Everything that scientists know about how the world works has been discovered by observation and experimentation.  None of us were around at the very beginning, so we can never be absolutely certain about how the world works, though we can be very certain that we understand how it works.

You can’t prove anything to be true in science.  This seems unintuitive to many people, including many of my former students, who used to insist that they had proven their point because the data supported their hypotheses.  But since we will never be absolutely certain about how the world works, we can never prove that any particular hypothesis or theory is absolutely true.  That’s why good scientists design experiments to disprove their hypotheses.  While you can’t prove anything to be true, you can prove things to be false.

So good scientists are forever questioning their assumptions, looking for evidence that their hypotheses and theories are wrong, open to the idea that they may have misinterpreted the data.  It’s vitally important for science teachers to remind their students to have this kind of healthy skepticism; scientific progress cannot easily proceed if people entrench themselves into opposing camps without regard for the data.

This is something that the High-Adventure Science investigations aim to do–immerse students in the data about climate change, finding extraterrestrial life, and freshwater resources–without making all-or-nothing judgements about the current state of the science.

“If you think that science is certain–well that’s just an error on your part.” ~Richard Feynman

http://online.wsj.com/article/SB10001424052970204630904577058111041127168.html?mod=WSJ_Opinion_LEADTop#articleTabs%3Darticle

Recently I was reminded about these types of spin-off projects when Richard Klancer relayed a conversation he had with Jeremy Ashkenas. Jeremy has been very successful in this area during his work on DocumentCloud.

We strive to make our individual projects successful, but often their technology is complex and not easy to re-use. The impact of the individual project is the research enabled by the technology, or demonstrating the usefulness of a new concept. However, the collection of technologies used in the project normally becomes a one-off: it is no longer used once the project reaches its 2-5 year end.

Alternatively, within these complex projects are reusable pieces of code that are simple, easy to maintain, and solve a common need. Because of this they have potential to be popular outside of our organization. We do have some partial successes with spin-offs like this.

• MozSwing – mostly abandoned, though it was used in at least one commercial product
• Java Sensor Library – collection of JAR files for communicating with a variety of sensors available in schools
• RaphaelViews – SproutCore 1.x library for creating fully fledged SproutCore 1.x views with Raphael
• SproutCore TestDriver – ruby gem for running SproutCore Jasmine and QUnit tests on a CI server

None of these has become a successful open source spin-off project. To be successful, such a project needs an active community that includes both developers and users. And the amount of work required to maintain it by Concord Consortium developers needs to be small enough that it doesn’t prevent us from reaching the goals of individual grant projects.

The MozSwing project would require too much maintenance. The Java sensor project is too intertwined in our other Java code. RaphaelViews and CapybaraTestrunner don’t have the above problems, but they have not been polished and announced to the right audience. I don’t think the polishing would take a lot of effort, but making the time and finding the support to do so is hard. We are always working on the next big thing, so it takes discipline to really finish up what is already working internally.

There are more potential open source spin-off projects within the technology at the Concord Consortium that have wider audiences than the ones above. With luck, we can change our culture to encourage this work more and make more of this great stuff accessible.

Do you agree that we should be spinning off more projects? Do you have experience with spinning off projects like these? Any tips?

From a Mac SE to a clickwheel iPod to an original Newton and eMate, we tried to capture most of Apple’s history from 1984 or so to the present day. (Yeah, we know that Steve’s role with the Newton was mainly to kill it, but hey – it was important and inspired by his work nonetheless.) The most impressive thing about this all was that it all came together via about 30 minutes of high-speed IM chatting as the news was rolling in that Wednesday night. By 9:00 the next morning, we had the whole thing assembled. Thanks to Ethan our Webmaster, we even managed to put together a one-day Web tribute within a few hours of the news as well.

It’s been a whirlwind week and a half thinking reflecting on just how much innovation Steve Jobs brought to us all. And it’s been even more inspiring to think about just how much joy he brought along with that innovation. Like many, his lasting work only inspires us more to try and create our own insanely great things, and to remember to always stay hungry and foolish.

Why are they using this technology on Earth?  We know that there is water on Earth; we know that there is life on Earth.

Firstly, it’s the only way that scientists can “see” underground structures.

“This demonstration is a critical first step that will hopefully lead to large-scale mapping of aquifers, not only improving our ability to quantify groundwater processes, but also helping water managers drill more accurately,” said Muhammad Al-Rashed, director of Kuwait Institute for Scientific Research’s Division of Water Resources.

We might have a lot of water on Earth, but it’s not distributed equally.  Knowing the availability of the water supply helps us to use it in a sustainable manner.

Secondly, it’s a good way to study the climactic history of these regions.

“This research will help scientists better understand Earth’s fossil aquifer systems, the approximate number, occurrence and distribution of which remain largely unknown,” said Essam Heggy, research scientist at NASA’s Jet Propulsion Laboratory. “Much of the evidence for climate change in Earth’s deserts lies beneath the surface and is reflected in its groundwater. By mapping desert aquifers with this technology, we can detect layers deposited by ancient geological processes and trace back paleoclimatic conditions that existed thousands of years ago, when many of today’s deserts were wet.”

Previously, climate research has focused on Earth’s polar regions and forests.  It is important to study those areas, but arid and semi-arid regions make up a big part of the planet, and they should be studied too.

This is a great story that shows how technology developed for one area of research can often be useful for several other fields of science–all of which are highlighted in our High-Adventure Science investigations!

Learn about searching for water on other planets in the High-Adventure Science space investigation, learn about aquifers and water sustainability in the High-Adventure Science water investigation, and learn about using geologic formations to reconstruct previous climates in the High-Adventure Science climate investigation.

http://www.sciencedaily.com/releases/2011/09/110915182850.htm

New research from Carnegie’s Global Ecology department, published last month in Environmental Research Letters, concludes that transpiration has a global effect as well.

How does this happen?  Water vapor is a greenhouse gas, so one might expect that more water vapor in the atmosphere would lead to higher temperatures.

But water vapor also condenses into clouds, which reflect sunlight, resulting in a cooling effect.  The increased transpiration from plants, combined with evaporation from bodies of water, results in lower-level clouds.  Lower clouds tend to reflect more sunlight, hence the cooling effect.

So you can plant trees locally, reap the cooling effect locally, and also help to cool globally!

Learn more about the relationship between clouds and climate in the High-Adventure Science climate investigation.

http://www.sciencedaily.com/releases/2011/09/110914161729.htm

Now researchers from Oxford University and the University of Western Australia are suggesting that life on Earth could have formed on floating rafts of pumice.

The researchers argue that pumice has a unique set of properties which would have made it an ideal habitat for the earliest organisms that emerged on Earth over 3.5 billion years ago.

‘Not only does pumice float as rafts but it has the highest surface-area-to-volume ratio of any type of rock, is exposed to a variety of conditions, and has the remarkable ability to adsorb metals, organics and phosphates as well as hosting organic catalysts, such as zeolites,’ said Professor Martin Brasier of Oxford University’s Department of Earth Sciences who led the work with David Wacey of the University of Western Australia. ‘Taken together these properties suggest that it could have made an ideal ‘floating laboratory’ for the development of the earliest micro-organisms.’

Floating pumice could have been exposed to lightning, oily residue and metals from hydrothermal vents, and ultraviolet light.  All of these conditions have the potential to generate the kinds of chemical reactions that scientists hypothesize created the first living cells.

The scientists plan to test their hypothesis by subjecting pumice rocks with cycles of heat and radiation to see if the process creates molecules associated with life.  They also plan to examine the early fossil record for evidence of fossils in pumice.

If scientists can determine how life on Earth began, they’ll be better prepared to search for evidence of life on other planets.

Learn about the search for extraterrestrial life in the High-Adventure Science space investigation.

http://www.sciencedaily.com/releases/2011/09/110903131404.htm

According to a new study from the University of Wisconsin-Madison, irrigation has increased agricultural productivity by an amount roughly equivalent to the entire agricultural output of the United States.  That’s a lot of increased productivity!

All of those growing plants take up more carbon dioxide, which could lead to slowing global warming.  But without the extra water required for irrigation, not as much carbon dioxide would be taken up by plants–and that could lead to more warming.

The study also shows quantitatively that irrigation increases productivity in a nonlinear fashion — in other words, adding even a small amount of water to a dry area can have a bigger impact than a larger amount of water in a wetter region. “More irrigation doesn’t necessarily mean more productivity,” Ozdogan says. “There are diminishing returns.”

This was already known on the field scale, he says, but is true globally as well. Interestingly, he found that, on average, worldwide irrigation is currently conducted close to the optimal level that maximizes gains. While this may be good news for current farmers, it implies limited potential for irrigation to boost future productivity even as food demands increase.

So what does this mean for us?

Be mindful of the amount of water that we use so that we can continue to irrigate fields, grow food to feed ourselves, and, along the way, reduce the amount of carbon dioxide in the atmosphere.

Learn about fresh water availability and climate change in our High-Adventure Science investigations.

http://www.sciencedaily.com/releases/2011/08/110825152457.htm

Look at the results.  Compare the results from the models with what happened in real life.

An August 2010 study published in Science claimed that drought induced a decline in global plant productivity during the past decade, posing a threat to global food security.  Zhao and Running, the authors of that study, set up their model based on their expectations that global plant productivity would continue to increase, as it had in the 1980s and 1990s.

A new study has found that Zhao and Running’s 2010 model was flawed.

… According to the new study, their model failed miserably when tested against comparable ground measurements collected in these forests. “The large (28%) disagreement between the model’s predictions and ground truth imbues very little confidence in Zhao and Running’s results,” said Marcos Costa, coauthor, Professor of Agricultural Engineering at the Federal University of Viçosa and Coordinator of Global Change Research at the Ministry of Science and Technology, Brazil.

What went wrong?

The authors of the original study included poor quality data and did not test trends for statistical significance.  They also didn’t test their assumptions against real-life.  There was a 28% disagreement between the model’s results and real-life results–far too much to make for a useful model!

So what’s the lesson from all this?  Don’t trust scientists?  Don’t trust models?

No.  The lesson is that scientific progress is made when scientists question their own and each others’ assumptions about what they think should happen.

Could all of this have been avoided?  Yes, if Zhao and Running had better tested their model against real-life to remove, as much as possible, their biases from their work.

Scientists, like all other humans, make errors.  Question the basic assumptions of each claim, and see how the models hold up to a real-life test.  That’s how you’ll know when you’re dealing with good science.

Learn some good science in the High-Adventure Science investigations on climate, water, and space.

http://www.sciencedaily.com/releases/2011/08/110825141621.htm

The HARPS telescope works by detecting the movement of stars.  A star with an orbiting planet will be pulled towards the planet as it orbits.  If the star moves toward and away from Earth, this movement can be detected and planets can be discovered.

Astronomers have pointed HARPS at 376 Sun-like stars, and over the past eight years, they have discovered more than 150 new planets.  At least one of the newly-discovered planets is potentially habitable; HD85512b is estimated to be only 3.6 times the mass of Earth and it orbits its star within a zone in which liquid water could exist.

The increasing precision of the new HARPS survey now allows the detection of planets under two Earth masses. HARPS is now so sensitive that it can detect radial velocity amplitudes of significantly less than 4 km/hour– less than walking speed.

“The detection of HD 85512 b is far from the limit of HARPS and demonstrates the possibility of discovering other super-Earths in the habitable zones around stars similar to the Sun,” adds Michel Mayor, of the University of Geneva, Switzerland.

This is just the beginning for finding Earth-like planets around other stars!

Learn more about planet hunting in the High-Adventure Science space investigation.

http://www.sciencedaily.com/releases/2011/09/110912143536.htm

What is new is the idea that high latitude areas will become a carbon source rather than a carbon sink.  The 2007 assessment report from the Intergovernmental Panel on Climate Change suggested that the thawed permafrost would allow for greater vegetation in polar regions, leading to carbon uptake.  But a recent study published in the Proceedings of the National Academy of Sciences contradicts that assertion.

The authors of that study–Charles Koven, of the U.S. Department of Energy’s Lawrence Berkeley National Laboratory and a team of scientists from France, Canada, and the United Kingdom–used a model that took into account how carbon behaves in different layers of the ground.

But unlike earlier models, the new model includes detailed processes of how carbon accumulates in high-latitude soil over millennia, and how it’s released as permafrost thaws. Because it includes these processes, the model begins with much more carbon in the soil than previous models. It also better represents the carbon’s vulnerability to decomposition as the soil warms.

New models lead to updated forecasts on what is likely to happen to Earth’s climate.  But this isn’t the final word.  Even the latest and greatest models can be refined to make ever-better forecasts of the future.

Koven adds that there are large uncertainties in the model that need to be addressed, such as the role of nitrogen feedbacks, which affect plant growth. And he says that more research is needed to better understand the processes that cause carbon to be released in permanently frozen, seasonally frozen, and thawed soil layers.

The quest to forecast the future continues.

To learn about how carbon dioxide affects Earth’s climate, try out the High-Adventure Science climate investigation.

http://www.sciencedaily.com/releases/2011/08/110823115651.htm

Photo by Walter Siegmund Beaver dam of Hat Lake and Hat Creek in foreground.  Bridge over Hat Creek on highway 89, Lassen Volcanic National Park. http://en.wikipedia.org/wiki/File:BeaverDam_8409.jpg

Beavers are rodents that live in and along streams and rivers.  They gnaw down trees and build dams, which back up the rivers and streams.  The standing water behind the dam can percolate into the ground, recharging the groundwater and raising the water table.  The dams minimize flooding during the wet season and keep water from drying up during the dry season.

It’s especially important to recharge the groundwater in areas that don’t have precipitation throughout the year.  As we draw water out of the ground for our own uses, the water table falls, so much so that natural watering holes dry up.  One solution is for us to simply use less water during the dry seasons.  Another solution for humans to build dams.  Using less water is a good start (for as much as that is possible during the dry season), but we can also turn to natural sources–such as beavers–to recharge the water supply AND restore natural habitats.

“We can spend $200,000 putting wood into a stream, cabling down logs. Sometimes it works and sometimes it doesn’t.  Put in a colony of beavers and it always works.”

-Celeste Coulter, stewardship director at the North Coast Land Conservancy, a Seaside, Oregon, group that urges developers to set aside land for beavers

Learn about the science behind groundwater recharge and the water table in the High-Adventure Science investigation, “Will there be enough fresh water?”.

http://online.wsj.com/article/SB10001424053111904253204576512391087253596.html?mod=ITP_AHED#articleTabs%3Darticle

Learn about how scientists use new data to make better models of Earth’s future climate and fresh water availability with High-Adventure Science investigations.

http://www.sfexaminer.com/news/2011/08/irene-forecasts-track-not-speed-wind

(Source:  http://xkcd.com/925/)

In the comic, the man in the hat has made a graph that shows the incidence of cancer in the United States with the number of cell phone users.  The incidence of cancer has been fairly steady over the past 30 years while the number of cell phone users has increased.

This means that cancer causes cell phones, right?  The graph shows that there are increases in cell phone users just as the cancer incidences start to plateau, so that conclusion makes sense, or does it?

Is there another–better–way to interpret this graph?  What does that graph really show?

Explore how good scientists draw conclusions from data in our High-Adventure Science investigations in climate, space, and water.

It was originally called Snow White because Mike Brown, a professor of planetary astronomy at Caltech, had guessed that it was an icy body formed by a breakup of another dwarf planet.  Since he thought the planet would be icy and water ice appears to be white, the name fit.

Dr. Brown and his team have discovered that the little planet known as Snow White is actually red.  And it is covered with water ice, which is usually white.  So what makes this little planet red?

The explanation is probably in Snow White’s disappearing atmosphere.  In 2002, Dr. Brown helped to discover a similar dwarf planet, Quaoar.

Quaoar was covered with volcanoes that spewed an icy slush.  Quaoar was too small to hold on to its atmosphere, so it has slowly drifted away into space. What was left behind was some methane, the heaviest gas thought to have been in its atmosphere.  The methane, after being exposed to space radiation, combined into long hydrocarbon chains.  The hydrocarbon chains rest on the icy surface, giving Quaoar a rosy hue.

The spectrum of “Snow White” looks similar to the spectrum of Quaoar, an indication that the planets’ atmospheric compositions may be the same.

“That combination — red and water — says to me, ‘methane,’” Brown explains. “We’re basically looking at the last gasp of Snow White. For four and a half billion years, Snow White has been sitting out there, slowly losing its atmosphere, and now there’s just a little bit left.”

Mike Brown doesn’t yet know that Snow White has methane; there’s no evidence, other than the comparison with Quaoar.  It will take more investigation, with a larger telescope, to determine that for sure.

And now that the astronomy community has determined that Snow White is an interesting object to study, it needs a real name.

Before the discovery of water ice and the possibility of methane, “2007 OR10″ might have sufficed for the astronomy community, since it didn’t seem noteworthy enough to warrant an official name. “We didn’t know Snow White was interesting,” Brown says. “Now we know it’s worth studying.”

That’s science–there’s always more to discover, even when it seems like all of the interesting discoveries have already been made.

Stay tuned to see what they re-name “Snow White.”

Explore how spectroscopy is used to determine the atmospheric composition of distant planets in our space investigation.

http://www.sciencedaily.com/releases/2011/08/110822124955.htm