Evaluating teachers based on “value-added” analysis of their students’ progress is unfair to teachers with lots of low-income students, argue teachers’ union leaders in Washington, D.C.
Ward 8, one of the poorest areas of the city, has only 5 percent of the teachers defined as effective under the new evaluation system known as IMPACT, but more than a quarter of the ineffective ones. Ward 3, encompassing some of the city’s more affluent neighborhoods, has nearly a quarter of the best teachers, but only 8 percent of the worst.
. . . Are the best, most experienced D.C. teachers concentrated in the wealthiest schools, while the worst are concentrated in the poorest schools? Or does the statistical model ignore the possibility that it’s more difficult to teach a room of impoverished children?
Value-added models compare a student’s previous progress with current progress: If Johnny has gained four months of learning for every year in school — because of poverty, disability, lack of English fluency or some other reason — and gains six months in Teacher X’s class, then the teacher has done well. If Jane has gained nine months a year in past years but only six months in Teacher Y’s class, the teacher gets the blame.
Adding demographic factors is unnecessary, if there’s at least three years of test-score data available, says William Sanders, a former University of Tennessee researcher who developed value-added analysis.
“If you’ve got a poor black kid and a rich white kid that have exactly the same academic achievement levels, do you want the same expectations for both of them the next year?”
However, D.C. uses one year of data, and factors in students’ poverty status.
A few value-added models factor in the concentration of disadvantaged students in a classroom.
Studies have found that students surrounded by more advantaged peers tend to score higher on tests than similarly performing students surrounded by less advantaged peers.
To some experts, this research suggests that a teacher with a large number of low-achieving minority children in a classroom, for example, might have a more difficult job than another teacher with few such students.
Controlling for the demographics of a whole class makes a complex model even more complicated — and may not make much difference. But the idea is being studied.