I’m still attracted to the idea of objective measurements of teaching performance.* But I’m wary of what appear to be some pretty big limitations in current implementations. It’s interesting reading the teacher comments on the LA Times’ teacher value added database, because many teachers appear to have a similar view – conceptually supportive, but wary … Continue reading “Teacher value added modeling – my bottom line”
In my last post, I showed that culling low-performance teachers can work surprisingly well, even in the presence of noise that’s as large as the signal. However, that involved two big assumptions: the labor pool of teachers is unlimited with respect to the district’s needs, and there’s no feedback from the evaluation process to teacher … Continue reading “Dynamics of teacher value added – the limits”
Suppose for the sake of argument that (a) maximizing standardized test scores is what we want teachers to do and (b) Value Added Modeling (VAM) does in fact measure teacher contributions to scores, perhaps with jaw-dropping noise, but at least no systematic bias. Jaw-dropping noise isn’t as bad as it sounds. Other evaluation methods, like … Continue reading “Dynamics of teacher value added”
The vision of teacher value added modeling (VAM) is a good thing: evaluate teachers based on objective measures of their contribution to student performance. It may be a bit utopian, like the cybernetic factory, but I’m generally all for substitution of reason for baser instincts. But a prerequisite for a good control system is a … Continue reading “Teacher value added modeling”
I can’t resist a dataset. So, now that I have the NYC teacher value added modeling results, I have to keep picking at it. The 2007-2008 results are in a slightly different format from the later years, but contain roughly the same number of teacher ratings (17,000) and have lots of matching names, so at … Continue reading “More NYC teacher VAM mysteries”
Does DNA IQ testing create a meritocracy, or merely reinforce existing biases?
A nice TED talk explaining how algorithms can reinforce unfairness, inequity and errors of judgment: Note the discussion of teacher value added modeling. This corresponds with what I found in my own assessment here.
It’s a good idea to read things you criticize; checking your sources doesn’t hurt either. One of the most frequent targets of uninformed criticism, passed down from teacher to student with nary a reference to the actual text, must be The Limits to Growth. In writing my recent review of Green & Armstrong (2007), I … Continue reading “On Limits to Growth”