Teacher Evaluation

Getting Teacher Evaluation Right

Linda Darling-Hammond’s new book, Getting Teacher Evaluation Right, is a detailed, practical guide about how to improve the teaching profession. It leverages the best research and best practices, offering actionable, illustrated steps to getting teacher evaluation right, with rich examples from the U.S. and abroad.

Here I offer a summary of the book’s main arguments and conclude with a couple of broad questions prompted by the book. But, before I delve into the details, here’s my quick take on Darling-Hammond’s overall stance.

We are at a crossroads in education; two paths lay before us. The first seems shorter, easier and more straightforward. The second seems long, winding and difficult. The big problem is that the first path does not really lead to where we need to go; in fact, it is taking us in the opposite direction. So, despite appearances, more steady progress will be made if we take the more difficult route. This book is a guide on how to get teacher evaluation right, not how to do it quickly or with minimal effort. So, in a way, the big message or take away is: There are no shortcuts.

Incentives And Behavior In DC's Teacher Evaluation System

A new working paper, published by the National Bureau of Economic Research, is the first high quality assessment of one of the new teacher evaluation systems sweeping across the nation. The study, by Thomas Dee and James Wyckoff, both highly respected economists, focuses on the first three years of IMPACT, the evaluation system put into place in the District of Columbia Public Schools in 2009.

Under IMPACT, each teacher receives a point total based on a combination of test-based and non-test-based measures (the formula varies between teachers who are and are not in tested grades/subjects). These point totals are then sorted into one of four categories – highly effective, effective, minimally effective and ineffective. Teachers who receive a highly effective (HE) rating are eligible for salary increases, whereas teachers rated ineffective are dismissed immediately and those receiving minimally effective (ME) for two consecutive years can also be terminated. The design of this study exploits that incentive structure by, put very simply, comparing the teachers who were directly above the ME and HE thresholds to those who were directly below them, and to see whether they differed in terms of retention and performance from those who were not. The basic idea is that these teachers are all very similar in terms of their measured performance, so any differences in outcomes can be (cautiously) attributed to the system’s incentives.

The short answer is that there were meaningful differences.

Comparing Teacher And Principal Evaluation Ratings

The District of Columbia Public Schools (DCPS) has recently released the first round of results from its new principal evaluation system. Like the system used for teachers, the principal ratings are based on a combination of test and non-test measures. And the two systems use the same final rating categories (highly effective, effective, minimally effective and ineffective).

It was perhaps inevitable that there would be comparisons of their results. In short, principal ratings were substantially lower, on average. Roughly half of them received one of the two lowest ratings (minimally effective or ineffective), compared with around 10 percent of teachers.

Some wondered whether this discrepancy by itself means that DC teachers perform better than principals. Of course not. It is difficult to compare the performance of teachers versus that of principals, but it’s unsupportable to imply that we can get a sense of this by comparing the final rating distributions from two evaluation systems.

Thoughts On Using Value Added, And Picking A Model, To Assess Teacher Performance

Our guest author today is Dan Goldhaber, Director of the Center for Education Data & Research and a Research Professor in Interdisciplinary Arts and Sciences at the University of Washington Bothell.

Let me begin with a disclosure: I am an advocate of experimenting with using value added, where possible, as part of a more comprehensive system of teacher evaluation. The reasons are pretty simple (though articulated in more detail in a brief, which you can read here). The most important reason is that value-added information about teachers appears to be a better predictor of future success in the classroom than other measures we currently use. This is perhaps not surprising when it comes to test scores, certainly an important measure of what students are getting out of schools, but research also shows that value added predicts very long run outcomes, such as college going and labor market earnings. Shouldn’t we be using valuable information about likely future performance when making high-stakes personnel decisions? 

It almost goes without saying, but it’s still worth emphasizing, that it is impossible to avoid making high-stakes decisions. Policies that explicitly link evaluations to outcomes such as compensation and tenure are new, but even in the absence of such policies that are high-stakes for teachers, the stakes are high for students, because some of them are stuck with ineffective teachers when evaluation systems suggest, as is the case today, that nearly all teachers are effective.

So Many Purposes, So Few Tests

In a new NBER working paper, economist Derek Neal makes an important point, one of which many people in education are aware, but is infrequently reflected in actual policy. The point is that using the same assessment to measure both student and teacher performance often contaminates the results for both purposes.

In fact, as Neal notes, some of the very features required to measure student performance are the ones that make possible the contamination when the tests are used in high-stakes accountability systems. Consider, for example, a situation in which a state or district wants to compare the test scores of a cohort of fourth graders in one year with those of fourth graders the next year. One common means of facilitating this comparability is administering some of the questions to both groups (or to some "pilot" sample of students prior to those being tested). Otherwise, any difference in scores between the two cohorts might simply be due to differences in the difficulty of the questions. If you cannot check that out, it's tough to make meaningful comparisons.

But it’s precisely this need to repeat questions that enables one form of so-called “teaching to the test," in which administrators and educators use questions from prior assessments to guide their instruction for the current year.

What Some Call Delay Is At Times Just Good Policy Making

U.S. Secretary of Education Arne Duncan recently announced that states will be given the option to postpone using the results of their new teacher evaluations for high-stakes decisions during the phase-in of the new Common Core-aligned assessments. The reaction from some advocates was swift condemnation – calling the decision little more than a “delay” and a “victory for the status quo."

We hear these kinds of arguments frequently in education. The idea is that change must be as rapid as possible, because “kids can’t wait." I can understand and appreciate the urgency underlying these sentiments. Policy change in education (as in other arenas) can sometimes be painfully slow, and what seem likes small roadblocks can turn out to be massive, permanent obstacles.

I will not repeat my views regarding the substance of Secretary Duncan’s decision – see this op-ed by Morgan Polikoff and myself. I would, however, like to make one very quick point about these “we need change right now because students can’t wait” arguments: Sometimes, what is called “delay” is actually better described as good policy making, and kids can wait for good policy making.

What Should The Results Of New Teacher Evaluations Look Like?

In a previous post, I discussed the initial results from new teacher evaluations in several states, and the fact that states with implausibly large proportions of teachers in the higher categories face a difficult situation – achieving greater differentiation while improving the quality and legitimacy of their systems.

I also expressed concern that pre-existing beliefs about the "proper" distribution of teacher ratings -- in particular, how many teachers should receive the lowest ratings -- might inappropriately influence the process of adjusting the systems based on the first round of results. In other words, there is a risk that states and districts will change their systems in a crude manner that lowers ratings simply for the sake of lowering ratings.

Such concerns of course imply a more general question: How should we assess the results of new evaluation systems? That’s a complicated issue, and these are largely uncharted waters. Nevertheless, I'd like to offer a few thoughts as states and districts move forward.

Relationship Counseling

A correlation between two variables measures the strength of the linear relationship between them. Put simply, two variables are positively correlated to the extent that individuals with relatively high or low values on one measure tend to have relatively high or low values on the other, and negatively correlated to the extent that high values on one measure are associated with low values on the other.

Correlations are used frequently in the debate about teacher evaluations. For example, researchers might assess the relationship between classroom observations and value-added measures, which is one of the simpler ways to gather information about the “validity” of one or the other – i.e., whether it is telling us what we want to know. In this case, if teachers with higher observation scores also tend to get higher value-added scores, this might be interpreted as a sign that both are capturing, at least to some extent, "true" teacher performance.

Yet there seems to be a tendency among some advocates and policy makers to get a little overeager when interpreting correlations.

On Teacher Evaluation: Slow Down And Get It Right

About Value-Added And "Junk Science"

One can often hear opponents of value-added referring to these methods as “junk science." The term is meant to express the argument that value-added is unreliable and/or invalid, and that its scientific “façade” is without merit.

Now, I personally am not opposed to using these estimates in evaluations and other personnel policies, but I certainly understand opponents’ skepticism. For one thing, there are some states and districts in which design and implementation has been somewhat careless, and, in these situations, I very much share the skepticism. Moreover, the common argument that evaluations, in order to be "meaningful," must consist of value-added measures in a heavily-weighted role (e.g., 45-50 percent) is, in my view, unsupportable.

All that said, calling value-added “junk science” completely obscures the important issues. The real questions here are less about the merits of the models per se than how they're being used.