0:02

Why is data so often used as

0:04

a gotcha, especially in education? As

0:07

a former teacher, administrator, and now consultant,

0:09

it's a question that I ask myself

0:11

all the time. Which is why

0:13

we're here. I'm Dr. Kurt, and

0:16

this is my takeover of data-driven

0:18

leadership. In this

0:20

four-episode miniseries, I'll be joined by several

0:23

industry experts who have made it their

0:25

mission to hunt, seek, and

0:27

destroy the systemic barriers to learning

0:29

through IT and data. We'll

0:31

share how IT and data can not only

0:34

meet unmet needs, but can actually accelerate

0:36

opportunities when done the right way. In

0:39

my role, I work with many state

0:41

education leaders across the country, which in

0:43

turn exposes me to a wide variety

0:45

of new and exciting strategies. I

0:48

look forward to bringing you these amazing leaders

0:50

to share those strategies with you. Let's

0:53

bring people, policy, and technology together so

0:55

that data can be our greatest ally.

0:58

Let's dive in.

1:02

It is really hard for me to

1:04

choose a favorite conversation of this miniseries,

1:06

but I will say that I have

1:08

been a long-time fan of the Data

1:10

Quality Campaign and specifically of

1:13

its Executive Vice President Paige Kowalski

1:15

and her thoughts and her leadership

1:17

around state data systems. This

1:19

conversation, whether you're from education or

1:22

outside of education, will give you

1:24

something to really think about in

1:26

terms of how states use the large

1:28

amounts of data that they compile for

1:31

the betterment of society as a whole.

1:34

Paige and the Data Quality Campaign

1:36

have been tireless advocates for advancing

1:38

the cause of data, of data

1:41

quality, and data accessibility. I

1:43

hope you enjoy the conversation. Paige,

1:49

thank you so much for joining us today. We really appreciate

1:51

you having on the show. Thanks,

1:54

Kurt. It's great to be here. Yeah, I am. Or should

1:56

I say Dr. Kurt? Yeah, Dr. Kurt. or

2:00

Dr. Drew a reference there.

2:02

Yeah. It's doesn't

2:07

get bold saying that. It's a newly minted

2:09

degree. I just finished so, but thank you.

2:13

Yeah. So Paige, I have really been

2:15

looking forward to this conversation with you.

2:17

I have always been a big fan

2:19

of the Data Quality Campaign and

2:21

in particular the content and the thought leadership

2:24

that you provide in this space. I

2:27

know that our listeners, whether they come

2:29

to us from an education background or

2:31

not, will lead with a really

2:33

insightful content

2:36

and thoughts from

2:38

you today. So

2:40

Paige, before we jump

2:42

in, I'd love for you to just

2:44

share a little bit with the audience what

2:47

is the Data Quality Campaign and

2:50

then how did you become involved

2:52

and where you are today? Yeah.

2:56

So the Data Quality Campaign

2:58

is a national, nonprofit policy

3:01

and advocacy organization and

3:03

our mission is quite simple. We just seek to

3:06

change the role the data plays in our decision-looking

3:08

at all levels. And I

3:10

know that sounds simple, but the fact that

3:12

we've been around for 18 years says that

3:14

that's easier said than done. And

3:17

I've been involved in this for the whole 18

3:20

years. I've been in DQC for, I think

3:22

this is my 16th year. It's getting harder and harder

3:24

to do the math. But prior

3:27

to that, I worked on some

3:29

national data initiatives with

3:31

CCSSO. So I've been around this

3:34

really since the beginning of

3:36

the conversation, if you will, around

3:38

staking the systems and the role

3:40

that data would play in improving

3:42

student outcomes. Amazing.

3:45

Do you mind sharing a little bit

3:47

about your backstory on how did you

3:50

become involved with this data movement and

3:52

why is data quality such

3:54

an important topic in the area of passion for

3:57

you personally? Yeah.

3:59

So... without saying

4:01

too much about how old I am, I actually

4:04

remember when great schools

4:06

watched. It was

4:08

in the 90s. I am from California. I grew

4:10

up in the Bay Area and I

4:13

come from... I'm a first-gen kid, so

4:15

first kid to go to college in

4:18

my family and I didn't

4:21

truly realize until I got

4:23

to college how much information

4:25

people had in

4:27

figuring all that out. Right now, we call it

4:29

college knowledge. I don't think we called it anything

4:32

back then. How much more information they had than

4:34

I had. So I made more

4:36

mistakes. I picked the wrong school. I didn't know how

4:38

to pick a major. I didn't

4:40

know anything. I didn't have AP classes in high

4:42

school. So I was

4:44

just less set up to succeed

4:46

and things seemed like they took longer

4:49

and there were more mistakes involved. And

4:52

when great schools launched, it

4:54

was the first time I had

4:57

really had access to see what

4:59

was going on in other schools and what kind

5:01

of outcomes they were achieving. I think

5:04

in my school, we'd always heard, well,

5:06

we don't have these kinds of programs.

5:09

Basically because your parents didn't go to college,

5:11

but the high school over there, they do

5:13

have these programs. And I just

5:16

sort of bought that. It sounded rational

5:18

and it wasn't until I was starting to

5:21

see this data come out about other schools

5:23

for the first time that I realized that

5:25

was garbage. That was an excuse. And

5:28

it really just drove

5:30

me to seek out ways of

5:32

getting out better information and lifting

5:34

up narratives and

5:37

stories and people

5:39

who are

5:41

trying to move that social

5:44

mobility ladder, but just get held

5:46

back because they just don't know.

5:48

They don't have access. And

5:50

I came out to DC and goodness,

5:53

I've been out here for 20 years now and

5:57

right after Niv Child Left Behind had passed, and

6:00

got involved in some of these conversations around,

6:02

all right, we're going to have a test

6:04

score in math and reading in every student

6:06

in America. What do we do with that?

6:09

How do we do more with it than

6:11

just tell the teacher and the parent or

6:14

send an accountability report to the feds for

6:17

our mini? How do we use

6:20

that information and actually start to

6:22

understand what's driving certain outcomes? Who

6:25

are the kids that are slipping through the

6:27

cracks in our grad rate? Who is not

6:30

going to college that could be going to

6:32

college with some better outreach and better understanding

6:34

of what kind of programs would lead to

6:36

that? And that's really what's driven

6:38

me for the last 18 years is

6:41

really thinking through, I

6:43

want to make sure that families

6:45

who don't have that information have

6:48

that information. And honestly, you can never have

6:50

too much information. So I have a

6:52

son in college and a son in high school. I

6:56

have all the college knowledge in

6:58

the world. Anybody could ever want.

7:00

And I still find myself digging

7:03

through admission rates and salaries

7:05

that kids are getting after college because

7:08

I ain't paying tuition for such and such.

7:11

It doesn't matter how you play that game.

7:13

So I'm using that information now as

7:16

an adult, thinking through how to help

7:18

my kids make better, smarter decisions. Amazing.

7:21

Thank you for sharing a little bit of that

7:23

lesson. I'd

7:25

really like to talk with you today

7:28

about state

7:30

longitudinal data systems or

7:32

sometimes just shortened to SLDS. We've

7:36

also maybe heard the phrase P20W, preschool,

7:42

kindergarten through college, workforce, P20W.

7:45

Crandall's a career, right? There's many

7:47

different names out there with some

7:50

subtle nuances to them.

7:52

But I would love your

7:54

perspective on these systems because these

7:56

systems really hold a promise, a

7:58

potential. provide

8:01

parents and educators and even critical

8:06

data that you were just talking

8:36

about, you know, what are the things that

8:38

are going to be involved in the data

8:40

systems and have evolved since then. And

8:43

there's just a lot of discussion around

8:46

these systems today. It's just very relevant, I think. So

8:48

just to open it up, could

8:50

you describe for us, you know, what is

8:54

an SLDS? And then we'll get into kind of

8:56

the history, I guess, in the evolution. Okay. Sure.

9:00

So I mentioned that Afterno Child Left Behind, we were

9:02

going to, for the first time,

9:05

send data at the state level. And I think

9:07

what most folks don't know is prior to that,

9:10

we've always had reporting, right? We've always

9:13

had data collection and education.

9:15

We've always had reporting up to the state

9:17

and to the federal government. That's how

9:19

many flows, send data up, many comes

9:22

down. And that data was never very good.

9:24

And it differed district to district,

9:26

right? We didn't have common definitions

9:28

or formulas. And so a

9:30

graduation rate from, you know,

9:32

I'm from California. So a graduation rate from

9:35

the Hinn County of Pentecost County, a little

9:37

bit very different than the graduation rate from

9:39

Cardin County, California, simply because the numerator and

9:41

the denominator is different and it's chosen by

9:43

the district. And so they would send the

9:45

graduate to the state, the state would report

9:47

it, they'd send it to the feds. And

9:50

so we didn't have very good data. So

9:53

when we, when states were charged by No

9:55

Child Left Behind to administer these assessments,

9:58

the state had an individual data point

10:00

for each student for the first time. So

10:03

the idea was what else can

10:06

we bring into the state level, to

10:08

the state at the

10:10

individual level that will help put some

10:12

context around that test score, right? We

10:14

get a lot of concern

10:17

about what is a test score even

10:19

mean for a child. Aren't

10:21

they growing up in certain conditions? Their

10:24

school isn't resourced properly, they don't have

10:26

aftercare or enrichment activities or access to

10:28

this or that. Those things are all

10:31

true and they're important context during the

10:33

test score as is family including. But

10:36

without a means to connect it to that

10:39

one single data point of the test score, we will

10:41

never know and we will

10:43

never be able to do any kind of

10:45

analysis and start to unpack the kinds

10:47

of questions that really matter to deriving

10:49

student achievement which is who is

10:52

it working for, who is it not working

10:54

for, how do we learn from

10:56

our high flyers from the schools

10:59

beating the odds beyond

11:02

income, right? Because it's the one thing

11:04

school systems can't really change. So to

11:07

do that, states started to build out what's

11:09

called an S, all the assets, statewide longitudinal

11:11

data system and you're right, it was around

11:13

0405 when federal dollars

11:15

began to flow for that. Coincidentally

11:18

we're not, that is around the same

11:20

time the data quality campaign was founded.

11:22

With the essential elements

11:24

of an SLDS to sort of provide

11:26

a roadmap for a state of like,

11:28

alright, y'all have test scores, let's

11:30

get courses and

11:33

grades, let's get student

11:35

demographics and attendance and behavior data, let's

11:37

get AP scores and

11:41

SAT scores, let's get outcomes, right?

11:44

Did the student graduate high school, did

11:46

a student drop out? There are actually

11:48

about 20 odd different ways

11:50

a student may leave a school, so

11:52

being able to incorporate all of that

11:54

and link it to the individual student

11:57

helps you better understand what's going on.

12:00

Longitudinal, so another you sort

12:02

of it's

12:05

a snapshot in time, it's a moment. It's

12:11

not going to define this whole life.

12:14

It was the Monday after day length savings

12:17

change so I don't know why they scheduled

12:20

SAT after now. They're not score

12:23

super high, we can have that

12:25

conversation but what longitudinal analysis

12:28

enables you to do is understand was

12:30

it a bad day for the students

12:32

or is it a bad day for the

12:35

students? So it allows you, it's sort of like

12:38

snapshot is a picture and longitudinal is like

12:40

a movie. It's moving, it's over time,

12:42

it's got a length to it and

12:44

while something may be a fluke, right, we all have bad

12:47

days but we don't do well, if you

12:49

see that consistently there's a clue

12:52

to a family and a school that there may be

12:54

a problem. The other thing it enables

12:56

you to do looking longitudally, especially

12:59

if you have these other data points like

13:01

AP scores and graduation

13:03

and such, is that once you have a

13:05

number of years of data, we can start

13:09

to answer those critical questions. Do

13:12

students who take more AP classes enroll at higher

13:14

rates in college and what

13:17

is that critical cutoff? Is it one AP? Is it ten?

13:19

Because that's going to drive

13:21

how principals and school district superintendents think

13:24

about their programming. If their goal is

13:26

we want to increase the number of students who do enroll,

13:29

if that's their goal, that's not the only goal

13:31

or even the best goal. But if

13:33

not your goal, you would want to know what am

13:35

I doing currently in my school that

13:37

is actually helping kids go on to enroll in school

13:40

and be successful in college and how do I do

13:42

more of that? How do I do

13:44

less of what's not working? Or are there

13:46

things that are working for some

13:48

students that aren't working for others and how do

13:51

I do that? And maybe nothing's working in my

13:53

school but now I want to know who

13:55

are the schools like mine in my state? This

13:57

is where that comparable data at this point

13:59

is state level comes

14:01

into play because if I call

14:05

the district next door to say how you

14:09

doing for your kids and it turns out well

14:14

I can change my denominator but it's

14:20

changing a number that you report. So

14:23

there are critical systems to

14:26

help folks whether you're a

14:28

governor or a legislator that's trying to allocate resources

14:30

and scale up programs, your parents

14:32

trying to understand your own child's trajectory or you

14:35

were a school principal or a district

14:37

superintendent trying

14:39

to understand what works in for home. I think

14:42

that's a great overview and the

14:44

data quality campaign you really did

14:46

set the pace, the standard of

14:48

these systems that was later codified

14:51

and did the federal law and has since

14:53

been kind of the guiding light for these systems

14:55

and you know that was several

14:58

years ago. There's been a lot

15:00

of evolution, there has

15:02

been the proliferation of AI and machine

15:05

learning and these

15:07

systems when done right and

15:09

when data is standardized talk

15:12

a lot about standardization there

15:14

holds a lot of potential and I would venture

15:17

to guess a lot of people who maybe

15:19

are outside of education don't realize that

15:22

these systems exist and

15:24

they do play a critical role in

15:27

decision-making as an example

15:29

you know is pivotal in

15:31

the COVID response and looking at

15:33

learning loss and trying to understand

15:35

you know what was the anticipated

15:37

gains or outcomes if we were

15:39

to have had our assessments

15:42

and compare that to where students are

15:44

showing up to try to quantify how

15:46

many years of academic

15:48

loss has been

15:50

endured. I

15:53

mean the list goes on and on, early warning

15:55

indicators to help predict students at risk of dropping

15:57

out all of these things are phenomenal. And

16:00

I'm curious, Paige, your take on over

16:03

the last two decades of

16:06

these systems being around, where

16:09

are we as a country

16:12

or as a majority of states? And

16:14

where do we still need to go in order to

16:17

really tap into the full

16:19

potential of these launch data

16:21

systems that are just amassing huge

16:24

amounts of data? And not just from

16:26

the education center, we see

16:28

them pull in workforce data,

16:30

mentioned employment and wage data.

16:32

We see early services, early

16:34

care and service data being pulled

16:37

in, social services, health data even.

16:40

Yeah, where are we now after investing in these

16:43

systems for as long as we have been? Yeah,

16:47

I mean, so we've made a ton

16:49

of progress. In

16:52

the beginning, these systems were largely funded

16:54

to be K-12 system. The

16:56

bulk of the work happened in K-12. That's the bulk

16:58

of the use of the data, the value of the

17:00

data. It's the bulk of the money going in, bulk

17:03

of the money that the state spends.

17:06

And so the question that states

17:09

began asking of these systems started

17:12

to make it clear that they

17:14

wanted to know more than just what was

17:16

happening inside of K-12. They wanted to understand

17:19

who's coming into our schools, what's happening in

17:21

birth to five, pre-K in

17:23

particular. And then they wanted to know,

17:25

OK, well, are kids are graduating

17:27

high school or not, where they're taking a

17:29

GED or what have you, what's

17:31

happening to them after? Do they

17:33

enroll in college? And if they do, is it a

17:36

two-year or four-year? Is it in state? Is it out

17:38

of state? Did they get financial aid? Are

17:40

they in debt? They want

17:42

to know them. Or did they go get a job?

17:46

Are they in an apprenticeship, which is

17:48

something that's a new conversation for us

17:50

all? Or do they enlist in the

17:52

military? There are a lot of successful

17:54

outcomes for young people. Even if you

17:56

don't graduate high school, there are successful

17:58

outcomes. And what have you done? having

18:00

that data be linked

18:03

up across time, across systems

18:05

and sectors, so pulling in

18:07

that early learning data, pulling

18:09

in that post-secondary, the

18:11

credentials including non-degree credentials

18:14

and certificate, apprenticeship,

18:16

military enlistment, UI

18:18

wage data, other kinds of workforce

18:21

data, that really starts to target

18:24

what is happening, what are

18:26

we doing, what are we spending, what are our programs

18:28

and what is it leading to? Because

18:30

if we keep focus on, did you enroll in

18:33

a four-year college and that's the definition of success,

18:35

first of all, we're never gonna win that. And

18:38

second of all, there are other

18:40

definitions of success. My dad enrolled in the

18:43

military, it's how we got here today. There

18:47

are many ways to get there and be

18:49

successful and we need to understand them

18:52

all and make sure that young

18:54

people are being guided correctly. If you don't

18:56

wanna go to college for whatever

18:58

reason, you can't and I know

19:00

this was the narrative when I was in high

19:03

school, you could go to college or you could

19:05

go up the street where the

19:07

mall was and apply at one of the

19:09

stores. Those were the choices I

19:11

was given. It was a no-brainer, I

19:13

did not wanna go work at the mall and

19:15

that was literally the biggest reason why I went to college

19:17

and I wish I were joking but I'm not. And

19:21

it worked out for me. But I

19:23

think there's gotta be better advice and

19:26

better information available to young people, especially

19:28

with the costs that we're looking at.

19:30

And can you calculate a really good

19:33

ROI for yourself, for your

19:35

family, is it you

19:37

or for your, is it, can

19:40

you take on the debt? What do

19:42

those jobs lead to? Do those colleges

19:44

actually getting jobs? So they

19:46

have become critical information

19:48

sources for that information.

19:51

You mentioned like, well, where do we have to

19:53

go? Our biggest problem, if

19:55

nobody has access. So Steve did

19:57

a great job of building the system. technology

20:00

is not the hard part. These

20:02

are not IT projects. These

20:05

are tools and systems that should

20:07

deliver information to people to make

20:09

better decisions. And that's the

20:11

big disconnect we have is you've

20:14

got to build it, but then you

20:16

have to build out access. Access

20:18

isn't magic. It's not organic.

20:20

You don't just turn a knob and

20:22

it slows out. You have to build

20:24

it into something so

20:26

that people know that it's there. They know

20:28

what it means. They know how to get

20:30

it. They know how to use it. Dashboards

20:34

are great. We could look at

20:36

KNY stacks in Kentucky. It's a

20:38

fabulous dashboard of very rich indicators

20:40

from early learning all the way

20:42

through workforce. But having something that

20:44

I as a parent or a teacher

20:46

could log into and pull data from

20:49

that system and helps me understand where

20:51

do kids from my kids high school

20:53

go? What kind of

20:55

salary could my child who's now applying

20:57

to the University of Kentucky Nursing Program,

20:59

what kinds of jobs do they go

21:02

get? And the best part

21:05

is if you really build it out, my

21:07

child is currently a freshman in

21:09

high school. Are they on track to

21:12

do that? What is the rest of their

21:14

high school course selection need to be to

21:16

be competitive to go to that college to

21:19

get that degree, to get that job and

21:21

earn that weight? And that's

21:23

where the hard work is left to do. And

21:26

it takes money. But

21:28

what it takes is the recognition upon

21:30

state leadership that everybody deserves

21:33

access to that information, that the

21:35

data doesn't belong to the state

21:37

or any particular agency and belongs

21:39

to all of us and

21:41

that it's time to get it out of the system in

21:43

the door. You made several great points.

21:45

I'll go back to the first one and

21:47

I often quib that states

21:50

have taken this notion of build it and

21:52

they will come. We will build

21:54

these huge infrastructures, these large amounts of data

21:56

and people will just come and use it

21:58

and it'll be great. That

22:01

didn't happen to your point about

22:04

accessibility. And so we have seen this

22:06

trend of serving data back

22:08

to schools, administrators,

22:10

whether it be publicly accessible

22:12

dashboards or even authenticated dashboards

22:15

to zero level data. And

22:17

for students to even see their own data

22:19

and to be able to manipulate and have

22:22

agency over their own data. You know, this

22:24

idea of kind of a golden record or

22:26

a student data backpack, right? People

22:29

by the students themselves. We'll

22:31

be sure to put a link in the in

22:34

the episode to KY stats and then also to

22:36

the Indiana graduates

22:38

prepare to succeed dashboard that resulted

22:40

built and looks at 17 data indicators

22:43

from pre-K all

22:45

the way through graduation and beyond. And

22:47

what that dashboard allows users to do

22:49

is look at the district and the

22:51

school level and say, of children

22:55

who graduated from this high school, where

22:58

are they now? What is their average

23:00

salary from this school? And so that

23:02

you're right is what we also

23:04

see as the next evolution of

23:06

these systems. And privacy

23:09

protected, right? We're not looking at her

23:11

graduated from this school and is

23:14

making this money. It is all

23:16

anonymous and de-identified. Absolutely. And

23:18

jump helps us all plan. That's right.

23:20

And we can look at it by student population.

23:22

So look at things like equity and

23:25

understand how are we serving student populations

23:28

to a point you made earlier,

23:30

where are there bright spots where

23:32

we're certain particular populations exceptionally well?

23:35

And how can we go learn from that if

23:37

there's a school system similar in demographic

23:39

and the geography and all of those

23:42

things that might have some

23:44

learning that they can do from the

23:46

school that's doing it exceptionally well. And

23:49

so I've often I think every episode now this

23:51

mini series, I've said this, so the list is

23:53

probably getting tired of it, but the power of

23:56

the data is not in the data itself or

23:58

in the dashboard. It's in the conversation. that

24:00

happens around it. And

24:03

you mentioned governance. And governance can be a

24:05

term that can really turn people off or

24:07

make people falsely. But it's

24:09

such a critical component. And I know

24:12

that the data quality campaign advocates for

24:15

data governance to be

24:17

articulated and well-defined of

24:20

where does this infrastructure live and how

24:22

do people access to this data. I'm

24:25

curious, could you share some bright spots

24:28

either states or some initiatives that are

24:30

doing this particularly well, ones

24:33

that we could learn from and

24:35

take some examples from? Every state has work

24:37

to do, first of all. No

24:40

state has truly realized the

24:43

potential of these incredible

24:45

assets in their state. But

24:47

we see a lot of bright

24:50

spots. I mentioned Kentucky. You mentioned

24:52

Indiana. These are two leaders of

24:54

getting the data in, getting it

24:56

to quality and putting out

24:59

information and tools in easily

25:01

accessible ways and

25:03

making sure that researchers and policymakers can

25:06

access information so that they can make

25:08

decisions better too. We're starting

25:10

to see the advent of

25:12

some tools that get more at what

25:15

can I as an individual do for myself.

25:18

Rather than just look at this aggregate data, how

25:20

can I look at my data or my child's

25:22

data and do something differently.

25:26

Idaho, for example, has, because

25:30

they've linked up their K-12 and their

25:32

post-secondary data, they have this direct admission.

25:35

I'm not sure if that's what it's called. Flipped admissions,

25:37

I think, is that I've read

25:40

a flipped admissions process where schools

25:42

are applying to students instead of students

25:44

applying to school. Because the state

25:47

has student information. And

25:49

they know, in front of would be IRS, when they're like,

25:51

you have to do your taxes, and you're like, don't you

25:53

already know what I are? It's

25:57

sort of the same way. The state already knows. Hey,

25:59

I took this. calculus and I got A's,

26:01

I took these AP classes and you have

26:03

my test scores and you paid for me

26:05

to take the SAT, you have that. Why

26:07

don't you tell me where I

26:09

can go to college and how much aid I might be able

26:12

to get? You have all this information and

26:14

so I know how I've done that. And

26:16

it's been amazing. I mean, I'm not a gen, wherever

26:19

you are in the system, getting a

26:21

letter from your flagship university

26:24

saying, Congrats you and you're 17 years

26:26

old. Congratulations. So

26:28

based on your GPA and your

26:30

test scores and such, you've been

26:32

admitted. So all of these tips,

26:35

to and for, are play for financial aid.

26:37

That's huge. That would have been huge for

26:39

me as a high school student to

26:41

know that I can do this. And

26:45

it streamlines it. You may be a kid that

26:47

was already going to do that but if anybody's

26:49

ever met a 17-year-old like streamlining

26:51

that process, really valuable. Yes.

26:54

And just less work for parents, less deadlines

26:56

to keep track, fewer deadlines to keep track

26:58

of. So that's a

27:00

great example of how do you use

27:02

all this information? Imagine doing research and

27:05

no indicators, but like it works for

27:07

people. We've got

27:09

examples in California. They're

27:12

just getting going, building their system, which

27:15

sometimes it's easier to build from the ground

27:18

up than to try to retrofit an existing

27:20

legacy system, as I'm sure you will know.

27:23

And in California, they were very intentional and thoughtful

27:25

on the front end as they are passing a

27:27

law to say, we're going to build a system,

27:30

here's who it's going to serve. All

27:32

of our data systems were

27:34

mostly originally designed to serve

27:36

policymakers and researchers. California

27:39

said, okay, we can do that

27:41

and we can build

27:43

out from the get-go intentionally with

27:46

parents and guidance counselors and community

27:49

college presidents and students in mind.

27:52

And so one of the things they're doing

27:54

is scaling up an existing tool in the

27:57

state called the California College Guidance Initiative, CCGRA.

28:00

enable every

28:02

student regardless of the

28:06

school. And then they have to get to the school.

28:08

So that's what this track is enrolled

28:10

in. To participate in a data-driven initiative

28:12

that they and their parents and their

28:14

high school guidance counselors can help them

28:17

chart their high school journey to

28:19

align with the post-secondary journey that

28:21

they would like that then

28:23

enables. Because too many kids are getting to their

28:25

junior and senior year and finding out, oh, you're

28:28

not on track to take calculus, you're senior. You'll

28:30

never get into the university in such and such.

28:33

I know, like, well, I needed to know that in

28:35

the eighth grade or the seventh grade. I don't need

28:37

to know that now. Like, now it's too late. So

28:40

let's not wait till it's too late. We

28:42

have the data to tell principals

28:45

and guidance counselors and moms and dads and kids

28:47

much earlier than senior year. And

28:50

that's what that initiative does. And then

28:52

streamlines the process to apply and also

28:54

delivers better information earlier, particularly

28:57

to community colleges in the California

28:59

State System and California around

29:01

enrollment projections. Because you have to

29:03

talk to any college president, they

29:06

want to know as soon as possible how many kids

29:09

are going to enroll in the fall. You've got to

29:11

plan housing, financial aid, courses. They've

29:14

got to have faculty. Like, they've got a lot of planning

29:16

to do. So finding out in,

29:18

you know, May, June, July, what August

29:20

looks like is not super helpful. Right.

29:23

And even helping close the gap to the

29:25

transition into the workforce. And so employers can

29:27

be able to say to school districts, these

29:30

are the type of skills or credentials that we're looking

29:32

for. And then the students

29:34

can all align to that to be able

29:36

to chart their course. And they

29:38

very well may change their mind. I know I sure

29:40

did. And the ability to

29:42

say, okay, now that I might want to become

29:44

a nurse, what now do I need to do

29:47

to prepare with the time I

29:49

have remaining in high school? And what are my

29:51

options? And what could the financial aid look like?

29:53

And that goes back to that student agency idea. And,

29:55

you know, when I taught, we would put all

29:58

the students' data in a binder. and

30:00

you know, print it off and three-hole punch

30:02

it, put it into a binder. But now

30:04

we're talking about being able to log in,

30:06

see that, manipulate it, project and

30:08

have this personalized learn, which is super,

30:11

super exciting. And, you know,

30:14

I don't know about everyone else, but I'm energized through

30:17

the conversation, the bright spots that are

30:19

happening across the country and see the

30:22

progress and see the road ahead, and

30:25

you mentioned California. That's a

30:27

great example, and we have

30:29

also seen this pendulum swing

30:31

of these systems for researchers

30:33

and policymakers towards the assistance

30:35

for citizens and other stakeholders

30:37

and really understanding the value

30:40

that they want to get from these systems

30:42

and making human-centered offerings to

30:44

make these systems more accessible.

30:48

I could probably spend hours

30:50

talking with you, Paige, but

30:53

before we end our conversation, I

30:55

would love your take on what

30:58

is data-driven leadership to

31:00

you? And

31:03

if you would provide your insights, I

31:05

know we would appreciate your perspective on

31:07

that. Yeah,

31:10

I think first and foremost,

31:12

it's about taking risks and

31:14

having courage. The

31:16

data doesn't always tell you what you want it

31:18

to tell you, and it doesn't always tell you

31:20

you're doing a good job or the right thing.

31:22

And so having the courage to dig in, honestly.

31:25

Thank you. And that's regardless of where you are. It doesn't

31:27

matter if you're a governor or a district

31:30

superintendent or somebody running a

31:33

household. If you can honestly

31:35

take a look at what the evidence is

31:37

showing you as a result of your hard

31:39

work, if you like

31:42

what you see, keep going. If you

31:44

don't like what you see, dig in

31:46

and bring people together to your

31:48

point that you made earlier, data

31:50

is the beginning of conversation. In

31:53

and of itself, it's not a decision. It's

31:56

information that should cause you to

31:58

ask deeper questions. question,

32:00

it should help you pivot

32:02

a working.

32:30

I added 20 AP classes to my high school. We're

32:49

still not seeing kids go to college. There's

32:54

something else. It doesn't mean get rid of AP classes,

32:56

but it's not having the intended benefit. It's

32:59

not going to be deeper. Do you have an attendance

33:01

problem? What

33:04

is going on in your school and have the courage to look at

33:06

that? I

33:09

think at the end of the day, that's all it really comes down

33:11

to is courage and risk-taking. And

33:14

the leaders that are under you, are you supporting

33:16

them in their risk-taking? Or

33:19

are you sort of communicating intentionally

33:21

or unintentionally, don't

33:25

take a risk because it's going to

33:28

look worse. And

33:31

that really matters. They need to feel the support

33:33

to take the risk out of what that data

33:35

is telling them. And using their professional judgment, right?

33:40

These are people that they're trained, they know

33:42

what they're doing, give

33:44

them information, let them poke

33:46

holes, give them a space to iterate a bit. Yeah,

33:49

teaching is an art and a science. And

33:52

we have to have both that data and

33:54

that experience to be able to make these

33:56

data-driven decisions. And I really

33:58

appreciate your commentary on that. And unfortunately,

34:00

in our history as

34:03

a profession, data has been used as

34:05

a gotcha or as a, you know,

34:07

what force instrument and being

34:10

able to use it as an

34:12

improvement tool and in that more

34:14

improvement science does take courage. Absolutely

34:16

right. Paige, thank you

34:18

so much for your time and for the

34:20

work that you do to advocate and to

34:23

just share knowledge around these systems and the

34:25

potential of data. Really do appreciate it. Thanks,

34:29

Kurt. And thanks for being a great partner. Pleasure.

34:32

Thank you for joining us

34:34

today on this episode of the Education

34:36

Mini-Series. I'm Dr. Kurt, your host. If

34:38

you're interested in learning more about the

34:40

Data Quality Campaign, you can visit dataqualitycampaign.org.

34:43

As we close out, I leave you

34:45

with a question. How

34:47

can you bring together people, policy, and

34:49

technology in a way that will drive

34:51

change? For more from me,

34:53

you can connect with me on LinkedIn at the link

34:55

in the show notes. And for

34:57

more from Resultant, sign up for

35:00

our education practice newsletter at

35:03

resultant.com/education.