Yet Another Problem With ‘Objective’ Measurement of Teaching

There have been many reports (including some from me) showing how unstable Value-Added Measurements (VAM) are for teachers.

Here in DC, Jason Kamras, Michelle Rhee and Kaya Henderson have put in place a system of supposedly “objective” evaluations of teachers, coupled with VAM for some of them, that has been used to evaluate teachers.

Now comes a report described in Education Week saying in effect that not only is VAM a sham, but there is a systematic bias in classroom observations against teachers who are assigned low-income and low-achieving students.

You don’t say, he said sarcastically.

Here is an excerpt from the EdWeek article:
———–
“As the rubber hits the road in the implementation of states’ revamped teacher-evaluation systems, new research illuminates a troubling source of bias. School principals-when conducting classroom observations-appear to give some teachers an unfair boost based on the students they’re assigned to teach, rather than based on their own instructional savvy.

“Observers tended to give the best marks to teachers whose incoming students were high performing, while those teachers working with academically struggling students were penalized, according to an analysis of thousands of observation scores.

The report, released today by the Brown Center on Education Policy at the Brookings Institution, a Washington think tank, raises a host of new concerns about the nation’s evolving systems for grading teachers. And it suggests that, in trying to manage the technical and political challenges posed by test-score-based approaches to evaluation, such as “value added” methods, policymakers may be missing problems in other features of the systems.”

Published in: on May 15, 2014 at 12:19 pm  Comments (1)  

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One CommentLeave a comment

  1. And the report recommends some super-duper idea, like observations given by strangers and soaked in VAM sauce http://curmudgucation.blogspot.com/2014/05/brookings-whips-up-some-teacher-eval.html

    Like


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