Districts and States Working with VARC

Districts and States Working with VARC

MinneapolisMilwaukee

Racine

Chicago

Madison

Tulsa

Atlanta

New York City

Los Angeles

Hillsborough County

NORTH DAKOTA

SOUTH DAKOTA

MINNESOTA

WISCONSIN

ILLINOISDenver

Collier County

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

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

Value-Added Model Description

The Oak Tree Analogy

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

Gardener A Gardener B

Gardener A Gardener B

Explaining Value-Added by Evaluating Gardener Performance

This method is analogous to using an Achievement Model.

• Using this method, Gardener B is the better gardener.

Gardener A Gardener B

61 in.

72 in.

Method 1: Measure the Height of the Trees Today (One Year After the Gardeners Began)

• How is this similar to how schools have been judged in New York?

• What information is missing?

Pause and Reflect

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

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

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

+14 in. +20 in.

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

Method 2: Compare Starting Height to Ending Height

• This is an “apples to oranges” comparison.• For our oak tree example, three environmental factors we will examine are:

Rainfall, Soil Richness, and Temperature.

Gardener A Gardener B

What About Factors Outside the Gardeners’ Influence?

External condition Oak Tree A Oak Tree B

Rainfall amount

Soil richness

Temperature

High LowLow HighHigh Low

Gardener A Gardener B

Gardener A Gardener B

• We compare the actual height of the trees to their predicted heights to determine if the gardener’s effect was above or below average.

• We need to analyze real data from the region to predict growth for these trees.

How Much Did These External Factors Affect Growth?

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 HighGrowth in inches

relative to the average

-3 -1 +2

Temperature Low Medium HighGrowth in inches

relative to the average

+5 -3 -8

Calculating Our Prediction Adjustments Based on Real Data

• Next, we will refine our 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.

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 Predictions for the Trees Based on Starting Height

Similarly, for having low rainfall, Oak B’s prediction is adjusted by -5 to compensate.For having high rainfall, Oak A’s prediction is adjusted by +3 to compensate.

70 in. 67 in.Gardener A Gardener B

47 in.52 in.

+20 Average+20 Average

+ 3 for Rainfall - 5 for Rainfall

Based on Real Data, Customize Predictions for Rainfall

For having rich soil, Oak B’s prediction is adjusted by +2.

For having poor soil, Oak A’s prediction is adjusted by -3.

67 in.69 in.Gardener A Gardener B

47 in.52 in.

+20 Average+20 Average

+ 3 for Rainfall

- 3 for Soil + 2 for Soil

- 5 for Rainfall

Adjusting for Soil Richness

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

59 in.

74 in.Gardener A Gardener B

47 in.52 in.

+20 Average+20 Average

+ 3 for Rainfall

- 3 for Soil + 2 for Soil

- 8 for Temp + 5 for Temp

- 5 for Rainfall

Adjusting for Temperature

+20 Average+20 Average

+ 3 for Rainfall

- 3 for Soil + 2 for Soil

- 8 for Temp + 5 for Temp_________+12 inchesDuring the year

_________+22 inches During the year

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

59 in.

74 in.Gardener A Gardener B

47 in.52 in.

- 5 for Rainfall

Our Gardeners are Now on a Level Playing Field

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

Oak A’s actual height is 2 inches more than predicted. We attribute this above-average result to the effect of Gardener A.

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

This is analogous to a Value-Added measure.

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 tree.

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

Oak Tree Analogy Value-Added in Education

What are we evaluating?

• Gardeners • Districts• Schools• Grades• Classrooms• Programs and Interventions

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

A Visual Representation of Value-Added

Year 2(Post-test)

Actual student achievement

scale score

Predicted student achievement (Based on observationally

similar students)

Value-AddedStarting student achievement scale score

Year 1(Prior-test)

District Model

Statewide Model

Unified Output and Messaging

Best Reference Point for Relative Growth Measurement

Largest Pool of Data to Refine Predictions

Customized Model Design

Producing a Value-Added Model for New York State

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

What do Value-Added Results Look Like?

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.

Transformation Example

10 15 200 5

Ineffective Developing Effective Highly Effective

Transformation Example

10 15 200 5

Ineffective Developing Effective Highly Effective

Transformation Example

10 15 200 5

Ineffective Developing Effective Highly Effective

Transformation Example

10 15 200 5

Ineffective Developing Effective Highly Effective

Transformation Example

10 15 200 5

Ineffective Developing Effective Highly Effective

Advisor Group

Value-Added Research

Center

New York BOCES and

Districts

Other New York

Stakeholders

Northwest Evaluation Association

How do we Translate Value-Added into the 0-20 Scale?

Output Formats

VARC Value-Added Output0-20 scale based on advisory group’s

recommended methodology

scale score standard deviations