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Speaker 1: Welcome to Stuff You Should Know, a production of iHeartRadio.

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Speaker 2: Hey, and welcome to the podcast. I'm Josh, and there's

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Speaker 2: Chuck and Jerry's lingering too. She's a lurker and this

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Speaker 2: is Stuff you Should Know. The Education edition.

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Speaker 3: Yeah, I'm pretty excited about this after learning more about it.

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Speaker 2: Yeah, this is your pick. Where'd you come up with this?

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Speaker 1: I don't know.

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Speaker 2: Well, that's good. I was hoping that it wasn't like, well,

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Speaker 2: I had a really bad experience with a teacher when

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Speaker 2: I was a kid.

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Speaker 3: No, I had always good experiences generally. But now I'm

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Speaker 3: worried that it was a listener because I've gotten a

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Speaker 3: few of those lately. Like, hey, when you said you

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Speaker 3: didn't know it was me, I usually make a note, but.

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Speaker 2: Sure, sure, I don't know. Well, if you suggest to

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Speaker 2: the Pygmalion effect, you're probably the only one, and you

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Speaker 2: can feel free to email and be like, hey, sorry,

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Speaker 2: and you said you didn't know it was me. Yeah,

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Speaker 2: Well we're talking about the pig melion effect and it

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Speaker 2: does have to do with education, but it has to

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Speaker 2: do with you know more than that too. And for

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Speaker 2: those of you who don't know the pigmalion effect, is

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Speaker 2: a kind of self fulfilling prophecy. It's called an expectancy bias,

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Speaker 2: I believe, and it basically says, in effect that if

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Speaker 2: you have high expectations for say a student or an

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Speaker 2: employee or something, they're likely to perform better than other people.

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Speaker 2: And it has something to do in all sorts of

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Speaker 2: different ways. It turns out from that relationship, that high expectation,

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Speaker 2: and it's pretty neat if you think about it. And

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Speaker 2: the Pygmalion it's named after, I guess an ovid metamorphosis story.

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Speaker 1: Right, yeah, I think it was. I believe it was

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Speaker 1: a statue, isn't that right?

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Speaker 2: I think Pigmalion was the sculptor, and the statue is Galateea.

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Speaker 1: Okay, I knew it from you know, because I'm not

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Speaker 1: the art major.

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Speaker 2: Well i'm not either.

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Speaker 3: I was the English major. So I read George bn

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Speaker 3: Ard Charles's play pig Malion in college in a class.

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Speaker 3: And then, of course My Fair Lady was based on

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Speaker 3: pig Malion, in which I think her name was Liza Doolittle.

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Speaker 3: Sort of, Hey, let's take this rough around the edges

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Speaker 3: young woman and make her into a fair lady.

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Speaker 2: I knew it is trading places.

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Speaker 1: Exactly, but you know, sort of a classic story.

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Speaker 3: The original play is great, and it all has to do,

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Speaker 3: like you said, with this sort of self this idea

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Speaker 3: of the self fulfilling prophecy, which had been around for

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Speaker 3: a long long time, but in the nineteen sixties, of course,

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Speaker 3: when psychology and doing studies on all kinds of things

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Speaker 3: was really blossoming and just sort of exploding.

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Speaker 1: In all directions.

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Speaker 3: Super hip tho was well, I don't know about that,

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Speaker 3: but maybe in those communities. But there was a psychology

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Speaker 3: name Robert Rosenthal who got pretty interested in this idea

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Speaker 3: of how bias can affect something like performance or assumptions

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Speaker 3: or you know, thinking like you know, it moved out

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Speaker 3: of the classroom, but initially like hey, this kid has

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Speaker 3: promised or this kid does it, and then they end

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Speaker 3: up being like that.

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Speaker 2: Yeah, yeah, for sure. And there's a lot of implications

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Speaker 2: obviously of you know, okay, well, then does that mean

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Speaker 2: that there's kids who are not performing as well as

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Speaker 2: they could because they're not being treated well by their teachers?

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Speaker 2: Like sure, there's a lot And I think one of

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Speaker 2: the things that I like about this is that it

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Speaker 2: just how much debate and research and argument has gone

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Speaker 2: into just this one segment of approaching education really just

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Speaker 2: goes to show how seriously we take education, or have,

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Speaker 2: at least in the past.

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Speaker 3: Yeah, I think so. I mean that certainly doesn't mean

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Speaker 3: we figured it out, but no, I think people have

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Speaker 3: long studied and tried and argued and debated on the

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Speaker 3: best way to help kids reach their potential.

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Speaker 1: Then that's a good thing. Yeah.

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Speaker 2: So there was a sociologist named Robert Mertin, and he

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Speaker 2: turns out to have been the person who coined the

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Speaker 2: term self fulfilling prophecy. I hope he copyrighted it because

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Speaker 2: I owe him some money, just me. Anyway, that was

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Speaker 2: back in nineteen forty eight, and even by then that

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Speaker 2: was a good almost fifty years after experimental data started

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Speaker 2: coming in that showed fulfilling prophecies existed. So I guess

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Speaker 2: our kind of hero or at least protagonist antagonist? I

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Speaker 2: guess it depends on how you look at him. Robert

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Speaker 2: Rosenthal in the sixties he hit upon a pretty great

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Speaker 2: study idea along with a colleague of his name, Kermit Fode,

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Speaker 2: which is a great name in writing out loud, blinked

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Speaker 2: out in Morse code, it's a great name all across

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Speaker 2: the board. But working together back in nineteen sixty three,

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Speaker 2: they took on running rats through may which was already

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Speaker 2: like just so cliched back then that it was like

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Speaker 2: a perfect thing to experiment on, because it was like

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Speaker 2: the people that they were actually experimenting on, the students

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Speaker 2: who were running the research, were the ones who were

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Speaker 2: being experimented on. But the rats amazes was just so

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Speaker 2: ubiquitous they didn't question that at all. It didn't even

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Speaker 2: occur to them that they would be being experimented on.

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Speaker 3: Yeah, and they ended up coming up what I think

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Speaker 3: should be just these words on a T shirt and

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Speaker 3: just don't even explain it, because what they told experimenters

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Speaker 3: that we're working with these rats, they said, all right,

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Speaker 3: you got some really great rats in this group, and

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Speaker 3: they were bred to be maze bright, but those other ones,

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Speaker 3: they're maze dull, and I just think that would be

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Speaker 3: fun on a T shirt. But actually these rats were

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Speaker 3: assigned randomly. But what they found out was that the

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Speaker 3: dull rats, the Mays dull ones, hit their peak performance

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Speaker 3: three days in and then started to go downhill where

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Speaker 3: these really bred to be Mayze bright rats just kept

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Speaker 3: on improving, and so the conclusion was, I think these

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Speaker 3: students are getting these rats that are Maze Bright and

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Speaker 3: are just you know, hey, little buddy, you can do it.

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Speaker 1: I know you got it.

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Speaker 3: Andy, Sure, you're a smart rat. Like they're handling them better,

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Speaker 3: they're talking them up, they're encouraging them, and it's working.

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Speaker 2: Yeah, because I mean again, you said that they were

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Speaker 2: assigned randomly and there was no such thing as Maze

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Speaker 2: Bright or Maze dol rats. They were all just the same.

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Speaker 2: So they had to have something to do with the

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Speaker 2: researchers because there was no difference between any of the

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Speaker 2: rats that were assigned. I think in the worst, the

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Speaker 2: worst interpretation is you could also suggest that the Maze

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Speaker 2: Bright rat student experimenters could could even have been fudging

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Speaker 2: the numbers a little bit to meet their expectations. Had

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Speaker 2: con but it's a possibility. And actually that kind of

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Speaker 2: led to one kind of branch of study that that

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Speaker 2: came out of that, Mays Bright May's Dole rat experiment.

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Speaker 2: How much expectancy bias affects researchers in scientific studies. That

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Speaker 2: was the first leap that it went to, but shortly

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Speaker 2: after that it ended up in the classroom because a

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Speaker 2: principle of Spruce School and Elementary in San Francisco read

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Speaker 2: about this rat experiment. I think it was an American

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Speaker 2: scientist in nineteen sixty three, yesh, and the principal, Leonora Jacobson,

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Speaker 2: wrote to Robert Rosenthal and said, Hey, if you ever

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Speaker 2: want to replace like rats and experimenters with students and teachers,

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Speaker 2: I'm your person. And very quickly Rosenthal took Leonora Jacobson

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Speaker 2: up on that.

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Speaker 3: Yeah, by very quickly, I guess in science terms. A

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Speaker 3: couple of years later, right, and they said, all right,

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Speaker 3: you know, we don't know it now, but this is

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Speaker 3: going to end up being a very very famous experiment

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Speaker 3: called the Pygmillion experiment. And again you know, named for

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Speaker 3: or the art and the play and.

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Speaker 1: What else was it?

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Speaker 2: Trading Places?

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Speaker 1: Trading Places? That's right.

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Speaker 2: I keep going to say forty eight hours, but that's

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Speaker 2: not it at all.

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Speaker 1: Yeah, but actually trade that came afterwards, so you know

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Speaker 1: what I mean? Sure, great movie, though, which one Trading Places?

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Speaker 1: I love it?

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Speaker 2: Okay, I've never seen forty eight hours?

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Speaker 1: Oh really, yeah, it's a good one.

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Speaker 2: Okay, So which one's better? Trading Places or forty eight Hours.

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Speaker 3: Well, I mean they're both kind of great. One is

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Speaker 3: just more of a straight up comedy, which is trading places. Sure,

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Speaker 3: forty eight hours was sort of in that cop buddy

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Speaker 3: movie action thing. Also has laughs, Yeah, for sure, but

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Speaker 3: you know it's prime Eddie Murphy.

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Speaker 2: Okay, that sound like more of a trading spaces person

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Speaker 2: to me.

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Speaker 1: Trading places, trading place, that's an HDTV show totally.

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Speaker 3: So boy, that was a good sidetrack. Eddie Murphy's got

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Speaker 3: a new Beverly Hills cop coming out, by the way.

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Speaker 2: Oh yeah, that's right. I wonder how that's going to be.

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Speaker 1: I wonder too.

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Speaker 2: I don't feel like he's aging poorly. He doesn't seem

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Speaker 2: to be getting less funny over time. Although I haven't

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Speaker 2: seen any of his stuff very recently.

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Speaker 1: We'll see I haven't either. I'm reserving my opinion untill

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Speaker 1: all right, that's fair, all right.

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Speaker 3: So Spruce School, San Francisco, it was performed on these kids,

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Speaker 3: white majority of Mexican American minority, but mostly working class kids.

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Speaker 3: This wasn't some like when I first heard Spruce School,

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Speaker 3: I thought it was some like Super heighty twenty Private.

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Speaker 2: Side I did too, sounds like it.

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Speaker 1: It sure it does, doesn't it, especially in San Francisco.

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Speaker 2: But one more thing, very crucially about this school. The

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Speaker 2: kids were grouped by reading ability. So if you weren't

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Speaker 2: a very good reader, you were in a group or

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Speaker 2: a class with other kids who weren't a very good reader,

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Speaker 2: and so on and so forth.

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Speaker 3: Yeah, and we're going to talk a lot about grouping

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Speaker 3: because it has a lot to It's not a great

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Speaker 3: thing to do, as it turns out, and it's got

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Speaker 3: a lot to do with a lot of this for sure.

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Speaker 3: So students at the school, they were given a test,

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Speaker 3: and the researchers told these teachers. And as we'll crucially

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Speaker 3: find out too, the test was not given by the researchers.

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Speaker 1: They were given by the teachers. Correct.

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Speaker 3: Yes, so that's going to come into play as well.

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Speaker 3: But they told the teachers, said, all right, we've got

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Speaker 3: these results. You've got some bloomers or quote unquote growth

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Speaker 3: spurters in your class, and they're probably just like these

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Speaker 3: maze bright rats. They were like the they're going to

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Speaker 3: really improve over the school year, just you watch. We

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Speaker 3: gave him this test. It was the Harvard's Test of

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Speaker 3: inflected acquisition, and it's supposed to assess their potential, which

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Speaker 3: was not true at all. What they actually took was

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Speaker 3: an IQ test called Toga Flanagan's Test of general ability,

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Speaker 3: and there were some problems with that right off the bat,

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Speaker 3: right with this Toga test.

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Speaker 2: So one thing chuck about that test of inflected acquisition.

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Speaker 2: It didn't exist. They just they made it up so

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Speaker 2: that teachers, if they were possibly familiar with the test

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Speaker 2: of general ability, they wouldn't be like, wait, this is

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Speaker 2: isn't This isn't what you would use to find gross

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Speaker 2: burgers or bloomers. They just made up a test. Because

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Speaker 2: this was a made up the results were supposed to

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Speaker 2: be made up too. Again, the teachers thought that they

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Speaker 2: were administering a test and that the results were real world,

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Speaker 2: but they were being lied to. They're being manipulated in

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Speaker 2: the exact same way those students were told that some

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Speaker 2: of their rats were mays, brighter, maze dull. Exact same experiment,

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Speaker 2: just with humans.

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Speaker 4: Now.

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Speaker 3: Yeah, because the idea is to see if teachers think

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Speaker 3: that a kid is supposed to have a growth spurt intellectually,

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Speaker 3: then that will end up being the self fulfilling prophecy.

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Speaker 3: So these students were chosen at random. The teachers were

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Speaker 3: given that information, and after months and months, they took

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Speaker 3: this test again, the Toga test at the eight month mark,

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Speaker 3: the one year mark, in the two year mark.

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Speaker 2: Yeah, and so, just as Rosenthal predicted in his hypothesis,

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Speaker 2: I should say Rosenthal and Jacobson, the principal the the

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Speaker 2: people who had been or the kids who had been

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Speaker 2: identified as growth spurners or bloomers actually did bloom academically.

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Speaker 2: They gained all sorts of IQ points over the course

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Speaker 2: of the eight months and then year and then two

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Speaker 2: years when they took and retook the tests. And that

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Speaker 2: even though the effects were mostly pronounced among first and

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Speaker 2: second graders, that was enough. That was enough to just

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Speaker 2: kind of show like this is a real deal. These

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Speaker 2: kids were no different than the other kids. The only

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Speaker 2: difference was that these bloomers were the ones whose teachers

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Speaker 2: were told keep an eye on them because they're going

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Speaker 2: to be amazing kids.

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Speaker 1: Yeah.

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Speaker 3: And what's interesting is for the uh, I think, the third, fifth,

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Speaker 3: and sixth graders, they showed that they actually improved at

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Speaker 3: the same rate the bloomers did the ones who were

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Speaker 3: assigned that tag at least at the same rate or

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Speaker 3: slightly even slower rate or lower rate did than the

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Speaker 3: control group, and the researchers.

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Speaker 1: Rosenthal basically said, well, that's because.

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Speaker 3: When you're younger, you're your mind is more malleable, and

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Speaker 3: so that that's probably it. And also because the school

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Speaker 3: and these teachers probably you know, think that their reputation

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Speaker 3: wasn't like they may have felt bad for these kids

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Speaker 3: who weren't who didn't get the bloomer tag, so they

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Speaker 3: may have like paid more attention to them or something.

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Speaker 2: Right, or the younger kids hadn't been at school long

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Speaker 2: enough to establish like, hey, I'm actually not that smart

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Speaker 2: or hey I'm actually really bright, so their their reputation

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Speaker 2: wasn't established. You know, no big man on campus label

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Speaker 2: had been applied yet.

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Speaker 3: Yeah, so if you're starting to sense like, oh wait

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Speaker 3: a minute, then he just immediately sort of explained away

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Speaker 3: something that didn't agree with his finding, you will see

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Speaker 3: that that kind of becomes part of the story.

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Speaker 1: Right.

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Speaker 2: So they published a study in nineteen sixty eight called

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Speaker 2: pig Malion in the Classroom, And again they named it

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Speaker 2: after Pigmalion because in that story from Ovid, the sculptor

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Speaker 2: Pigmalion sculpts a beautiful woman falls in love with her

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Speaker 2: and loves the statue so much that the goddess Venus says,

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Speaker 2: I'm going to make you a real live person. So

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Speaker 2: the attention that Pygmalion paid to Galatea his statue created

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Speaker 2: a magical transformation in his statue from statue to human.

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Speaker 2: There was some sort of magical intervention. So it actually

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Speaker 2: is a really great, great name. And I can't think

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Speaker 2: that that didn't have something to do with how much

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Speaker 2: it exploded onto the scene, because it's really difficult to

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Speaker 2: understate what just to bomb. This dropped not just in academia,

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Speaker 2: but in popular culture. It got picked up and talked

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Speaker 2: about four years afterward.

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Speaker 1: It sounds like a great place to break.

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Speaker 3: A I thought you might say that, all right, well,

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Speaker 3: let's take a break and we'll be right back and

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Speaker 3: talk about this explosion of understanding right after.

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Speaker 4: This shot shot.

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Speaker 3: All right, So where we left off. The study was

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Speaker 3: called Pigmilion in the Classroom, published in sixty eight as

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Speaker 3: the paper and then also notably is a full book.

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Speaker 3: If it was just the paper, it may have just

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Speaker 3: sort of been, you know, passed around through academia. But

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Speaker 3: because it was a book, it became very popular, and

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Speaker 3: all of a sudden, Barbara Walters is interviewing Rosenthal, and

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Speaker 3: the New York Times has got it on the front page,

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Speaker 3: and you know, like the mainstream media is all over

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Speaker 3: this is you know, basically saying and you know, kind

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Speaker 3: of like the media does with something like this. They're

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Speaker 3: not digging into the data like academia will, as we'll see,

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Speaker 3: but they'll run big headline and they'll say this is

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Speaker 3: really significant because we all knew that the way we

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Speaker 3: teach our kids is wrong, and this kind of proves it.

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Speaker 2: Yeah, So I think that was another reason why it

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Speaker 2: had such a huge effect on like the larger culture,

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Speaker 2: because people have been suspecting for a while that putting

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Speaker 2: kids into groups by you know, reading ability was a

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Speaker 2: bad idea. It was doing a disservice to him. Now

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Speaker 2: there's this paper that showed demonstrably that that was absolutely true,

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Speaker 2: that was a terrible idea. And yeah, like you said,

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Speaker 2: there were headlines all over the place. People were discussing it,

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Speaker 2: and Rosenthal he was basically the ringleader, the ring master

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Speaker 2: to all this stuff. He was very much on board

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Speaker 2: with not pointing out, oh, actually, you guys are missing

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Speaker 2: a lot of nuance. It's not quite that cut and dry.

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Speaker 2: He was like, yep, absolutely, like exactly what you're saying,

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Speaker 2: this black and white thing, where like, yes, this is

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Speaker 2: absolute proof. I'm totally going to go along with that.

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Speaker 2: And he got criticized just for that alone, just not

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Speaker 2: intervening in how his science and findings was being communicated

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Speaker 2: to the larger public and in fact kind of playing

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Speaker 2: a role in making that happen, just kind of capitalizing

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Speaker 2: on the general populations. In comprehension of statistical analysis, we

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Speaker 2: don't know what that is or how to do it,

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Speaker 2: so we rely on scientists to explain it to us

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Speaker 2: in terms we can understand or the press, and if

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Speaker 2: the scientist, as we've covered many times Chuck isn't forthright

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Speaker 2: or honest, that stuff can get turned into all sorts

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Speaker 2: of misunderstandings or overblown findings.

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Speaker 1: Yeah.

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Speaker 3: And one of the big things too that we should

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Speaker 3: point out was that the fact that we told you

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Speaker 3: earlier that before the break, that these tests really kind

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Speaker 3: of showed this effect for these younger kids in first

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Speaker 3: and second grade, but not for the you know, the

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Speaker 3: kids in the older grades, and in fact, it showed

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Speaker 3: a negative correlation sometimes in some of the older grades.

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Speaker 3: They didn't even put that in the book at all,

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Speaker 3: so they're already sort of cherry picking stuff. And the

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Speaker 3: book was the thing that really blew up more so

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Speaker 3: than the paper. Yeah, and so of course the press

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Speaker 3: isn't covering that aspect of it, probably because they didn't

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Speaker 3: even know about it.

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Speaker 2: Yeah. So there's two tracks. The popular press like the

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Speaker 2: New York Times or Today Show or whatever, they're covering

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Speaker 2: it in glowing terms like it's absolute proof of what

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Speaker 2: everybody always suspected. The other track was a pretty wide

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Speaker 2: river of criticism coming out of the halls of academia

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Speaker 2: from other psychologists of different stripes who just were teeing

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Speaker 2: off on this paper. And even though it didn't necessarily

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Speaker 2: capture the attention of the larger public in academia, there

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Speaker 2: was a thorough debate that started right after the paper

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Speaker 2: came out and went on for a good decade and

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Speaker 2: actually turned out to be really healthy, not just for

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Speaker 2: Rosenthal's paper, synhal and Jacobson's paper, but for I guess

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Speaker 2: statistical analysis as a whole. But I think because Rosenthal

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Speaker 2: ended up inadvertently creating the meta analysis study. But before

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Speaker 2: we get to that, took. Let's talk about some of

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Speaker 2: the stuff that was wrong with the paper statistically speaking.

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Speaker 3: So yeah, I mean the Toga test we should talk

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Speaker 3: about right out of the gate, because this test was

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Speaker 3: not supposed to be used on first graders or with

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Speaker 3: kids with an IQ below sixty, and that alone probably

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Speaker 3: accounts for, or at least accounts for some of the

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Speaker 3: fact that these low results were coming in on these

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Speaker 3: kids in the younger grades, and then they would obviously

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Speaker 3: gain much more ground because they've then aged into the

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Speaker 3: test by their time, they're taking this when they're really

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Speaker 3: supposed to.

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Speaker 2: Be exactly and there was something that that was Rosenthal

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Speaker 2: like responded to that. It even said, hey, even if

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Speaker 2: that test does and apply to these younger kids, the

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Speaker 2: fact that the same kid is taking the same test

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Speaker 2: over time, it really it renders that moot. It's still

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Speaker 2: going to show accurate results.

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Speaker 3: I see what he's saying there, but it's just moot

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Speaker 3: to me because it wasn't even supposed to be given

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Speaker 3: to a kid that.

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Speaker 2: Young, right. But also, he's totally full of beans right there.

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Speaker 2: He's so the first initial findings that first test produced

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Speaker 2: such totally skewed results that as those kids aged into

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Speaker 2: the test and started getting normal results, and you compared

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Speaker 2: those those later results to the first results, you would

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Speaker 2: see all sorts of crazy gains that were completely incorrect,

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Speaker 2: like they just weren't true. That was a big part

378
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Speaker 2: of it. That Toga test was not set up for

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Speaker 2: kids with IQs under sixty, which is a big problem

380
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Speaker 2: because first graders in the United States on average had

381
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Speaker 2: IQs of fifty eight, and so you can see it

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Speaker 2: reflected in some of those results, like some kid had

383
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Speaker 2: an IQ of eighteen. That's almost impossible and certainly they

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Speaker 2: wouldn't be like reading at that point. Same with the kid,

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Speaker 2: I think with thirty. And then one of those kids

386
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Speaker 2: later went from like thirty to like one hundred, which

387
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Speaker 2: is coming close to maybe even gifted level. Like the

388
00:21:18,359 --> 00:21:21,359
Speaker 2: results were just terrible and even worse than that. In

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Speaker 2: the book as an academic should they didn't include any

390
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Speaker 2: of the raw data either.

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Speaker 3: Yeah, so that means, you know, you can't go out

392
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Speaker 3: as another researcher and sort of try and replicate that.

393
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Speaker 3: It just kind of occurred to me what he was

394
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Speaker 3: sort of saying with that initial defense was like, you know,

395
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Speaker 3: you have a broken scale that doesn't say what your

396
00:21:42,200 --> 00:21:44,720
Speaker 3: true weight is, but you can still you know, it's

397
00:21:44,720 --> 00:21:47,600
Speaker 3: still accurate because you can see how much weight you

398
00:21:47,720 --> 00:21:50,240
Speaker 3: gain or lose by using that same scale, right, And

399
00:21:50,280 --> 00:21:52,000
Speaker 3: you're like, yeah, but you still don't know how much

400
00:21:52,040 --> 00:21:52,880
Speaker 3: somebody weighs.

401
00:21:53,400 --> 00:21:56,120
Speaker 2: Yes, that's true, But then also the thing that makes

402
00:21:56,200 --> 00:22:00,240
Speaker 2: him dishonest in that response is that imagine and it's

403
00:22:00,280 --> 00:22:02,600
Speaker 2: broken the first time you weigh yourself, and then you

404
00:22:02,640 --> 00:22:05,840
Speaker 2: fix it, and you weigh yourself after that, and so

405
00:22:06,040 --> 00:22:08,560
Speaker 2: those are the right results, but you're comparing them to

406
00:22:08,600 --> 00:22:11,960
Speaker 2: that first broken result. It's completely useless.

407
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Speaker 1: Why does anyone even have scales anyway?

408
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Speaker 2: I don't know, doesn't make any Why for God's sakes

409
00:22:19,080 --> 00:22:21,600
Speaker 2: do they keep them at hotels like at the Beach?

410
00:22:22,760 --> 00:22:23,320
Speaker 2: I don't know.

411
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Speaker 1: Man.

412
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Speaker 2: There was one in one of my rooms when we

413
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Speaker 2: were on tour, and this one was in San Francisco.

414
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Speaker 2: I'm like, I just glared at it a couple of times,

415
00:22:31,600 --> 00:22:33,920
Speaker 2: and it eased itself back under the vanity.

416
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Speaker 3: I mean, hey, I weigh myself to keep track of things,

417
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Speaker 3: but for God's sakes, don't weigh yourself on vacation.

418
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Speaker 4: No.

419
00:22:40,520 --> 00:22:42,879
Speaker 2: I was gonna say I do at home, but not

420
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Speaker 2: on vacation, not even on tour.

421
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Speaker 3: So anyway, scale diversion aside, Like you said, he didn't

422
00:22:48,119 --> 00:22:50,760
Speaker 3: include raw data. That means you can't come along afterward

423
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Speaker 3: and try and replicate it, So that's a big problem.

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Speaker 1: Yeah.

425
00:22:54,000 --> 00:22:58,720
Speaker 3: Other people chimed in and said things like I think

426
00:22:58,960 --> 00:23:03,960
Speaker 3: Richard snow Is psychologists who said, also, you know, apparently

427
00:23:04,119 --> 00:23:06,879
Speaker 3: teachers couldn't remember, and a lot of them reported that

428
00:23:06,920 --> 00:23:09,359
Speaker 3: they even didn't really even glance at this list on

429
00:23:09,400 --> 00:23:12,480
Speaker 3: who was a bloomer or not a bloomer, so, which

430
00:23:12,520 --> 00:23:14,840
Speaker 3: is very strange. It sounds like some of these teachers

431
00:23:15,280 --> 00:23:19,040
Speaker 3: didn't even fully realize or care much that they had

432
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Speaker 3: an experiment going on.

433
00:23:20,920 --> 00:23:23,760
Speaker 2: Yeah. I thought that was kind of weird too, because

434
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Speaker 2: I didn't get the impression that these were anything but

435
00:23:26,080 --> 00:23:29,200
Speaker 2: you know, normal dedicated teachers. But I don't know, maybe

436
00:23:29,200 --> 00:23:31,320
Speaker 2: they suspected that this was made up, or that this

437
00:23:31,520 --> 00:23:33,880
Speaker 2: was Maybe they were like, there's no test that can

438
00:23:33,920 --> 00:23:35,520
Speaker 2: really pick that up, so I'm not even going to

439
00:23:35,560 --> 00:23:37,200
Speaker 2: pay attention to that kind of thing.

440
00:23:37,400 --> 00:23:39,480
Speaker 1: Or they were busy teaching that.

441
00:23:39,240 --> 00:23:42,560
Speaker 2: That could be it too, for sure. Another one was

442
00:23:42,600 --> 00:23:47,120
Speaker 2: that the teachers themselves administered the tests, the initial tests,

443
00:23:48,480 --> 00:23:52,359
Speaker 2: so they weren't administered by professional child psychologists. They were

444
00:23:52,520 --> 00:23:55,840
Speaker 2: ministered by teachers who already had an impression of the kids.

445
00:23:55,840 --> 00:23:58,640
Speaker 2: They were administering the tests too, because it was the

446
00:23:58,680 --> 00:24:02,080
Speaker 2: previous year's teacher. So if you were saying second grade,

447
00:24:02,200 --> 00:24:04,680
Speaker 2: your first grade teacher was the one who administered your test.

448
00:24:05,000 --> 00:24:08,360
Speaker 2: I didn't get that, but that was another criticism for academia.

449
00:24:08,760 --> 00:24:09,119
Speaker 1: Yeah.

450
00:24:09,160 --> 00:24:14,360
Speaker 3: Absolutely, So people are debating this, they're starting to sort

451
00:24:14,400 --> 00:24:18,320
Speaker 3: of argue positions, you know, foreign against over time. There

452
00:24:18,400 --> 00:24:21,040
Speaker 3: was a twenty eighteen overview of a lot of these

453
00:24:21,040 --> 00:24:26,240
Speaker 3: debates from someone named Thomas L. Goood and Natasha Stirsinger

454
00:24:26,359 --> 00:24:29,960
Speaker 3: and Alison Levine and hats off Olivia for like getting

455
00:24:29,960 --> 00:24:32,119
Speaker 3: all these names mcau. There's a lot of people that

456
00:24:32,160 --> 00:24:35,280
Speaker 3: did a lot of follow up stuff, so nice job. Yeah,

457
00:24:35,320 --> 00:24:40,000
Speaker 3: but they noted that the individual the individual students result

458
00:24:40,359 --> 00:24:45,119
Speaker 3: results varied a lot on the different post tests, saying,

459
00:24:45,240 --> 00:24:47,600
Speaker 3: you know, basically, we just don't have a lot of

460
00:24:47,720 --> 00:24:50,840
Speaker 3: evidence that these IQs really improved at all.

461
00:24:51,800 --> 00:24:55,439
Speaker 2: Yes, but here's the thing. This is what's astounding of it.

462
00:24:55,480 --> 00:24:58,840
Speaker 2: I think it was. Robert Snow in his book review

463
00:24:58,880 --> 00:25:03,199
Speaker 2: of it, wrote that it's possible that the pygmalion in

464
00:25:03,280 --> 00:25:08,199
Speaker 2: the in the classroom like study actually did turn up

465
00:25:08,320 --> 00:25:12,879
Speaker 2: evidence of you know, this, this idea that we've all considered,

466
00:25:12,960 --> 00:25:15,200
Speaker 2: you know, for a long time as possible that teachers

467
00:25:15,280 --> 00:25:18,919
Speaker 2: expectations affect student performance. Right, But if it did, it

468
00:25:18,960 --> 00:25:21,840
Speaker 2: did it by accident, because he was just saying, like,

469
00:25:22,119 --> 00:25:26,040
Speaker 2: the study was so poorly executed. And it seems like

470
00:25:26,119 --> 00:25:31,480
Speaker 2: Robert Snow was correct in that guess that somehow, some

471
00:25:31,600 --> 00:25:35,280
Speaker 2: way this study did show this is a real thing.

472
00:25:35,720 --> 00:25:39,120
Speaker 2: And over time from this ten year long debate over

473
00:25:39,160 --> 00:25:41,280
Speaker 2: the results and the methodology and all that stuff and

474
00:25:41,440 --> 00:25:44,120
Speaker 2: has off to Rosenthal. He didn't just like, he didn't

475
00:25:44,200 --> 00:25:45,840
Speaker 2: just like throw the study out and run off with

476
00:25:45,880 --> 00:25:48,080
Speaker 2: a big bag of money with a dollar sign on it.

477
00:25:49,240 --> 00:25:52,040
Speaker 2: He stood there and he answered his critics he had.

478
00:25:52,160 --> 00:25:55,040
Speaker 2: He engaged in the debate for a good decade, and

479
00:25:55,119 --> 00:25:57,680
Speaker 2: over the course of that decade, more studies with better

480
00:25:57,720 --> 00:26:04,359
Speaker 2: methodology and better execute were created and studied the same effect,

481
00:26:04,359 --> 00:26:07,720
Speaker 2: this pigmalion effect, and they found Nope, he was right.

482
00:26:08,160 --> 00:26:10,080
Speaker 2: Whether it was a bad study or not, this is

483
00:26:10,200 --> 00:26:14,640
Speaker 2: it produced some sort of correct results that we do

484
00:26:14,760 --> 00:26:18,920
Speaker 2: realize now. The pigmalion effect does have it is real

485
00:26:18,960 --> 00:26:19,679
Speaker 2: to some degree.

486
00:26:20,400 --> 00:26:22,520
Speaker 3: Yeah, and depending on who you were, you could come

487
00:26:22,560 --> 00:26:25,040
Speaker 3: at it from a different angle, and each of you

488
00:26:25,280 --> 00:26:28,879
Speaker 3: have a point because, as Livia points out, like just

489
00:26:28,880 --> 00:26:32,520
Speaker 3: sort of politically, as far as being hard on teachers

490
00:26:32,640 --> 00:26:35,040
Speaker 3: or not, it would play out in different ways. A

491
00:26:35,040 --> 00:26:38,480
Speaker 3: writer for the San Francisco Chronicle said like, see, here

492
00:26:38,520 --> 00:26:44,640
Speaker 3: you go, these low expectations on these children of lesser income.

493
00:26:45,119 --> 00:26:48,480
Speaker 3: That's what's causing them to fall behind and maybe even

494
00:26:48,560 --> 00:26:52,159
Speaker 3: drop out of school later on. Whereas the Albert Shanker,

495
00:26:52,240 --> 00:26:55,720
Speaker 3: who was with it United Federation of Teachers said, no,

496
00:26:56,080 --> 00:26:59,560
Speaker 3: it's not the teacher's fault. It's you know, it's poverty itself,

497
00:26:59,840 --> 00:27:02,159
Speaker 3: and we have too many kids in these classes and

498
00:27:02,200 --> 00:27:03,480
Speaker 3: we don't have the right materials.

499
00:27:04,440 --> 00:27:07,719
Speaker 2: Yeah, and regardless of where you fall on it, there

500
00:27:07,800 --> 00:27:11,199
Speaker 2: was still a big push to do away with like

501
00:27:11,280 --> 00:27:15,679
Speaker 2: advanced placement classes or gifted tracks or even remedial stuff.

502
00:27:15,680 --> 00:27:17,960
Speaker 2: They were like, just because you think of kids are medial,

503
00:27:18,040 --> 00:27:20,000
Speaker 2: do not put them in a remedial class. Put them

504
00:27:20,040 --> 00:27:24,000
Speaker 2: in like a mixed aptitude class, and they'll do way

505
00:27:24,040 --> 00:27:26,280
Speaker 2: better than if you put them in a remedial class.

506
00:27:26,880 --> 00:27:29,880
Speaker 2: That was a big deal. I don't guess it wasn't

507
00:27:29,880 --> 00:27:33,240
Speaker 2: successful because there was still plenty of AP classes and

508
00:27:33,440 --> 00:27:37,040
Speaker 2: snotty little AP students in the nineties. When I was

509
00:27:37,040 --> 00:27:39,560
Speaker 2: in high school, I just loved to shove it in

510
00:27:39,600 --> 00:27:42,240
Speaker 2: your face. Oh I'm in AP history.

511
00:27:42,359 --> 00:27:45,800
Speaker 3: Did you not take any AP classes? No, I took

512
00:27:45,840 --> 00:27:49,639
Speaker 3: a couple. I took AP history in English. But looking back, like,

513
00:27:50,960 --> 00:27:53,320
Speaker 3: I don't know, I can definitely see like they were

514
00:27:53,359 --> 00:27:57,119
Speaker 3: great classes, and I felt like the teachers were better,

515
00:27:57,160 --> 00:28:01,280
Speaker 3: But it also may have been my own bias because

516
00:28:01,320 --> 00:28:05,159
Speaker 3: it was AP and also like a student that you know,

517
00:28:06,680 --> 00:28:09,000
Speaker 3: they don't think should have tested into there, if they

518
00:28:09,000 --> 00:28:11,040
Speaker 3: had been thrown in there, maybe they would have risen

519
00:28:11,080 --> 00:28:14,440
Speaker 3: to that level. So it's, you know, with with adult eyes,

520
00:28:14,480 --> 00:28:16,239
Speaker 3: I know, look back at kind of how messed up

521
00:28:16,240 --> 00:28:17,000
Speaker 3: all that stuff was.

522
00:28:17,320 --> 00:28:20,639
Speaker 2: Well, if it was better teaching from better teachers with

523
00:28:20,720 --> 00:28:24,520
Speaker 2: better material, then the argument for people who are like

524
00:28:24,600 --> 00:28:29,080
Speaker 2: against that would say, then all classes should be like that,

525
00:28:29,280 --> 00:28:31,600
Speaker 2: Every history class should be taught like that. Don't just

526
00:28:31,680 --> 00:28:34,040
Speaker 2: make it for the ones who you think are gifted

527
00:28:34,160 --> 00:28:37,520
Speaker 2: or whatever. So that was a I think that still

528
00:28:37,520 --> 00:28:40,120
Speaker 2: probably is a big deal. I'm not particularly up on

529
00:28:41,320 --> 00:28:44,960
Speaker 2: the state of education today or early childhood education. Yeah,

530
00:28:45,040 --> 00:28:47,680
Speaker 2: so I don't know if they're still putting kids in classes,

531
00:28:47,720 --> 00:28:51,840
Speaker 2: different separate classes or not, But if not, I'm sure

532
00:28:51,880 --> 00:28:53,800
Speaker 2: there's still people arguing against it.

533
00:28:54,320 --> 00:28:56,120
Speaker 3: Yeah, I mean, I'm not sure yet as far as

534
00:28:56,280 --> 00:28:58,760
Speaker 3: upper grades. You know, all my experience right now is

535
00:28:58,760 --> 00:29:02,080
Speaker 3: with Ruby and the third grade at her little hippie

536
00:29:02,120 --> 00:29:05,800
Speaker 3: dippy private school, where of course everybody is you know,

537
00:29:05,840 --> 00:29:08,000
Speaker 3: treated equally and given the same opportunities.

538
00:29:08,120 --> 00:29:10,920
Speaker 1: Everyone wins or loses.

539
00:29:12,000 --> 00:29:14,360
Speaker 3: So one kind of cool thing that came out of

540
00:29:14,400 --> 00:29:18,200
Speaker 3: this was because there was there was it was so famous,

541
00:29:18,240 --> 00:29:20,920
Speaker 3: and because there were so many people sort of criticizing it,

542
00:29:20,960 --> 00:29:23,480
Speaker 3: so many people defending it, and so many people doing

543
00:29:23,520 --> 00:29:26,440
Speaker 3: other studies. Because as we'll see, this soon leapt into

544
00:29:27,200 --> 00:29:31,320
Speaker 3: the private sector with like business, into the military, like

545
00:29:31,320 --> 00:29:33,280
Speaker 3: people started sort of applying this kind of thing to

546
00:29:33,320 --> 00:29:37,320
Speaker 3: all kinds of you know, stuff outside the classroom. It

547
00:29:37,440 --> 00:29:40,880
Speaker 3: led to Rosenthal saying, well, hey, now I can look

548
00:29:40,920 --> 00:29:44,600
Speaker 3: at like all these studies together, and like, was that

549
00:29:44,640 --> 00:29:46,640
Speaker 3: the literal birth of meta analysis?

550
00:29:46,680 --> 00:29:47,560
Speaker 1: Was he one of the first.

551
00:29:47,680 --> 00:29:50,480
Speaker 2: That's how I took it. Yeah, that shame. Nineteen seventy eight,

552
00:29:50,480 --> 00:29:53,400
Speaker 2: he got together with a colleague Donald Rubin, who was

553
00:29:53,440 --> 00:30:00,479
Speaker 2: the head of Harvard's statistics or statistical analysis department. Uh huh,

554
00:30:00,560 --> 00:30:03,480
Speaker 2: So this guy's like as good as it gets with statistics.

555
00:30:04,400 --> 00:30:06,800
Speaker 2: And they got together three hundred and forty five studies

556
00:30:07,080 --> 00:30:11,400
Speaker 2: that looked at expectancy effects and found that there was

557
00:30:12,120 --> 00:30:16,040
Speaker 2: like there was a pronounced effect that was detected in

558
00:30:16,080 --> 00:30:19,480
Speaker 2: the if you just looked at the high quality studies on.

559
00:30:19,480 --> 00:30:23,280
Speaker 1: It, right, Okay, so they're looking at this stuff.

560
00:30:24,720 --> 00:30:27,800
Speaker 3: Like you pointed out earlier, the people couldn't replicate because

561
00:30:27,840 --> 00:30:31,920
Speaker 3: there wasn't raw data, and there were psychologists and neuroscience

562
00:30:31,960 --> 00:30:34,680
Speaker 3: researchers that were pointing this stuff out like, hey, we

563
00:30:34,680 --> 00:30:37,960
Speaker 3: can't even replicate this thing. There were also people pointing

564
00:30:38,040 --> 00:30:41,560
Speaker 3: out that the people that are criticizing it and the

565
00:30:41,560 --> 00:30:43,720
Speaker 3: people that are defending it, like sometimes they're not even

566
00:30:43,760 --> 00:30:45,520
Speaker 3: looking at the same data, right.

567
00:30:46,280 --> 00:30:49,600
Speaker 2: Yeah. So Rosenthal was like, hey, this if we're looking

568
00:30:49,640 --> 00:30:52,840
Speaker 2: at actual student progress based on teacher expectations and you're

569
00:30:53,000 --> 00:30:57,719
Speaker 2: just looking at gains in IQ testing, yeah, that's not

570
00:30:57,760 --> 00:31:00,000
Speaker 2: looking at the whole picture. Like if you also take

571
00:31:00,160 --> 00:31:05,480
Speaker 2: into into account scores from year end achievement tests or

572
00:31:05,680 --> 00:31:11,720
Speaker 2: teacher assessments on improvement or how many books a kid

573
00:31:11,760 --> 00:31:15,000
Speaker 2: can walk around with on their head without spilling them

574
00:31:15,000 --> 00:31:16,840
Speaker 2: over because they have really good posture. If you take

575
00:31:16,880 --> 00:31:19,000
Speaker 2: all this stuff into account, you get a much clearer

576
00:31:19,040 --> 00:31:21,520
Speaker 2: picture of whether the student actually did improve or not

577
00:31:21,880 --> 00:31:23,920
Speaker 2: thanks to teacher expectation.

578
00:31:24,600 --> 00:31:28,240
Speaker 3: Yeah, exactly so. And you know, I mentioned they did

579
00:31:28,240 --> 00:31:30,600
Speaker 3: it outside the classroom. I think they found the biggest

580
00:31:30,640 --> 00:31:34,800
Speaker 3: gains in military settings. Yeah, which, yeah, the idea that

581
00:31:34,840 --> 00:31:37,080
Speaker 3: you know, you probably just have more sway as a

582
00:31:37,200 --> 00:31:38,959
Speaker 3: drill sergeant than you do as a teacher.

583
00:31:39,000 --> 00:31:39,280
Speaker 1: Maybe.

584
00:31:39,480 --> 00:31:42,040
Speaker 2: Yeah, for sure, you have that much more influence in

585
00:31:42,160 --> 00:31:45,320
Speaker 2: the more influence and control or you have over somebody,

586
00:31:45,920 --> 00:31:49,520
Speaker 2: the more effect your expectations can have on them.

587
00:31:49,880 --> 00:31:50,120
Speaker 1: Yeah.

588
00:31:50,120 --> 00:31:53,040
Speaker 3: But they definitely like they saw this play out, so

589
00:31:53,080 --> 00:31:55,640
Speaker 3: it's not like we're this is an episode on how

590
00:31:55,720 --> 00:31:59,120
Speaker 3: this isn't a thing, because it is a thing, whether

591
00:31:59,120 --> 00:32:01,280
Speaker 3: they found it by acts and or not. Like there

592
00:32:01,360 --> 00:32:04,680
Speaker 3: was one example that Lyvia found where, uh, there were

593
00:32:04,800 --> 00:32:08,560
Speaker 3: employees putting together medical kits and they brought in this

594
00:32:08,600 --> 00:32:11,120
Speaker 3: group of new hires and told the managers like, hey,

595
00:32:11,360 --> 00:32:14,120
Speaker 3: these people they're they're mays happy.

596
00:32:14,160 --> 00:32:16,360
Speaker 1: What was it, may bright?

597
00:32:17,000 --> 00:32:18,920
Speaker 3: Yeah, they're maze bright, like you ought to see them

598
00:32:18,920 --> 00:32:21,640
Speaker 3: put together these kids like you're going to do great.

599
00:32:21,680 --> 00:32:24,440
Speaker 3: They got a lot of potential here, and that that

600
00:32:24,480 --> 00:32:26,960
Speaker 3: group ended up breaking records for production levels.

601
00:32:27,200 --> 00:32:31,880
Speaker 2: Right. And so if you're in management science, you're teaching

602
00:32:31,920 --> 00:32:35,000
Speaker 2: everybody this, like just just go out and lie to

603
00:32:35,080 --> 00:32:40,120
Speaker 2: your managers, and your employees will start like actually producing

604
00:32:40,240 --> 00:32:43,720
Speaker 2: way better than you would think for for no reason

605
00:32:43,760 --> 00:32:46,960
Speaker 2: other than their manager has higher expectations thinks they're better

606
00:32:47,000 --> 00:32:50,000
Speaker 2: at their job than other people. And that is a huge,

607
00:32:50,560 --> 00:32:54,920
Speaker 2: huge part of all of this. Yeah, there doesn't seem

608
00:32:55,000 --> 00:32:58,600
Speaker 2: to be the same effect if you are forthright and

609
00:32:58,720 --> 00:33:02,160
Speaker 2: honest with the teacher, because the whole thing seems to

610
00:33:02,200 --> 00:33:05,200
Speaker 2: be rooted in the idea that the teacher or the

611
00:33:05,240 --> 00:33:09,160
Speaker 2: manager has to genuinely believe that this kid or this

612
00:33:09,320 --> 00:33:14,840
Speaker 2: student or this employee is above average and expect above

613
00:33:14,880 --> 00:33:16,240
Speaker 2: average results from him.

614
00:33:16,720 --> 00:33:18,880
Speaker 3: Yeah, I say we take another break and we talk.

615
00:33:19,160 --> 00:33:20,720
Speaker 3: We dive a little bit more into that after this.

616
00:33:20,840 --> 00:33:51,000
Speaker 2: Eh sounds good, ma'am. Shot shot, So, Chuck. One thing

617
00:33:51,160 --> 00:33:53,960
Speaker 2: that I think probably everybody listening to this episode so

618
00:33:54,080 --> 00:33:58,120
Speaker 2: far has come across as a question, is okay, if

619
00:33:58,440 --> 00:34:03,040
Speaker 2: teacher's expectations actually influenced student performance. How what are teachers

620
00:34:03,120 --> 00:34:06,320
Speaker 2: doing that that can have that effect? And that's been

621
00:34:06,360 --> 00:34:08,720
Speaker 2: a big thread of this study as well.

622
00:34:09,360 --> 00:34:11,799
Speaker 3: Yeah, because you know, to put this into you know,

623
00:34:11,840 --> 00:34:14,359
Speaker 3: to implement this is kind of the important thing. Sure,

624
00:34:14,360 --> 00:34:16,120
Speaker 3: it's not just to sit back and say, well, we

625
00:34:16,160 --> 00:34:18,680
Speaker 3: know all this stuff now, because hopefully the goal is

626
00:34:18,719 --> 00:34:21,640
Speaker 3: to help kids, you know, learn better. So they did

627
00:34:21,680 --> 00:34:25,280
Speaker 3: put together some broad categories over the years of how

628
00:34:26,480 --> 00:34:29,800
Speaker 3: if you're a teacher, you might be transmitting positive expectations

629
00:34:30,440 --> 00:34:32,279
Speaker 3: you might not be and not even know it by

630
00:34:32,360 --> 00:34:35,320
Speaker 3: saying certain things. And they put together a four point

631
00:34:35,360 --> 00:34:37,239
Speaker 3: thing which after I read it, I was like, oh

632
00:34:37,280 --> 00:34:39,840
Speaker 3: my god, why weren't they already doing all this?

633
00:34:40,080 --> 00:34:41,799
Speaker 2: I know it's kind of sad, but.

634
00:34:42,080 --> 00:34:45,520
Speaker 3: Here it is climate that is giving a warm emotional

635
00:34:45,640 --> 00:34:50,720
Speaker 3: environment input, giving them more and tougher assignments these students. Output,

636
00:34:50,840 --> 00:34:54,760
Speaker 3: allowing the students more opportunity to engage with that material,

637
00:34:54,960 --> 00:34:58,000
Speaker 3: and then the fourth win is give more detailed feedback.

638
00:34:58,120 --> 00:35:04,080
Speaker 2: Right, so teaching essentially ideally, well yeah, exactly. So what

639
00:35:04,200 --> 00:35:09,560
Speaker 2: they found was that given that the idea that some

640
00:35:09,640 --> 00:35:12,719
Speaker 2: of their students were growth spurgers or we're going to

641
00:35:12,760 --> 00:35:16,359
Speaker 2: really you know, make some crazy good moves. This school year.

642
00:35:17,000 --> 00:35:21,040
Speaker 2: Teachers did different stuff with that information, Like, they didn't

643
00:35:21,080 --> 00:35:24,920
Speaker 2: all just follow what Rosenthal would have expected, which is they,

644
00:35:25,239 --> 00:35:28,760
Speaker 2: you know, create these high expectations of warm learning environment

645
00:35:28,800 --> 00:35:33,200
Speaker 2: for those those growth spurgers or bloomers. Instead, some of

646
00:35:33,239 --> 00:35:35,600
Speaker 2: them were like, Okay, well then that kid's good. Let

647
00:35:35,600 --> 00:35:38,560
Speaker 2: me go focus my attention on.

648
00:35:38,080 --> 00:35:40,440
Speaker 1: The lower amazing students.

649
00:35:40,520 --> 00:35:42,000
Speaker 2: Yeah, the maze dull students.

650
00:35:42,200 --> 00:35:43,000
Speaker 1: Yeah, you know.

651
00:35:43,200 --> 00:35:46,239
Speaker 2: And so what was interesting about that too, because that

652
00:35:46,280 --> 00:35:49,040
Speaker 2: actually is kind of sensible. It's a sensible strategy if

653
00:35:49,080 --> 00:35:51,960
Speaker 2: you have a finite amount of time and attention to

654
00:35:52,480 --> 00:35:57,360
Speaker 2: give to all your students. They found that in some cases.

655
00:35:57,480 --> 00:36:01,480
Speaker 2: There was a psychologist, Rona Weinstein, who found that when

656
00:36:01,520 --> 00:36:05,480
Speaker 2: that was done, in some cases, the low performing students

657
00:36:05,480 --> 00:36:08,640
Speaker 2: who got more attention actually still did worse than the

658
00:36:08,719 --> 00:36:13,719
Speaker 2: higher performing students. Yeah, and she hypothesized that that was

659
00:36:13,719 --> 00:36:16,879
Speaker 2: because those kids were basically being patronized, and even though

660
00:36:16,880 --> 00:36:20,600
Speaker 2: they're six, they still understand that on some innate level,

661
00:36:20,960 --> 00:36:23,759
Speaker 2: and so they were still getting signals that there the

662
00:36:23,840 --> 00:36:25,319
Speaker 2: expectations for them were low.

663
00:36:26,320 --> 00:36:29,879
Speaker 3: Yeah, or maybe they were already separated out, which kind

664
00:36:29,880 --> 00:36:32,280
Speaker 3: of goes to that whole idea that like putting kids

665
00:36:32,320 --> 00:36:34,840
Speaker 3: in a group just labeled, and you know, and a

666
00:36:34,880 --> 00:36:39,080
Speaker 3: lot of times, you know, they would. I remember my school,

667
00:36:39,160 --> 00:36:43,120
Speaker 3: even where my father was principal, the you know, the

668
00:36:43,400 --> 00:36:47,120
Speaker 3: the troubled Kids program, and this wasn't necessarily academically, but

669
00:36:47,200 --> 00:36:50,440
Speaker 3: the behaviorally troubled kids were all put in a special

670
00:36:50,520 --> 00:36:53,279
Speaker 3: group that had a label. I can't remember. It was

671
00:36:53,320 --> 00:36:57,319
Speaker 3: an acronym that basically indicated kind of how great they were,

672
00:36:57,719 --> 00:37:00,560
Speaker 3: which is, you know, it's a good things like you

673
00:37:00,560 --> 00:37:03,799
Speaker 3: should definitely shouldn't say like call them like these are

674
00:37:03,880 --> 00:37:07,359
Speaker 3: the bad kids or whatever. So I think you're they

675
00:37:07,360 --> 00:37:09,960
Speaker 3: would put labels on them that would hopefully give them

676
00:37:09,960 --> 00:37:13,359
Speaker 3: an aspirational expectation or something, or they or they did.

677
00:37:13,360 --> 00:37:17,120
Speaker 3: In the seventies, my dad's whole thing was outdoor programs.

678
00:37:17,160 --> 00:37:19,279
Speaker 3: He was the first person in the i think in

679
00:37:19,320 --> 00:37:21,839
Speaker 3: the state, definitely in the county that started like all

680
00:37:21,840 --> 00:37:24,239
Speaker 3: these camping programs and he really believed that getting kids

681
00:37:24,239 --> 00:37:28,319
Speaker 3: out in nature if they had behavioral problems could really

682
00:37:28,400 --> 00:37:31,399
Speaker 3: you could see gains there and stuff like that. So yeah, yeah,

683
00:37:31,640 --> 00:37:36,000
Speaker 3: it was pretty cool, great, great principle, Yeah, and full stop.

684
00:37:39,160 --> 00:37:42,480
Speaker 2: So what your point is is that if you if

685
00:37:42,520 --> 00:37:45,719
Speaker 2: you are if you separate kids, or you even talk

686
00:37:45,840 --> 00:37:49,040
Speaker 2: about certain kids in certain ways, if you even have

687
00:37:49,120 --> 00:37:53,480
Speaker 2: them separated mentally, it's going to be transmitted or telegraphed

688
00:37:53,800 --> 00:37:55,879
Speaker 2: to both groups of students as a whole.

689
00:37:56,960 --> 00:37:59,480
Speaker 3: Sure, And they found that even if they weren't separated,

690
00:38:00,160 --> 00:38:02,200
Speaker 3: just sort of the language that teachers would use in

691
00:38:02,239 --> 00:38:05,319
Speaker 3: the class would divide them, the way they talk to

692
00:38:05,320 --> 00:38:07,040
Speaker 3: certain kids and other kids.

693
00:38:07,120 --> 00:38:10,080
Speaker 2: Right, Yeah, that's what I was saying, okay, which is,

694
00:38:10,200 --> 00:38:13,279
Speaker 2: you know, it's pretty interesting. But again, all of it

695
00:38:13,320 --> 00:38:15,520
Speaker 2: comes down to this, and I shouldn't say again because

696
00:38:15,520 --> 00:38:17,880
Speaker 2: I haven't made this point yet. What this is all

697
00:38:17,920 --> 00:38:22,920
Speaker 2: predicated on the fact that teachers are human beings with biases,

698
00:38:23,040 --> 00:38:29,040
Speaker 2: with prejudices, with just thoughts that they can't, you know,

699
00:38:29,080 --> 00:38:32,880
Speaker 2: avoid unconscious ways that you treat or act towards certain

700
00:38:32,960 --> 00:38:37,080
Speaker 2: kids where you favor some over others. And then there

701
00:38:37,120 --> 00:38:39,239
Speaker 2: was something that stuck out to me because there was

702
00:38:39,320 --> 00:38:43,160
Speaker 2: a researcher from New Zealand named Christine Ruby Davies. No

703
00:38:43,280 --> 00:38:47,239
Speaker 2: bet when she talks it sounds awesome, but she has

704
00:38:47,280 --> 00:38:50,399
Speaker 2: set up a project called the Teacher Expectation Project where

705
00:38:50,440 --> 00:38:53,080
Speaker 2: she's like Hey, remember how you guys said a minute

706
00:38:53,120 --> 00:38:56,440
Speaker 2: ago that for this to be effective, you have to

707
00:38:56,480 --> 00:38:58,880
Speaker 2: lie to the teachers. You have to mislead them so

708
00:38:58,920 --> 00:39:02,239
Speaker 2: that they genuinely believe that the students are gifted. I

709
00:39:02,400 --> 00:39:04,600
Speaker 2: say nuts to that. I'm going to figure out a

710
00:39:04,640 --> 00:39:10,560
Speaker 2: way to teach teachers to be high expectance or high

711
00:39:10,600 --> 00:39:13,759
Speaker 2: expectancy teachers so that they for everybody right, so that

712
00:39:13,800 --> 00:39:16,839
Speaker 2: they have those effects on everybody without them, you know,

713
00:39:16,880 --> 00:39:19,319
Speaker 2: being duped. But one of the things she came up

714
00:39:19,360 --> 00:39:22,239
Speaker 2: with that to me was like, yes, I think that's

715
00:39:22,960 --> 00:39:27,120
Speaker 2: seventy percent of it right there. Teachers don't know all

716
00:39:27,200 --> 00:39:30,520
Speaker 2: of their students equally well in the classroom. And if

717
00:39:30,520 --> 00:39:33,000
Speaker 2: you've ever been one of the students who you your

718
00:39:33,080 --> 00:39:36,439
Speaker 2: teacher didn't really know you very well and clearly knew

719
00:39:36,440 --> 00:39:37,520
Speaker 2: other students better.

720
00:39:38,000 --> 00:39:38,200
Speaker 1: Yeap.

721
00:39:38,320 --> 00:39:41,640
Speaker 2: That is a that is an isolating feeling, and it's

722
00:39:41,680 --> 00:39:44,520
Speaker 2: not as easy to learn as it is when you're

723
00:39:44,600 --> 00:39:48,040
Speaker 2: one of the students that the teacher knows that kid.

724
00:39:48,320 --> 00:39:51,400
Speaker 2: And so that's one of the things that Christine Ruby

725
00:39:51,480 --> 00:39:55,799
Speaker 2: Davies teaches, like, know all of your kids equally well.

726
00:39:55,840 --> 00:39:56,800
Speaker 2: It's very important.

727
00:39:57,480 --> 00:39:58,320
Speaker 1: Yeah, for sure.

728
00:39:58,680 --> 00:40:01,280
Speaker 3: I mean I was well known by all my teachers

729
00:40:01,280 --> 00:40:05,040
Speaker 3: because I didn't consciously make a point to but I

730
00:40:05,120 --> 00:40:07,839
Speaker 3: was the class clown and I was always involved in

731
00:40:08,560 --> 00:40:10,719
Speaker 3: trying to crack jokes and being funny, and I may

732
00:40:10,719 --> 00:40:13,560
Speaker 3: have been disruptive, but the teachers also loved me because

733
00:40:13,560 --> 00:40:18,239
Speaker 3: it wasn't usually like a like super negative disruption. I

734
00:40:18,239 --> 00:40:20,160
Speaker 3: would just see a good opportunity for a joke and

735
00:40:20,239 --> 00:40:20,759
Speaker 3: run with it.

736
00:40:21,640 --> 00:40:24,000
Speaker 2: But as you know, well plus your dad would have

737
00:40:24,000 --> 00:40:25,720
Speaker 2: fired them if they gave you any back.

738
00:40:25,640 --> 00:40:29,799
Speaker 1: Talk, right yeah, right, well into high school too, you know.

739
00:40:29,840 --> 00:40:32,560
Speaker 3: But as a long story short, I was well liked

740
00:40:32,560 --> 00:40:36,040
Speaker 3: by teachers and so they paid me more attention. Livy

741
00:40:36,120 --> 00:40:41,360
Speaker 3: also points out something really important about grouping kids is

742
00:40:41,520 --> 00:40:43,759
Speaker 3: if you just throw kids in a group of like,

743
00:40:44,280 --> 00:40:46,360
Speaker 3: you know, may's dull group.

744
00:40:47,560 --> 00:40:48,759
Speaker 1: Some of these kids.

745
00:40:48,520 --> 00:40:52,759
Speaker 3: May have dyslexia, some may have ADHD, some may have

746
00:40:52,840 --> 00:40:56,480
Speaker 3: insecure housing and family issues and be stress. Some may

747
00:40:57,560 --> 00:41:01,960
Speaker 3: have limited English fluency. So you're throwing all these different

748
00:41:01,960 --> 00:41:05,359
Speaker 3: issues in as one group, and of course that's going

749
00:41:05,400 --> 00:41:06,160
Speaker 3: to be an issue.

750
00:41:06,560 --> 00:41:11,640
Speaker 2: Yeah yeah, so yeah, that's why they say use mixed group,

751
00:41:11,880 --> 00:41:14,640
Speaker 2: mixed ability groups. That's when I'm easually teaching the teacher

752
00:41:14,680 --> 00:41:18,440
Speaker 2: Expectation Project. Another thing that was touched on is creating

753
00:41:18,480 --> 00:41:22,080
Speaker 2: a caring, non threatening environment where you just it's a

754
00:41:22,120 --> 00:41:26,000
Speaker 2: warm environment for all students and you use respectful language.

755
00:41:26,480 --> 00:41:29,560
Speaker 2: You can't be like, gosh, you're so dumb, you dumb dumb.

756
00:41:29,600 --> 00:41:33,120
Speaker 2: You shouldn't say that to students, right. Yeah, And this

757
00:41:33,160 --> 00:41:36,239
Speaker 2: is another one too, working with students to set their

758
00:41:36,239 --> 00:41:39,080
Speaker 2: own goals, which a lot of teachers would be like,

759
00:41:39,560 --> 00:41:43,440
Speaker 2: you can't actually do that, but apparently Ruby Davis research

760
00:41:43,440 --> 00:41:45,919
Speaker 2: has shown, or some research out there that Ruby Davy

761
00:41:46,000 --> 00:41:49,120
Speaker 2: sites has shown, if you allow students to set their

762
00:41:49,160 --> 00:41:52,640
Speaker 2: own learning goals, they will actually shoot for something that's

763
00:41:52,800 --> 00:41:56,279
Speaker 2: challenging but doable. They probably aren't going to be like, well,

764
00:41:56,320 --> 00:42:01,479
Speaker 2: I'm just gonna learn to draw huckleberry hound this year.

765
00:42:01,560 --> 00:42:05,279
Speaker 2: That's my learning goal, you know, like they're going to

766
00:42:05,320 --> 00:42:07,560
Speaker 2: do something a little more challenging than that, and they'll

767
00:42:07,640 --> 00:42:10,000
Speaker 2: learn along the way, and they will have a sense

768
00:42:10,040 --> 00:42:13,040
Speaker 2: of like agency and a stake in their learning, Like

769
00:42:13,080 --> 00:42:15,640
Speaker 2: they'll take it that much more seriously, and they'll know

770
00:42:16,200 --> 00:42:20,080
Speaker 2: if you plot and chart their learning through learning goals

771
00:42:20,320 --> 00:42:24,640
Speaker 2: and allow them to track it themselves, they will they

772
00:42:24,640 --> 00:42:28,479
Speaker 2: will know when they've learned rather than I saw having

773
00:42:28,480 --> 00:42:30,000
Speaker 2: to look to the teacher to be like, yes, you

774
00:42:30,120 --> 00:42:31,200
Speaker 2: just learned something. Way to go.

775
00:42:31,719 --> 00:42:35,120
Speaker 3: Yeah, what near's the horse? If I remember correctly, you

776
00:42:35,120 --> 00:42:36,640
Speaker 3: can draw it ye of a horse?

777
00:42:36,800 --> 00:42:41,080
Speaker 2: I used to. I lost it as I approved on Instagram.

778
00:42:41,320 --> 00:42:43,319
Speaker 1: No, everyone should go check that out. I thought it

779
00:42:43,360 --> 00:42:44,800
Speaker 1: was a great drawing of a horse.

780
00:42:45,000 --> 00:42:45,360
Speaker 2: Thanks.

781
00:42:46,200 --> 00:42:49,000
Speaker 3: Another thing that they said, as far as the high

782
00:42:49,040 --> 00:42:54,200
Speaker 3: expectations teaching from Christine Ruby Davies goes is praising effort

783
00:42:54,280 --> 00:42:58,080
Speaker 3: rather than accuracy. Very big deal and working equally with

784
00:42:58,120 --> 00:43:02,360
Speaker 3: all students. And I'm not going to name my daughter's

785
00:43:02,360 --> 00:43:04,960
Speaker 3: school for obvious reasons, but like they're doing it right

786
00:43:05,680 --> 00:43:09,040
Speaker 3: and it's just great to see that happening. So just

787
00:43:09,120 --> 00:43:12,840
Speaker 3: big props to her teachers and everyone at her school.

788
00:43:12,840 --> 00:43:16,040
Speaker 3: And it is not just her school. It's happening more

789
00:43:16,080 --> 00:43:18,400
Speaker 3: than when we were kids at more and more schools,

790
00:43:18,400 --> 00:43:22,000
Speaker 3: but it's still not as much as it should in

791
00:43:22,040 --> 00:43:22,840
Speaker 3: the same breath, you know.

792
00:43:23,000 --> 00:43:27,640
Speaker 2: Yeah. Instead, two more just related effects that have to

793
00:43:27,680 --> 00:43:31,120
Speaker 2: do with the Pigmalion effect, the Gollum effect, which is

794
00:43:31,160 --> 00:43:34,319
Speaker 2: the opposite if you have low expectations at least the

795
00:43:34,360 --> 00:43:38,160
Speaker 2: lower performance which makes sense too, and that galatea effect

796
00:43:38,200 --> 00:43:42,160
Speaker 2: named after Pigmalion statue that what we expect for ourselves

797
00:43:43,080 --> 00:43:50,000
Speaker 2: impacts our performance, mostly because it mediates how the people

798
00:43:50,000 --> 00:43:53,479
Speaker 2: in authority, a teacher or a manager or something, sees us.

799
00:43:53,600 --> 00:43:55,560
Speaker 2: So the way that they see us impacts how we

800
00:43:55,600 --> 00:43:58,719
Speaker 2: see ourselves, which impacts how we perform, which impacts how

801
00:43:58,840 --> 00:44:02,760
Speaker 2: the manager sees us. And it's just like our boros.

802
00:44:03,040 --> 00:44:06,120
Speaker 2: That's right, pretty interesting stuff. Man, good pick Chuck. I

803
00:44:06,600 --> 00:44:08,200
Speaker 2: think it's so great. You just came up with this

804
00:44:08,280 --> 00:44:09,120
Speaker 2: all by yourself.

805
00:44:09,360 --> 00:44:10,560
Speaker 1: Oh man, I hope I did.

806
00:44:11,200 --> 00:44:13,520
Speaker 2: I hope you did too. Well. Since we both hope

807
00:44:13,520 --> 00:44:15,919
Speaker 2: that Chuck came up with this by himself, it's time,

808
00:44:15,960 --> 00:44:17,120
Speaker 2: of course for a listener mail.

809
00:44:19,840 --> 00:44:21,600
Speaker 3: Hey guys, I live in Rhode Island, where I run

810
00:44:21,640 --> 00:44:24,719
Speaker 3: a Charter Books, an independent bookstore opened in the spring

811
00:44:24,719 --> 00:44:25,480
Speaker 3: of twenty twenty one.

812
00:44:25,640 --> 00:44:25,839
Speaker 2: Nice.

813
00:44:26,000 --> 00:44:28,920
Speaker 3: We report to the New York Times Bestseller List. Nice,

814
00:44:28,920 --> 00:44:31,000
Speaker 3: and I can confirm that you guys really nailed just

815
00:44:31,040 --> 00:44:32,000
Speaker 3: about everything about it.

816
00:44:32,040 --> 00:44:34,040
Speaker 1: And I thought you might like a few more tidbits.

817
00:44:34,160 --> 00:44:34,720
Speaker 2: Yes, please.

818
00:44:35,400 --> 00:44:39,239
Speaker 3: Every week we export a CSV document from our bookstore

819
00:44:39,320 --> 00:44:42,800
Speaker 3: point of sales software, upload it to the bestseller list portal,

820
00:44:43,440 --> 00:44:46,040
Speaker 3: and as mighty as they are, it's still amusing to

821
00:44:46,080 --> 00:44:49,920
Speaker 3: see that it's basically just comes down to us emailing

822
00:44:49,960 --> 00:44:53,240
Speaker 3: them at Spreadsheet, along with all the other booksellers. Of course,

823
00:44:53,960 --> 00:44:56,000
Speaker 3: if we haven't done it by eleven am on Monday,

824
00:44:56,000 --> 00:44:59,040
Speaker 3: they send a gentle reminder if we inadvertently miss a week,

825
00:44:59,480 --> 00:45:03,120
Speaker 3: because they that you report all fifty two weeks. They

826
00:45:03,120 --> 00:45:05,520
Speaker 3: send a message about how much they value our input

827
00:45:05,840 --> 00:45:07,680
Speaker 3: and how disappointed they are that we forgot.

828
00:45:07,920 --> 00:45:10,280
Speaker 1: Oh wow, a little passive aggressive.

829
00:45:11,120 --> 00:45:13,320
Speaker 3: And then every week they also send an email asking

830
00:45:13,320 --> 00:45:16,000
Speaker 3: about any bulk orders, which you explained very well in

831
00:45:16,040 --> 00:45:19,319
Speaker 3: the episode. You are correct in implying how powerful it

832
00:45:19,320 --> 00:45:19,600
Speaker 3: can be.

833
00:45:20,239 --> 00:45:20,640
Speaker 1: The list.

834
00:45:20,680 --> 00:45:24,040
Speaker 3: That is, authors, publishers, publicists, and other entities in the

835
00:45:24,080 --> 00:45:28,120
Speaker 3: industry frequently ask if we report to the Times. And

836
00:45:28,239 --> 00:45:30,720
Speaker 3: years ago, when I was with another bookstore, we received

837
00:45:30,719 --> 00:45:34,000
Speaker 3: a weird order for twenty copies of a random ya

838
00:45:34,040 --> 00:45:36,520
Speaker 3: fantasy book. Turned out to be a bungled effort by

839
00:45:36,560 --> 00:45:40,080
Speaker 3: an obscure publisher to do some book laundering, as Chuck

840
00:45:40,400 --> 00:45:43,200
Speaker 3: would say, so hours after we took the order, we

841
00:45:43,280 --> 00:45:46,880
Speaker 3: received a sternly worded message from The New York Times

842
00:45:46,880 --> 00:45:50,600
Speaker 3: that they wanted documentation of all orders, basically asking.

843
00:45:50,320 --> 00:45:51,520
Speaker 1: For our receipts.

844
00:45:52,040 --> 00:45:53,880
Speaker 3: None of this is our shattering to you guys, probably,

845
00:45:53,920 --> 00:45:55,239
Speaker 3: but it was fun to hear you talk about my

846
00:45:55,320 --> 00:45:56,040
Speaker 3: day to day work.

847
00:45:56,920 --> 00:45:58,520
Speaker 1: That is Steve from Charter Books.

848
00:45:58,520 --> 00:46:01,839
Speaker 3: So hey, if you're near Charter Books and Rhode Island,

849
00:46:01,920 --> 00:46:03,640
Speaker 3: support your indie bookstores.

850
00:46:03,760 --> 00:46:06,600
Speaker 2: Yeah, no, matter where you live, support your indie bookstore

851
00:46:06,640 --> 00:46:07,520
Speaker 2: friends for sure.

852
00:46:08,200 --> 00:46:11,279
Speaker 3: That was Steve right, Yeah, any similar picture they had

853
00:46:11,280 --> 00:46:12,440
Speaker 3: our book on display?

854
00:46:12,480 --> 00:46:15,480
Speaker 2: Awesome, Thanks Steve. We love it when people round out

855
00:46:15,760 --> 00:46:19,000
Speaker 2: information that we've talked about. And if you want to

856
00:46:19,000 --> 00:46:20,920
Speaker 2: be like Steve and do something like that, you can

857
00:46:20,960 --> 00:46:23,600
Speaker 2: do it via email. Send it off to stuff podcast

858
00:46:23,680 --> 00:46:29,200
Speaker 2: at iHeartRadio dot com. Stuff you Should Know is a

859
00:46:29,200 --> 00:46:30,480
Speaker 2: production of iHeartRadio.

860
00:46:31,000 --> 00:46:34,200
Speaker 1: For more podcasts my heart Radio, visit the iHeartRadio app,

861
00:46:34,360 --> 00:46:37,280
Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows,