1. Douglas D. Ready, Ph.D. Teachers College, Columbia University
Achievement vs. Learning in Oregon: Unpacking Both and Understanding the Difference
Douglas D. Ready, Ph.D.
Teachers College, Columbia University

2. A Different Approach to Student Performance
The major aim of schooling is student learning, not student achievement.
Meaning, we should focus on learning gaps, not “achievement gaps.”
Why? Sizable racial/ethnic and income gaps exist when children enter kindergarten.
Individual schools can influence student learning, not the skills students bring with them.

4. SAME CHILDREN OVER TIME

5. Exploring Oregon Achievement Data
These analyses use RIT scores, not benchmarks. Although very informative, benchmarks cannot accurately measure learning over time.
All estimates are within-school, not state-wide averages.
All students in the analyses had pre- and post-tests, and information on race/ethnicity, income and LEP status.

6. Exploring Oregon Achievement Data
Although this approach is similar to Oregon’s recent growth model efforts, it is quite different in most respects. My work is rooted in explanation, not accountability.
These analyses are meant to provide examples of different ways to think about student performance. They are by no means meant to be an exhaustive exploration.

7. Student Samples High School: 32,377 tenth graders in 233 high schools
Middle School: 25,158 eighth graders in 220 middle schools
Junior High: 5,766 eighth graders in 27 junior highs

16. 10th Grade Reading Achievement (RIT Scores)

17. Reading Gain Between 8th and 10th Grade (RIT Scores)

19. Thinking about Achievement
Race/Ethnicity
Demographic Background

20. -2.62
-4.23
-5.84

21. Unpacking Data is the First Step
We need to measure student achievement and student learning.
These relationships between student background and achievement suggest limited probabilities, but unlimited possibilities.
Demography is not destiny.

22. What’s Required
The ability to measure student academic growth among the same students over time.
The use of child-level and school-level data simultaneously: schools don’t learn, students do.

23. LA/HG
HA/HG
HA/LG
LA/LG

24. The “Action” is Within Schools
Although between-school differences are important, great variability in achievement exists within your school.
In fact, there is much more variability within than between schools.

27. Next Steps
Growth models vs. growth curve models (but both can be “value added models”)
Oregon will very soon be able to model children’s learning over multiple years.