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Wednesday, June 11, 2014

Controversial data-driven research behind the California court’s decision to reject teacher tenure | Education By The Numbers

Controversial data-driven research behind the California court’s decision to reject teacher tenure | Education By The Numbers:



Controversial data-driven research behind the California court’s decision to reject teacher tenure

Underlying the California court’s decision on June 10, 2014 to reject teacher tenure as unconstitutional is a controversial body of academic research on teacher effectiveness.  The argument that won out was that tenure rules often force school districts to retain their worst teachers. Those ineffective teachers tend to end up at the least desirable schools that are packed with low-income and minority students. As a result, teacher tenure ends up harming low-income students who don’t have the same access as rich students to high-quality teaching.
But for this argument to carry weight we have to be able to distinguish good teachers from bad. How can we prove that California’s low-income schools are filled with teachers who are inferior to the teachers at high-income schools?
The nine plaintiffs, including Beatriz Vergara, who brought suit against the state. This slide, without names, was shown in court.
The nine plaintiffs, including Beatriz Vergara, who brought suit against the state. This slide, without names, was shown in court.
Dan Goldhaber, a labor economist at the University of Washington, and Eric Hanushek, a senior fellow at the Hoover Institution at Stanford, were two of the expert witnesses who spoke against teacher tenure in Vegara v. California. Both employ quantitative economic analysis in the field of education. They are both big proponents of using value-added measures to determine who is an effective teacher.
In value-added analysis, you  begin by creating a model that calculates how much kids’ test scores, on average, increase each year. (Test score year 2 minus test score year 1). Then you give a high score to teachers who have students who post test-score gains above the average. And you give a low score to teachers whose students show smaller test-score gains. There are lots of mathematical tweaks, but the general idea is to build a model that answers this question: are the students of this particular teacher learning more or less than you expect them to?  
Indeed, researchers using this value-added measure approach have sometimes found low-income schools have a high number of teachers who teach students with below-average test score gains.
Many researchers are questioning whether test-score gains are a good measure of teacher effectiveness. Part of the problem are the standardized tests themselves. In some cases, theControversial data-driven research behind the California court’s decision to reject teacher tenure | Education By The Numbers: