7 Tips for Motivating Elementary School Kids During Distance Learning

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- Hi, everyone.

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Thank you for joining today on our webinar

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on 7 Tips for Motivating
Elementary School Kids

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During Distance Learning.

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Now, the tips we'll be sharing today

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are tailored to this moment,

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but they're really applicable
more broadly as needed.

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I'm Dan Tu,

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and I'll be kicking us off today

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as well as moderating the Q&A segment.

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But before we get started,

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we want to say a special thank
you to several organizations

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who have really helped us
with philanthropic support,

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enabling us to bring you
programming like today's session.

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So we want to say a special
thanks to Bank of America,

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Google.org, AT&T,

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Fastly, and Novartis.

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So I'd like to introduce
you all to Kristen DiCerbo,

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who will be walking
through the seven tips,

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and sharing her expert advice with you all

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while we answer your live questions.

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Kristen's our Chief Learning
Officer here at Khan Academy,

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and an expert in learning science.

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So if you have any
questions on today's topic,

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feel free to enter them into the chat.

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We have a team collecting
them for the Q&A portion,

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and all of this is going
to be recorded and posted.

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Since you've registered,

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this will automatically be emailed to you.

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So don't worry if you miss
anything or need to go back.

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So with that, Kristin, take it away.

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- Great, thanks.

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And thanks all of you for joining today.

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I'm going to spend about 15 minutes or so

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going through some of the
ideas and the research

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behind the tips,

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and then we'll open it up for questions,

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which I'm sure you all
have lots of in this time.

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So to start off with,

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as Dan said, I'm the
Chief Learning Officer,

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and one of my roles is to bring research

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about learning motivation
to our understanding

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of how to help students learn.

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So all of that that you'll
see here is based on

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what we understand about
motivation from research

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about how to keep kids motivated.

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And one of the first things to understand

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is that we can really boil
down a lot of the research

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on motivation to a pretty simple formula.

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We like to do things when first,

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we think we're gonna
be successful at them,

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and second, when we
think there's some value

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to doing that activity,

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which probably makes sense for you

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just as much as it does for your kids.

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Who wants to do something when you think,

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"Oh, I'm probably gonna fail at this"?

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You're not likely to do it.

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And second, you think
you want to do things

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that are important to you.

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And so, as we think about
how to motivate our kids,

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we can think about how
can I help them feel

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like they're likely to be successful

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and how can I help them
see some of the value

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in this activity?

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And so this kind of simple
formula drives a lot of the tips

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that I'm gonna be talking about.

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And one of the biggest
ways to help kids feel

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like they can be successful
at doing something

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is to help them experience some success.

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And one really good way to
do that is to set goals,

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because goals, by definition,

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help us see that we're making progress.

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And so, as you think about
this with elementary-aged kids,

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you want to set really short-term goals.

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So if you're talking
kindergartners or first graders,

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it could be something as simple as,

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"Hey, this afternoon,

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we're going to read this book together,"

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or "We're going to write three sentences."

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What are those things
that you want to work on

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just very short-term?

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And then at the end you can say,

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"Yes, look, we said we were going to try

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to write three sentences.

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We wrote four sentences."

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There's an experience of success.

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As kids get a little older,
you can stretch it out.

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You can think about in the morning

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setting goals for the day.

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You can think about
maybe setting a longer,

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maybe a week goal that then you
break down into daily goals.

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And the other piece that
when you're setting goals

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is to help kids think about
how they're gonna know

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if they're on track.

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So then one of the important pieces here

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in both goal setting and monitoring

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and making sure they're on track is

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to bring the kids into it.

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So instead of you saying,
"Our goal is this,"

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have the conversation and say,

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"So we want to set a goal
about writing sentences.

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How many sentences do you
think you can write today?"

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So think about helping
them set realistic goals,

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and that's then gonna help them be able

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to also not just see success,

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but also they're going to
value the activity more

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because they owned some
of the goal setting

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that went along with it.

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And so you get actually the
double plus there of thinking

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about both success and
value of the activity.

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So very strong.

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And that's why I encourage
a lot of goal setting

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and thinking about how to do that.

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But then when you meet them,

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definitely celebrate that success.

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So this can be everything from
high fives to thinking about,

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you know, small, different
things that kids like to do

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that they might find successful.

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We do everything online from, you know,

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a little online confetti to
all kinds of things that just,

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you know, recognize that
there's an achievement there.

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Also, this can be a good time to reflect.

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"Hey, what was it that
helped you be successful

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in meeting that goal?"

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And "Hey, was there a time
when you were trying to do this

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and it was hard and how
did you get over that?

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How did you get through the
place where you were stuck?"

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And that kind of reflection
helps them then think about,

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hey, next time they get
stuck, what might be the,

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what might be some strategies

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that they could do and work on.

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So definitely tip two is
celebrate those successes.

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Then thinking about those roadblocks,

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think about how you can help
your children have strategies

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for when they're going
to get into that place

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where they don't know what to do.

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And that's going to happen
when you're learning,

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because learning is hard.

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And when you're actually
engaged in learning,

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your learning edge, right when
you're learning new things,

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we know that often you
need a little bit of help

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to get over that.

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But what you want to do is
for your kids to have a plan

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for what to do so they don't
get frustrated in that moment.

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And before they get frustrated,
you want to make the plan.

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So this can be everything
from asking you for help.

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If you're also, say, working
from home at the same time

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your kids are in school, you
might want to think about,

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okay, how do you signal to your kids

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when it's okay to come
to you and ask for help

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and when they should wait,

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and what does that look like?

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Are there ways they can
ask their friends for help?

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How do they reach out to their teacher

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in this kind of place?

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So come up with a list of things

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that you might be able to work through

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to help your kids think about,

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"Hey, if I'm stuck,

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what are things I can do to get unstuck?"

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So you get before they get
to the place of frustration,

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they have strategies to use.

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After that, think about emphasizing

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that effort leads to success.

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And there's been a lot of writing recently

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about growth mindset and this
idea that if you work hard,

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your brain is like a muscle.

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And the more you work at it,

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the more it's ready to learn new things.

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And there's actually
physiology in your brain.

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The cells in your brain
are called neurons.

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And when we learn new things,

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it makes new connections to new neurons.

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And that in itself then helps you remember

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the things that you've learned.

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It makes you a stronger, better learner.

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All of that is better in a better message.

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And research shows,

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kids who understand and get
some of that understanding

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of how their brain works

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actually do persist more
than students who just say,

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"Hey, keep trying, hey, keep going."

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And so actually at Khan Academy,

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we have a growth mindset course

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where we have a couple of videos

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that explain some of
this in language for kids

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to understand that can
be good to just, again,

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start thinking of those
messages that your brain

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is like other muscles and
it needs to keep practicing

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and keep learning new things.

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That'll actually make you
better and a better learner.

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So that's the fourth tip,

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thinking about effort leading to success.

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The fifth one is to
engage their curiosity.

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So lots of kids are curious
about different things

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that happen in the world.

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It could be when they're
riding their bikes

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and they're thinking, "How does, you know,

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pushing on this pedal actually
make the wheels turn?"

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any of those kinds of questions.

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It's okay if you don't know the answer.

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Think about how you can find out answers

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to those things together.

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And when we think about kids

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that are curious about the world,

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those are the kids that keep learning

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and keep trying to find new things.

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And so you want to encourage
those kinds of questions.

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You can also model asking
those kinds of questions.

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So as you're doing
things around the house,

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or as you're doing things outside,

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asking questions about how the world works

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and thinking about, "Hey,
did you ever wonder,"

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and "How can you find
the answers to those?"

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All of those help build up
that internal motivation

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to find answers and understand the world.

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The sixth tip I have is
to establish relevance.

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So it relates to what I was just saying

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about linking things to the world.

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But the other way to do this is

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think about what your kids are learning

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and help them link that to things

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that are of interest to them.

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You know your kid's
interests better, really,

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than even their teachers do.

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So if you can think about
how do I help them see

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how this relates to a hobby
that they're interested in?

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How does this relate to
something they want to be

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when they grow up?

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How does this relate to something
we talked about yesterday?

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All of those things help
students establish the value

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of what they're learning.

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So if we go back to that equation,

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that second piece of
why should I learn this,

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what is this going to help me do,

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how does this relate to
things I'm interested in,

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if you can help make those connections,

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kids will value the activity more

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and be more likely to
want to engage in it.

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And finally, the seventh tip.

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I hear lots of concerns and we
get lots of questions about,

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"Should I use rewards?

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I don't want to be bribing
my student to work."

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And I would say rewards are
absolutely a part of what we do

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and how we reward ourselves.

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Even those of us who, you know,

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are successful and are
good students have tricks

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to keep ourselves motivated.

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I often say and do, when
I'm writing, I will say,

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"If I write one page here,

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I'll reward myself with
a piece of chocolate."

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You know, all of that is fine,

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but you want to think about
how to do this wisely.

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So the danger of rewards is sometimes

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that if a kid is already
interested in doing something,

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like it's already bringing
them happiness and joy,

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and you start rewarding it
with external things like candy

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and those kinds of things,

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they start to lose, actually,

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some of their internal
motivation and start thinking,

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"Oh, I'm just doing this for the reward."

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So that's what we want to avoid.

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But rewards can be really good for things

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that students just
don't want to do at all,

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but need a little bit of
kickstart to get going,

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and then maybe experience
some of the success,

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experience meeting a goal in it,

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to get from thinking about

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how we can use those extrinsic rewards

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to getting to more of those intrinsic,

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"Oh, actually I am good at this.

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I can learn math."

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Those extrinsic rewards
are really good at that.

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And they don't have to be things.

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They can be picking what's for dinner.

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They can be picking
which TV show you watch,

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spending, you know, another
15 minutes of screen time,

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taking a walk with you,

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having some of your time individually,

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all of those things are good rewards.

00:11:49.576 --> 00:11:52.940
And it's also good to ask your kids

00:11:52.940 --> 00:11:54.120
what's rewarding to them,

00:11:54.120 --> 00:11:56.600
what would they like to
be rewards for things

00:11:56.600 --> 00:12:00.100
that meeting goals and making progress.

00:12:00.100 --> 00:12:01.050
And that way, again,

00:12:01.050 --> 00:12:02.600
you're making sure you've got something

00:12:02.600 --> 00:12:06.220
that's meaningful to them
and helping them learn

00:12:06.220 --> 00:12:08.860
how to think about rewarding themselves.

00:12:08.860 --> 00:12:11.470
And when they need to
get a little extra kick

00:12:11.470 --> 00:12:14.799
to get something done,
to be able to think about

00:12:14.799 --> 00:12:18.160
how to use rewards wisely for themselves.

00:12:18.160 --> 00:12:22.320
So that said, that
gives us our seven tips,

00:12:22.320 --> 00:12:26.450
thinking about setting goals
and monitoring progress,

00:12:26.450 --> 00:12:31.450
celebrating success, help your
kids manage those roadblocks,

00:12:31.562 --> 00:12:35.350
emphasize that effort leads to success,

00:12:35.350 --> 00:12:39.620
encourage curiosity, establish relevance,

00:12:39.620 --> 00:12:41.853
and use rewards wisely.

00:12:42.700 --> 00:12:44.660
So with that,

00:12:44.660 --> 00:12:45.670
I'm going to open it up,

00:12:45.670 --> 00:12:48.240
because I'm sure there are
specific questions that you have

00:12:48.240 --> 00:12:49.870
about your situations

00:12:49.870 --> 00:12:52.320
and how to deal with some of the things,

00:12:52.320 --> 00:12:54.320
the specifics that you're facing.

00:12:54.320 --> 00:12:57.643
So would be glad to
hear some of those, Dan?

00:12:58.510 --> 00:13:00.380
- Yeah, well, so before we get to that,

00:13:00.380 --> 00:13:02.930
just a couple of things
that we'd all ask you to do.

00:13:02.930 --> 00:13:06.250
First, there's, in the handout
section of the webinar,

00:13:06.250 --> 00:13:09.910
you can download a printable
version of these seven tips.

00:13:09.910 --> 00:13:12.000
That's available in the download section.

00:13:12.000 --> 00:13:13.090
And again, if you registered

00:13:13.090 --> 00:13:15.190
and you're actually
watching this live now,

00:13:15.190 --> 00:13:18.320
once this video, this webinar's over,

00:13:18.320 --> 00:13:20.980
you'll get an email with the
recording of this webinar,

00:13:20.980 --> 00:13:23.420
as well as a link to the downloadable.

00:13:23.420 --> 00:13:24.970
So again, if you want to go back

00:13:24.970 --> 00:13:28.530
and both watch any of
the tips in particular

00:13:28.530 --> 00:13:31.390
or read about them, that'll
be available to you.

00:13:31.390 --> 00:13:32.440
And then secondly,

00:13:32.440 --> 00:13:34.970
go ahead and start putting in
any of the questions you have.

00:13:34.970 --> 00:13:36.840
Even specific examples would be great.

00:13:36.840 --> 00:13:37.970
And we'll try to get through

00:13:37.970 --> 00:13:40.540
as many of them as possible.

00:13:40.540 --> 00:13:41.740
So with that, Kristen,

00:13:41.740 --> 00:13:44.680
the first question we
have is from Rebecca.

00:13:44.680 --> 00:13:46.640
And I think you mentioned
this in terms of tip,

00:13:46.640 --> 00:13:48.580
I believe it's tip number six.

00:13:48.580 --> 00:13:51.280
How can you help kids feel
like what they're learning

00:13:51.280 --> 00:13:54.160
in school and what they
do is really relevant

00:13:54.160 --> 00:13:55.393
to their lives?

00:13:57.370 --> 00:14:02.370
- So the biggest thing here is
to think about understanding

00:14:03.470 --> 00:14:06.150
what is interesting to your kids.

00:14:06.150 --> 00:14:08.800
So what are things that
they're interested in

00:14:08.800 --> 00:14:11.710
is the first piece and then
relating things to them.

00:14:11.710 --> 00:14:15.620
So the most common place
we hear this is in math.

00:14:15.620 --> 00:14:16.850
And so there's some things

00:14:16.850 --> 00:14:20.930
that are pretty easy relatable to math.

00:14:20.930 --> 00:14:22.880
So one thing is cooking.

00:14:22.880 --> 00:14:25.360
Cooking, it turns out,
involves a lot of math,

00:14:25.360 --> 00:14:27.640
specifically, a lot of fractions.

00:14:27.640 --> 00:14:30.550
There can be, you know, if
you're doing a double batch,

00:14:30.550 --> 00:14:33.050
how do you think about
doubling a fraction?

00:14:33.050 --> 00:14:34.680
What does that look like?

00:14:34.680 --> 00:14:38.210
Thinking about how that actually applies,

00:14:38.210 --> 00:14:42.480
the things that they're doing
apply to their daily life.

00:14:42.480 --> 00:14:45.750
So cooking is always a good example

00:14:45.750 --> 00:14:48.520
for a lot of math issues.

00:14:48.520 --> 00:14:51.630
There are also things
that you can think about.

00:14:51.630 --> 00:14:54.430
What do they want to be when they grow up?

00:14:54.430 --> 00:14:57.140
Maybe they don't know. That's okay, too.

00:14:57.140 --> 00:15:00.360
You can think about what kinds of careers

00:15:00.360 --> 00:15:04.550
are the skills they're learning good for.

00:15:04.550 --> 00:15:06.470
So, hey, these are the kinds of things,

00:15:06.470 --> 00:15:10.130
I know we don't know what you
want to be when you grow up.

00:15:10.130 --> 00:15:12.213
You haven't decided what you
want to be when you grow up.

00:15:12.213 --> 00:15:15.380
It's a good idea to kind of
experience a bunch of different,

00:15:15.380 --> 00:15:18.140
find out about a bunch of
different possibilities.

00:15:18.140 --> 00:15:21.368
So, "Hey, this thing
that we're learning now,

00:15:21.368 --> 00:15:23.300
it turns out that this is something

00:15:23.300 --> 00:15:24.530
that accountants use a lot,"

00:15:24.530 --> 00:15:28.933
or "This is something that
someone who's doing a lot of,

00:15:30.450 --> 00:15:34.850
who's an oceanographer learns
a lot about these topics

00:15:34.850 --> 00:15:37.154
that you're learning
about in science today,

00:15:37.154 --> 00:15:40.090
thinking about those,
the kinds of careers,

00:15:40.090 --> 00:15:42.950
that link to the different
things that they're learning

00:15:42.950 --> 00:15:46.590
is another way to bring
some of this to life.

00:15:46.590 --> 00:15:48.960
And I would also not hesitate to reach out

00:15:48.960 --> 00:15:51.967
to your kids' teachers for
help in thinking about,

00:15:51.967 --> 00:15:55.720
"Hey, can you give us a
preview of what's coming?"

00:15:55.720 --> 00:15:58.420
And what do they think
are good ways to help them

00:15:58.420 --> 00:16:01.430
relate that to things
they see in their lives?

00:16:01.430 --> 00:16:03.620
But there's lots of things
in our everyday lives.

00:16:03.620 --> 00:16:05.520
I gave the bicycle example.

00:16:05.520 --> 00:16:08.570
It turns out that's all about
simple machines and pulleys

00:16:08.570 --> 00:16:09.920
and chains and all of that.

00:16:09.920 --> 00:16:13.820
So all of those kinds of
examples of how the world works,

00:16:13.820 --> 00:16:16.240
we can find things that kids are learning

00:16:16.240 --> 00:16:17.587
that relate to them.

00:16:17.587 --> 00:16:20.040
But it takes some work, I will admit.

00:16:20.040 --> 00:16:20.930
- No, that's great, Kristen.

00:16:20.930 --> 00:16:23.100
I would say like one thing, as well, is

00:16:23.100 --> 00:16:25.330
use all of your senses as a parent.

00:16:25.330 --> 00:16:27.530
Like there are things that
the child might not tell you,

00:16:27.530 --> 00:16:29.110
like they want to do this,
they want to do that.

00:16:29.110 --> 00:16:30.890
But you know, your child best.

00:16:30.890 --> 00:16:33.850
And if you know that they love
doing this activity, yeah,

00:16:33.850 --> 00:16:35.900
and just be observant of
that and try to relate that

00:16:35.900 --> 00:16:38.643
back to the, again, back to the subject.

00:16:39.960 --> 00:16:40.793
Cool.

00:16:40.793 --> 00:16:44.090
So Kristen, we have a
question from Jay Tinder.

00:16:44.090 --> 00:16:46.590
Apologies if I mispronounce the name.

00:16:46.590 --> 00:16:49.280
Any tips for helping motivate
elementary kids transitioning

00:16:49.280 --> 00:16:51.580
to middle school in a
distance learning environment?

00:16:51.580 --> 00:16:54.750
So really that transition
period, meeting new friends,

00:16:54.750 --> 00:16:55.583
those types of things.

00:16:55.583 --> 00:16:57.333
So any advice you have there.

00:16:58.840 --> 00:17:01.310
- So there's a number of
things that go on when kids

00:17:01.310 --> 00:17:02.810
transition to middle school.

00:17:02.810 --> 00:17:04.870
So you mentioned, Dan,
meeting new friends.

00:17:04.870 --> 00:17:08.490
There's also kids that are
taking on and required to take on

00:17:08.490 --> 00:17:10.490
a little bit more ownership,

00:17:10.490 --> 00:17:12.840
a little bit more self responsibility.

00:17:12.840 --> 00:17:14.030
And it's in,

00:17:14.030 --> 00:17:15.610
when we talk about distance learning,

00:17:15.610 --> 00:17:17.710
one of the things we know
about distance learning is

00:17:17.710 --> 00:17:19.300
that kids who are best at it

00:17:19.300 --> 00:17:22.630
are kids who are what we
call self-regulated learners.

00:17:22.630 --> 00:17:23.463
So those are kids

00:17:23.463 --> 00:17:25.680
that are actually
capable of setting goals,

00:17:25.680 --> 00:17:28.930
capable of monitoring their own progress,

00:17:28.930 --> 00:17:33.563
kids that can create, organize
schedules for themselves.

00:17:35.080 --> 00:17:38.470
Those are all skills that
you can help your child with.

00:17:38.470 --> 00:17:40.870
So think about helping them set up,

00:17:40.870 --> 00:17:43.880
what is their schedule for the day.

00:17:43.880 --> 00:17:46.330
Because the other piece that
often happens in middle school

00:17:46.330 --> 00:17:48.220
is you start switching teachers.

00:17:48.220 --> 00:17:50.450
Where in elementary
school, you just had one,

00:17:50.450 --> 00:17:52.200
now you have multiple teachers.

00:17:52.200 --> 00:17:53.610
So help your child think about

00:17:53.610 --> 00:17:56.100
how do they set out a schedule
of what the day looks like

00:17:56.100 --> 00:17:59.500
so they make sure to know
what the expectations are.

00:17:59.500 --> 00:18:04.440
Help them think about and let
them be part of the process.

00:18:04.440 --> 00:18:05.790
Let them, you know,

00:18:05.790 --> 00:18:08.820
you can maybe give them a
skeleton and let them fill it out.

00:18:08.820 --> 00:18:11.250
And next time they create what the,

00:18:11.250 --> 00:18:13.640
next week they create what
the schedule looks like.

00:18:13.640 --> 00:18:17.260
So kind of gradually
pulling back from the things

00:18:17.260 --> 00:18:19.737
that you're doing and letting
them have some ownership

00:18:19.737 --> 00:18:21.410
and some agency.

00:18:21.410 --> 00:18:24.480
And it turns out that a lot of kids

00:18:24.480 --> 00:18:25.690
are really motivated by that.

00:18:25.690 --> 00:18:27.790
If they know that you've
kind of got them there

00:18:27.790 --> 00:18:31.417
as a safety net and that you're
there to kind of catch them

00:18:31.417 --> 00:18:33.410
if things get too rough.

00:18:33.410 --> 00:18:36.180
So I would say, give them
a little bit more agency.

00:18:36.180 --> 00:18:38.750
They're going to then find more ownership

00:18:38.750 --> 00:18:40.210
in valuing the activities

00:18:40.210 --> 00:18:43.440
because they're driving
them and choosing them.

00:18:43.440 --> 00:18:46.150
And help them see that

00:18:46.150 --> 00:18:48.700
and take on a little bit
more of that ownership.

00:18:48.700 --> 00:18:50.329
Dan, you mentioned, you know,

00:18:50.329 --> 00:18:53.730
the pieces about making new friends,

00:18:53.730 --> 00:18:55.700
and often middle schools
are bringing together

00:18:55.700 --> 00:18:57.220
a bunch of elementary schools.

00:18:57.220 --> 00:18:58.650
And now that we're at distance,

00:18:58.650 --> 00:19:02.790
it's so difficult to create
some of those peer connections.

00:19:02.790 --> 00:19:05.250
And peers start to become
more and more important

00:19:05.250 --> 00:19:07.480
for kids at this age level.

00:19:07.480 --> 00:19:10.880
So the more you can help facilitate things

00:19:10.880 --> 00:19:12.610
in this distance environment,

00:19:12.610 --> 00:19:15.690
we're really limited, but
facilitating, you know,

00:19:15.690 --> 00:19:18.570
at least calls or Hangouts,

00:19:18.570 --> 00:19:22.300
ways for kids to informally
connect with each other

00:19:22.300 --> 00:19:26.000
and be able to do some
activities together online

00:19:26.000 --> 00:19:29.420
that maybe they wouldn't be
able to do in person these days,

00:19:29.420 --> 00:19:33.020
but to help keep some of
those peer relationships going

00:19:33.020 --> 00:19:36.440
and to try to build them
in these new places.

00:19:36.440 --> 00:19:39.164
But understand the other piece you may do

00:19:39.164 --> 00:19:41.927
is to empathize with your kids and say,

00:19:41.927 --> 00:19:44.060
"I know this is tough right now,

00:19:44.060 --> 00:19:47.270
and this is going to be difficult
as we're moving forward.

00:19:47.270 --> 00:19:48.660
I know you miss your friends,"

00:19:48.660 --> 00:19:52.170
and just give them a
chance to share with you

00:19:52.170 --> 00:19:54.380
and to express some of
what they're feeling, too,

00:19:54.380 --> 00:19:57.063
as they're kind of making
some of these transitions.

00:19:57.930 --> 00:19:59.630
- Yeah, I think that's
a great point, Kristen,

00:19:59.630 --> 00:20:00.463
great points.

00:20:00.463 --> 00:20:02.200
I think when you transition
to middle school,

00:20:02.200 --> 00:20:03.720
not only do you have more teachers,

00:20:03.720 --> 00:20:06.420
but you have to control
your schedules more,

00:20:06.420 --> 00:20:08.110
you know, which classrooms
are you going to when.

00:20:08.110 --> 00:20:10.340
So you're learning a lot of like agency

00:20:10.340 --> 00:20:12.960
and being responsible and
getting to class on time.

00:20:12.960 --> 00:20:13.793
And like you said,

00:20:13.793 --> 00:20:16.260
you're meeting like tons of
new kids from different schools

00:20:16.260 --> 00:20:19.020
that you're going to at the same time.

00:20:19.020 --> 00:20:22.763
So I do think there's a lot
of challenges for those kids.

00:20:23.750 --> 00:20:24.583
- Absolutely.

00:20:24.583 --> 00:20:27.420
And if you can pinpoint which
things your kid is good at

00:20:27.420 --> 00:20:28.530
and highlight those

00:20:28.530 --> 00:20:31.010
and find out where some
of the weaknesses are

00:20:31.010 --> 00:20:32.860
and help support those,

00:20:32.860 --> 00:20:36.670
that's a good way for
you to help support them

00:20:36.670 --> 00:20:38.570
and tailor it to what their needs are.

00:20:39.920 --> 00:20:41.550
- So we have a question from Dora.

00:20:41.550 --> 00:20:42.650
I think we get,

00:20:42.650 --> 00:20:44.000
we hear this question a lot,

00:20:44.000 --> 00:20:45.700
is how do you manage roadblocks?

00:20:45.700 --> 00:20:47.720
So when your child has self-doubt

00:20:47.720 --> 00:20:50.090
or developing low self esteem,

00:20:50.090 --> 00:20:52.600
especially in this distance
learning environment,

00:20:52.600 --> 00:20:53.900
how do you manage those

00:20:53.900 --> 00:20:56.000
and help the child overcome those moments?

00:20:57.160 --> 00:21:00.760
- Yeah, so kids who are, you know,

00:21:00.760 --> 00:21:03.450
not feeling like they're learners

00:21:03.450 --> 00:21:05.900
or they're feeling like,
"Math is not for me.

00:21:05.900 --> 00:21:09.250
I'm not, you know, not
good at these things.

00:21:09.250 --> 00:21:10.900
I don't know how to do this,"

00:21:10.900 --> 00:21:15.390
all of those are things
where kids can really benefit

00:21:15.390 --> 00:21:19.280
from things like the growth mindset piece

00:21:19.280 --> 00:21:20.360
that I was talking about.

00:21:20.360 --> 00:21:24.840
This idea that none of us
is fixed in our ability

00:21:24.840 --> 00:21:25.960
to do things.

00:21:25.960 --> 00:21:30.433
We can always get better and
getting better takes work.

00:21:30.433 --> 00:21:34.677
But it's actually pretty
rewarding when you start to see,

00:21:34.677 --> 00:21:39.040
"Hey, I used to be able to do
this and now I can do this.

00:21:39.040 --> 00:21:40.650
Here's a new thing that I can do now

00:21:40.650 --> 00:21:42.250
that I couldn't do before."

00:21:42.250 --> 00:21:46.550
So one, give them the
messages about thinking about

00:21:46.550 --> 00:21:48.170
we can always improve.

00:21:48.170 --> 00:21:51.790
Our brains improve. Our brains
change as we try new things.

00:21:51.790 --> 00:21:53.370
That's really important.

00:21:53.370 --> 00:21:56.260
But also thinking about helping them

00:21:56.260 --> 00:21:57.880
experience some success.

00:21:57.880 --> 00:22:01.340
So those small goals and thinking about

00:22:01.340 --> 00:22:05.330
how to get them to see
a little bit of progress

00:22:05.330 --> 00:22:06.163
that they're making.

00:22:06.163 --> 00:22:08.357
And even a little bit of progress is good.

00:22:08.357 --> 00:22:10.290
"Yesterday you couldn't do

00:22:10.290 --> 00:22:14.440
the two-digit by minus two-digit problems.

00:22:14.440 --> 00:22:17.370
And today you did three
of them. That's great."

00:22:17.370 --> 00:22:19.313
Like legitimately, that's progress.

00:22:20.320 --> 00:22:21.220
That's very good.

00:22:21.220 --> 00:22:23.910
So helping them see, again,

00:22:23.910 --> 00:22:25.980
that they can have a little bit of success

00:22:25.980 --> 00:22:27.733
and that really does build on itself

00:22:27.733 --> 00:22:31.600
and help kids start to see
themselves as learners.

00:22:31.600 --> 00:22:34.953
And that's really the key that
then will kick things off.

00:22:35.900 --> 00:22:38.120
- Great. So Kristen, we
have a lot of questions.

00:22:38.120 --> 00:22:40.420
And it goes back to the very first tip,

00:22:40.420 --> 00:22:41.470
which is setting goals.

00:22:41.470 --> 00:22:45.210
We have a couple of questions
around goals, first from Sue.

00:22:45.210 --> 00:22:46.670
She loves the idea of setting goals,

00:22:46.670 --> 00:22:48.660
but they're already
being set by the school.

00:22:48.660 --> 00:22:50.640
So how do you match what your,

00:22:50.640 --> 00:22:52.650
the goals that your schools are setting?

00:22:52.650 --> 00:22:55.570
Another question from Flavio is like,

00:22:55.570 --> 00:22:57.600
how do we know we're setting
goals at the appropriate level?

00:22:57.600 --> 00:23:00.890
And you know, whether
it's too challenging,

00:23:00.890 --> 00:23:02.570
not challenging enough.

00:23:02.570 --> 00:23:03.950
And then the third is from Ed,

00:23:03.950 --> 00:23:06.290
who's asking for very specific examples

00:23:06.290 --> 00:23:09.090
for their grade school kid.

00:23:09.090 --> 00:23:11.750
They're having challenges
with getting their child

00:23:11.750 --> 00:23:13.663
to help set their own goals.

00:23:14.690 --> 00:23:17.650
- Got it. So, yeah, lots
of, lots of good stuff.

00:23:17.650 --> 00:23:20.240
So the first thing I
would say that underlies

00:23:20.240 --> 00:23:22.370
all of those is,

00:23:22.370 --> 00:23:25.720
think about talking to
your child's teacher,

00:23:25.720 --> 00:23:27.250
even in this distance world,

00:23:27.250 --> 00:23:29.920
keeping those lines of communication open.

00:23:29.920 --> 00:23:32.937
So if a teacher starts hearing, like,

00:23:32.937 --> 00:23:36.350
"All of the things that you're
asking us to do every day

00:23:36.350 --> 00:23:37.290
is too much.

00:23:37.290 --> 00:23:40.200
Like, the kids can't handle that much."

00:23:40.200 --> 00:23:42.420
And they start hearing that from parents,

00:23:42.420 --> 00:23:44.250
they're gonna adjust what they're doing.

00:23:44.250 --> 00:23:45.330
Or they're gonna, you know,

00:23:45.330 --> 00:23:47.740
start to find ways to help the students

00:23:47.740 --> 00:23:50.800
and provide more support,
one or the other.

00:23:50.800 --> 00:23:53.810
But if the teacher doesn't know
that the kids are struggling

00:23:53.810 --> 00:23:54.643
to do this,

00:23:54.643 --> 00:23:56.600
and it can be difficult to know that

00:23:56.600 --> 00:24:00.226
when they are in a distance
learning situation,

00:24:00.226 --> 00:24:04.050
then they don't know that
the kids need extra help

00:24:04.050 --> 00:24:06.220
or that they need to think about

00:24:06.220 --> 00:24:08.290
how much they're putting on kids.

00:24:08.290 --> 00:24:11.650
Because a lot of teachers
may have tons of experience

00:24:11.650 --> 00:24:14.400
doing all of this in
the regular classroom,

00:24:14.400 --> 00:24:17.480
but it's not clear how
much kids can take on

00:24:17.480 --> 00:24:19.340
in these distance learning situations.

00:24:19.340 --> 00:24:24.340
So that's my first piece of
advice is to think about how to,

00:24:25.287 --> 00:24:27.120
you know, talk to your teacher

00:24:27.120 --> 00:24:28.969
about if the goals are too much,

00:24:28.969 --> 00:24:32.720
or if they're thinking about, you know,

00:24:32.720 --> 00:24:36.230
if they're not meeting them
or where they need to go.

00:24:36.230 --> 00:24:37.350
And Dan, I have lost,

00:24:37.350 --> 00:24:38.850
there were a bunch of questions there,

00:24:38.850 --> 00:24:42.630
and I lost, (giggles)
in my short term memory,

00:24:42.630 --> 00:24:43.900
a couple of the other points.

00:24:43.900 --> 00:24:45.677
Could you remind me of what those were?

00:24:45.677 --> 00:24:49.430
- So the other one is, how do we know when

00:24:49.430 --> 00:24:52.390
it's like the right level
of goal for the child,

00:24:52.390 --> 00:24:55.291
in addition to your teacher one.

00:24:55.291 --> 00:24:57.700
And then I think the third one was,

00:24:57.700 --> 00:25:00.491
how do you get your kids to
kind of play into goal setting

00:25:00.491 --> 00:25:02.880
if your kid's reluctant to kind of engage

00:25:02.880 --> 00:25:04.380
with the goal setting exercise, right?

00:25:04.380 --> 00:25:07.340
So if they're not happy to
share with you what they think,

00:25:07.340 --> 00:25:10.090
like, how do you pull that out of them?

00:25:10.090 --> 00:25:11.200
- Great. Yep.

00:25:11.200 --> 00:25:16.200
So the level of goals
piece is another one to,

00:25:17.020 --> 00:25:19.670
it's really good to talk
to your student's teacher,

00:25:19.670 --> 00:25:22.080
talk to your kid's
teacher about, you know,

00:25:22.080 --> 00:25:24.690
what kinds of levels that they're at,

00:25:24.690 --> 00:25:27.500
because it is going to
be individual for kids.

00:25:27.500 --> 00:25:30.320
But then it should be as
something that you're looking for,

00:25:30.320 --> 00:25:34.210
you want kids to, if you're
kind of observing them do work,

00:25:34.210 --> 00:25:36.960
you want to think about there's
something where they have to

00:25:36.960 --> 00:25:38.020
put in effort.

00:25:38.020 --> 00:25:39.330
And there's something that we talked about

00:25:39.330 --> 00:25:41.280
that's called productive struggle.

00:25:41.280 --> 00:25:43.130
And it means that kids are,

00:25:43.130 --> 00:25:45.250
they're having to put in
some cognitive efforts,

00:25:45.250 --> 00:25:48.437
like think hard about this
and reason through it.

00:25:48.437 --> 00:25:50.620
And it's not just coming easy to them.

00:25:50.620 --> 00:25:53.540
If it's coming really easy,
it's probably too low a goal.

00:25:53.540 --> 00:25:55.000
If they're getting really frustrated,

00:25:55.000 --> 00:25:56.510
it's probably too high a goal.

00:25:56.510 --> 00:25:58.770
So something kind of in between there

00:25:58.770 --> 00:26:00.590
is what you're looking to hit,

00:26:00.590 --> 00:26:03.320
where there is a little
bit of struggle going on.

00:26:03.320 --> 00:26:04.940
Maybe they're a little bit confused.

00:26:04.940 --> 00:26:06.740
Maybe they're not sure how to do this.

00:26:06.740 --> 00:26:10.050
Maybe they need a little bit
of help. That is all okay.

00:26:10.050 --> 00:26:12.620
Those are all signs
that they're right about

00:26:12.620 --> 00:26:13.610
at the right place.

00:26:13.610 --> 00:26:15.320
They don't need to be
doing it independently.

00:26:15.320 --> 00:26:17.160
If they could just breeze
through it independently,

00:26:17.160 --> 00:26:18.083
probably too easy.

00:26:19.400 --> 00:26:21.930
So that's kind of what you're
looking for in terms of

00:26:21.930 --> 00:26:24.360
where the step, what that is.

00:26:24.360 --> 00:26:26.543
But a good guide is looking at the things

00:26:26.543 --> 00:26:28.003
that your teachers,

00:26:28.003 --> 00:26:30.440
that your child's teacher's assigning

00:26:30.440 --> 00:26:33.080
and thinking about,
you know, how that can,

00:26:33.080 --> 00:26:35.120
how you can help break that down.

00:26:35.120 --> 00:26:36.980
The other piece to that
first question was, you know,

00:26:36.980 --> 00:26:39.670
it seems to be too much,
that's being assigned.

00:26:39.670 --> 00:26:42.380
Again, think about breaking
down into small pieces.

00:26:42.380 --> 00:26:45.627
So you can a little bit of
progress even after an hour.

00:26:45.627 --> 00:26:47.980
"What are we going to
try to do in this hour?

00:26:47.980 --> 00:26:49.880
I know it's a whole bunch of stuff.

00:26:49.880 --> 00:26:52.250
Let's just focus on this
thing we can get done now,

00:26:52.250 --> 00:26:54.810
and then we can worry about
the rest of it later."

00:26:54.810 --> 00:26:56.580
So all of that is good goal setting.

00:26:56.580 --> 00:26:58.630
And then if you've got kids that are just,

00:26:58.630 --> 00:27:00.620
that are not engaging

00:27:00.620 --> 00:27:03.997
and are not that good at setting goals,

00:27:03.997 --> 00:27:07.660
we can think about, again,

00:27:07.660 --> 00:27:12.660
that idea of rewards and
thinking about engaging them,

00:27:13.000 --> 00:27:17.480
and maybe if you're
initially setting a goal

00:27:17.480 --> 00:27:19.320
and then having them in the conversation

00:27:19.320 --> 00:27:22.110
about what they think maybe
would be a good reward

00:27:22.110 --> 00:27:25.750
for getting into that, reaching that goal.

00:27:25.750 --> 00:27:29.810
So all of that can kind of help
bring them into the process

00:27:29.810 --> 00:27:30.643
and what that is.

00:27:30.643 --> 00:27:33.086
So maybe they're not
interested in setting the goal

00:27:33.086 --> 00:27:34.580
the first time.

00:27:34.580 --> 00:27:38.120
'Cause then you can think about
what's the reward for that.

00:27:38.120 --> 00:27:41.600
Or you can think about
rewards for helping set goals.

00:27:41.600 --> 00:27:43.870
We're getting very kind of meta there.

00:27:43.870 --> 00:27:47.127
But then again, rewards can
be good for helping kids

00:27:47.127 --> 00:27:50.790
start doing something that
they're not interested in doing.

00:27:50.790 --> 00:27:53.137
So think about, you know,

00:27:53.137 --> 00:27:56.470
"Hey, let's set this goal and
then we can go and, you know,

00:27:56.470 --> 00:27:59.340
chase around in the backyard
with the dog for 30 minutes,

00:27:59.340 --> 00:28:02.100
and then we can get back
to work on a meeting."

00:28:02.100 --> 00:28:05.410
Those kinds of things, you
know, are all good ways

00:28:05.410 --> 00:28:07.713
to start to kind of
encourage that behavior.

00:28:08.950 --> 00:28:09.783
- That's great.

00:28:09.783 --> 00:28:10.740
And we have a question,

00:28:10.740 --> 00:28:12.810
we have a couple questions around feedback

00:28:12.810 --> 00:28:15.040
and that portion of the tips.

00:28:15.040 --> 00:28:16.600
So Rebecca asks,

00:28:16.600 --> 00:28:18.627
what type of feedback
is most helpful for kids

00:28:18.627 --> 00:28:21.950
and does it depend on the stage
of where they're learning?

00:28:21.950 --> 00:28:25.733
So things like the frequency
of the feedback, the timing,

00:28:25.733 --> 00:28:28.239
when to acknowledge, you know,

00:28:28.239 --> 00:28:32.180
basically effort versus
results, things like that?

00:28:32.180 --> 00:28:35.730
- Yup, yup. All good, excellent questions.

00:28:35.730 --> 00:28:36.840
This is actually some place

00:28:36.840 --> 00:28:40.080
where there's a pretty good
amount of research out there.

00:28:40.080 --> 00:28:43.420
So when you were just
learning a new thing,

00:28:43.420 --> 00:28:46.326
you want to give immediate feedback

00:28:46.326 --> 00:28:49.700
that is both about whether
it's correct or not,

00:28:49.700 --> 00:28:52.820
but also why it's correct or not.

00:28:52.820 --> 00:28:55.570
So that's what we call elaborated feedback

00:28:55.570 --> 00:28:58.410
because it's not just the
correctness, but it's the why.

00:28:58.410 --> 00:29:01.750
And so you want that to
be specific to the task

00:29:01.750 --> 00:29:03.070
that they're doing.

00:29:03.070 --> 00:29:05.417
You don't want to give
that general feedback like,

00:29:05.417 --> 00:29:06.560
"Oh, you're so smart."

00:29:06.560 --> 00:29:10.490
You want to keep it as "You
got that question right.

00:29:10.490 --> 00:29:11.677
Good job."

00:29:11.677 --> 00:29:15.517
"That answer is right because XYZ."

00:29:17.040 --> 00:29:19.970
or "You didn't get that
one right that time.

00:29:19.970 --> 00:29:24.580
That answer's not correct
because of XYZ. Let's try again."

00:29:24.580 --> 00:29:28.610
Again, don't make it about
characteristics of the child.

00:29:28.610 --> 00:29:31.330
You want to think about
trying to give feedback.

00:29:31.330 --> 00:29:32.950
So feedback on correctness

00:29:32.950 --> 00:29:35.890
and about why it's correct or not is good.

00:29:35.890 --> 00:29:38.080
And you can also then combine that

00:29:38.080 --> 00:29:40.320
with feedback about effort.

00:29:40.320 --> 00:29:42.630
So something like, "Wow,"

00:29:42.630 --> 00:29:44.380
if you're seeing that productive struggle

00:29:44.380 --> 00:29:45.340
that I was talking about,

00:29:45.340 --> 00:29:48.187
like they've worked pretty hard
to get to this answer, like,

00:29:48.187 --> 00:29:51.320
"Wow, I saw you really
thinking hard about that.

00:29:51.320 --> 00:29:52.900
That's great.

00:29:52.900 --> 00:29:55.470
Let's take a look at whether
we got it right or wrong,

00:29:55.470 --> 00:29:57.030
or whether we want to try again."

00:29:57.030 --> 00:30:00.090
And then you can move into
the correctness feedback.

00:30:00.090 --> 00:30:02.670
As kids are more advanced

00:30:02.670 --> 00:30:05.400
and they're getting into
more advanced topics

00:30:05.400 --> 00:30:08.090
and are kind of more
comfortable with things,

00:30:08.090 --> 00:30:11.370
it's actually good to
have them have a chance

00:30:11.370 --> 00:30:15.210
to reflect on whether they think
something's correct or not.

00:30:15.210 --> 00:30:16.977
So we do things like saying,

00:30:16.977 --> 00:30:18.990
"How confident are you in that answer?

00:30:18.990 --> 00:30:19.830
How sure are you,

00:30:19.830 --> 00:30:21.902
how sure are you that that's right?"

00:30:21.902 --> 00:30:25.970
And get them to actually start
building some of those skills

00:30:25.970 --> 00:30:29.170
where they're kind of mentally
checking their own work,

00:30:29.170 --> 00:30:32.340
which is a good skill
to have and think about,

00:30:32.340 --> 00:30:34.180
and then give them the feedback.

00:30:34.180 --> 00:30:35.700
It can be a little bit more delayed.

00:30:35.700 --> 00:30:37.416
It doesn't need to be quite as immediate

00:30:37.416 --> 00:30:39.766
as kids are getting a
little bit more advanced.

00:30:40.800 --> 00:30:43.330
- So perhaps we have time
for one more question.

00:30:43.330 --> 00:30:44.920
Unfortunately, we have
a lot more questions

00:30:44.920 --> 00:30:45.753
than we have time for.

00:30:45.753 --> 00:30:48.170
So we'll try to, you know,

00:30:48.170 --> 00:30:50.790
potentially address it in the
future in a future webinar.

00:30:50.790 --> 00:30:54.340
But Melissa asks, and it's a
little bit related to feedback,

00:30:54.340 --> 00:30:57.630
which is, when should you
step back and allow a break,

00:30:57.630 --> 00:31:00.823
if your child's just really
reluctant or really frustrated.

00:31:02.400 --> 00:31:05.910
When you recognize that and
then when do you actually engage

00:31:05.910 --> 00:31:07.880
or when do you disengage?

00:31:07.880 --> 00:31:09.710
- Yeah. Excellent question.

00:31:09.710 --> 00:31:11.360
So first I would say,

00:31:11.360 --> 00:31:16.360
we want to prioritize our
kids' emotional wellbeing

00:31:16.526 --> 00:31:17.900
in all of this.

00:31:17.900 --> 00:31:19.900
We don't want to let kids get to the point

00:31:19.900 --> 00:31:20.770
where they're, you know,

00:31:20.770 --> 00:31:22.750
breaking down in tears about things,

00:31:22.750 --> 00:31:26.310
because that's going to
take a lot to then walk back

00:31:26.310 --> 00:31:29.940
and get down and get
settled and where that is.

00:31:29.940 --> 00:31:34.810
So the more you can be proactive
in scheduling in breaks

00:31:34.810 --> 00:31:37.090
and taking a step away

00:31:37.090 --> 00:31:40.638
even before they're showing
signs of being upset,

00:31:40.638 --> 00:31:43.330
that's actually good too.

00:31:43.330 --> 00:31:45.270
So don't be hesitant to take breaks.

00:31:45.270 --> 00:31:47.220
Breaks are good for everybody.

00:31:47.220 --> 00:31:50.130
But if you've kind of
gotten through there,

00:31:50.130 --> 00:31:52.910
but your child is still
like really frustrated,

00:31:52.910 --> 00:31:54.433
absolutely take a break.

00:31:55.490 --> 00:31:57.607
Go back to those strategies.

00:31:57.607 --> 00:32:00.810
"Okay, let's take a
break, do something else."

00:32:00.810 --> 00:32:02.140
And then I tend to go back,

00:32:02.140 --> 00:32:03.470
have a little bit of discussion

00:32:03.470 --> 00:32:06.600
about what's really frustrating them.

00:32:06.600 --> 00:32:08.220
Where is that road block?

00:32:08.220 --> 00:32:10.870
Is it they don't know what to do next?

00:32:10.870 --> 00:32:14.170
They can't get this right?
They can't understand it?

00:32:14.170 --> 00:32:17.160
You know, then you can start
to problem solve about,

00:32:17.160 --> 00:32:21.440
okay, if you understand what
it is that's so frustrating.

00:32:21.440 --> 00:32:24.610
Is it that it's just too much
that they're thinking about?

00:32:24.610 --> 00:32:25.830
Is it that, you know,

00:32:25.830 --> 00:32:29.310
they're worried about all
of the other things going on

00:32:29.310 --> 00:32:32.100
in the world, or they,
where are those things?

00:32:32.100 --> 00:32:34.740
So the more you can have a
conversation, take a break,

00:32:34.740 --> 00:32:35.610
get away from it,

00:32:35.610 --> 00:32:40.070
then have a conversation
about how are they feeling,

00:32:40.070 --> 00:32:41.370
what are the things that
they're worried about?

00:32:41.370 --> 00:32:44.310
What are the things that are
frustrating them and see if you

00:32:44.310 --> 00:32:46.050
can problem solve some of those together.

00:32:46.050 --> 00:32:49.740
But absolutely look for
those body language signs

00:32:49.740 --> 00:32:53.440
that kids are getting
really upset and frustrated

00:32:53.440 --> 00:32:55.310
and try to cut things off if you can,

00:32:55.310 --> 00:32:57.810
before it gets too far down that road.

00:32:57.810 --> 00:32:59.860
- Well, I think that's
great advice for adults

00:32:59.860 --> 00:33:00.700
as much as kids.

00:33:00.700 --> 00:33:03.403
I think that's some good
advice for us all to follow.

00:33:04.240 --> 00:33:07.373
So huge apologies. We have
so many more questions.

00:33:08.220 --> 00:33:11.700
Like I said, perhaps we can
tackle more in a future webinar.

00:33:11.700 --> 00:33:12.533
But with that,

00:33:12.533 --> 00:33:15.640
I'd like to thank Kristen for
her sharing her expert advice

00:33:15.640 --> 00:33:16.780
with our audience.

00:33:16.780 --> 00:33:18.190
And we want to thank you, our audience,

00:33:18.190 --> 00:33:21.040
for taking time out of your
busy schedule to be with us.

00:33:21.040 --> 00:33:23.197
We know you're incredibly
busy during this time.

00:33:23.197 --> 00:33:26.110
And so we really appreciate
you investing your time

00:33:26.110 --> 00:33:27.360
to this session.

00:33:27.360 --> 00:33:30.020
If you missed anything or want
to go back and review again,

00:33:30.020 --> 00:33:31.850
this is being recorded.

00:33:31.850 --> 00:33:33.880
And not only are we going to post it,

00:33:33.880 --> 00:33:35.840
but for those of you who have registered,

00:33:35.840 --> 00:33:38.610
this will automatically be emailed to you.

00:33:38.610 --> 00:33:39.590
And in addition to that,

00:33:39.590 --> 00:33:42.180
as well as the downloadable
with the seven tips

00:33:42.180 --> 00:33:43.630
that Kristen mentioned before.

00:33:43.630 --> 00:33:44.740
So don't miss, you know,

00:33:44.740 --> 00:33:46.460
don't worry if you missed anything.

00:33:46.460 --> 00:33:49.900
And we also have quite a few
resources at khanacademy.org

00:33:49.900 --> 00:33:52.410
if you need more information.

00:33:52.410 --> 00:33:53.440
And before we sign off,

00:33:53.440 --> 00:33:55.890
we want to ask that you
do two things for us.

00:33:55.890 --> 00:34:00.680
First, please answer the
poll that happens at the end,

00:34:00.680 --> 00:34:02.630
that pops up at the end of this webinar.

00:34:02.630 --> 00:34:03.530
We want to ask, you know,

00:34:03.530 --> 00:34:06.580
how can we make future
iterations of this session

00:34:06.580 --> 00:34:07.920
even better for you?

00:34:07.920 --> 00:34:09.870
And then secondly, what
kind of other sessions

00:34:09.870 --> 00:34:12.240
would you like to see us
develop in the future?

00:34:12.240 --> 00:34:13.220
And we're here to help you.

00:34:13.220 --> 00:34:15.630
So please let us know
how we can best do that.

00:34:15.630 --> 00:34:18.050
And so from all of us at
Khan Academy, thank you.

00:34:18.050 --> 00:34:18.993
And goodbye.


Types of studies

WEBVTT
Kind: captions
Language: en

00:00:00.840 --> 00:00:02.520
- [Instructor] In this video,
we're gonna get our bearings

00:00:02.520 --> 00:00:04.610
on the different types of studies

00:00:04.610 --> 00:00:08.820
you might statistically
analyze or statistical studies.

00:00:08.820 --> 00:00:11.250
So first of all, it's
worth differentiating

00:00:11.250 --> 00:00:16.250
between an experiment and
an observational study.

00:00:16.790 --> 00:00:19.200
I encourage you pause this video

00:00:19.200 --> 00:00:20.600
and think about what the difference is,

00:00:20.600 --> 00:00:22.750
at least in your head
between an experiment

00:00:22.750 --> 00:00:24.980
and an observational.

00:00:24.980 --> 00:00:27.963
Observational study.

00:00:30.130 --> 00:00:33.130
Well, you might already be
familiar with experiments.

00:00:33.130 --> 00:00:35.450
You oftentimes have a hypothesis

00:00:35.450 --> 00:00:38.000
that if you do something to one group,

00:00:38.000 --> 00:00:40.800
that it might have some type
of statistically significant

00:00:40.800 --> 00:00:42.960
impact on them relative to a group

00:00:42.960 --> 00:00:44.450
that you did not do it to.

00:00:44.450 --> 00:00:46.270
And you would be generally right.

00:00:46.270 --> 00:00:48.770
That is the flavor of
what we're talking about

00:00:48.770 --> 00:00:50.470
when we're talking about an experiment.

00:00:50.470 --> 00:00:53.410
An experiment we're
actively putting people

00:00:53.410 --> 00:00:57.010
or things into a control
versus treatment group.

00:00:57.010 --> 00:01:01.250
In the treatment group, you put the people

00:01:01.250 --> 00:01:03.510
and you usually would want
to randomly select people

00:01:03.510 --> 00:01:04.343
into the treatment group.

00:01:04.343 --> 00:01:06.060
Maybe it's a new type of medication.

00:01:06.060 --> 00:01:07.730
And maybe in the treatment group,

00:01:07.730 --> 00:01:11.240
they actually get the medication
while in the control group,

00:01:11.240 --> 00:01:14.250
which you would put people into randomly,

00:01:14.250 --> 00:01:16.030
whether they're in control or treatment,

00:01:16.030 --> 00:01:18.210
here they might get a placebo

00:01:18.210 --> 00:01:20.370
where they get a pill that
looks just like the medication,

00:01:20.370 --> 00:01:22.140
but it really doesn't do anything.

00:01:22.140 --> 00:01:24.460
And then you wait some
time and you can see,

00:01:24.460 --> 00:01:26.900
is there a statistically
significant difference

00:01:26.900 --> 00:01:29.070
between the treatment group on average

00:01:29.070 --> 00:01:30.800
and the control group.

00:01:30.800 --> 00:01:32.370
So that's what an experiment does.

00:01:32.370 --> 00:01:34.520
It's kind of this act of
sorting and figuring out

00:01:34.520 --> 00:01:39.250
whether some type of stimulus
is able to show a difference

00:01:39.250 --> 00:01:41.190
while an observational study,

00:01:41.190 --> 00:01:43.870
you don't actively put into groups.

00:01:43.870 --> 00:01:46.760
Instead, you just collect data

00:01:46.760 --> 00:01:49.100
and see if you can have some insights

00:01:49.100 --> 00:01:50.400
from that data.

00:01:50.400 --> 00:01:54.660
If you can say, okay, the data,
there's a population here,

00:01:54.660 --> 00:01:56.110
can I come up with some statistics

00:01:56.110 --> 00:01:57.800
that are indicative of the population?

00:01:57.800 --> 00:01:59.620
I might just wanna look at averages,

00:01:59.620 --> 00:02:03.800
or I might wanna find some
correlations between variables.

00:02:03.800 --> 00:02:06.580
But even when we're talking
about an observational study,

00:02:06.580 --> 00:02:07.880
there are different types of it,

00:02:07.880 --> 00:02:10.360
depending on what type
of data we're looking at,

00:02:10.360 --> 00:02:13.420
whether the data is
backward-looking, forward-looking

00:02:13.420 --> 00:02:16.320
or it's data that we are
collecting right now,

00:02:16.320 --> 00:02:19.750
based on what people
think or say right now.

00:02:19.750 --> 00:02:22.560
So if we're thinking about
an observational study

00:02:22.560 --> 00:02:24.603
that is looking at past data,

00:02:25.940 --> 00:02:27.890
and I could imagine
doing something like this

00:02:27.890 --> 00:02:28.723
at Khan Academy,

00:02:28.723 --> 00:02:32.550
where we could look at
maybe usage of Khan Academy

00:02:32.550 --> 00:02:33.383
over time.

00:02:33.383 --> 00:02:35.560
We have these things in our server logs,

00:02:35.560 --> 00:02:38.150
and we're able to make
do some analysis there.

00:02:38.150 --> 00:02:41.010
Maybe we're able to analyze and say,

00:02:41.010 --> 00:02:45.660
on average students are
spending two hours per month

00:02:45.660 --> 00:02:49.500
on Khan Academy over in 2019.

00:02:49.500 --> 00:02:51.340
That would be past data.

00:02:51.340 --> 00:02:54.750
And that type of observational
study would be called

00:02:54.750 --> 00:02:57.690
a retrospective study.

00:02:57.690 --> 00:03:02.330
Retro for backwards and spective, looking.

00:03:02.330 --> 00:03:05.290
So retrospective observational study

00:03:05.290 --> 00:03:08.810
would sample past data
in order to come up with

00:03:08.810 --> 00:03:10.370
some insights.

00:03:10.370 --> 00:03:12.590
Now you can imagine there
might be the other side.

00:03:12.590 --> 00:03:16.970
What if we are trying to
observe things into the future?

00:03:16.970 --> 00:03:19.790
Well, here you might
take a sample of folks

00:03:19.790 --> 00:03:22.560
who you think are
indicative of a population

00:03:22.560 --> 00:03:25.830
and you might want to
just track their data.

00:03:25.830 --> 00:03:29.730
So you could even consider
that to be future data.

00:03:29.730 --> 00:03:32.030
So you pick the group,
the sample ahead of time,

00:03:32.030 --> 00:03:34.980
and then you track their data over time.

00:03:34.980 --> 00:03:37.630
I'm just gonna try these little arrows

00:03:37.630 --> 00:03:39.050
that you're tracking their data,

00:03:39.050 --> 00:03:40.760
and then you see what happens.

00:03:40.760 --> 00:03:43.890
For example, you might
randomly select hopefully

00:03:45.403 --> 00:03:47.340
a random sample of a hundred women,

00:03:47.340 --> 00:03:49.980
and you wanna see in the coming year,

00:03:49.980 --> 00:03:54.570
how many eggs do they
eat on average per day?

00:03:54.570 --> 00:03:57.310
Well, what you would do is
you selected those folks

00:03:57.310 --> 00:03:59.010
and then you would track that data

00:03:59.010 --> 00:04:00.590
for each of them every day.

00:04:00.590 --> 00:04:02.480
And then once you have the data,

00:04:02.480 --> 00:04:04.380
you could actually do it
while you're collecting it.

00:04:04.380 --> 00:04:05.310
But at the end of the study,

00:04:05.310 --> 00:04:07.740
you'll be able to see
what those averages are.

00:04:07.740 --> 00:04:09.090
But you can also keep track of it

00:04:09.090 --> 00:04:11.250
while you're taking that data.

00:04:11.250 --> 00:04:13.670
And you could imagine
what this was called.

00:04:13.670 --> 00:04:16.350
Instead of retrospective,
we're now looking forward.

00:04:16.350 --> 00:04:21.350
So it is prospective, forward
looking observational study.

00:04:22.961 --> 00:04:24.800
Last but not least, some of
y'all are probably thinking,

00:04:24.800 --> 00:04:27.430
what about if we're doing something now?

00:04:27.430 --> 00:04:32.080
If we go out there and we were
to survey a bunch of people

00:04:32.080 --> 00:04:34.800
and say, how many eggs did you eat today?

00:04:34.800 --> 00:04:37.410
Or who are you going to vote for?

00:04:37.410 --> 00:04:38.930
What might we call that?

00:04:38.930 --> 00:04:40.980
It's tempting to call it
something with a prefix

00:04:40.980 --> 00:04:41.980
and then spective.

00:04:41.980 --> 00:04:43.200
So it all matches,

00:04:43.200 --> 00:04:45.680
but it turns out that the
terminology that statisticians

00:04:45.680 --> 00:04:50.680
will typically use is a sample survey.

00:04:52.520 --> 00:04:55.740
That right now you're gonna
take a hopefully random sample

00:04:55.740 --> 00:04:58.840
of individuals from the
population that you care about.

00:04:58.840 --> 00:05:01.170
And you are just gonna
serve them right now

00:05:01.170 --> 00:05:03.030
and ask them, say some questions

00:05:03.030 --> 00:05:06.610
or observe some data about them right now.

00:05:06.610 --> 00:05:07.770
So I'll leave you there.

00:05:07.770 --> 00:05:09.870
This video is to just
give you a little bit of

00:05:09.870 --> 00:05:12.620
the vocabulary and a
little bit of a taxonomy

00:05:12.620 --> 00:05:14.880
on the types of studies
that you'll see in general,

00:05:14.880 --> 00:05:16.560
which is especially useful to know

00:05:16.560 --> 00:05:19.170
when you're exploring
the world of statistics.


Influential points in regression

WEBVTT
Kind: captions
Language: en

00:00:00.670 --> 00:00:01.860
- [Instructor] I'm pretty sure I just tore

00:00:01.860 --> 00:00:03.240
my calf muscle this morning

00:00:03.240 --> 00:00:05.370
while sprinting with my son.

00:00:05.370 --> 00:00:07.304
But the math must not stop,

00:00:07.304 --> 00:00:09.980
(chuckles) so I'm here to help us

00:00:09.980 --> 00:00:13.060
think about what we could
call influential points

00:00:13.060 --> 00:00:14.550
when we're thinking about regressions.

00:00:14.550 --> 00:00:15.430
And to help us here,

00:00:15.430 --> 00:00:18.990
I have this tool from BFW Publishing.

00:00:18.990 --> 00:00:22.200
I encourage you to go here
and use this tool yourself.

00:00:22.200 --> 00:00:25.020
But what it allows us to
do is to draw some points.

00:00:25.020 --> 00:00:28.470
So just like that, let
me draw some points.

00:00:28.470 --> 00:00:31.360
And then fit a least-squares line.

00:00:31.360 --> 00:00:33.470
So that's the least-squares
line right over there.

00:00:33.470 --> 00:00:35.650
And you can not only see the line,

00:00:35.650 --> 00:00:37.520
we can see our correlation coefficient.

00:00:37.520 --> 00:00:40.610
It's pretty good, 0.8156.

00:00:40.610 --> 00:00:41.720
It's pretty close to one.

00:00:41.720 --> 00:00:44.910
So we have a pretty good
fit right over here.

00:00:44.910 --> 00:00:47.720
But we're gonna think about our points

00:00:47.720 --> 00:00:51.070
that might influence, or it
might be overly influential,

00:00:51.070 --> 00:00:55.160
we could say, to different
aspects of this regression line.

00:00:55.160 --> 00:00:58.950
So one type of influential
point is known as an outlier.

00:00:58.950 --> 00:01:01.210
And a good way of identifying an outlier

00:01:01.210 --> 00:01:03.720
is it's a very bad fit to the line,

00:01:03.720 --> 00:01:06.280
or it has a very large residual.

00:01:06.280 --> 00:01:10.460
And so if I put a point right
over here, that is an outlier.

00:01:10.460 --> 00:01:13.330
So what happens when we
have an outlier like that?

00:01:13.330 --> 00:01:18.330
So before we had a correlation
coefficient of 0.8 something,

00:01:18.710 --> 00:01:20.960
you put one outlier like that out of,

00:01:20.960 --> 00:01:23.520
it's now one out of 16 points,

00:01:23.520 --> 00:01:25.970
it dramatically lowered
our correlation coefficient

00:01:25.970 --> 00:01:29.060
because we have a really large
residual right over here.

00:01:29.060 --> 00:01:32.210
So an outlier like this
has been very influential

00:01:32.210 --> 00:01:33.920
on the correlation coefficient.

00:01:33.920 --> 00:01:38.630
It didn't impact the slope of
the line a tremendous amount.

00:01:38.630 --> 00:01:39.633
It did a little bit.

00:01:40.710 --> 00:01:42.910
Actually, when I put it
there, it didn't impact

00:01:42.910 --> 00:01:44.270
the slope much at all.

00:01:44.270 --> 00:01:48.210
And it does impact the
Y-intercept a little bit.

00:01:48.210 --> 00:01:49.550
Actually, when I put it out here,

00:01:49.550 --> 00:01:51.520
it doesn't impact the
Y-intercept much at all.

00:01:51.520 --> 00:01:53.320
If I put it a little bit more to the left,

00:01:53.320 --> 00:01:54.870
it impacts it a little bit.

00:01:54.870 --> 00:01:58.100
But these outliers that are at least close

00:01:58.100 --> 00:02:00.830
to the mean X value,

00:02:00.830 --> 00:02:03.020
these seem to be most relevant

00:02:03.020 --> 00:02:05.610
in terms of impacting, or most influential

00:02:05.610 --> 00:02:08.720
in terms of the correlation coefficient.

00:02:08.720 --> 00:02:11.140
Now, what about an outlier

00:02:11.140 --> 00:02:14.040
that's further away from the mean X value?

00:02:14.040 --> 00:02:16.760
And something, a point whose X value

00:02:16.760 --> 00:02:19.810
is further away from the mean X value

00:02:19.810 --> 00:02:21.480
is considered a high leverage point.

00:02:21.480 --> 00:02:22.313
And the way you could think

00:02:22.313 --> 00:02:23.760
about that is if you imagine

00:02:23.760 --> 00:02:26.100
this as being some type of a seesaw

00:02:26.100 --> 00:02:28.730
somehow pivoted on the mean X value,

00:02:28.730 --> 00:02:30.730
well, if you put a point out here,

00:02:30.730 --> 00:02:32.090
it looks like it's pivoting down.

00:02:32.090 --> 00:02:34.510
It's like someone's sitting
at this end of the seesaw.

00:02:34.510 --> 00:02:37.330
And so that's where I think
the term leverage comes from.

00:02:37.330 --> 00:02:39.820
And you can see what I put an outlier,

00:02:39.820 --> 00:02:42.830
if I put a high leverage outlier out here

00:02:42.830 --> 00:02:44.600
that does many things.

00:02:44.600 --> 00:02:47.750
It definitely drops the
correlation coefficient.

00:02:47.750 --> 00:02:52.310
It changes the slope and
it changes the Y-intercept.

00:02:52.310 --> 00:02:53.850
So it does a lot of things.

00:02:53.850 --> 00:02:56.310
So it's highly influential for everything

00:02:56.310 --> 00:02:57.720
I just talked about.

00:02:57.720 --> 00:02:59.430
And if I have a high leverage point

00:02:59.430 --> 00:03:02.710
that's maybe a little
bit less of an outlier,

00:03:02.710 --> 00:03:03.880
something like this,

00:03:03.880 --> 00:03:05.240
based on the points that I happen to have,

00:03:05.240 --> 00:03:07.240
it didn't hurt the
correlation coefficient.

00:03:07.240 --> 00:03:08.880
In fact, in that example,
it's actually improved

00:03:08.880 --> 00:03:12.820
it a little bit, but it did
change the Y intercept a bit,

00:03:12.820 --> 00:03:14.440
and it did change the slope a bit.

00:03:14.440 --> 00:03:15.940
Although, obviously, not as dramatic

00:03:15.940 --> 00:03:17.360
as when you do something like that.

00:03:17.360 --> 00:03:20.820
And that kills the correlation
coefficient as well.

00:03:20.820 --> 00:03:22.720
Let's see what happens if
we do things over here.

00:03:22.720 --> 00:03:27.060
So if I have a high
leverage outlier over here,

00:03:27.060 --> 00:03:28.330
you see the same thing.

00:03:28.330 --> 00:03:32.270
A high leverage outliers
seems to influence everything.

00:03:32.270 --> 00:03:34.070
If it is a high leverage point

00:03:34.070 --> 00:03:35.680
that is less of an outlier.

00:03:35.680 --> 00:03:37.370
Actually, once again, it improved

00:03:37.370 --> 00:03:39.140
the correlation coefficient.

00:03:39.140 --> 00:03:40.760
You could say that it's still influential

00:03:40.760 --> 00:03:42.820
on the correlation
coefficient in this case,

00:03:42.820 --> 00:03:46.170
it's improving it, but
it's less influential

00:03:46.170 --> 00:03:48.250
in terms of the slope and the Y-intercept,

00:03:48.250 --> 00:03:50.280
although it is making a difference there.

00:03:50.280 --> 00:03:51.810
So I encourage you to play with this.

00:03:51.810 --> 00:03:53.750
Think about different points.

00:03:53.750 --> 00:03:57.680
How far they are away
from the mean X value,

00:03:57.680 --> 00:03:59.230
how large of a residual they have,

00:03:59.230 --> 00:04:00.540
are they an outlier?

00:04:00.540 --> 00:04:04.420
And how influential they
are to the various aspects

00:04:04.420 --> 00:04:07.410
of a least-squares line,
the slope, the Y-intercept,

00:04:07.410 --> 00:04:08.570
or the correlation coefficient.

00:04:08.570 --> 00:04:10.210
When we're talking about
correlation coefficient,

00:04:10.210 --> 00:04:11.680
also known as the R value,

00:04:11.680 --> 00:04:14.080
which is of course the
square root of R squared.


Systematic random sampling

WEBVTT
Kind: captions
Language: en

00:00:00.430 --> 00:00:02.460
- [Instructor] In this video,
we're going to talk about

00:00:02.460 --> 00:00:05.240
random sampling, which
we've already talked about

00:00:05.240 --> 00:00:06.500
in other videos.

00:00:06.500 --> 00:00:08.620
And we're going to compare
what we already know

00:00:08.620 --> 00:00:11.670
about simple random sampling to a new type

00:00:11.670 --> 00:00:13.670
of random sampling that
we're going to introduce

00:00:13.670 --> 00:00:14.503
in this video.

00:00:14.503 --> 00:00:19.140
And that is systematic random sampling.

00:00:19.140 --> 00:00:20.810
So let's look at an example.

00:00:20.810 --> 00:00:24.750
Let's say that there is a
concert that is happening

00:00:24.750 --> 00:00:29.370
and we expect approximately 10,000 people

00:00:29.370 --> 00:00:31.110
to attend the concert.

00:00:31.110 --> 00:00:34.020
And we want to randomly
sample people at the concert.

00:00:34.020 --> 00:00:36.740
Maybe we wanna do a study on how do people

00:00:36.740 --> 00:00:38.090
get to the concert.

00:00:38.090 --> 00:00:43.090
How do people get to the concert?

00:00:45.100 --> 00:00:48.820
Do they drive and park? Do
they ride with a friend?

00:00:48.820 --> 00:00:52.550
Do they take an Uber
or a cab of some kind?

00:00:52.550 --> 00:00:57.180
And so we wanna find a random
sample, ideally without bias,

00:00:57.180 --> 00:00:58.880
to survey people.

00:00:58.880 --> 00:01:00.490
So there's a couple of
ways you could do it.

00:01:00.490 --> 00:01:04.980
You could try to do a
simple random sample.

00:01:04.980 --> 00:01:07.670
And that might be a case
of if you could somehow

00:01:07.670 --> 00:01:10.620
get the names of all
10,000 people and put them

00:01:10.620 --> 00:01:12.830
into a big bowl like this.

00:01:12.830 --> 00:01:15.830
And then let's say you
want to sample 100 people.

00:01:15.830 --> 00:01:20.830
Let's say you want to sample
approximately 100 people.

00:01:22.750 --> 00:01:25.290
You could just mix up all the names

00:01:25.290 --> 00:01:28.660
that may be on these little
pieces of paper, 10,000 of them,

00:01:28.660 --> 00:01:31.900
and then pull them out and
pull out a random sample

00:01:31.900 --> 00:01:33.080
of 100 of them.

00:01:33.080 --> 00:01:36.440
That would be a simple random sample.

00:01:36.440 --> 00:01:38.420
But you could already
imagine there might be some

00:01:38.420 --> 00:01:40.170
logistic difficulties of doing this.

00:01:40.170 --> 00:01:41.970
How are you going to get the 10,000 names?

00:01:41.970 --> 00:01:43.840
You're gonna write 'em
on a piece of paper.

00:01:43.840 --> 00:01:46.290
That's gonna be a you'd
have to really mix 'em good

00:01:46.290 --> 00:01:49.120
so it's truly random
who you're picking out.

00:01:49.120 --> 00:01:51.480
So are there other ways
of doing a random sample?

00:01:51.480 --> 00:01:53.090
And as you can imagine, yes, there are.

00:01:53.090 --> 00:01:56.920
And that's where systematic
random sampling is useful.

00:01:56.920 --> 00:01:59.370
One way to think about
systematic random sampling

00:01:59.370 --> 00:02:03.430
is you're going to randomly
sample a subset of the people

00:02:03.430 --> 00:02:06.080
who are maybe walking into the concert.

00:02:06.080 --> 00:02:08.760
So let's say people get to
the concert and they start

00:02:08.760 --> 00:02:12.930
forming a line to get into the concert.

00:02:12.930 --> 00:02:15.860
What you wanna do in
systematic random sampling

00:02:15.860 --> 00:02:18.130
is randomly pick your first person.

00:02:18.130 --> 00:02:20.300
There's a bunch of ways
that you could do that.

00:02:20.300 --> 00:02:22.070
Let's say you have a
random number generator

00:02:22.070 --> 00:02:24.770
that'll generate a number from one to 100.

00:02:24.770 --> 00:02:26.800
And that's going to be the
first person you survey.

00:02:26.800 --> 00:02:29.470
If that random number
generator generates a 37,

00:02:29.470 --> 00:02:32.870
then you're going to start
with the 37th person in line.

00:02:32.870 --> 00:02:36.950
So you pick that first person
randomly. You survey them.

00:02:36.950 --> 00:02:39.930
And remember, our goal is
to sample about 100 people

00:02:39.930 --> 00:02:40.763
out of 10,000.

00:02:40.763 --> 00:02:43.970
So we wanna roughly sample
one out of every 100 people.

00:02:43.970 --> 00:02:47.440
And so what you do there is
once you have that first person

00:02:47.440 --> 00:02:49.610
that you're sampling, you then sample

00:02:49.610 --> 00:02:51.640
every 100th person after that.

00:02:51.640 --> 00:02:53.800
That's called sometimes
the sample interval.

00:02:53.800 --> 00:02:56.300
And the reason why 100 people
is because if you sample

00:02:56.300 --> 00:02:59.110
every 100th person after
that, you're gonna roughly get

00:02:59.110 --> 00:03:02.850
100 people in your sample
out of a total of 10,000.

00:03:02.850 --> 00:03:04.560
So this is going to be after 100,

00:03:04.560 --> 00:03:06.660
you're going to sample someone else.

00:03:06.660 --> 00:03:07.820
And then after another 100, you're going

00:03:07.820 --> 00:03:09.930
to sample someone else.

00:03:09.930 --> 00:03:13.000
Now, the reason why this
is useful is you could say

00:03:13.000 --> 00:03:14.630
okay, that first person was random.

00:03:14.630 --> 00:03:16.410
And then every person after that.

00:03:16.410 --> 00:03:18.410
It doesn't seem like there'd be any bias

00:03:18.410 --> 00:03:19.960
for why they would be the 100th person

00:03:19.960 --> 00:03:21.820
after that first person.

00:03:21.820 --> 00:03:23.660
You don't wanna just
do the first 100 people

00:03:23.660 --> 00:03:25.370
because those might be the early birds,

00:03:25.370 --> 00:03:28.200
the people who may be
disproportionately went parking

00:03:28.200 --> 00:03:30.650
or planned earlier or had
some bias in some way.

00:03:30.650 --> 00:03:32.690
So you do wanna make sure
that you're getting, you know,

00:03:32.690 --> 00:03:35.190
both the beginning, the middle,
and the end of the line,

00:03:35.190 --> 00:03:36.490
which this thing helps.

00:03:36.490 --> 00:03:38.140
Now, we have to be careful.

00:03:38.140 --> 00:03:41.750
Even systematic random
sampling is not foolproof.

00:03:41.750 --> 00:03:43.870
There's a situation where inadvertently

00:03:43.870 --> 00:03:45.830
even this system has bias.

00:03:45.830 --> 00:03:47.720
Let's say that this is the arena.

00:03:47.720 --> 00:03:50.550
This is a top view of the
arena right over here.

00:03:50.550 --> 00:03:54.580
And this is the line of people coming in.

00:03:54.580 --> 00:03:58.780
And this is where you are
standing and you are counting

00:03:58.780 --> 00:04:00.220
every 100th person.

00:04:00.220 --> 00:04:04.430
But maybe, and let's say
there's a tree right over here.

00:04:04.430 --> 00:04:09.000
And maybe there's a road. I'm
making this quite elaborate.

00:04:09.000 --> 00:04:11.940
So maybe there is a road right over here.

00:04:11.940 --> 00:04:15.560
And a lot of people, maybe all
of the people who are walking

00:04:15.560 --> 00:04:19.180
or taking a cab are coming
from this direction.

00:04:19.180 --> 00:04:21.670
And maybe all of the
people from the parking lot

00:04:21.670 --> 00:04:24.070
are coming from this direction.

00:04:24.070 --> 00:04:26.770
And maybe you have a police
officer right over here

00:04:26.770 --> 00:04:30.360
who is doing crowd control,
who lets 50 people,

00:04:30.360 --> 00:04:34.510
50 of these people in, followed
by 50 of these people in.

00:04:34.510 --> 00:04:37.590
Well, in that situation,
every 100th, you might end up

00:04:37.590 --> 00:04:40.170
just sampling one side or the other.

00:04:40.170 --> 00:04:42.340
So you have to make sure
that there isn't some bias

00:04:42.340 --> 00:04:44.840
that's being introduced
into this line somehow

00:04:44.840 --> 00:04:46.433
that might distort your sample.


Analyzing mosaic plots

WEBVTT
Kind: captions
Language: en

00:00:00.220 --> 00:00:02.050
- [Instructor] We're told that
administrators at a school

00:00:02.050 --> 00:00:04.190
are considering a policy change.

00:00:04.190 --> 00:00:06.850
They survey a group of
students, staff members,

00:00:06.850 --> 00:00:09.630
and parents about
whether or not they agree

00:00:09.630 --> 00:00:11.180
with the new policy.

00:00:11.180 --> 00:00:15.300
The following mosaic plot
summarizes their results.

00:00:15.300 --> 00:00:17.780
Which of the following
statements can we justify

00:00:17.780 --> 00:00:19.810
from the mosaic plot?

00:00:19.810 --> 00:00:22.720
So pause this video and try to
figure this out on your own.

00:00:22.720 --> 00:00:25.030
Pick which of these
statements can be justified

00:00:25.030 --> 00:00:28.963
and there could be more than
one based on this mosaic plot.

00:00:30.310 --> 00:00:32.500
All right, now let's work
through this together.

00:00:32.500 --> 00:00:33.880
So before I even look at the choices,

00:00:33.880 --> 00:00:35.890
let me see if I can interpret this.

00:00:35.890 --> 00:00:36.930
So this mosaic plot,

00:00:36.930 --> 00:00:39.780
what it does above and
beyond a segmented bar chart,

00:00:39.780 --> 00:00:43.040
is it gives us the width
that shows us how many,

00:00:43.040 --> 00:00:45.750
for example, students
versus staff versus parents

00:00:45.750 --> 00:00:47.630
were sampled or surveyed.

00:00:47.630 --> 00:00:50.520
And it looks like more than half

00:00:50.520 --> 00:00:52.620
of the people surveyed were students.

00:00:52.620 --> 00:00:55.160
And then staff and parents seem similar.

00:00:55.160 --> 00:00:58.020
In terms of who is
agreeing with the policy,

00:00:58.020 --> 00:00:59.600
so that's that light blue color.

00:00:59.600 --> 00:01:02.950
It seems like students are
not very likely to agree

00:01:02.950 --> 00:01:03.783
with the policy.

00:01:03.783 --> 00:01:07.060
It looks like staff is very
likely to agree with the policy,

00:01:07.060 --> 00:01:09.250
that the bulk of staff
is agreeing with it.

00:01:09.250 --> 00:01:12.330
And parents are kind of on both sides.

00:01:12.330 --> 00:01:14.980
So let's see which of these
statements are consistent

00:01:14.980 --> 00:01:16.530
with what we just looked at.

00:01:16.530 --> 00:01:19.840
Parents were the least likely
to agree with the new policy.

00:01:19.840 --> 00:01:21.460
No, that's not true.

00:01:21.460 --> 00:01:24.600
The least likely to
agree with the new policy

00:01:24.600 --> 00:01:27.150
that's students, right over here.

00:01:27.150 --> 00:01:28.730
They were definitely the least likely.

00:01:28.730 --> 00:01:30.750
The lowest percentage of students

00:01:30.750 --> 00:01:32.010
are agreeing with the policy.

00:01:32.010 --> 00:01:35.260
So I don't like that choice.

00:01:35.260 --> 00:01:38.600
More than half of the total
responses came from students.

00:01:38.600 --> 00:01:40.330
And that does look like the case.

00:01:40.330 --> 00:01:43.700
'Cause if you view this entire
width as the total responses,

00:01:43.700 --> 00:01:46.190
it looks like the student
width right over here,

00:01:46.190 --> 00:01:47.870
that is more than half of it.

00:01:47.870 --> 00:01:50.070
It looks like it's about
50 something percent

00:01:50.070 --> 00:01:51.370
or even 60 percent.

00:01:51.370 --> 00:01:55.270
So I like this choice right over here.

00:01:55.270 --> 00:01:57.070
And then last but not least,

00:01:57.070 --> 00:02:01.880
there were more total "No"
responses from students

00:02:01.880 --> 00:02:04.370
than from staff and parents combined.

00:02:04.370 --> 00:02:07.360
So let's look at the no
responses from students.

00:02:07.360 --> 00:02:09.300
No, that's that darker blue color.

00:02:09.300 --> 00:02:14.300
The no responses for students
is this area right over here.

00:02:15.780 --> 00:02:19.400
And then the nos, so I can,
maybe I'll shade all of that in,

00:02:19.400 --> 00:02:22.490
and then the no responses from staff

00:02:22.490 --> 00:02:27.490
and parents combined, that
is this area right over here.

00:02:28.590 --> 00:02:32.900
And it does look like indeed
the total no responses

00:02:32.900 --> 00:02:35.400
from students, this area is much larger

00:02:35.400 --> 00:02:37.040
than this area right over here.

00:02:37.040 --> 00:02:39.373
So I like this choice as well.


Mosaic plots and segmented bar charts

WEBVTT
Kind: captions
Language: en

00:00:00.280 --> 00:00:01.113
- [Instructor] Let's say we're looking

00:00:01.113 --> 00:00:02.550
at some type of disease

00:00:02.550 --> 00:00:05.100
and we want to see if
there's any relationship

00:00:05.100 --> 00:00:09.080
between people having
antibodies for that disease

00:00:09.080 --> 00:00:12.880
and whether they are
adults, children or infants.

00:00:12.880 --> 00:00:14.870
And if you don't know what antibodies are,

00:00:14.870 --> 00:00:17.670
these are things that your
immune system keeps around

00:00:17.670 --> 00:00:20.970
so it's very easy to
recognize future infection.

00:00:20.970 --> 00:00:22.680
But you don't have to
worry too much about that

00:00:22.680 --> 00:00:23.700
for this video.

00:00:23.700 --> 00:00:25.010
In this video, we're just
trying to think about

00:00:25.010 --> 00:00:28.000
how we can visualize data to understand

00:00:28.000 --> 00:00:31.040
if there's a relationship
between having antibodies

00:00:31.040 --> 00:00:33.500
and the age of the individual.

00:00:33.500 --> 00:00:35.980
So let's say we go out and
collect a bunch of data.

00:00:35.980 --> 00:00:37.990
So we test 120 adults

00:00:37.990 --> 00:00:40.660
and 114 have antibodies, six don't.

00:00:40.660 --> 00:00:41.960
We test 60 children,

00:00:41.960 --> 00:00:44.400
54 have antibodies, six don't.

00:00:44.400 --> 00:00:46.530
We test 20 infants, and then eight

00:00:46.530 --> 00:00:49.590
have antibodies and 12 don't.

00:00:49.590 --> 00:00:51.110
So we can just look at this data,

00:00:51.110 --> 00:00:52.540
but this really still doesn't give us

00:00:52.540 --> 00:00:55.630
a visual representation
of what's going on.

00:00:55.630 --> 00:00:57.590
One step we can take,
what's still doesn't give us

00:00:57.590 --> 00:00:59.380
a fully visual representation,

00:00:59.380 --> 00:01:01.130
is to just think about percentages

00:01:01.130 --> 00:01:03.110
that might help us think
about the likelihood

00:01:03.110 --> 00:01:04.790
of having antibodies.

00:01:04.790 --> 00:01:06.510
So if we calculate the percentages,

00:01:06.510 --> 00:01:08.530
we might see something like this.

00:01:08.530 --> 00:01:12.400
For example, 114 over 120

00:01:12.400 --> 00:01:17.090
is 95%, or 95% have antibodies.

00:01:17.090 --> 00:01:19.900
That 114 over one 20 is 95%.

00:01:19.900 --> 00:01:22.820
And then the number that
don't have antibodies,

00:01:22.820 --> 00:01:24.370
this six right over here,

00:01:24.370 --> 00:01:27.360
that is 5%, six over 120.

00:01:27.360 --> 00:01:30.040
And you can do that for
each of the categories.

00:01:30.040 --> 00:01:34.470
54 over 60 is 90%.

00:01:34.470 --> 00:01:36.760
While six over 60,

00:01:36.760 --> 00:01:39.570
you can do that math in your head, is 10%.

00:01:39.570 --> 00:01:41.870
And we can do the same
thing for the infants.

00:01:41.870 --> 00:01:45.370
Eight out of 20 is 40%

00:01:45.370 --> 00:01:50.210
while 12 out of 20 is 60%.

00:01:50.210 --> 00:01:51.500
So that helps us a little bit.

00:01:51.500 --> 00:01:52.750
It helps us think about,

00:01:52.750 --> 00:01:54.490
well, what's the percentage of adults

00:01:54.490 --> 00:01:57.540
that have the antibody
or children or infants?

00:01:57.540 --> 00:01:58.990
But if we really want to visualize it,

00:01:58.990 --> 00:02:02.260
we can look at two different
types of visualizations.

00:02:02.260 --> 00:02:06.200
One we can call a segmented bar chart,

00:02:06.200 --> 00:02:08.640
and I will show a segmented bar chart

00:02:08.640 --> 00:02:10.670
for this data right over here.

00:02:10.670 --> 00:02:13.370
Now in a segmented bar chart, we group,

00:02:13.370 --> 00:02:16.070
we have a bar for each category here

00:02:16.070 --> 00:02:18.570
and we're making adults,
children and infants

00:02:18.570 --> 00:02:20.610
the different categories
because we're thinking

00:02:20.610 --> 00:02:23.230
maybe that has something
to do with the likelihood

00:02:23.230 --> 00:02:24.720
of having antibodies.

00:02:24.720 --> 00:02:28.020
And then for each bar, for
example, this adult bar,

00:02:28.020 --> 00:02:31.090
you can see the percentage
that have the antibodies

00:02:31.090 --> 00:02:32.220
and the percentage that don't.

00:02:32.220 --> 00:02:36.050
So 95% of the adult bar is filled in blue.

00:02:36.050 --> 00:02:38.140
That's for yes, they have the antibodies.

00:02:38.140 --> 00:02:41.060
And 5% is filled in red.

00:02:41.060 --> 00:02:42.250
And then for children,

00:02:42.250 --> 00:02:45.330
you can see that 90% is filled in blue

00:02:45.330 --> 00:02:46.760
and 10% is filled in red

00:02:46.760 --> 00:02:48.720
because 10% don't have the antibodies.

00:02:48.720 --> 00:02:50.890
And then for infants, you can see

00:02:50.890 --> 00:02:53.300
that 40% is filled in blue

00:02:53.300 --> 00:02:56.630
and 60% don't have the antibodies.

00:02:56.630 --> 00:02:58.550
Now, this by itself is pretty useful

00:02:58.550 --> 00:03:00.850
to visually see, alright,
it looks like adults

00:03:00.850 --> 00:03:03.240
are much more likely
to have the antibodies

00:03:03.240 --> 00:03:05.580
than children, and children
are far more likely

00:03:05.580 --> 00:03:08.240
to have the antibodies than infants.

00:03:08.240 --> 00:03:11.390
And so it looks like
this idea of making a bar

00:03:11.390 --> 00:03:13.700
for each of adults, children or infants

00:03:13.700 --> 00:03:16.490
was a good way to start to understand

00:03:16.490 --> 00:03:18.620
the likelihood of having antibodies.

00:03:18.620 --> 00:03:20.280
You could have done it other ways.

00:03:20.280 --> 00:03:23.170
You could have had a
bar for have antibodies

00:03:23.170 --> 00:03:25.000
and another bar for not have antibodies.

00:03:25.000 --> 00:03:26.980
And then you could have
segmented the bar chart

00:03:26.980 --> 00:03:29.410
by whether they are adults,
children, or infants.

00:03:29.410 --> 00:03:30.440
But if you did that,

00:03:30.440 --> 00:03:31.870
that would have been trying to understand

00:03:31.870 --> 00:03:34.330
whether having antibodies
or not having antibodies

00:03:34.330 --> 00:03:37.140
is predictive of whether you're
an adult, child or infant

00:03:37.140 --> 00:03:39.030
while this one makes, at least to me,

00:03:39.030 --> 00:03:40.760
a little bit more sense that
whether you're an adult,

00:03:40.760 --> 00:03:42.900
child or infant might be predictive

00:03:42.900 --> 00:03:45.070
of whether or not you have antibodies.

00:03:45.070 --> 00:03:46.940
But there is some information lost

00:03:46.940 --> 00:03:48.900
in this segmented bar chart.

00:03:48.900 --> 00:03:50.820
For example, we have lost the fact

00:03:50.820 --> 00:03:53.390
that we have sampled, or we have tested

00:03:53.390 --> 00:03:54.820
a lot more adults than children

00:03:54.820 --> 00:03:57.540
and far more children than infants.

00:03:57.540 --> 00:03:59.710
So one way to incorporate that data

00:03:59.710 --> 00:04:01.460
back into a visualization

00:04:01.460 --> 00:04:03.360
to essentially show how many people

00:04:03.360 --> 00:04:05.660
you sampled in each of these categories,

00:04:05.660 --> 00:04:09.253
we can generate what's
known as a mosaic plot.

00:04:09.253 --> 00:04:11.920
So this is a mosaic plot right over here.

00:04:11.920 --> 00:04:13.750
And one way to think about it is

00:04:13.750 --> 00:04:15.600
we have just adjusted the width

00:04:15.600 --> 00:04:17.320
of each of these bars

00:04:17.320 --> 00:04:19.910
based on how many people we tested.

00:04:19.910 --> 00:04:21.870
So we tested 200 people.

00:04:21.870 --> 00:04:24.770
And so you can view this
width right over here

00:04:24.770 --> 00:04:26.800
as being 200.

00:04:26.800 --> 00:04:30.840
And you can see that we tested 120 adults.

00:04:30.840 --> 00:04:32.990
So the width of this first bar,

00:04:32.990 --> 00:04:33.823
I guess you could say,

00:04:33.823 --> 00:04:35.750
although now we're dealing
with a mosaic plot,

00:04:35.750 --> 00:04:38.150
this width right over here would be

00:04:38.150 --> 00:04:40.400
60% of this entire width,

00:04:40.400 --> 00:04:42.000
which you can see that it is.

00:04:42.000 --> 00:04:45.680
And then the children are 60
of the 200 that we tested.

00:04:45.680 --> 00:04:47.970
And so this width right over here

00:04:47.970 --> 00:04:51.380
would be 60 over the entire 200,

00:04:51.380 --> 00:04:54.040
or it would be about
30% of the entire width.

00:04:54.040 --> 00:04:55.880
And we can see that we tested

00:04:55.880 --> 00:04:57.240
the fewest number of infants.

00:04:57.240 --> 00:04:59.760
And so this 20 right over here

00:04:59.760 --> 00:05:02.890
represents the 20 infants we tested.

00:05:02.890 --> 00:05:04.810
And the reason why this mosaic plot

00:05:04.810 --> 00:05:06.010
conveys more information,

00:05:06.010 --> 00:05:07.980
it conveys all the same information

00:05:07.980 --> 00:05:10.010
that our segmented bar chart does.

00:05:10.010 --> 00:05:12.630
But it also gives us
a sense that we tested

00:05:12.630 --> 00:05:13.717
more adults than children

00:05:13.717 --> 00:05:16.370
and far more children than infants.

00:05:16.370 --> 00:05:19.200
And it's also easy to then
look at it and say, okay,

00:05:19.200 --> 00:05:22.440
of the total number of people
who don't have the antibodies,

00:05:22.440 --> 00:05:25.490
so that would be the red
area right over here,

00:05:25.490 --> 00:05:28.450
even though we tested the
fewest number of infants,

00:05:28.450 --> 00:05:31.100
it looks like infants
make up a large chunk

00:05:31.100 --> 00:05:32.650
of the total number of folks

00:05:32.650 --> 00:05:35.000
who don't have antibodies.

00:05:35.000 --> 00:05:36.020
So I'll leave you there.

00:05:36.020 --> 00:05:38.460
The whole point of this
video is to just understand

00:05:38.460 --> 00:05:41.170
why a segmented bar chart or a mosaic plot

00:05:41.170 --> 00:05:42.640
will be useful in future videos.

00:05:42.640 --> 00:05:45.473
We'll get more practice analyzing them.