John Cronin, Ph.D.John Cronin, Ph.D.DirectorDirector

The Kingsbury Center @ NWEAThe Kingsbury Center @ NWEA

Measuring and Modeling Growth in a High Measuring and Modeling Growth in a High Stakes EnvironmentStakes Environment

Presenter - John Cronin, Ph.D.

Contacting us:Rebecca Moore: 503-548-5129E-mail: rebecca.moore@nwea.org

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Measuring and Modeling Growth in a High Stakes Environment

How tests are used to evaluate teachers

Three principles

• Ineffective should mean egregiously incompetent.

• You must support teachers with an ineffective rating through improvement or dismissal.

• You shouldn’t dismiss a teacher that can’t be replaced with someone more effective.

What question is being answered? – Performance Management

Is the progress produced by this teacher dramatically greater or

less than teaching peers that deliver instruction to comparable

students?

Our nation has moved from a model of education reform that focused on fixing schools to a model that is focused on fixing the teaching profession.

Difficulty of New York Proficient Level

Difficulty of ACT college readiness standards

Moving from Proficiency to Growth

All students count when accountability is measured

through growth.

One district’s change in 5th grade math performance relative to Kentucky cut scores

One district’s change in 5th grade math performance relative to Kentucky cut scores

proficiency college readiness

Number of 5th grade students meeting math growth target in the same district

Number of 5th grade students meeting math growth target in the same district

Issues in the use of growth and value-added measures

Measurement design of the instrument

Many assessments are not designed to measure growth. Others do not measure growth equally well for all students.

Tests are not equally accurate for all students

California STAR NWEA MAP

Tests are not equally accurate for all students

Grade 6 New York Mathematics

Issues in the use of growth and value-added measures

“Among those who ranked in the top category on the TAKS reading test, more than 17% ranked among the lowest two categories on the Stanford. Similarly more than 15% of the lowest value-added teachers on the TAKS were in the highest two categories on the Stanford.”

Corcoran, S., Jennings, J., & Beveridge, A., Teacher Effectiveness on High and Low Stakes Tests, Paper presented at the Institute for Research on Poverty summer workshop, Madison, WI (2010).

Issues in the use of growth and value-added measures

Instability of results

A variety of factors can cause value-added results to lack stability.

Results are more likely to be stable at the extremes. The use of multiple-years of data is highly recommended.

Teachers with growth scores in lowest and highest quintile over two years using NWEA’s Measures of Academic Progress

Bottom quintile Y1&Y2

Top quintile Y1&Y2

Number 59/493 63/493

Percent 12% 13%

r .64 r2 .41

Typical r values for measures of teaching effectiveness range between .30 and .60 (Brown Center on Education Policy, 2010)

Reliability of teacher value-added estimates

Issues in the use of growth and value-added measures

Control for statistical error

All models attempt to address this issue. Nevertheless, many teachers value-added scores will fall within the range of statistical error.

Range of teacher value-added estimates

Issues in the use of growth and value-added measures

Instructional alignment

Tests should align to the teacher’s instructional responsibilities.

Issues in the use of growth and value-added measures

Measurement sensitivity

Assessments must align with the should be instructionally sensitive.

Issues in the use of growth and value-added measures

Model Wars

There are a variety of models in the marketplace. These models may come to different conclusions about the effectiveness of a teacher or school. Differences in findings are more likely to happen at the extremes.

Issues in the use of growth and value-added measures

Lack of random assignment

The use of a value-added model assumes that the school doesn’t add a source of variation that isn’t controlled for in the model.

e.g. Young teachers are assigned disproportionate numbers of students with poor discipline records.

Issues in the use of growth and value-added measures

Uncovered Subjects and Teachers

High quality tests may not be administered, or available, for many teachers and grades. Subjects like social studies may be particularly problematic.

Issues in the use of growth and value-added measures

Idiosyncratic cases

In self-contained classrooms, one or two idiosyncratic cases can have a large effect on results.

Other issues

Security and Cheating

When measuring growth, one teacher who cheats disadvantages the next teacher.

Security considerations

• Teachers should not be allowed to view the contents of the item bank or record items.

• Districts should have policies for accomodation that are based on student IEPs.

• Districts should consider having both the teacher and a proctor in the test room.

• Districts should consider whether other security measures are needed for both the protection of the teacher and administrators.

Other issues

Proctoring

Proctoring both with and without the classroom teacher raises possible problems.

Documentation that test administration procedures were properly followed is important.

Potential Litigation Issues

The use of testing data for high stakes personnel decisions does not yet have a strong, coherent, body of case law.

Expect litigation if value-added results are the lynchpin evidence for a teacher-dismissal case until a body of case law is established.

Possible legal issues

• Title VII of the Civil Rights Act of 1964 – Disparate impact of sanctions on a protected group.

• State statutes that provide tenure and other related protections to teachers.

• Challenges to a finding of “incompetence” stemming from the growth or value-added data.

Recommendations

• Embrace the formative advantages of growth measurement as well as the summative.

• Create comprehensive evaluation systems with multiple measures of teacher effectiveness (Rand, 2010)

• Select measures as carefully as value-added models.• Use multiple years of student achievement data.• Understand the issues and the tradeoffs.