Arne-Ology & the Bad Incentives of Evaluating Teacher Prep with Student Outcome Data

Posted on April 25, 2014



As I understand it, USDOE is going to go ahead with the push to have teacher preparation programs rated in part based on the student growth outcomes of children taught by individuals receiving credentials from those programs. Now, the layers of problems associated with this method are many and I’ve addressed them previously here and in professional presentations.

  1. This post summarizes my earlier concerns about how the concept fails both statistically and practically.
  2. This post explains what happens at the ridiculous extremes of this approach (a warped, endogenous cycle of reformy awesomeness)
  3. These slides present a more research based, and somewhat less snarky critique

Now, back to the snark.

This post builds on my most recent post in which I challenged the naive assertion that current teacher ratings really tell us where the good teachers are. Specifically, I pointed out that in Massachusetts, if we accept the teacher ratings at face value, then we must accept that good teachers are a) less likely to teach in middle schools, b) less likely to teach in high poverty schools and c) more likely to teach in schools that have more girls than boys.

Slide4

Extending these findings to the policy of rating teacher preparation programs by the ratings their teachers receive… working on the assumption that these ratings are quite strongly biased by school context, it would make sense for Massachusetts teacher preparation institutions to try to get their teachers placed in low poverty elementary schools that have fewer boys.

Given that New Jersey growth percentile data reveal even more egregious patterns of bias, I now offer insights for New Jersey colleges of education as to where they should try to place their graduates – that is, if they want to win at the median growth percentile game.

Slide2

It’s pretty simple – New Jersey colleges of education would be wise to get their graduates placements in schools that are:

  • 20% of fewer free lunch (to achieve good math gains)
  • 5% or lower black (to achieve good math gains)
  • 11% or lower free lunch (to achieve good LAL gains)
  • 2% or lower black ( to achieve good LAL gains)

Now, the schools NJ colleges of ed should avoid (for placing their grads) are those that are:

  • over 50% free lunch
  • over 30% black

That is, if colleges of education want to play this absurd game of chasing invalid metrics.

Let’s take a look at some of the specific districts that might be of interest.

Here are the districts with the highest and lowest growth producing teachers (uh… assuming this measure has any attribution to teacher quality).

Slide3

Now, my New Jersey readers can readily identify the differences between these groups, with a few exceptions. Ed schools in NJ would be wisest to maximize their placements in locations like Bernards Twp, Essex Fells, Princeton, Mendham and Ridgewood. After all, what young grads wouldn’t want to work in these districts? And of course, Ed schools would be advised to avoid placing any grads in districts like East Orange, Irvington or Newark.

Let me be absolutely clear here. I AM NOT ACTUALLY ADVOCATING SUCH DETRIMENTAL UNETHICAL BEHAVIOR.

Rather, I am pointing out that newly adopted USDOE regulations in fact endorse this model by requiring that this type of data actually be used to consequentially evaluate teacher preparation programs.

It’s simply wrong. It’s bad policy. And it must stop!

And yes… quite simply… this is WORSE THAN THE STATUS QUO!

For further discussion on this point, I refer you to this post!

 

 

 

 

 

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