Teacher Quality

There is currently a push to evaluate teacher preparation programs based in part on the value-added of their graduates. Predictably, this is a highly controversial issue, and the research supporting it is, to be charitable, still underdeveloped. At present, the evidence suggests that the differences in effectiveness between teachers trained by different prep programs may not be particularly large (see here, here, and here), though there may be exceptions (see this paper).

In the meantime, there’s an interesting little conflict underlying the debate about measuring preparation programs’ effectiveness, one that’s worth pointing out. For the purposes of this discussion, let’s put aside the very important issue of whether the models are able to account fully for where teaching candidates end up working (i.e., bias in the estimates based on school assignments/preferences), as well as (valid) concerns about judging teachers and preparation programs based solely on testing outcomes. All that aside, any assessment of preparation programs using the test-based effectiveness of their graduates is picking up on two separate factors: How well they prepare their candidates; and who applies to their programs in the first place.

In other words, programs that attract and enroll highly talented candidates might look good even if they don’t do a particularly good job preparing teachers for their eventual assignments. But does that really matter?

Education researchers have paid a lot of attention to the sorting of teachers across schools. For example, it is well known that schools serving more low-income students tend to employ teachers who are, on average, less qualified (in terms of experience, degree, certification, etc.; also see here).

Far less well-researched, however, is the issue of sorting within schools – for example, whether teachers with certain characteristics are assigned to classes with different students than their colleagues in the same school. In addition to the obvious fact that which teachers are in front of which students every day is important, this question bears on a few major issues in education policy today. For example, there is evidence that teacher turnover is influenced by the characteristics of the students teachers teach, which means that classroom assignments might either exacerbate or mitigate mobility and attrition. In addition, teacher productivity measures such as value-added may be affected by the sorting of students into classes based on characteristics for which the models do not account, and a better understanding of the teacher/student matching process could help inform this issue.

A recent article, which was published in the journal Sociology of Education, sheds light on these topics with a very interesting look at the distribution of students across teachers' classrooms in Miami-Dade between 2003-04 and 2010-11. The authors’ primary question is: Are certain characteristics, most notably race/ethnicity, gender, experience, or pre-service qualifications (e.g., SAT scores), associated with assignment to higher or lower-scoring students among teachers in the same school, grade, and year?

In a story for Education Week, always reliable Stephen Sawchuk reports on what may be a trend in states’ first results from their new teacher evaluation systems: The ratings are skewed toward the top.

For example, the article notes that, in Michigan, Florida and Georgia, a high proportion of teachers (more than 90 percent) received one of the two top ratings (out of four or five). This has led to some grumbling among advocates and others, citing similarities between these results and those of the old systems, in which the vast majority of teachers were rated “satisfactory," and very few were found to be “unsatisfactory."

Differentiation is very important in teacher evaluations – it’s kind of the whole point. Thus, it’s a problem when ratings are too heavily concentrated toward one end of the distribution. However, as Aaron Pallas points out, these important conversations about evaluation results sometimes seem less focused on good measurement or even the spread of teachers across categories than on the narrower question of how many teachers end up with the lowest rating - i.e., how many teachers will be fired.

A few weeks ago, Students First NY (SFNY) released a report, in which they presented a very simple analysis of the distribution of “unsatisfactory” teacher evaluation ratings (“U-ratings”) across New York City schools in the 2011-12 school year.

The report finds that U-ratings are distributed unequally. In particular, they are more common in schools with higher poverty, more minorities, and lower proficiency rates. Thus, the authors conclude, the students who are most in need of help are getting the worst teachers.

There is good reason to believe that schools serving larger proportions of disadvantaged students have a tougher time attracting, developing and retaining good teachers, and there is evidence of this, even based on value-added estimates, which adjust for these characteristics (also see here). However, the assumptions upon which this Students First analysis is based are better seen as empirical questions, and, perhaps more importantly, the recommendations they offer are a rather crude, narrow manifestation of market-based reform principles.