Sunday, January 5, 2014

Data, Portfolios & the Path Forward for NYC (& Elsewhere) | School Finance 101

Data, Portfolios & the Path Forward for NYC (& Elsewhere) | School Finance 101:

Data, Portfolios & the Path Forward for NYC (& Elsewhere)

Posted on January 5, 2014

 
 
 
 
 
 
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As the new year begins, I’ve been pondering what I might recommend as guiding principles for the path forward for education policy in New York City under its new Mayor, Bill de Blasio, who is often referred to on Twitter as BDB. So here are my thoughts for the way forward, from one BDB (Bruce D. Baker) to another.
Note that I had drafted much of this content last spring when convening with a group of scholars to discuss the path forward for NYC education policies. Not being as well versed in the specifics of NYC education policies, but having at least written academically about some, I kept my ideas broad, and applicable to many educational settings across the U.S.
My recommendations fall into two broad categories:

Develop a robust, balanced, least intrusive system of indicators for evaluating New York City Schools and then use that information appropriately

NYC BOE policies of the past ten years have been rife with data abuse (though at times, merely in an effort to comply with state required data abuse). School closures have been based on ill-conceived measures of “school failure” which do little more than target the city’s neediest student populations, imposing on them repeated disruptions.
New York City’s teacher performance reports, albeit better than many, apply the worst form of statistical reductionism to quantify teacher “quality,” taking noisy statistical estimates of the association between teachers-of-record and assigned students test score gains (applying only the most convenient statistical corrections) in limited curricular areas and grades, and assuming levels of precision and accuracy that are completely unwarranted.
Such data abuse – on both counts [school closures and teacher ratings] – is reprehensible.
Right-sized (NOT BIG) data can indeed be useful for guiding decision-making in large, complex