The Blanket Fort Building Challenge
The legendary CHAOSMaker "Mysterious Building Challenges" - show up, be given a mostly random set of materials and a problem, and then work in teams under strict time pressures to come up with a solution. It's the perfect way to teach about prototyping, resource management, planning, working in teams...and how to handle pressure in a fun environment.
The 6th Mysterious Building Challenge started in Mr. Boyd's room on a Wednesday lunch. I had been spreading a few confusing rumors to throw off student detectives trying to figure out the plan, so participants were a little nervous: "Why do we need headlamps?" "Why are you wearing kneepads & a helmet?" "Were we all supposed to bring spatulas?".
MBC #6: Build the longest, continuous, one-way, fully enclosed, lighted blanket tunnel possible using the entire CHAOS Room and only the provided materials. Oh, and at some point there had to be a "Disco Palace" big enough to stand up and dance in.
One other big change from previous challenges: all 14 students had to work together as one big team (instead of competing against each other). They had until Friday at noon (3 lunches) to complete their task and show it off to the entire school (gulp).
Day 1
And away we went. Students started with a traditional planning session and mapped out a route. But it quickly became clear that the plan and the available materials wouldn't match up, so they switched to an area-based system.
Day 2
There was some major progress, as students came in at both recess & lunch, and a number got permission to skip their regular classes to keep building. They discovered the technique of using string supported by chairs, and the room quickly became a spiderweb. Available chairs became precious. No matter, students used tables, blankets, tablecloths, curtains, scrap fabric, tarps, duct tape, clothes pins, to rig their tunnel.
It became hard to move around the room, so students would pop up out of the blankets and yell "duct tape, need some duct tape over here". Supplies would be tossed, pass from person to person, or delivered by the mouse-team that would scurry through the tunnels.
At the end of the day we had a pretty reasonable tunnel system, and so we brought out the lasers, sound system and fog machine to make the Disco Palace even cooler. Oh, and the legend of the toe-eating Spatula Rat was born.
Day 3
By recess the tunnels were structurally finished, although repair and improvement teams kept crawling through the system trying to improve and fix problem areas. We were down to our last few clothespins and roll of duct tape, so resources were scarce. The Spatula Rat station had gained a 3D printed foot-bone with a toe missing.
I passed around the EL wire and turned off the lights, and soon we had a foggy, crazy, crawly tunnel experience. We never did measure the tunnel length, although the system took up the entire room and could easily have more than 20 people moving in it at the same time without congestion. We rigged up an access control system using glowsticks to count who was in the fort. There were enough nooks and crannies that students would enter the one-way tunnel and then not come out for quite a while.
The build team was proud of their work, so we cycled through all 5 Grade 8 classes, a number of Grade 7 classes, some Grade 5 classes, random siblings, and a number of teachers. All came out with big smiles on their faces.
The best way to experience it all is through video edited by Colin F. Click to play it!
We have done a number of fun projects over the last couple of years, but I was particularly proud of how students handled this one. They solved difficult physical challenges, dealt with resource management, repair, and redesign - all while working together. And then, once their project was "finished" they shared it with others, managing access control, safety, student fears, and various other problems. It was true leadership and ownership in action!
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The Great Robotic Ping Pong Ball Migration
Another challenge from Ken Symington's "Design Thinking and Media Communications" class : carry as many ping-pong balls across the room as possible without dropping them or getting stuck. Oh, and use a robot. Once each Sphereo was successful, the students joined their ping-pong bot carriers to create the ultimate three-bot ball carrying machine.
Click for some vertical video!
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3D Printing and Painting - Part 2
We have a new Instagram feed that gets updated a lot more regularly than our website (see the right side tabs for more social media). In case you missed them, here are a few of our recent 3D prints. All the paint jobs are by students! Click on a picture for a better view. Or, just follow us on Instagram for the full dose of witty captions and hashtags.
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3D Printing - Bridging International Boundaries
This semester CCHS had an international student from Germany visiting and attending classes. He discovered the 3D printer and quickly became enthralled (we know the feeling!) because his own school back in Germany did not have one. Every class he parked himself in front of the printer and worked on various projects. Some cool results included a bike spoke wrench, souvenir key chains, and hand-lamp and skull. This semester we have more international students keen to try out the 3D printer! [Click for a larger version]
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Newspaper Tower Design Challenge
Another interesting problem from Ken Symington's "Design Thinking and Media Communications" class. In this design challenge, students were tasked with building the strongest structure using only newspaper and masking tape. The winning structure held 14 textbooks and stood almost a metre tall! It probably could have handled more but we ran out of textbooks. Prototyping, design, testing - simple materials can lead to complex learning.
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Machine Learning and Recycling, Part II
Rien has been working on her Machine Learning with Google AIY Vision 20-time project and it's time for an update. After walking through all the demos (joy detector, object recognition) she started the process of training a machine learning classifier on some new data.
The overall goal is to use machine learning and computer vision to help sort recycling and non-recycling, either as part of an automated sorter or a reverse recycling machine. To determine what is recycling and what is not, she needed training data. The first pass involved taking hundreds of pictures of 2 types of bottles and 2 types of cans, all in front of a little stage that allowed for a controlled background.
We made the assumption that in a reverse-recycling or recycling-stream sorting situation, we'd be able to control what our background looked like. Each object was photographed from all angles, upside down, and even crushed.
Example training data.
Example training data.
Example training data.
Example training data.
She then ran a Tensorflow model adapted from the Tensorflow-for-Poets demo. The classifier was able to place testing data into the 4 categories with 100% accuracy. She then expanded her training data to 13 categories of bottles and cans - again, taking hundreds of pictures from all angles. Even with a much wider set of possibilities, the machine learning classifier was able to place an incoming picture into one of the 13 categories at almost 100% - that is, it was able to determine that a Coke can was a Coke can, a Nestea bottle was a Nestea bottle, etc even though it had never seen that image before.
Finally, she grouped the 13 categories into "recycling" and "non-recycling" categories and retrained the model. As expected, this worked well - but the real challenge was testing the trained machine learning classifier on some pictures of objects it had never seen before. These were not examples of training data, these were entirely new objects (ie, beer cans, new branded bottles, etc). The classifier got 7 of 9 correct, with both the 2 misclassified objects the same object (a Brisk bottle).
This bottle was missclassified.
Possibly confused by similar colors?
By the nature of a neural network's operation, it is not immediately obvious how the model is coming to a conclusion. Is it color, shape, reflection, size? We hypothesize it is using color and mixing it up similar bright yellows on the Nestea can. The obvious solution of course, is to take more training data. In a real recycling sorting scenario, it would be important to get pictures of as many types of possible containers as possible (not just 13 brands), so if a never-before seen Brisk bottle was being missclassified, some pictures of the Brisk bottle and retraining should solve the problem.
This approach seems like it has some real commercial potential. Even if the machine learning was not able to classify everything properly, given a fairly controlled camera & background environment, it should be able to successfully identify a large number of recycling/non-recycling objects. You can imagine a conveyor-belt system where a robot arm pre-sorts known objects, leaving only the "unknown" and "unsure" objects for a human. This could greatly reduce the cost of sorting a city's recycling stream, and make recycling a much more cost-effective and wide-spread.
A very interesting 20-Time project!
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- LGMS 20 Time
The Egg and the Roller Coaster
Speed, acceleration, velocity - and possible wet splatty impact!? As part of Ken Symington's Physics 20 class, students needed to build an amusement park ride that took their precious cargo (an egg) on a wild ride without loss of eggy life. The egg amusement ride needed to take the "passenger" a minimum of 45 cm off the ground and needed to be in motion between 30 seconds and 2 minutes. Check out the pictures to see the wide range of designs! All designs were successful until an overexcited ride operator cranked up the ferris wheel ride to ludacris speed and - SPACE FLIGHT. No video of that unfortunately, since everyone was ducking and taking cover.
A fun way to learn why physics is so important in making sure our real-world machines prevent humans from going splat.
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