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5 Things to Remember About Teachers for Back to School

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Earlier this year you may have heard about a “War on Teachers,” where discussions of how little teachers work compared to how much they're paid got a lot of play in the media. With school starting again, let's take a closer look at our teachers.

1. They Don’t Do It for the Money

Since more money is the way to motivate employees in most businesses, some school districts with less than stellar test grades have tried offering large bonuses to teachers if they get their kids to a higher level. In 2007, New York City put aside $75 million, breaking down to $3,000 per teacher per year, if they increased their students’ test scores by enough. The scheme was ineffective, with very few schools claiming the bonuses, and was quietly done away with two years later.

The problem seems to be that unlike other jobs, just “working harder” isn’t enough. There are too many variables when it comes to teaching, and no amount of monetary incentive is enough to overcome all of them. It’s not usually the teacher’s work ethic that is the problem.

2. They Don’t Do It for the Money – Part 2

Even if you think teachers get paid too much, what isn’t in dispute is that most public schools have too little money for other areas. That is why every year teachers spend hundreds of dollars of their own money on classroom supplies, usually without any chance of being reimbursed. Studies have shown that 92% of K-12 teachers spend personal money on their classrooms. While the amount varied from around $350 to $550 per teacher per year in the last decade, in 2010 it added up to $1.3 billion.

And the numbers went down as the recession deepened and teachers had to cut corners in all areas of their lives. So a bad economy isn’t just bad for you at home -- it affects your kids at school as well, even if state funding isn’t cut.

3. It’s Not a Part-Time Job

One of the accusations often leveled against teachers is that they only work half days, since they are out of their classrooms by 3pm. Nothing could be further from the truth. A 2001 study found that the average teacher works 50 hours a week, because their work doesn’t end when the bell rings. Grading takes hours a day, not to mention any school extracurricular activities they may lead, like clubs, sports teams, or theater groups. There are long meetings outside of school hours, and someone has to be there if they assign a kid detention. Not to mention time spent making lesson plans so the whole school day runs smoothly.

While teachers may have more vacation days than most jobs in the US, it doesn’t mean they can afford them. Many teachers teach summer school or get a retail or restaurant job over the holidays, just like their students, in order to supplement their income.

4. They Are a Huge Influence on Your Child

Who had the biggest effect on your kid’s academic success? Their teachers. A large-scale Australian study found that a good teacher-student relationship meant better grades, even more so than good parental or peer relationships, especially during middle and high school.

When it comes to bad grades, most Americans think the blame lies with the parents. A study found that 68% of parents deserve “heavy blame” for failing students, while only 35% said teachers. While this was personal opinion and not based on proven fact, it shows that most people understand failing schools are not the sole fault of the teachers.

5. It Might Be Your Fault the Good Teachers Are Leaving

OK, not always. But studies have shown that the main contributor to young teachers burning out and leaving the profession is pushy parents. When new teachers come into the classroom ready to change the lives of their students, they are often shell-shocked by the number of complaints and sometimes outright abuse that they receive from those students’ parents. Many new teachers feel pressure to be perfect right away, and perfect always means making sure the child of that particular parent is happy and getting good grades. Multiply this by 25 or 30 parents (or more) in a school year and you get the mass exodus from teaching that America is now faced with.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Scientists Think They Know How Whales Got So Big
May 24, 2017
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It can be difficult to understand how enormous the blue whale—the largest animal to ever exist—really is. The mammal can measure up to 105 feet long, have a tongue that can weigh as much as an elephant, and have a massive, golf cart–sized heart powering a 200-ton frame. But while the blue whale might currently be the Andre the Giant of the sea, it wasn’t always so imposing.

For the majority of the 30 million years that baleen whales (the blue whale is one) have occupied the Earth, the mammals usually topped off at roughly 30 feet in length. It wasn’t until about 3 million years ago that the clade of whales experienced an evolutionary growth spurt, tripling in size. And scientists haven’t had any concrete idea why, Wired reports.

A study published in the journal Proceedings of the Royal Society B might help change that. Researchers examined fossil records and studied phylogenetic models (evolutionary relationships) among baleen whales, and found some evidence that climate change may have been the catalyst for turning the large animals into behemoths.

As the ice ages wore on and oceans were receiving nutrient-rich runoff, the whales encountered an increasing number of krill—the small, shrimp-like creatures that provided a food source—resulting from upwelling waters. The more they ate, the more they grew, and their bodies adapted over time. Their mouths grew larger and their fat stores increased, helping them to fuel longer migrations to additional food-enriched areas. Today blue whales eat up to four tons of krill every day.

If climate change set the ancestors of the blue whale on the path to its enormous size today, the study invites the question of what it might do to them in the future. Changes in ocean currents or temperature could alter the amount of available nutrients to whales, cutting off their food supply. With demand for whale oil in the 1900s having already dented their numbers, scientists are hoping that further shifts in their oceanic ecosystem won’t relegate them to history.

[h/t Wired]