0:02

Why is data so often use

0:04

as a gotcha? Especially in education?

0:06

As a former teacher administrator and

0:08

now consultant, it's a question that

0:10

I ask myself all the time,

0:12

which is why we're here. I'm

0:14

doctor current and this is my

0:17

take over of data driven leadership.

0:20

In this for episode mini series I'll

0:22

be joined by several industry expert who

0:24

have made it their mission to hunt,

0:26

seek and destroy the systemic barrier. Still

0:29

learning through I T and data will

0:31

share how I T. And data can

0:33

not only me unmet needs, but can

0:35

actually accelerate opportunities when done the right

0:38

way. In my role, I work with

0:40

many state education leaders across the country,

0:42

which in turn exposed me to a

0:45

wide variety of new and exciting strategies.

0:48

I. Look forward to bringing you these amazing

0:51

leaders to share those strategies with you.

0:53

Let's bring people, policy, and technology together

0:55

so that data can be our greatest

0:57

ally. But dive. In.

1:02

Today's episode you will have the pleasure to

1:04

hear from Doctor David Ramadan. Doctor.

1:06

Ramadan. As a seasoned executive

1:08

and international consultant with there were

1:11

twenty five years of advocacy, business

1:13

and government relations experience. David.

1:16

Is served two terms and the Virginia House

1:18

of Delegates. He. Is also an

1:20

alumnus of George Mason University, where he

1:23

is now a professor of practice and

1:25

on faculty. And. From Twenty ten

1:27

to Twenty twelve, he was a member

1:29

of the George Mason University for hims

1:31

visitors appointed by the Governor of Virginia.

1:35

David. Immigrated from the United States from

1:37

Lebanon in Nineteen Eighty Nine. After it's

1:39

and in the International College in Beirut.

1:41

He graduated from Mason with a bachelor's

1:43

degree and government and politics and a

1:46

master's degree in International trade and transactions.

1:48

I. Had the pleasure of meeting

1:50

David through my coursework at Vanderbilt

1:53

University. And. I actually defended my

1:55

thesis in front of David where he was

1:57

the first to call me doctor and so

1:59

I. The Social connection with David. Really?

2:02

Enjoyed a relationship and getting snow him

2:04

and his work in the space. He.

2:06

Brings an interesting perspective in the

2:08

conversation. Is. Experience of

2:10

the policymaker bid is training

2:13

as an educator and. Time

2:15

spent as a consultant. Really?

2:18

Gives a unique perspective into how people

2:20

process and technology interact with policy. I

2:23

hope you enjoyed today's conversation. Thank.

2:25

You for joining. A

2:30

Low and welcome to this episode

2:32

of the Data Driven Leadership The

2:34

Education mini series I'm Your Host

2:37

Doctor Kirk And today I am

2:39

honored to have Doctor David Ramadan.

2:41

Who. Is a former house a delegate

2:43

member and state of Virginia. Professor.

2:46

Scholar and I should also say the

2:48

very first person to call me doctor

2:50

as I was going through my program

2:53

at Vanderbilt and defending my Capstone project

2:55

and ah for that reason and many,

2:57

many others As you will all find

3:00

out shortly, I'm excited to have to

3:02

David with us! Welcome Thank you for

3:04

joining us today. And. Que doctor

3:06

com or love good to see and gang

3:08

them that it it's still it's still sounds

3:11

good as a didn't that it does I'm

3:13

the first time few months ago. It.

3:15

Does It does. never gets old and I

3:18

just really appreciate you and your background and

3:20

and how you got to be are you

3:22

are. I hope to get into some of

3:24

that today because I think it's just so.

3:28

Amazing to to hear how you got to be

3:30

where you are today and so honored for you

3:32

to share little bit of that with us. Ah,

3:35

Here on this episode. We.

3:38

Are the Data Driven Leadership

3:40

Podcast and so with that

3:42

we take a focus around

3:44

Data I T A and

3:46

you bring a unique perspective

3:48

to the conversation. Having lived

3:50

in the policy making world.

3:52

Ah, But also as a consultant in the

3:54

eighties face as well and so I expect

3:57

to learn a lot from us. I'm sure

3:59

our list. Nurse or as well. So.

4:02

We're. Gonna jump right in. And.

4:05

I'm curious. If you could

4:07

share with us your thoughts on. The

4:10

topic of data driven leadership. And

4:12

specifically from your perspective given your

4:15

history and your background. How.

4:18

Can that be applied in

4:20

shaping education policies? To meet

4:23

the needs of our students. It

4:25

Sam, it's an important subject. And.

4:27

A subject that. Despite

4:30

every when he recognizing how

4:32

important detail is in in.

4:35

To Education Today. We.

4:38

Still, unfortunately are not

4:40

utilizing data. As much

4:42

as we should. Specially. And

4:44

policy making. An. Online.

4:48

Education as you and I

4:51

or proponents off has not

4:53

caught out. As

4:57

much as we wanted to

4:59

do so, specially in traditional

5:01

universities, And part of

5:03

the reason. Is

5:06

an. I think. Turf

5:08

protection. Is

5:11

these Universities. As. Our

5:13

own mom, Honor had figured out. Really

5:17

use data. And

5:19

really sink in. really utilize

5:22

the data they collect. On

5:25

the need for exile education, the need

5:27

for hybrid education, the need for remote

5:29

an online education and those can be

5:31

synonyms or they could differ quite a

5:34

bit depending on. On the

5:36

context of the conversation, They.

5:38

Would realize that All

5:40

Forward Education. Should be

5:43

data driven. most of it should

5:45

be online or or some sort

5:47

of and and child delivery. And

5:51

should be the base for

5:53

all education policies. But.

5:56

That is not the case. Today's.

5:58

Education policies are continue. The

6:00

Asian. Aws. Way.

6:02

Of deciding where we gonna go. Turf

6:06

protection. Building. More

6:08

buildings and brick and

6:10

mortar. Protecting.

6:13

That brick and mortar. And.

6:15

Feeling good about having students around

6:18

us whether we are serving them

6:20

correctly or not? He.

6:24

Is we collected enough data?

6:26

And then if we analyze that

6:28

data, We. Would be

6:30

able to. Make.

6:33

Sure that we perform better

6:35

for students in education feel.

6:38

We. Would have better see

6:41

back for educators operationalize.

6:44

Our. Services to be

6:46

more efficient and get

6:48

better uses of dollars.

6:51

That we are that that we are

6:53

so short of all the time and

6:55

education in therefore we would allocate resources.

6:58

More. Effective. Now.

7:01

I'm generalizing them same in general.

7:03

Were not being. As

7:06

good stewards them as good of

7:08

users of. Data. Driven

7:10

Leadership and in education policies.

7:12

News there of course there

7:15

are of course. Outlier

7:17

service in there are some shining stars

7:19

and and in this field. But

7:22

in general I think we still have

7:24

quite a bit of. What?

7:27

It would work to do, specially

7:29

in the policy decision making when

7:31

it comes to education. Who.

7:35

Added appreciate that perspective it it's san

7:37

What you're describing is. A

7:40

that oh yeah I seen a to of

7:42

people going to as protection mode will be

7:44

reservation or maybe in the presence of data

7:46

and and maybe it's out of fear. Of

7:49

what it might show or say or

7:51

am the myriad of things I think

7:53

what you're touching on as that. The.

7:57

Mindset around Data A

7:59

Li. There. Would.

8:02

Be well advised to

8:04

really self reflect on.

8:07

How. They think about and embrace data would you?

8:09

Would you agree? Hundred percent.

8:12

On. A project I'm actually I'm recording

8:14

this podcast with you. While.

8:16

I'm on the road. I'm down in Richmond,

8:18

Virginia. Helping gum.

8:21

An advocate for a couple

8:23

projects in Virginia. They're

8:27

not education but I'm going to use

8:29

that data the medication related but I'm

8:31

going to use a that aspect of

8:34

that com and as an example. An

8:36

ambien anonymised windows were those

8:39

projects are One project in

8:41

particular is a yam. Economic

8:44

development projects that could bring

8:46

that the vast thousand jobs

8:48

to the Commonwealth. How

8:51

the numbers are incredible. A

8:53

date as incredible did that.

8:56

The projections are incredible. Project.

8:59

Is probably dead. It has five

9:01

percent at this point chance of

9:04

surviving the General Assembly this year.

9:07

Not because we didn't a ham

9:10

because it a dozen their the

9:12

data their. It's because

9:14

the data was in utilize and the

9:16

decision making is happening with some political

9:18

bases not based on not data driven.

9:21

Now. We see that medication as well.

9:24

I if I send the hallways today

9:26

of have any. General

9:29

Assembly Any legislature in the nation,

9:32

You'll. See the. Good.

9:34

Hard working. Representatives.

9:37

of traditional university said are roaming

9:39

those always. Pushing. For.

9:42

The term pushing for protecting

9:44

when they currently have. Insects

9:47

coming around same lock The data

9:49

shows. Ah, The

9:52

opposite of always told you for years

9:54

but that's fine linen little utilize that

9:56

and and and use I see in

9:58

the Usa. I. Instead

10:00

on Stifle. The.

10:03

Them Innovation. Oh I'm

10:05

you don't see those discussions happening and I

10:07

think it's some people like you mean that

10:09

were so excited to be on your show

10:11

today. Ah, By your

10:13

Podcast To date because we still

10:15

have a lot of work in

10:18

order to get innovation in data

10:20

particular driven discussions happening even at

10:22

our research universities that are supposed

10:25

to be mostly about data. Yeah.

10:29

And. That's what a I would love read. It

10:31

may be shared little bit about your background and

10:33

in. The. What brought you to the

10:35

that perspectives and the belief that you have because.

10:38

As. A researcher. As a scholar you

10:40

are looking at and holding data in

10:43

one way, but you've also been as

10:45

you mentioned, the House of Delegates within

10:47

the Commonwealth see seen the political side

10:49

of it to and. What

10:51

you described as as his political as

10:54

they should have data as sometimes people

10:56

might describe it as though weaponization of

10:58

data or says the stifling of the

11:01

data. Yeah. How we

11:03

get there have we have a lot

11:05

of work? How do we get there

11:07

is your app. So I ran for

11:09

office and twenty eleven and and had

11:11

the honor and privilege of becoming the

11:13

first and don't immigrant and Jefferson's house.

11:16

Doesn't. In the long while longest

11:18

running legislature in the Western Hemisphere.

11:21

And dub ya little old me, an

11:23

immigrant from from Lebanon. First generation immigrant

11:25

came the seventy two thousand dollars in

11:28

my pockets in a dream. Ended

11:30

up serving in the same houses: Jefferson

11:32

Madison and Patterns and Amazing. So

11:35

it is. It was truly an honor to

11:37

serve. I surfer to germs and then went

11:40

back to the private sector. Now remain very

11:42

involved in Virginia and and politics overall. I'm.

11:47

There's. Never been a shortage of

11:49

data were gathering in different you

11:51

Today we're analyzing and differently. Date

11:53

has always existed and policymaking back

11:56

from the. Sounders lay

11:58

some till today. The. Problem

12:00

has been continuously is.

12:03

An arm the mistrust and with

12:05

data. That brought us to

12:07

when would you call thought sensation of

12:10

data which which is now it's And

12:12

it's because we don't trust it. Keep

12:15

in mind that politicians specially

12:17

and and modern era politics. Were.

12:19

Elected officials Iowa com of politicians whose or

12:21

lot of truly says the legislature's all over

12:24

the nation that under their to do a

12:26

desert work. But

12:28

elected officials? Before they get

12:31

into a policy making position,

12:33

they go through campaigns. Campaigns

12:36

that or a dependent on

12:38

polls. Polls. That

12:40

are pulling size polls

12:43

Lead or am. Is.

12:45

Due to one way or another. And.

12:48

And we got to a point where

12:50

the polls are unbelievable. Unbuttoned

12:52

none believable rights and and been

12:54

sailing again then again and again

12:57

year after year. And

12:59

brings this decision makers to a

13:01

point where they don't trust the

13:03

data. So

13:05

our role. As

13:07

researchers and as people that come

13:10

from the academic a world. Is

13:12

to make sure to differentiate between

13:15

political data. And. Real

13:17

days of it is needed in order

13:19

for them to do or to take

13:21

the correct of decisions are to make

13:23

the correct decision. As

13:26

a week that was in

13:29

New. Eat the.

13:31

New tools that are out there. Whether

13:33

we're talking about a i will were

13:35

talking about of quantum computers that that

13:38

that are do an incredible level us

13:40

of calculations. Ought. To

13:42

be used to verify data.

13:45

Ought. To be used in a way

13:47

that gives. Tourist in

13:50

data. From. The.

13:52

Collection of data as easy senses.

13:54

Been doing it since mankind. It's

13:57

how do it to, how do we utilize

13:59

it harder? The plane at how do we

14:01

bring it to a point? Where. You

14:03

can make decisions and nowadays be

14:05

agile. Those decisions put it in

14:08

a no por. You're moving quickly

14:10

in reaffirming just decisions for changing

14:12

those decisions based on the changing

14:14

world. You

14:18

know it that there's a semi different

14:20

directions week ago and this conversation share

14:22

those are listening or are thinking of

14:24

just at the thousand and that need.

14:27

some of which are going through my mind to. You.

14:29

Mentioned trust and that is such

14:31

a critical element And. I'm

14:33

an interesting topic given that the state of

14:36

where we are today and and the how

14:38

a lot of people do feel a around

14:40

his politics in general whether be left or

14:42

a be right. On the

14:44

big mentioned: trust and trust in

14:46

the source and trust in the

14:48

findings. Ah, That.

14:52

Brings with it a level of vulnerability

14:54

Transparency. right? Where

14:57

have you seen that done particularly

14:59

well? Or were have there been

15:01

promising glimmers? were us seen. Trust.

15:05

Be extended or are generated between

15:08

the citizenry. And.

15:10

Maybe. A state agency or or just

15:12

the government. A large. It's

15:17

at Logan and it in

15:19

a detention here. Who am

15:21

I think Zero is. Trust

15:23

him. Just

15:26

performances of students. Trust

15:29

in enrollment numbers per

15:31

se. Completion. Numbers

15:34

match of collation numbers. That.

15:36

The you can three hundred of nowadays,

15:38

right? Nor can you play with them.

15:43

And and I think Zandt him

15:45

brought us to the realization of

15:48

how many. The are

15:50

missing. the boat on on. Graduating.

15:53

From schools. Within I am I

15:55

think I looked at. It

15:57

though there are numbers in the

15:59

millions. across the

16:01

nation of individuals that have

16:03

some college but no college

16:05

degrees. Those

16:09

are ranging between 20 to 25

16:11

percent of the population that

16:15

has some college but no college

16:17

degrees and that has been a

16:19

number that it varies

16:21

from one area to another, it varies if

16:23

there is a big military base or not,

16:27

it varies if we're counting community

16:29

college as part of some college

16:31

but no degree. However,

16:33

overall if we mix all that together

16:35

we get to a point where like

16:37

okay we have a problem, we've

16:40

discovered that problem because of the data that's out

16:42

there and the data says that we've got about

16:44

20% of our population that got some college and

16:46

never got a college degree. The

16:49

data has not given us is where

16:53

and what are these people doing

16:55

nowadays and

16:58

I think one of

17:00

the biggest in

17:02

college education, talking undergraduate college

17:05

education here, one of the

17:07

biggest gaps is trying

17:09

to figure out all right we know that is

17:11

the number but what

17:13

age groups are they, where are they

17:15

today, what are they doing today, do

17:18

they still need that education or not,

17:21

are they all potential

17:28

clients, I'm going to use the commercial

17:31

word clients because I see education today

17:33

as a client-based

17:35

right. Students should

17:38

be clients of universities and should

17:40

be lifelong clients, lifelong learners not

17:42

just for years. So

17:44

are these some college no

17:47

degree folks

17:50

potential clients or they

17:53

did they become somebody else's clients,

17:55

they become somebody else's users of

17:57

technology, are

17:59

they not in need of further

18:01

education because of data

18:08

that's missing there. We

18:11

started with good Senate data and

18:13

data-driven decision-making for a

18:16

lot of money that was spent on

18:18

this problem, by the way, with yet

18:20

very little success. Yeah.

18:25

Yeah. We collect mounds

18:27

of data, right? Mounds

18:30

and mounds of data. And we've talked a

18:32

great length about state longitudinal data

18:35

systems and how states

18:37

and the federal government have

18:39

spent in amassing this large

18:41

amounts of data. And

18:44

what's the return, right? What is the

18:46

change in practice or the improvement

18:49

in outcomes? And not to say that there haven't

18:51

been as a result of these systems, but I

18:54

think to your point, it has seen

18:56

little in return. And

19:00

so what I

19:03

appreciate you mentioning in that example, whether you

19:05

kind of knowing it or not, you're touching

19:07

on this trust building piece and you laid

19:09

it out in a very pragmatic way, I

19:12

think, in that interpreting,

19:15

discussing, analyzing data takes

19:17

a psychological safety, right?

19:25

And to get there, we

19:27

know that trust is critical, trust building.

19:29

And so you mentioned a

19:31

couple of things. I jotted it down. You mentioned finding

19:34

common ground. Where are we

19:36

appreciative of one another and

19:39

our roles and responsibilities? How

19:41

do we demonstrate empathy for

19:44

where each other are at? And then finally, and this

19:46

is, I think, the point you are just making of

19:49

resolving issues with data really

19:52

says we can count on one another. And I think

19:55

that full exchange is needed to really

19:58

see The value. You

20:00

and nice in data systems are using

20:02

data driven leadership and a healthy productive

20:04

not the nicest way. An

20:06

unfortunate I think because there have been instances

20:09

where people have felt that. Maybe.

20:12

They've been dylan eyes or data has been used

20:14

against them. That that trust has eroded.

20:17

A sob. Yeah. What? What?

20:19

What? What you think of that is and I

20:21

totally off base or do you A D C

20:24

Some examples of that. You're.

20:26

Absolutely right arm he brings us

20:28

back to to a point that

20:30

have a are we talked about

20:32

which is. Which is

20:34

creating data driven cultures neck

20:36

concerns have and it multiple

20:39

sets off individuals month just

20:41

individuals or multiple types of

20:43

of players. Mom. Players

20:46

have horses, universities, The. Other

20:48

players are definitely the the

20:50

I T professionals. Mm

20:53

this third the third saw here

20:55

is gonna be the the decision

20:57

makers. the political decision makers when

20:59

their their policy or political or

21:01

or the could be the money

21:03

decision makers right? This could be

21:05

private sector I can be to

21:07

venture capitalists. In. In in

21:10

some scenarios. And.

21:12

When you put this together, they need

21:15

to. They need to work together. They

21:17

need to collaborate. They. Need

21:19

to have a clear set

21:21

of communication. And

21:24

then they're nice to be an arbitrator.

21:26

Dad couldn't. That guarantees.

21:29

Am too old players that

21:31

they're all playing in and

21:33

clean sandbox here yet there

21:36

are no no third rails

21:38

than are trying to ruin

21:40

with their do it. More

21:42

than men get a society where their

21:45

that by the at that point as

21:47

education whether that society is is there.

21:50

Are a Nuclear Physics or that

21:52

society? in his his. Supply

21:55

chain for food. Yeah,

21:58

We're we're We're dealing with. lot

22:00

of environmental issues. I'll tell

22:02

you what, we're dealing with

22:04

energy production issues that requires

22:07

all these sets of players as

22:09

well. And I think

22:12

education and the world

22:14

of education and academics

22:17

and academia overall has

22:19

quite a bit of a role to

22:21

play there because let's use environment as

22:23

an example. The role of

22:25

academia and environment has been unfortunately

22:27

over the last 20 years is to

22:30

tell everybody that we have an

22:32

environmental problem, right? Well,

22:34

we do have an environmental problem.

22:36

I think everybody at this point

22:39

had realized we have an environmental

22:41

problem. The question becomes, okay, now

22:43

what do we do about it? How

22:45

do we solve this? And the misconception

22:48

or the mistrust, going back to the

22:50

trust decision has been on,

22:52

okay, how do we use current

22:54

resources versus no resources

22:57

at all? Do we still

23:00

use any of

23:02

our national resources that exist today or

23:05

do we go totally off and don't

23:07

use any of the fossil fuels? And

23:10

there's been this mistrust in between

23:12

where the data became missing, right?

23:15

Because I don't trust your data. You don't

23:17

trust my data. You want me to rid

23:19

of all fossil fuel. I'm like,

23:21

no, no, you're a far

23:24

whatever left environmentalist. And

23:28

then the data becomes the victim

23:30

here versus the data being the

23:32

base of that communication and that

23:35

culture in between.

23:38

So the data here needs to

23:40

be really the common, not

23:42

just common ground, but the common

23:45

factor that we start with. This is

23:47

the base. These

23:49

numbers are facts. Facts

23:52

should not be questioned because

23:54

facts need to be facts,

23:57

not opinions, not skewed facts.

24:00

not the data

24:02

should be the fact of the problem. And

24:05

based on that data, now we can differ

24:08

on where and

24:10

how we want to do things.

24:12

We can differ on how long

24:14

this should last or not. But

24:16

we need to start with a culture

24:19

that accepts data as

24:21

facts. That's

24:23

that data-driven culture versus

24:26

a culture that says, no,

24:28

no, the data is an

24:30

aspect here of this discussion.

24:34

It should be the fact of the discussion. It's

24:36

going to be the base, the common

24:38

ground. We start here, from here we

24:40

go on. I love

24:42

that. And there's a big picture to

24:46

share with data. And it's what you're

24:48

describing, I was thinking to my mind,

24:51

we all run to our data points to

24:53

justify or defend our position on

24:55

a policy or on a particular initiative.

24:58

And it's almost like we talk past each

25:00

other because we are so either against that

25:03

data as being wrong or not accurate or

25:05

just can't believe it and our data is

25:07

right. I think what

25:09

you're describing is we're missing the opportunity to say, let's just

25:11

get curious about what this data is telling us. And

25:16

I love that because that's data as a strategic

25:18

asset. There's

25:20

a direct relationship between, you

25:23

can almost plot it on a horizontal

25:25

and vertical axis to say the level

25:27

of trust in data as an asset

25:30

and the level of maturity in people

25:32

process technology go

25:36

hand in hand to get to that

25:38

ideal state where you're optimizing your data.

25:40

And so we need data-driven culture for

25:43

our agencies, for our governments, our organizations,

25:45

and for our society, it sounds like.

25:48

Absolutely. And

25:50

you're showing off your true colors

25:53

there, Dr. Kurtz. You're using X

25:55

and Y axis in the discussion,

25:57

right? I was not good in

25:59

the quantitative. I'm a qual researcher all

26:01

day long, but that data science class,

26:03

not in the end, but boy, do

26:05

I have a greater appreciation for it.

26:07

Yeah. And, you

26:10

know, which leads me to say, you know, as I've

26:12

gotten more curious about data and learned more about it,

26:14

I can appreciate it more. And

26:16

sadly, we just don't take

26:18

enough time, I think, to really listen what

26:20

the data is telling us. And

26:23

I've often said, you know, data is kind of a

26:25

smoke signal to lead us to

26:27

where the fire is. And it's a piece,

26:29

it's an asset that we

26:32

need to look at and ask questions

26:34

and challenge it, yes, but not

26:36

just dismiss it outright and miss what it has to tell

26:39

us. And it sounds like you'd agree.

26:42

Yeah. Yeah. We

26:47

need to take the politics out of the

26:49

data. I always see

26:51

things because of my background and

26:53

involvement in the political world. And

26:56

I see how data is politicized all the

26:58

time, whether it is data

27:01

collected by cops on

27:04

the roads, whether it is census data,

27:06

whether it is the data of

27:09

purchases and cell

27:11

phone usages. And

27:14

I think the role of

27:16

policy overall here, not just education,

27:18

we're talking beyond education, the

27:21

role of policy here is

27:23

to make sure to anonymize the

27:26

data and make data

27:28

collection acceptable as that

27:30

of that data-driven culture

27:33

we talked about so

27:35

that we can use the data for

27:37

the betterment of society. If

27:41

we're going to keep being cynics

27:43

about the data and we're going to keep

27:45

question the real need and

27:48

usage of data collection in

27:51

policy is going to be pretty hard to use

27:53

that data. We're going to

27:55

continue to postpone the lack

27:58

of the scientific. analysis

28:01

of data for better policy

28:04

and because

28:08

we could be spending a couple

28:11

decades and hundreds of millions of dollars

28:14

on less efficient. I'm not saying

28:16

useless. I

28:18

mean I don't think we waste money on

28:21

anything that is academic because

28:23

you're always learning something but

28:25

with a world of finite

28:28

resources we

28:31

need to better use those

28:33

resources and I think we can do that

28:35

with better data or better use of data

28:37

that is available out there. And

28:40

identify those barriers to

28:43

learning or those inequities that have existed

28:45

and not be

28:48

an exercise in pointing fingers but

28:51

you mentioned a couple times where do the

28:53

resources need to go? Where does the emphasis

28:55

need to be? How do

28:57

we change this outcome? It's

29:00

really powerful to think about that. And so my

29:03

last question for you, go on

29:05

for hours, but my last question

29:07

for you, are there specific

29:09

areas within education policy

29:11

where you believe advancements

29:13

in technology such as

29:16

AI will have

29:18

significant impact and how should

29:20

policymakers prepare for these changes

29:23

in light of everything we just talked about? I

29:31

think online education

29:34

and personalized education

29:37

is where AI could make a

29:39

huge difference. We

29:42

don't learn the same

29:44

way and

29:46

even though there's always going to

29:48

be the need

29:51

and the advantage of

29:54

people like yourself and I to

29:56

sit down in a classroom anywhere

29:58

and to sit discuss and

30:00

talk and negotiate and debate

30:02

and think and plot and

30:04

challenge each other's thoughts.

30:09

The basic line of education that

30:11

happens, especially on an undergraduate level,

30:15

does not have to continue the same way we've

30:17

done it. We don't have to put 30 kids

30:19

in a classroom

30:21

in my mind. We

30:24

don't have to give them their education

30:26

in a classroom to start with.

30:30

All 30 kids do not need to

30:32

get the exact same message and same

30:34

lesson plan, even undergraduate. I

30:37

think we can personalize education. I

30:40

think we can use IT for better

30:42

and more secure delivery, especially

30:49

when we're personalizing education,

30:54

because we want to design it for them. We

30:56

want to deliver it in a way that makes

30:58

sense to them, and we want to make sure

31:00

to secure it so that

31:02

there's no question, is that personalized

31:05

education going to jeopardize either

31:07

my advancement or

31:09

my lacking behind

31:12

where everybody else is. I think technology

31:14

is going to be quite important to

31:16

personalize education and to

31:19

make sure that it's delivered where people need

31:23

it, when they need it,

31:25

whether that is on a degree level or whether they're

31:27

on lifelong learning. I think we

31:29

should have students that remain the clients

31:32

of our educational system

31:34

for life. My joke, we used to,

31:36

not joke, but I used to say that I'm not a student, but I'm

31:38

not a

31:42

student. My joke, we used to, not

31:44

joke, but I used the example of, it

31:47

wasn't too long ago when

31:50

we graduated kids from undergrad

31:52

in a degree in computer science or in IT. They

31:56

could code in C++ and they could for whatever reason,

31:58

but that's not the case. light.

32:01

I mean today you graduate

32:04

IT and into pure science kids that

32:07

can code in 20 languages and in

32:09

six months those 20 languages could be

32:11

integrated. Absolutely, yeah that's right. So

32:16

that part of personalized

32:18

lifelong learning is

32:20

quite important. The question is how are we

32:23

going to deliver it and

32:27

how we're going to track it with

32:30

data sets that makes us continuous

32:32

and agile so that we can

32:35

continue to change and

32:38

continue to make it

32:40

worthwhile and do significant

32:42

impact. I

32:46

couldn't agree with you more. I hear

32:48

a lot of states talking about

32:51

from a policy perspective the idea

32:53

of graduation pathways and

32:55

redesigning high school around these.

32:57

I think this perfectly illustrates

32:59

your point. They're

33:01

personalized. Well how do we

33:03

help the student? How do

33:05

we personalize or tailor, curate

33:07

that experience that keeps

33:09

their interests and

33:12

abilities in mind and also the

33:14

needs of this diverse ever-changing workforce

33:16

in mind and that

33:19

continuous, you're almost like

33:21

recalibrating as you're progressing

33:23

through a pathway. It might

33:25

take a little turn here or there but technology

33:27

and I agree with you absolutely can be a

33:30

force for supporting the delivery

33:32

and the monitoring the tracking of

33:34

these types of policies and there's

33:36

a great book. I'm still working

33:38

through it, Recoding

33:41

America and I love how Jen

33:44

describes this that and it kind

33:46

of paraphrased the quote but policy

33:49

often fails on the jagged

33:52

rocks of implementation. Yep

33:55

and I think that's so true especially in education.

33:57

I hate to say it we have these great

33:59

well-intended The Policies. And.

34:02

I'm. Unfortunately,

34:06

They're not either implemented with fidelity

34:08

or integrity or we we lack

34:10

the tools to support the delivery,

34:12

the monitoring, and they fail. Because.

34:15

What we celebrate and what we.

34:17

What? We prioritize as the policy making and then

34:19

we and then we almost I have seen as

34:22

I am his me time zones kind of. Regulate.

34:25

The implementation out and. These.

34:27

Policies don't get to live to their full

34:29

potential. And. I think what you

34:31

just is a road where a path where

34:33

we can use date and I t. To

34:36

help ensure our policies have the intended and

34:38

packed. And. They're serving people

34:41

well, and they're not

34:43

inadvertently creating wider inequities.

34:46

So. I love that pryke that do whole

34:48

other episode just on that topic. Salutes

34:50

And look, If that doesn't mean that

34:52

every policy gonna be good to or

34:54

it's gonna rain is implemented correctly, it's

34:57

gonna. it's gonna be. And the correct

34:59

policy. Of and that's okay so

35:01

can do steaks sodium in what ways that

35:03

to an evidence based. Research is

35:06

all about and continuously changing right?

35:09

It's okay to have policies that

35:11

don't pan out. His.

35:13

Arm: as long as we recognize that

35:15

they're not panning out and recognize it

35:17

quickly and be able to change quickly.

35:20

Vs. Waiting decades to make

35:22

the change. When he got generations

35:24

that are missing out. Because

35:26

we were not tracking where we

35:29

are. And. That one day then

35:31

data driven decision making and policy comes

35:33

in hand. And. To

35:35

not only continue in advance

35:38

the good policies, But.

35:40

Also to be able to

35:42

stop him recalibrate or or

35:44

refocus. So. that we can

35:46

move away from of. Bad.

35:48

Decisions were decisions that in

35:50

the time. Sounded. Good,

35:53

but ended up with unintended consequences.

35:55

That's right, that's right. Beautiful.

35:58

Point: I think that is a pet. That.

36:01

A perfect ending. Add to the conversation

36:03

analysts I can add much more beyond

36:05

that and. I. Just want to get

36:07

thank you for joining us Think you to our

36:09

listeners for joining us on this episode of the

36:12

Education mini series. I'm. Doctor Kurt

36:14

your hosts. Be sure to follow

36:16

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36:20

rate and review and share how

36:23

these discussions on education data in

36:25

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36:27

within your or. Stay

36:30

tuned for next episode where will continue

36:32

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