I'd like to extend this discussion about UDL and accommodations.  In the
summer (July 18-20), we are planning more training for current Bridges
trainers.  I've begun some discussions about the use of UDL and
accommodations and how we can bring this information together...and then
have an impact on the way the GED looks at this for testing.  I'd be
interested in hearing how states are doing this and would welcome any
suggestions about speakers and/or other resources for trainers.  Dr.
Christopher Lee has agreed to work with me on this but we are open to ideas.
June Justice Crawford
NIFL

-----Original Message-----
From: learningdisabilities-bounces at nifl.gov
[mailto:learningdisabilities-bounces at nifl.gov]On Behalf Of Michael Tate
Sent: Thursday, March 23, 2006 11:31 AM
To: The Learning Disabilities Discussion List
Subject: [LearningDisabilities 370] Re: Hello



Thanks Glenn!  It's great to see a formula for predicting the cost benefit
of providing students with what they need to be successful.  

 

When the universal design for learning (UDL) approach we are using here In
Washington State gets plugged into your formula, an even greater cost
benefit is realized since UDL reduces the number of students who need to get
"legal accommodations" to be successful in their GED prep classes.  The
costs of both the assessments and the accommodations are reduced.  

 

Unfortunately, many standardized test protocols say the discrepancy model as
the only eligibility model, excluding students who can't afford the
assessments, or live in areas without enough psychologists, or who are out
side the norm because they didn't attend school in the American model, or
because their languages and cultures are not supported by the IQ test or the
achievement test.  

 

There isn't a formula that could show that it is worth disenfranchising so
many people to protect the reliability and validity of a test.  We need to
find a new eligibility model that better fits the students who come to our
classes.


  _____  


From: learningdisabilities-bounces at nifl.gov
[mailto:learningdisabilities-bounces at nifl.gov] On Behalf Of Glenn Young
Sent: Monday, March 20, 2006 4:36 PM
To: 'The Learning Disabilities Discussion List'
Subject: [LearningDisabilities 361] Re: Hello

 

While writing a message to Shash, I had a thought about how to demonstrate
the cost benefit of LD diagnostic testing

 

I wonder if this makes sense, and if anyone other there potentially have the
data to plug into this formula to so its value?

 

 

The premise is that in many states, if not all, the cost of testing is often
stated as the major problem for not addressing the LD issue.  However, if we
can show there is cost-benefit of investing in the diagnostic testing and
getting people out of the GED process quicker and with more success, rather
then people stuck in the process for years, we can show that the cost of
testing and accommodating actually is more cost effective then standard
approaches.

 

To do so we would need the cost benefit formula ... this is one I drew up
(just an idea)  Is X+A<Y x Z1 - (Y x (Z2)+A)

 

Where: 

*  Y = Annual investment in GED Prep for student with LD; including direct
(adult ed, test prep, etc) and indirect costs (students loss of income due
to lack of GED, etc)

*  X = Cost of diagnostic testing for LD

*  A = Cost of Accommodations

*  Z1 = Number of years of perpetration for GED, based on standard GED prep
process (without LD diagnostics or accommodations 

*  Z2 = Numbers of years of preparation for the GED for the student
receiving training for and taking test with accommodations, based on LD
diagnostics. 

 

So  Is X+A (cost of testing and accommodations < (is less than) Y(annual
costs) x Z1 (years without LD services) - Y (annual costs) x Z2 )(number of
years in preparation based on LD approach)+ A (accommodations) then testing
is cost effective.

 

So,  say that the testing cost 1,000 and the accommodations cost 500  (both
potentially high)

 

and,  the Annual costs are 2,000 per year (low considering the indirect
costs)  and the average person takes 3 years to get the GED  

 

But the LD person takes only one year, with the accommodated training and
test taking  .... 

 

So ... then is $1500 < 2,000x3 - 2000 +500

 

                $1500 < 6,000 - 2500

                $1500 < 3,500

 

So we can show that testing saves 2000 per student and allows the person to
pass the test in one year instead of three ... there is great cost benefit
in doing the testing.

 

The costs may not be right ... but you get the idea of what I am suggesting.

 

Now, does anyone out there have real data that we can use in the formula?   

 

 

Glenn Young

505 East Braddock Rd # 608

Alexandria VA 22314

703-684-1750

gyoungxlt at comcast.net

 

 

 


  _____  


From: Glenn Young [mailto:gyoungxlt at comcast.net] 
Sent: Monday, March 20, 2006 3:11 PM
To: 'The Learning Disabilities Discussion List'
Subject: RE: [LearningDisabilities 359] Hello

Shash,

 

First of all - Go Huskies (both men's and women's teams --- I am an alum and
very happy so far with the tournaments)

 

Second - Welcome to the List Serve -  I am familiar with the great work you
all are doing in Washington State ... I have a copy of the report that was
developed on the project covering the 2000-2004 period ... This is some of
the best data available in the country and Israel Mendoza, Mike Tate and the
others in the state should be congratulated for your work.  

 

You say that we can be of help to you.  I hope we can.  However, I think you
could be of great help to all of us around the country, because of the great
data you have collected on the LD students entering your programs.  

 

Your findings can be of great significance, and can impact both state and
national policy ... However in the report I have, there is some difficulty
in how the data is presented ... and therefore the data is not as useful as
it could be.  I am not sure if what I am raising is just a presentation
issue,  or if you need to look at the data differently.  

 

I raise this issue only because I think Washington State can better
influence policy issues locally if you think about the questions and are
able to show your data to answer them.  Since you have the data base, you
all may be the best to provide the rest of the country with data in such a
format that it could also influence the national picture.  (OK a lot to put
on you just when your joining the list serve ... but ... ) 

 

(Also ... if other programs have data to answer some of these questions
please chime in ... )

 

But here is the problem --- As we know, we do not have data to really been
able to systematically and statistically show that recognition of LD and
addressing LD in adult ed and other programs have a major impact on success
rates of students.  And also, even it we can show impact, we can not show
the "cost benefit" do making changes to programs.

 

You potentially have that data.  However, how it is presented in the report
I have, the data does not really provide the answers that policy people need
to have;   How many people are involved may have LD, compared to what, and
is there a cost benefit to the investment in the interventions?

 

The major problem is that the data you are showing in the report does not
compare itself to a baseline or other factors, nor give any definitive
results,  so it is hard to tell if there is any impact from what you are
doing ... not to say that there is not impact, its just in this report it is
hard to tell ...  

 

So ...  here are some questions that may help you think about your data that
can show results we need to know ... and also help all of us out here .... 

 

First Screening:  Although you state in the report I have, the number
screened (1,144), you do not state what that number represents in comparison
to students entering the programs, nor do you say the follow-up piece of
data,  that is, the % of the 1,144 that scored within the LD range in the
screening process ... So simply to say that 1,144 were screened, and then
there were consequences ... does not answer the key questions.

 

So the first pieces of information needed are:

*	What percent of those entering the program(s) were referred for LD
screening? (1,144 of how many over all?) 

The next question is:

*	Of those screened, what % of those were found to be at risk for LD ?
(Of the 1,144, what rate was positive in LD risks?) 


>From those two pieces of data we can better infer what % of all students

entering the program are at risk for LD.

*	Except ....If 1,144 is like 1% of all people entering, then even if
100% were found to be LD ... it does not show a high rate. it is very
important to know whom and how many this 1,144 represented.  Also, if 1,144
is a small amount of the whole population ... were there criteria used to
limit the evaluation pool? (ESL, etc)  So, in other words, what % of the
eligible people, after addressing criteria, did the 1,144 represent?) 

*	 Also, If everyone, or almost everyone, who is being referred by the
intake and orientation process for screening is actually found to be at risk
for LD, it strongly suggests that the referral process is being too
selective,  and that, in fact, many others who are LD or at risk for being
LD, are being not referred through the current process ...   A good referral
process needs to be such that it includes people who are not LD, but may
appear so ... not just catching obvious ones and therefore being 100%
correct in the referrals, but as a result missing a major part of those who
are LD in the process.  Therefore, knowing what % of those 1,114 were found
at risk for LD gives not just the information, but also credibility to the
process. 


>From the way the data is presented, I can not understand if the referral

process is sending too many, too few or just about the right numbers for
screening.  This can only be determined by seeing how many of the 1144
screened were found to be at risk. What the actual LD rate is,  and many
other questions, cannot be addressed in how the data is presented, and
therefore would not make this information as interesting to policy people.
So what is needed is a report that shows:

 

All students entering service in X areas during Y period

 

All students who did not meet LD potential screening process based on Z
criteria issues)

 

All students referred at:

*	Intake 

*	Orientation 

*	from Classroom 

Total
1,144 (x% of all potential students)

 

All students who were found at risk for LD based on screening procedure
XYZ (x% of those screened)

 

 

Second - Impact of Screening

 

As a result of the screening, you were able to get accommodations in the GED
for about 1/3 of those screened, either based on school records or new
testing.  However, there is no information on what happened to them with the
accommodations.

*	Did the accommodations have in impact?  Can you show how that 1/3
who did get accommodated did in taking the test, as opposed to the general
rate of those taking the GED?  

*	Can you show that the accommodations were successful in enabling
those taking the GED with accommodations to pass the GED? 

Also, can you track how those people whom you screened, and found to be at
risk for LD, but who could not get the diagnostic work up needed to get the
accommodations did on the GED, or in other words  ... 

*	Can you track if there is a difference between the success rates of
those whom are found to be at risk for LD and do get tested and then
accommodated, versus those whom are found to be at risk, and then are not
able to be tested and therefore do not get the accommodations.  

So a report something like this

*	All those who took the GED - rate of success
X% 

*	All those who took the GED with accommodations based on LD - rate of
success     Y % 

*	All those who took the GED, found to be at risk for LD, but not able


             to get accommodations - rate of success
Z %

 

(In theory,  X and Y should be close to each other,  were Z should be much
lower ...)

 

These pieces of data can show the value of getting people tested and then
accommodated ,,, and therefore increase public officials interest in getting
success, through the diagnostic process.  

 

Also,  you had another major category in your report that I have ... those
who received LD specific education approaches 

*	Can you track how those who you find to be at risk for LD and whom
received specific LD instruction (47% of those whom you screened), did in
comparison with the general adult education population, who are not screen
(holding accountable things like second language, etc.) and,  therefore do
not get any LD specific instruction. 

For example, do you have data that show?:

 
Rate of Increase             Years needed

 
in Reading level per year   to pass GED

General ABE population

ABE population receiving LD interventions (47%)

ABE population found at risk for LD not receiving LD interventions (53%???)

 

 

Three - Cost benefit.

 

Many point to the cost of testing as a great problem in addressing the LD
issue.  However, if we can show there is cost-benefit of investing in
testing and getting people out of the GED process quicker and with more
success, rather then people stuck in the process for years, we can show that
the cost of testing and accommodating actually is more cost effective then
standard approaches.

 

To do so we would need the cost benefit formula ... this is one I drew up
(just an idea)  Is X+A<Y x Z1 - (Y x (Z2)+A)

 

Where: 

*  Y = Annual investment in GED Prep for student with LD; including direct
(adult ed, test prep, etc) and indirect costs (students loss of income due
to lack of GED, etc)

*  X = Cost of diagnostic testing for LD

*  A = Cost of Accommodations

*  Z1 = Number of years of perpetration for GED, based on standard GED prep
process (without LD diagnostics or accommodations 

*  Z2 = Numbers of years of preparation for the GED for the student
receiving training for and taking test with accommodations, based on LD
diagnostics. 

 

So  Is X+A (cost of testing and accommodations < (less than) Y(annual costs)
x Z1 (years without LD services) - Y (annual costs Z2 )(number of years in
preparation based on LD approach)+ A (accommodations) 

 

So say that the testing cost 1,000 and the accommodations cost 500  (both
high)

 

and,  the Annual costs are 2,000 per year (low considering the indirect
costs)  and the average person takes 3 years to get the GED  

 

But the LD person takes only one year, with the accommodated training and
test taking  .... 

 

So ... then is $1500 < 2,000x3 - 2000 +500

 

                $1500 < 6,000 - 2500

                $1500 < 3,500

 

So we can show that testing saves 2000 per student and allows the person to
pass the test in one year instead of three ... there is great cost benefit
in doing the testing.

 

Now, can your data show this time saving factor in the process?  

 

Again,  I only bring all of  this up to you because you all have some of the
best data in the nation, and if it can be organized to answer these
questions, you can give us some tools and data that are really needed in
this area. 

 

Hope this is helpful and continued luck in the project...  

 

 

Glenn Young

505 East Braddock Rd # 608

Alexandria VA 22314

703-684-1750

gyoungxlt at comcast.net

 

 

 


  _____  


From: learningdisabilities-bounces at nifl.gov
[mailto:learningdisabilities-bounces at nifl.gov] On Behalf Of Shash Woods
Sent: Friday, March 17, 2006 4:59 PM
To: LearningDisabilities at nifl.gov
Subject: [LearningDisabilities 359] Hello

Hi Rochelle and all -

I'm a regional professional development coordinator for Washington State
Office of ABE.  My colleague, KC Andrew, and I are taking Bridges to
Practice training, and I want to learn more/keep up with the field.  Here in
Washington State we have two significant initiatives: Learning Disabilities
Quality Initiative and Universal Design for Learning that are at various
stages of development, piloting, and growing to scale around the state.  I
hope what I learn from participating in this listserv will help me support
these initiatives, and the continuuing LD professional development needs of
the instructors in the field.

- Shash

Shash Woods
Professional Development Coordinator
Region 1
Washington State OABE
(206) 276-3745


-----Original Message-----
From: RKenyon721 at aol.com [ mailto:RKenyon721 at aol.com]
Sent: Fri 3/17/2006 2:33 PM
To: Shash Woods
Subject: Welcome

Hi Shash,



Welcome to the National Institute for  Literacy Learning Disabilities
Discussion List.  I look forward to your  participation.  Please introduce
yourself
online and tell our  subscribers what your interests are as related to LD.

You can view the interesting array of archived  messages from the LD
Discussion List at:
_ http://www.nifl.gov/lincs/discussions/list_archives.html_
( http://www.nifl.gov/lincs/discussions/list_archives.html)


Thanks,

Rochelle Kenyon, Moderator
National Institute for Literacy Learning  Disabilities Discussion List
_RKenyon721 at aol.com_ ( mailto:RKenyon721 at aol.com) 





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