1. Leveraging Value-Added Assessment Data to Craft Instructional Design
Conrad Martin, Ed.D
Cleveland Early College High School
Renow Group

2. Gratitude

3. Can your ego handle value-added assessment?

4. Agenda Value-added assessment for the non-statistician Teacher Reports
A look at North Carolina’s use of value-added assessment
Teacher Reports
What they mean
Tools for increased differentiation

5. True or False Growing higher achievers is more difficult
In any given year, 75% of schools will meet or exceed growth

6. What we think…

7. Pitchforks and torches
“teaching to the test”
“I teach these kids, it’s unfair to compare them to those kids.”

8. Quick Glossary Analysis of Variance (ANOVA) 20+ years of test scores
Determines how much the test scores (Independent Variable Y) are affected by each Dependent Variable, such as race, mother’s education level, school, teacher, etc. (X)

9. Quick Glossary Formula for using raw score to determine growth
variance of all Dependent Variables (X) added together to derive the Raw Score or Independent Variable (Y)
specific Dependent Variables and their Variances or Weights was determined by ANOVA on 20+ years of test score data

10. Quick Glossary Formula for using raw score to determine growth
Looks like this:
X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10 = Y

11. 10 Dependent Variables SAS Institute is a private company
does not publish this information

12. Based on our research, variables are as follows:
X1 = Race
X2 = Mother’s Education
X3 = Father’s Income
X4 = Teacher
X5 = School District
X6 = School
X7 = Grade
X8 = Age
X9 = Gender
X10 = Previous Test Scores

13. Dependent Variables affected by school
X4 = Teacher (10%)
X8 = Age (5%)
% approximate

14. Regression Equation
how the target raw score is calculated to determine if a student has made a year of growth

15. Example Math EOG has 180 questions
the total raw score that any student could achieve would be 180
What score out of 180 would represent a years growth?
regression equation is used to determine what that score would be

16. Sample Kid – Jeremy (3rd grade)
Dependent Variables
Hispanic (15 out of 30) [White would be 30 out of 30]
Mother less than HS Diploma (5 out of 15) [Beyond 4 year degree would be 15]
No father in home (0 out of 15) [Father's income in excess of 150K would be 15]
Teacher (10)
CMS (5) [high wealth district; low wealth would be 2]

17. Sample Kid – Jeremy (3rd grade)
Dependent Variables
Happysville ES (0) [Take over school; High Growth school would be 5]
Grade (5) (not a repeater)
Age (3) (9 years old; was developmentally delayed and started school at age 6)
Gender (5) [Female would be 4 due to differential in Male and Female Math scores; would be opposite for Reading EOG]
Previous Scores (2) (was not on grade level according to data coming out of 2nd grade)

18. Sample Kid – Jeremy (3rd grade)
equation looks like this:
= 50%
Total Possible Raw score of 180 x .50 = 90
So, Jeremy has to achieve a raw score of 90 on the 3rd Grade Math EOG to have made a year’s growth

19. Which leaves

20. Reflection – what we know
retaining students so that their age and grade do not match hurts test scores
quality teachers can contribute 10% of the variance in test scores
curriculum at the school level is all about how to teach not what to teach

21. EVAAS Achievement

22. EVAAS Growth

23. Some Tools for Practice

25. Did we cover… Value-added assessment for the non-statistician
A look at North Carolina’s use of value-added assessment
Teacher Reports
What they mean
Tools for increased differentiation

26. If the horse dies, dismount!
DO NOT
1. Stay on the horse.
2. Switch riders.
3. Move the horse to a new location.
4. Buy a stronger whip.
5. Tighten the cinch.
6. Try a new bit or bridle.
7. Say things like: "We've always ridden our horses this way."
8. Visit other sites where they ride dead horses in different ways.
9. Complain about the state of horses nowadays.
10. Blame the breeding.

28. All the resources at this link:

29. Thank you!
Conrad Martin, Ed.D

30. References Slide 21 – 22 from EVAAS
Slide 19, from John Hattie Visible Learning, us/nam/the-visible-learning-research