Classroom Observation

The Details Matter In Teacher Evaluations

Throughout the process of reforming teacher evaluation systems over the past 5-10 years, perhaps the most contentious, discussed issue was the importance, or weights, assigned to different components. Specifically, there was a great deal of debate about the proper weight to assign to test-based teacher productivity measures, such estimates from value-added and other growth models.

Some commentators, particularly those more enthusiastic about test-based accountability, argued that the new teacher evaluations somehow were not meaningful unless value-added or growth model estimates constituted a substantial proportion of teachers’ final evaluation ratings. Skeptics of test-based accountability, on the other hand, tended toward a rather different viewpoint – that test-based teacher performance measures should play little or no role in the new evaluation systems. Moreover, virtually all of the discussion of these systems’ results, once they were finally implemented, focused on the distribution of final ratings, particularly the proportions of teachers rated “ineffective.”

A recent working paper by Matthew Steinberg and Matthew Kraft directly addresses and informs this debate. Their very straightforward analysis shows just how consequential these weighting decisions, as well as choices of where to set the cutpoints for final rating categories (e.g., how many points does a teacher need to be given an “effective” versus “ineffective” rating), are for the distribution of final ratings.

Student Sorting And Teacher Classroom Observations

Although value added and other growth models tend to be the focus of debates surrounding new teacher evaluation systems, the widely known but frequently unacknowledged reality is that most teachers don’t teach in the tested grades and subjects, and won’t even receive these test-based scores. The quality and impact of the new systems therefore will depend heavily upon the quality and impact of other measures, primarily classroom observations.

These systems have been in use for decades, and yet, until recently, relatively little is known about their properties, such as their association with student and teacher characteristics, and there are, as yet, only a handful of studies of their impact on teachers’ performance (e.g., Taylor and Tyler 2012). The Measures of Effective Teaching (MET) Project, conducted a few years ago, was a huge step forward in this area, though at the time it was perhaps underappreciated the degree to which MET’s contribution was not just in the (very important) reports it produced, but also in its having collected an extensive dataset for researchers to use going forward. A new paper, just published in Educational Evaluation and Policy Analysis, is among the many analyses that have and will use MET data to address important questions surrounding teacher evaluation.

The authors, Rachel Garrett and Matthew Steinberg, look at classroom observation scores, specifically those from Charlotte Danielson’s widely employed Framework for Teaching (FFT) protocol. These results are yet another example of how observation scores share most of the widely-cited (statistical) criticisms of value added scores, most notably their sensitivity to which students are assigned to teachers.

Evidence From A Teacher Evaluation Pilot Program In Chicago

The majority of U.S. states have adopted new teacher evaluation systems over the past 5-10 years. Although these new systems remain among the most contentious issues in education policy today, there is still only minimal evidence on their impact on student performance or other outcomes. This is largely because good research takes time.

A new article, published in the journal Education Finance and Policy, is among the handful of analyses examining the preliminary impact of teacher evaluation systems. The researchers, Matthew Steinberg and Lauren Sartain, take a look at the Excellence in Teaching Project (EITP), a pilot program carried out in Chicago Public Schools starting in the 2008-09 school year. A total of 44 elementary schools participated in EITP in the first year (cohort 1), while an additional 49 schools (cohort 2) implemented the new evaluation systems the following year (2009-10). Participating schools were randomly selected, which permits researchers to gauge the impact of the evaluations experimentally.

The results of this study are important in themselves, and they also suggest some more general points about new teacher evaluations and the building body of evidence surrounding them.

The Magic Of Multiple Measures

Our guest author today is Cara Jackson, Assistant Director of Research and Evaluation at the Urban Teacher Center.

Teacher evaluation has become a contentious issue in U.S.  Some observers see the primary purpose of these reforms as the identification and removal of ineffective teachers; the popular media as well as politicians and education reform advocates have all played a role in the framing of teacher evaluation as such.  But, while removal of ineffective teachers was a criterion under Race to the Top, so too was the creation of evaluation systems to be used for teacher development and support.

I think most people would agree that teacher development and improvement should be the primary purpose, as argued here.  Some empirical evidence supports the efficacy of evaluation for this purpose (see here).  And given the sheer number of teachers we need, declining enrollment in teacher preparation programs, and the difficulty disadvantaged schools have retaining teachers, school principals are probably none too enthusiastic about dismissing teachers, as discussed here.

Of course, to achieve the ambitious goal of improving teaching practice, an evaluation system must be implemented well.  Fans of Harry Potter might remember when Dolores Umbridge from the Ministry of Magic takes over as High Inquisitor at Hogwarts and conducted “inspections” of Hogwart’s teachers in Book 5 of J.K. Rowling’s series.  These inspections pretty much demonstrate how not to approach classroom observations: she dictates the timing, fails to provide any of indication of what aspects of teaching practice she will be evaluating, interrupts lessons with pointed questions and comments, and evidently does no pre- or post-conferencing with the teachers. 

Research On Teacher Evaluation Metrics: The Weaponization Of Correlations

Our guest author today is Cara Jackson, Assistant Director of Research and Evaluation at the Urban Teacher Center.

In recent years, many districts have implemented multiple-measure teacher evaluation systems, partly in response to federal pressure from No Child Left Behind waivers and incentives from the Race to the Top grant program. These systems have not been without controversy, largely owing to the perception – not entirely unfounded - that such systems might be used to penalize teachers.  One ongoing controversy in the field of teacher evaluation is whether these measures are sufficiently reliable and valid to be used for high-stakes decisions, such as dismissal or tenure.  That is a topic that deserves considerably more attention than a single post; here, I discuss just one of the issues that arises when investigating validity.

 The diagram below is a visualization of a multiple-measure evaluation system, one that combines information on teaching practice (e.g. ratings from a classroom observation rubric) with student achievement-based measures (e.g. value-added or student growth percentiles) and student surveys.  The system need not be limited to three components; the point is simply that classroom observations are not the sole means of evaluating teachers.   

In validating the various components of an evaluation system, researchers often examine their correlation with other components.  To the extent that each component is an attempt to capture something about the teacher’s underlying effectiveness, it’s reasonable to expect that different measurements taken of the same teacher will be positively related.  For example, we might examine whether ratings from a classroom observation rubric are positively correlated with value-added.

Multiple Measures And Singular Conclusions In A Twin City

A few weeks ago, the Minneapolis Star Tribune published teacher evaluation results for the district’s public school teachers in 2013-14. This decision generated a fair amount of controversy, but it’s worth noting that the Tribune, unlike the Los Angeles Times and New York City newspapers a few years ago, did not publish scores for individual teachers, only totals by school.

The data once again provide an opportunity to take a look at how results vary by student characteristics. This was indeed the focus of the Tribune’s story, which included the following headline: “Minneapolis’ worst teachers are in the poorest schools, data show." These types of conclusions, which simply take the results of new evaluations at face value, have characterized the discussion since the first new systems came online. Though understandable, they are also frustrating and a potential impediment to the policy process. At this early point, “the city’s teachers with the lowest evaluation ratings” is not the same thing as “the city’s worst teachers." Actually, as discussed in a previous post, the systematic variation in evaluation results by student characteristics, which the Tribune uses to draw conclusions about the distribution of the city’s “worst teachers," could just as easily be viewed as one of the many ways that one might assess the properties and even the validity of those results.

So, while there are no clear-cut "right" or "wrong" answers here, let’s take a quick look at the data and what they might tell us.

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.

On Teacher Evaluations, Between Myth And Fact Lies Truth

Controversial proposals for new teacher evaluation systems have generated a tremendous amount of misinformation. It has come from both “sides," ranging from minor misunderstandings to gross inaccuracies. Ostensibly to address some of these misconceptions, the advocacy group Students First (SF) recently released a "myth/fact sheet” on evaluations.

Despite the need for oversimplification inherent in “myth/fact” sheets, the genre can be useful, especially about topics such as evaluation, about which there is much confusion. When advocacy groups produce them, however, the myths and facts sometimes take the form of “arguments we don’t like versus arguments we do like."

This SF document falls into that trap. In fact, several of its claims are a little shocking. I would still like to discuss the sheet, not because I enjoy picking apart the work of others (I don’t), but rather because I think elements of both the “myths” and “facts” in this sheet could be recast as "dual myths” in a new sheet. That is, this document helps to illustrate how, in many of our most heated education debates, the polar opposite viewpoints that receive the most attention are often both incorrect, or at least severely overstated, and usually serve to preclude more productive, nuanced discussions.

Let’s take all four of SF’s “myth/fact” combinations in turn.

A Few Points About The Instability Of Value-Added Estimates

One of the most frequent criticisms of value-added and other growth models is that they are "unstable" (or, more accurately, modestly stable). For instance, a teacher who is rated highly in one year might very well score toward the middle of the distribution – or even lower – in the next year (see here, here and here, or this accessible review).

Some of this year-to-year variation is “real." A teacher might get better over the course of a year, or might have a personal problem that impedes their job performance. In addition, there could be changes in educational circumstances that are not captured by the models – e.g., a change in school leadership, new instructional policies, etc. However, a great deal of the the recorded variation is actually due to sampling error, or idiosyncrasies in student testing performance. In other words, there is a lot of “purely statistical” imprecision in any given year, and so the scores don’t always “match up” so well between years. As a result, value-added critics, including many teachers, argue that it’s not only unfair to use such error-prone measures for any decisions, but that it’s also bad policy, since we might reward or punish teachers based on estimates that could be completely different the next year.

The concerns underlying these arguments are well-founded (and, often, casually dismissed by supporters and policymakers). At the same time, however, there are a few points about the stability of value-added (or lack thereof) that are frequently ignored or downplayed in our public discourse. All of them are pretty basic and have been noted many times elsewhere, but it might be useful to discuss them very briefly. Three in particular stand out.

A Look At The Changes To D.C.'s Teacher Evaluation System

D.C. Public Schools (DCPS) recently announced a few significant changes to its teacher evaluation system (called IMPACT), including the alteration of its test-based components, the creation of a new performance category (“developing”), and a few tweaks to the observational component (discussed below). These changes will be effective starting this year.

As with any new evaluation system, a period of adjustment and revision should be expected and encouraged (though it might be preferable if the first round of changes occurs during a phase-in period, prior to stakes becoming attached). Yet, despite all the attention given to the IMPACT system over the past few years, these new changes have not been discussed much beyond a few quick news articles.

I think that’s unfortunate: DCPS is an early adopter of the “new breed” of teacher evaluation policies being rolled out across the nation, and any adjustments to IMPACT’s design – presumably based on results and feedback – could provide valuable lessons for states and districts in earlier phases of the process.

Accordingly, I thought I would take a quick look at three of these changes.