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UNDERNEWS

Undernews is the online report of the Progressive Review, edited by Sam Smith, who covered Washington during all or part of ten of America's presidencies and who has edited alternative journals since 1964. The Review, which has been on the web since 1995, is now published from Freeport, Maine. We get over 5 million article visits a year. See prorev.com for full contents of our site

February 19, 2010

WHY TYING TEACHER TENURE TO TEST SCORES FLUNKS

Justin Snider, Ed Week - First, when a student fails to flourish, it is rarely the result of one party. Rather, it tends to be a confluence of confounding factors, often involving parents, teachers, administrators, politicians, neighborhoods, and even the student himself. If we could collect data that allowed us to parse out these influences accurately, then we might be able to hold not just teachers but all parties responsible. At present, however, we are light-years away from even understanding how to collect such data.

Second, learning is not always, or easily, captured by high-stakes tests. A student's performance on a given day reflects a whole lot more than what his teacher has or hasn't taught him.

When it comes to school accountability, today's favorite catchphrase is "value added" assessment. The idea is that by measuring what students know at both the beginning and the end of the school year, and by simply subtracting the former from the latter, we're able to determine precisely how much "value" a given teacher has "added" to his or her students' education. Then we can make informed decisions about tenure and teacher compensation. After all, why shouldn't teachers whose students learn more than most be better compensated than their colleagues? Why shouldn't teachers whose students learn little be fired?

The short answer to both questions is because our current data systems are a complete mess. We tend to collect the wrong kinds of data, partly to save money and partly because we're not all that good at statistical analysis.

The accountability measures of the federal No Child Left Behind Act, for instance, are based on cross-sectional rather than longitudinal data. In layman's terms, this means that we end up comparing how one set of 7th graders performs in a given year with how a different set of 7th graders performs the following year. Experts in data analysis agree that this is more than a little problematic. A better system-one based on longitudinal data-would instead compare how the same set of students performs year after year, thereby tracking change over time. But these are not the data we currently collect, in large part because doing so is difficult and expensive. . .


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