new york town hall value added - varc

VALUE-ADDED NEW YORK TOWN HALL MEETING

Value-Added Research Center’s (VARC) Role in NWEA’s APPR Strategy

Testing

Metric (Growth Score)

Analysis (Value Added)

State APPR Rating (0-20)

NWEA

VARC

The Power of Two

&A more

complete picture of student learning

Achievement Value-AddedCompares students’

performance to a standard

Does not factor in students’ background characteristics

Measures students’ performance at a single

point in time

Critical to students’ post-secondary opportunities

Measures students’ individual academic growth longitudinally

Factors in students’ background characteristics

outside of the school’s control

Critical to ensuring students’ future academic success

Measures the impact of teachers and schools on

academic growth

Adapted from materials created by Battelle for Kids

Value-Added Basics – The Oak Tree Analogy

Andrew Rice

I am a little concerned that there isn't enough about why we care about VAUltimately it is because of the state Regs… should we mention that more forcefully?

The Oak Tree Analogy

Gardener A Gardener B

Explaining Value-Added by Evaluating Gardener Performance

For the past year, these gardeners have been tending to their oak trees trying to maximize the height of the trees.

This method is analogous to using an Achievement Model.


61 in.

72 in.

Method 1: Measure the Height of the Trees Today (One Year After the Gardeners Began) Using this method, Gardener B is the more effective gardener.

Pause and Reflect

How is this similar to how schools have been evaluated in the past?

What information is missing from our gardener evaluation?

61 in.

72 in.Gardener A Gardener B

Oak AAge 4

(Today)

Oak BAge 4

(Today)

Oak AAge 3

(1 year ago)

Oak BAge 3

(1 year ago)

47 in.52 in.

This Achievement Result is not the Whole Story

We need to find the starting height for each tree in order to more fairly evaluate each gardener’s performance during the past year.

This is analogous to a Simple Growth Model, also called Gain.

61 in.


Oak AAge 4

(Today)

Oak BAge 4

(Today)

Oak AAge 3

(1 year ago)

Oak BAge 3

(1 year ago)

47 in.52 in.+14 in. +20 in

.

Method 2: Compare Starting Height to Ending Height

Oak B had more growth this year, so Gardener B is the more effective gardener.


What About Factors Outside the Gardener’s Influence? This is an “apples to oranges” comparison. For our oak tree example, three environmental factors we will examine are:

Rainfall, Soil Richness, and Temperature.

External condition Oak Tree A Oak Tree B

Rainfall amount

Soil richness

Temperature

High LowLow HighHigh Low



How Much Did These External Factors Affect Growth? We need to analyze real data from the region to predict growth for these trees. We compare the actual height of the trees to their predicted heights to

determine if the gardener’s effect was above or below average.

In order to find the impact of rainfall, soil richness, and temperature, we will plot the growth of each individual oak in the region compared to its environmental conditions.

Rainfall Low Medium HighGrowth in

inches relative to

the average

-5 -2 +3

Soil Richness

Low Medium High

Growth in inches

relative to the average

-3 -1 +2Temperatu

reLow Medium High

Growth in inches

relative to the average

+5 -3 -8

Calculating Our Prediction Adjustments Based on Real Data

Oak AAge 3

(1 year ago)

Oak BAge 3

(1 year ago)

67 in.72 in.Gardener A Gardener B

Oak APrediction

Oak BPrediction

47 in.52 in.

+20 Average+20 Average

Make Initial Prediction for the Trees Based on Starting Height

Next, we will refine out prediction based on the growing conditions for each tree. When we are done, we will have an “apples to apples” comparison of the gardeners’ effect.

70 in. 67 in.Gardener A Gardener B

47 in.52 in.


+ 3 for Rainfall - 5 for Rainfall

Based on Real Data, Customize Predictions based on Rainfall

For having high rainfall, Oak A’s prediction is adjusted by +3 to compensate.

Similarly, for having low rainfall, Oak B’s prediction is adjusted by -5 to compensate.

67 in.69 in.Gardener A Gardener B

47 in.52 in.


+ 3 for Rainfall

- 3 for Soil + 2 for Soil

- 5 for Rainfall

Adjusting for Soil Richness For having poor soil, Oak A’s prediction is adjusted by -3. For having rich soil, Oak B’s prediction is adjusted by +2.

59 in.


47 in.52 in.


+ 3 for Rainfall


- 8 for Temp + 5 for Temp

- 5 for Rainfall

Adjusting for Temperature For having high temperature, Oak A’s prediction is adjusted by -8. For having low temperature, Oak B’s prediction is adjusted by +5.


+ 3 for Rainfall


- 8 for Temp + 5 for Temp_________

+12 inchesDuring the year

_________+22 inches During the year

59 in.


47 in.52 in.

- 5 for Rainfall

Our Gardeners are Now on a Level Playing Field

The predicted height for trees in Oak A’s conditions is 59 inches. The predicted height for trees in Oak B’s conditions is 74 inches.

PredictedOak A

PredictedOak B

ActualOak A

ActualOak B

59 in.

74 in.Gardener A Gardener B61 in.

72 in.+2-2

Compare the Predicted Height to the Actual Height Oak A’s actual height is 2 inches more than predicted. We attribute this to the effect

of Gardener A. Oak B’s actual height is 2 inches less than predicted. We attribute this to the effect

of Gardener B.

This is analogous to a Value-Added measure.

Above Average

Value-Added

Below Average

Value-Added

PredictedOak A

PredictedOak B

ActualOak A

ActualOak B

59 in.

74 in.Gardener A Gardener B61 in.

72 in.+2-2

Method 3: Compare the Predicted Height to the Actual Height

By accounting for last year’s height and environmental conditions of the trees during this year, we found the “value” each gardener “added” to the growth of the trees.

Value-Added Basics – Linking the Oak Tree Analogy to Education

Oak Tree Analogy Value-Added in Education

What are we evaluating?

• Gardeners • Districts• Schools• Grades• Classrooms

How does this analogy relate to value added in the education context?

What are we using to measure success?

• Relative height improvement in inches

• Relative improvement on standardized test scores

Sample • Single oak tree • Groups of students

Control factors • Tree’s prior height

• Other factors beyond the gardener’s control:

• Rainfall• Soil richness• Temperature

• Students’ prior test performance (usually most significant predictor)

• Other demographic characteristics such as:

• Grade level• Gender• Race / Ethnicity• Low-Income Status• ELL Status• Disability Status• Section 504 Status

Another Visual Representation

Spring NWEAMAP Score

Actual student

achievement

RIT score

Predicted student achievement

(Based on observationally similar students)

Value-Added

Starting student

achievement RIT score

Fall NWEAMAP Score

The Education Context

VARC Data Output

What do Value-Added Results Look Like?

The Value-Added model typically generates a set of results measured in scale scores.

Teacher Value-Added

Teacher A +10

Teacher B -10

Teacher C 0

This teacher’s students gained 10 more points on

the RIT scale than observationally similar

students across the state. (10 points more

than predicted)

10 points fewer than predicted

These students gained exactly as many points

as predicted

Value-Added in “Tier” Units

Grade 4 30

-2 -1 0 1 2

0.9

In some cases, Value-Added is displayed on “Tier” scale based on standard deviations (z-score) for reporting purposes.

About 95% of estimates will fall between -2 and +2 on the scale.

Using NWEA’s MAP + VARC within New York’s Annual Professional Performance Review (APPR)

60%20%

20%

APPRObservations State Test GrowthNWEA + VARC

State Tested Grades / Subjects

Other Grades / Subjects for which there is an approved NWEA test

60%20%

20%

APPRObservations Local MeasureNWEA + VARC

APPR’s 0-20 Local Measure Descriptions of Categories

A teacher’s results are compared to district or BOCES-adopted expectations for growth or achievement of student learning standards for grade/subject Ineffective – Results are well-below

expectations Developing – Results are below

expectations Effective – Results meet expectations Highly Effective – Results are well-above

expectations

Andrew Rice

I am wary of the expectations statement here

What are the Rules for APPR’s Local 0-20?

Score Ranges 0-2 Ineffective 3-8 Developing 9-17 Effective 18-20 Highly Effective


Scores must use the full range (For example: not all teachers can be labeled “Effective”)

How can we translate Value-Added estimates into this 0-20 scale in a fair and responsible way? Who gets labeled “Ineffective” Resources to support these teachers

Transformation Example

10 15 200 5

Ineffective Developing Effective Highly Effective

VARC Data Output File

Example VARC Output File

What is included in these results?

Levels of Results

District School Teacher Grade Subject

District A School 1 Ms. Smith 4 Math

District A School 1 Ms. Smith 4 Reading

District A School 2 Mr. Jones 6 Math

District A School 3 Mr. Thomas 1 Language Usage

District A School 4 Mrs. Meyer 10 Reading

Results will be provided for (provided a large enough sample of students) Math grades K-10 Reading grades K-10 Language Usage grades K-10

Result Formats

RIT Score Confidence Interval

+10 +7 to +13

0 -2 to +2

-4 -6 to -2

Tier Confidence Interval

+1.9 +1.7 to +2.1

0 -0.2 to +0.2

-0.8 -1.0 to -0.6

0-20 APPR

18

10

7

Scale score growth difference than average

for observationally similar students

“z-scores” of the RIT score differences. This

answers the question of “how good is good?”

Default 0-20 to comply

with law (to be decided)

VARC Data Needs

What Data Does VARC Need? Data identifying and linking

students/teachers State Student ID linkable to NWEA data School ID Teacher ID

What Data Does VARC Need? Student Test Data

Fall Test Data for Math, Reading, Language Usage (Date, Score, SEM)

Spring Test Data for Math, Reading, Language Usage(Date, Score)

Student Demographics Grade, Gender, Race/Ethnicity, Special

Education Status, ELL Status, FRL Status, etc.

What is the Timeline?

Testing windows in the 2012-2013 school year Need Fall/Spring testing

Collection strategy for student demographic data Data from the state update Contingency plan for collection from

RIC/district

What is the Timeline?

Our production timeline can only begin once we’ve received clean student-teacher linking data from supplier (state, RIC, district)

Timeline for Value-Added analysis Drop-dead date for data transfer to VARC Time to run analysis and quality check Return results back to districts’

superintendants or designee Special case of summer 2012

• Individual student-level MAP growth targets vs. the need for Value-Added for APPR

• 0-20 local measure within APPR 0-100• Transformation of Value-Added to 0-20• Consistent messaging and meaning across

NWEA partners• Approving this solution through the New

York SED

Questions / concerns for the advisory committee to address?

VALUE-ADDED NEW YORK ADVISORY GROUP MEETING

Existing VARC Projects

Minneapolis Milwauke

eRacine

Chicago

Madison

Tulsa

Atlanta

New York City

Los Angeles

Hillsborough County

NORTH DAKOTA

SOUTH DAKOTA

MINNESOTA

WISCONSIN

ILLINOIS

Districts and States Working with VARC

Denver

Collier County

Wisconsin

Opt-in statewide Value-Added system (2010)

Statewide advisory group with quarterly meetings District-led annual meetings

on responsible use and messaging

Expansion of piloted MAP Value-Added (Racine and Milwaukee) to statewide model

Same model and messaging across districts

A Value-Added Model of Classroom Performance: Recipe for a Statistician

1 0

1 1 1 1 1(school) (school) (classroom)

i i i

k ik jk ijk ik k j

Y Y X

S C

What does that mean in English?

Post-Test

Post-on-Pre Link

Pre-TestStudent

Characteristics

Classroom Effect

Unknown Student

Characteristics

= * + + +

Spring MAP Result

Adjustment to account for

starting point

Fall MAP Result

Adjustment to account for

student demographics

Classroom contribution to

student learning(Value-Added)

Error term for unknown factors,

(reduces with increased sample

size)

Los Angeles, California

Phase 1 (May 2011) Grades 3-8 Math and ELA Grade 9 ELA

Phase 2 (Nov 2011) Grades 3-11 ELA Grades 3-8 General Math High School subjects

Math, ELA, Science, Social Studies

Phase 3 (Nov 2012) Other Assessments

Example Documentation

http://portal.battelleforkids.org/BFK/LAUSD/Training_Materials.html?sflang=en

Excerpt from LAUSD’s teacher-level Value-Added

Model documentation

Transparency of the model is our goal



Hillsborough County, Florida

Began July 2010 Subject / Grade Coverage

Models from Art to Welding Multiple Measures

Charlotte Danielson observational ratings

Combined use of student outcomes and observational data in evaluation system

Use of Value-Added Fiscal awards Future uses being developed

together with union

New York, New York

In the past, Value-Added based on state exams Dangers related to the

release of teacher-level data

Constructive use of data Currently calculating

local measures based on MAP

Advising NYC on Transformation to 0-20

Some Common Features of VARC’s Value-Added Models

Prior test scores to predict current test scores Single prior test or multiple tests (sometimes across

subjects) Growth of a teacher’s students is compared to growth of

similarly achieving students across the state Student demographics

Typically Gender, Race/Ethnicity, Low-Income Status, Special Education Status, English Language Learner Status, other student-level data available for all students

Measurement error correction Dosage (when enrollment data is available) Statistical shrinkage estimation VARC motto: Simpler is better unless it’s wrong

Continuous improvement of the model based on latest research and improving data quality

Translating Value-Added to the 0-20 Scale Required by APPR

Using NWEA’s MAP + VARC within New York’s Annual Professional Performance Review (APPR)

60%20%

20%

APPRObservations State Test GrowthNWEA + VARC

State Tested Grades / Subjects

Other Grades / Subjects for which there is an approved NWEA test

60%20%

20%

APPRObservations Local MeasureNWEA + VARC

Can NWEA’s MAP be used for the other 20% where NWEA tests are approved?

What about grades / subjects not covered by NWEA’s assessments?

APPR’s 0-20 Local Measure Descriptions of Categories

A teacher’s results are compared to district or BOCES-adopted expectations for growth or achievement of student learning standards for grade/subject Ineffective – Results are well-below

expectations Developing – Results are below

expectations Effective – Results meet expectations Highly Effective – Results are well-above

expectations


Score Ranges 0-2 Ineffective 3-8 Developing 9-17 Effective 18-20 Highly Effective

Scores must use the full range (For example: not all teachers can be labeled “Effective”)

How can we translate Value-Added estimates into this 0-20 scale in a fair and responsible way?

Transformation Example

10 15 200 5

Ineffective Developing Effective Highly Effective

0-20 Consideration Topics

Implications of a given translation Percentage of teachers labeled “Ineffective”

relative to resources for support Disagreement between Value-Added in

subject areas For example: a 4th grade teacher gets a “0”

in math and “20” in reading Do we do a weighted average of those two

to get a single cross-subject Value-Added? Do we take the higher of the two?

0-20 Consideration Topics

What about teachers teaching multiple grades? Same solution as multi-subject?

Once multiple years of data are available, do we use the most recent year or a multi-year average? If an average, how many years?

What about estimates based on very few students? Is there a minimum threshold for reporting

out? Is there any way to consider the confidence

interval around estimates?

15 Minutes

Break

Why does VARC recommend including student demographic data?How do we decide what to include?

Modeling Decisions

(Proxy measures for causal factors)

How does VARC choose what to control for?

How does VARC choose what to control for?• Imagine we want to evaluate another pair of gardeners and we notice that there is

something else different about their trees that we have not controlled for in the model.

• In this example, Oak F has many more leaves than Oak E. • Is this something we could account for in our predictions?

Oak EAge 5

Oak FAge 5

Gardener E Gardener F

73 in. 73 in.

In order to be considered for inclusion in the Value-Added model, a characteristic must meet several requirements:

Check 1: Is this factor outside the gardener’s influence?

Check 2: Do we have reliable data?

Check 3: If not, can we pick up the effect by proxy?

Check 4: Does it increase the predictive power of the model?

Check 1: Is this factor outside the gardener’s influence?

Outside the gardener’s influence

Starting tree height

Rainfall

Soil Richness

Temperature

Starting leaf number

Gardener can influence

Nitrogen fertilizer

Pruning

Insecticide

Watering

Mulching


Category Measurement Coverage

Yearly record of tree height

Height (Inches) 100%

Rainfall Rainfall (Inches) 98%

Soil Richness Plant Nutrients (PPM)

96%

Temperature Average Temperature

(Degrees Celsius)

100%


Individual Leaf Count

7%

Canopy diameter Diameter (Inches) 97%

Check 3: Can we approximate it with other data?

Category Measurement Coverage

Yearly record of tree height

Height (Inches) 100%

Rainfall Rainfall (Inches) 98%

Soil Richness Plant Nutrients (PPM)

96%

Temperature Average Temperature

(Degrees Celsius)

100%


Individual Leaf Count

7%

Canopy diameter Diameter (Inches) 97%

?

Canopy diameter as a proxy for leaf count• The data we do have available about canopy diameter might help us measure the effect of

leaf number.• The canopy diameter might also be picking up other factors that may influence tree

growth.• We will check its relationship to growth to determine if it is a candidate for inclusion in the

model.

Oak EAge 5

Oak FAge 5

Gardener E Gardener F

33 in. 55 in.

If we find a relationship between starting tree diameter and growth, we would want to control for starting diameter in the Value-Added model.

The Effect of Tree Diameter on Growth

Gro

wth

fro

m Y

ear

5 t

o 6

(in

ches)

Tree Diameter (Year 5 Diameter in Inches)

0 20 40 60 800

5

10

15

20

25

30

35

40

Tree Diameter?

If we find a relationship between starting tree diameter and growth, we would want to control for starting diameter in the Value-Added model.

The Effect of Tree Diameter on Growth

Gro

wth

fro

m Y

ear

5 t

o 6

(in

ches)

Tree Diameter (Year 5 Diameter in Inches)

0 20 40 60 800

5

10

15

20

25

30

35

40

Tree Diameter

What happens in the education context?

Check 1: Is this factor outside the school or teacher’s influence?




Outside the school’s influence

At home support

English language learner status

Gender

Household financial resources

Learning disability

Prior knowledge

School can influence

Curriculum

Classroom teacher

School culture

Math pull-out program at school

Structure of lessons in school

Safety at the school


Let’s use “Household financial resources” as an example


What we want

• Household financial resources

Check 3: Can we approximate it with other data?

Using your knowledge of student learning, why might “household financial resources” have an effect on

student growth?

What we have

• Free/reduced lunch status

Related data

What we want

• Household financial resources

Check 4: “Does it increase the predictive power of the model?” will be determined by a multivariate linear regression model based on real data from your district or state (not pictured) to determine whether FRL status had an effect on student growth.

What about race/ethnicity?

What we have

• Race/ethnicity

What we want

• General socio-economic status• Family structure• Family education• Social capital• Environmental stress

Related complementary data may correlate with one another(not a causal relationship)

Race/ethnicity causes higher or lower performance

Check 4 will use real data from your district or state to determine if race/ethnicity has an effect on student growth.If there is no effect, it will not be included in the model.

What about race/ethnicity?

If there is a detectable difference in growth rates

We attribute this to a district or state challenge to be addressed

Not as something an individual teacher or school should be expected to overcome on their own

Checking for Understanding

What would you tell a 5th grade teacher who said they wanted to include the following in the Value-Added model for their results?:A. 5th grade reading curriculumB. Their students’ attendance during 5th

gradeC. Their students’ prior attendance during 4th

gradeD. Student motivation





Small Group Discussion

Group 1

Nate (NWEA)

Sean (VARC)

Group 2

John (NWEA)

Andrew (VARC)

Key discussion topics: Advisory council’s role in

selecting a consistent “standard” model and 0-20 translation and Value-Added model

Questions / concerns about selecting a 0-20 translation of Value-Added

Questions / concerns about modeling features (we do not yet know what data will be available to VARC)

Wrap-Up

Top concerns and questions from small group discussion

Where do we need more information? What are the challenges we face?

How can we work together to address those challenges?

What are our next steps? Next advisory group meeting What topics should we cover?

Quasi-experimental design structureVisualizing Achievement vs. Value-AddedControlling for starting pointComparison to a different model – Student Growth Percentiles

Additional Resources

Value-Added Model Description

Design• Quasi-

experimental statistical model

• Controls for non-school factors (prior achievement, student and family characteristics)

Output• Productivity

estimates for contribution of educational units (schools, classrooms, teachers) to student achievement growth

Objective• Valid and fair

comparisons of school productivity, given that schools may serve very different student populations

The Power of Two - Revisited

1 2 3 540

20

40

60

80

100

Value-Added (2009-2010)

Perc

en

t P

rof/

Ad

v (

2009)

Scatter plots are a way to represent Achievement and Value-Added together

Ach

ievem

ent

Value-Added

The Power of Two - Revisited

1 2 3 540

20

40

60

80

100

Value-Added (2009-2010)

Perc

en

t P

rof/

Ad

v (

2009)

Schools in your district

A

A. Students know a lot and are growing faster than predicted

B

B. Students are behind, but are growing faster than predicted

C

C. Students know a lot, but are growing slower than predicted

D

D. Students are behind, and are growing slower than predicted

E

E. Students are about average in how much they know and how fast they are growing

(high or low achieving students)

What about tall or short trees?

1. What about tall or short trees?• If we were using an Achievement Model, which gardener would you rather be?

Gardener C Gardener D

Oak CAge 4

Oak DAge 4

• How can we be fair to these gardeners in our Value-Added Model?

28 in.

93 in.

Why might short trees grow faster?• More “room to grow”• Easier to have a “big impact”

Gardener C Gardener D

Why might tall trees grow faster?• Past pattern of growth will continue • Unmeasured environmental factors

How can we determine what is really happening?

Oak CAge 4

Oak DAge 4

In the same way we measured the effect of rainfall, soil richness, and temperature, we can determine the effect of prior tree height on growth.

The Effect of Prior Tree Height on Growth

Gro

wth

fro

m Y

ear

4 t

o 5

(in

ches)

Prior Tree Height (Year 4 Height in Inches)

0 20 40 60 80 100 1200

5

10

15

20

25

30

35

40

Prior Tree H...

Oak C

(28 in)

9 in

Oak D

(93 in)

30 in

Our initial predictions now account for this trend in growth based on prior height. • The final predictions would also account for

rainfall, soil richness, and temperature.

Oak CAge 4

Oak DAge 4

Oak CAge 5

(Prediction)

Oak DAge 5

(Prediction)

+9 in.

+30 in.

How can we accomplish this fairness factor in the education

context?

Analyzing test score gain to be fair to teachers

Student 3rd Grade Score 4th Grade Score

Abbot, Tina 244 279

Acosta, Lilly 278 297

Adams, Daniel 294 301

Adams, James 275 290

Allen, Susan 312 323

Alvarez, Jose 301 313

Alvarez, Michelle 256 285

Anderson, Chris 259 277

Anderson, Laura 304 317

Anderson, Steven 288 308

Andrews, William 238 271

Atkinson, Carol 264 286

High

Low

Test ScoreRange

High Achiever

Low Achiever

If we sort 3rd grade scores high to low, what do we notice about the students’ gain from test to test?

Student 3rd Grade Score 4th Grade Score Gain in Score from 3rd to 4th

Allen, Susan 312 323 11

Anderson, Laura 304 317 13

Alvarez, Jose 301 313 12

Adams, Daniel 294 301 7

Anderson, Steven 288 308 20

Acosta, Lilly 278 297 19

Adams, James 275 290 15

Atkinson, Carol 264 286 22

Anderson, Chris 259 277 18

Alvarez, Michelle 256 285 29

Abbot, Tina 244 279 35

Andrews, William 238 271 33

High

Low

Test ScoreRange

If we find a trend in score gain based on starting point, we control for it in the Value-Added model.

Student 3rd Grade Score 4th Grade Score Gain in Score from 3rd to 4th

Allen, Susan 312 323 11

Anderson, Laura 304 317 13

Alvarez, Jose 301 313 12

Adams, Daniel 294 301 7

Anderson, Steven 288 308 20

Acosta, Lilly 278 297 19

Adams, James 275 290 15

Atkinson, Carol 264 286 22

Anderson, Chris 259 277 18

Alvarez, Michelle 256 285 29

Abbot, Tina 244 279 35

Andrews, William 238 271 33

High

Low

Test ScoreRange

High

Low

Gain

What do we usually find in reality? Looking purely at a simple growth

model, high achieving students tend to gain about 10% fewer points on the test than low achieving students.

In a Value-Added model we can take this into account in our predictions for your students, so their growth will be compared to similarly achieving students.

School A School B School C

AdvancedProficientBasicMinimal

StudentPopulation

Why isn’tthis fair?

Comparisons of gain at different schoolsbefore controlling for prior performance

HighAchievement

MediumAchievement

LowAchievementArtificially inflated

gain

Artificially lower gain

Comparisons of Value-Added at different schools

after controlling for prior performance

School A School B School C

Fair Fair Fair

StudentPopulation

AdvancedProficientBasicMinimal

Checking for Understanding

What would you tell a teacher or principal who said Value-Added was not fair to schools with: High achieving students? Low achieving students?

Is Value-Added incompatible with the notion of high expectations for all students?

STUDENT GROWTH PERCENTILES (SGP)

Draft Explanation

Gardener A

Oak AAge 4

(Today)

Oak AAge 3

(1 year ago)

47 in.

How Would SGP Measure Oak A?

Oak A’s growth will be compared to all Oaks in the region who started at the same height last year.

Identify all Oaks that were 47” last year

Oak AAge 3

(1 year ago)

Oak ZOak YOak XOak WOak VOak UOak T

Find the Height of Those Trees Today

Oak AAge 4

(Today)


Reorder the Trees Shortest to Tallest

Oak AAge 4

(Today)


Oak W Oak AAge 4

(Today)

Oak U Oak T Oak Z Oak Y Oak X Oak V

Reorder the Trees Shortest to Tallest

The percentage of trees equal or shorter than Oak A is Oak A’s growth percentile.

2/8 = 0.25 25th Growth Percentile

Gardener A

Oak AAge 4

(Today)

Oak AAge 3

(1 year ago)

47 in.

Assigning SGP to the Gardener

If Gardener A is assigned to multiple trees, the median SGP of Gardener A’s trees is assigned to the Gardener.

25th Percentile

61 in.

Pause and Reflect

What might happen if Oak A is in a different environment than the other trees it was compared against?

Is SGP measuring the effect of just the gardener?

new york town hall value added - varc

Education

height oak b

oak b oak bage

oak trees

oak aoak b oak b age

effect of gardener

gardener performance

gardener b61

gardener a72