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

Gratitude

Can your ego handle value-added assessment?

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

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

What we think…

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

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)

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

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

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

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

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

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

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

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]

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)

Sample Kid – Jeremy (3rd grade) equation looks like this: 15 + 5 + 0 + 10 + 5 + 0 + 5 + 3 + 5 + 2 = 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

Which leaves

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

EVAAS Achievement

EVAAS Growth

Some Tools for Practice

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

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All the resources at this link: https://wakelet.com/wake/b4c18242-b8cf-47c9-9843-93ea684430f0

Thank you! Conrad Martin, Ed.D cfmartin3@gmail.com conrad@renowgroup.com

References Slide 21 – 22 from EVAAS Slide 19, from John Hattie Visible Learning, https://us.corwin.com/en- us/nam/the-visible-learning-research