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Are "Learning Styles" Backed By Scientific Evidence?

When I was in sixth grade, a psychologist came into my English class and gave us a "learning styles" test. I was going to a magnet school at the time, a school that might today be lumped in the TAG category. I don't remember the specifics of the test, but I distinctly remember the outcome. As the psychologist stood in front of our class before the test, she said, roughly: "Most of you will be visual learners; the rest will be auditory. [Explanation of those learning styles, and how smart people can use them while studying and such.] Oh, and kinesthetic learners are good with their hands, so they're usually mechanics. None of you will fall into that category." Guess who the only kinesthetic learner in the class was? Yup, me! I was a very quiet, non-kinesthetic kid, so I was baffled (though I was a fairly good pianist). When I asked what I could do with this new learning style information, the psychologist shrugged and said, "Learn to type?" So I did.

In the years since, I often wondered about learning styles and whether I was an odd duck, being a kinesthetic learner who was also a writer and an extremely verbal person. For example, I would write (or later type) notes in class, but I never, ever went back to read them. There was a time in high school when I would "type" my notes on a nonexistent keyboard on my desk, since there was no way I could afford a laptop, and I wouldn't read the notes later anyway. It looked weird, but it worked. In college, when I finally had a laptop, I took notes using my blindingly fast typing ability (and was paid for this -- the university gave me a stipend for taking notes to be given to other students who could not take their own), but I never reviewed my own notes, since it didn't seem to matter; I either knew the material or I didn't. And typically, sitting through a class, I knew it. So perhaps there was something to the notion that the "kinesthetic" activity of typing the notes was what made me learn them. Or was it simply that I had convinced myself that this was the case, because at a formative moment, someone told me how my mind worked?

NPR has a good article (and audio piece) on the topic. It seems that some recent studies and surveys of existing studies apparently disprove the notion that learning styles are actually significant in terms of classroom teaching -- this doesn't mean that the learning styles don't exist (though there is apparently a lack of super-solid science on that too), but it may mean that tailoring lessons to a particular, single style may be a mistake. Here's a snippet:

We've all heard the theory that some students are visual learners, while others are auditory learners. And still other kids learn best when lessons involve movement.

But should teachers target instruction based on perceptions of students' strengths? Several psychologists say education could use some "evidence-based" teaching techniques, not unlike the way doctors try to use "evidence-based medicine."

Psychologist Dan Willingham at the University of Virginia, who studies how our brains learn, says teachers should not tailor instruction to different kinds of learners. He says we're on more equal footing than we may think when it comes to how our brains learn. And it's a mistake to assume students will respond and remember information better depending on how it's presented.

Read the rest and be sure to listen to the audio piece -- it's fascinating stuff, and sure to rile the many people who have based careers on learning style theories.

What Do You Think?

Are you a teacher, learner, or other person with an interest in learning styles? Share your thoughts in the comments.

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iStock // Ekaterina Minaeva
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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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Nick Briggs/Comic Relief
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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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