Accountability

One of the more interesting recurring education stories over the past couple of years has been the release of results from several states’ and districts’ new teacher evaluation systems, including those from New York, Indiana, Minneapolis, Michigan and Florida. In most of these instances, the primary focus has been on the distribution of teachers across ratings categories. Specifically, there seems to be a pattern emerging, in which the vast majority of teachers receive one of the higher ratings, whereas very few receive the lowest ratings.

This has prompted some advocates, and even some high-level officials, essentially to deem as failures the new systems, since their results suggest that the vast majority of teachers are “effective” or better. As I have written before, this issue cuts both ways. On the one hand, the results coming out of some states and districts seem problematic, and these systems may need adjustment. On the other hand, there is a danger here: States may respond by making rash, ill-advised changes in order to achieve “differentiation for the sake of differentiation,” and the changes may end up undermining the credibility and threatening the validity of the systems on which these states have spent so much time and money.

Granted, whether and how to alter new evaluations are difficult decisions, and there is no tried and true playbook. That said, New York Governor Andrew Cuomo’s proposals provide a stunning example of how not to approach these changes. To see why, let’s look at some sound general principles for improving teacher evaluation systems based on the first rounds of results, and how they compare with the New York approach.*

As a frequent critic of how states and districts present and interpret their annual testing results, I am also obliged (and indeed quite happy) to note when there is progress.

Recently, I happened to be browsing through New York City’s presentation of their 2014 testing results, and to my great surprise, on slide number four, I found proficiency rate changes between 2013 and 2014 among students who were in the sample in both years (which they call “matched changes”). As it turns out, last year, for the first time, New York State as a whole began publishing these "matched" year-to-year proficiency rate changes for all schools and districts. This is an excellent policy. As we’ve discussed here many times, NCLB-style proficiency rate changes, which compare overall rates of all students, many of whom are only in the tested sample in one of the years, are usually portrayed as “growth” or “progress.” They are not. They compare different groups of students, and, as we’ll see, this can have a substantial impact on the conclusions one reaches from the data. Limiting the sample to students who were tested in both years, though not perfect, at least permits one to measure actual growth per se, and provides a much better idea of whether students are progressing over time.

This is an encouraging sign that New York State is taking steps to improve the quality and interpretation of their testing data. And, just to prove that no good deed goes unpunished, let’s see what we can learn using the new “matched” data – specifically, by seeing how often the matched (longitudinal) and unmatched (cross-sectional) changes lead to different conclusions about student “growth” in schools.

Our guest authors today are Frank Hernandez, Corinne Mantle-Bromley and Benjamin Riley. Dr. Hernandez is the dean of the College of Education at the University of Texas of the Permian Basin, and previously served as a classroom teacher and school and district administrator for 12 years. Dr. Mantle-Bromley is dean of the University of Idaho’s College of Education and taught in rural Idaho prior to her work preparing teachers for diverse K-12 populations. Mr. Riley is the founder of Deans for Impact, a new organization composed of deans of colleges of education working together to transform educator preparation in the US. 

Students of color in the U.S., and those who live in rural communities, face unique challenges in receiving a high-quality education. All too often, new teachers have been inadequately prepared for these students’ specific needs. Perhaps just as often, their teachers do not look like them, and do not understand the communities in which these students live. Lacking an adequate preparation and the cultural sensitivities that come only from time and experience within a community, many of our nation’s teachers are thrust into an almost unimaginably challenging situation. We simply do not have enough well-prepared teachers of color, or teachers from rural communities, who can successfully navigate the complexities of these education ecosystems.

Some have described the lack of teachers of color and teachers who will serve in rural communities as a crisis of social justice. We agree. And, as the leaders of two colleges of education who prepare teachers who serve in these communities, we think the solution requires elevating the expectations for every program that prepares teachers and educators in this country.

In education, we hear the terms “failing school” and “low-performing school” quite frequently. Usually, they are used in soundbyte-style catchphrases such as, “We can’t keep students trapped in ‘failing schools.’” Sometimes, however, they are used to refer to a specific group of schools in a given state or district that are identified as “failing” or “low-performing” as part of a state or federal law or program (e.g., waivers, SIG). There is, of course, interstate variation in these policies, but one common definition is that schools are “failing/low-performing” if their proficiency rates are in the bottom five percent statewide.

Putting aside the (important) issues with judging schools based solely on standardized testing results, low proficiency rates (or low average scores) tell you virtually nothing about whether or not a school is “failing.” As we’ve discussed here many times, students enter their schools performing at different levels, and schools cannot control the students they serve, only how much progress those students make while they’re in attendance (see here for more).

From this perspective, then, there may be many schools that are labeled “failing” or “low performing” but are actually of above average effectiveness in raising test scores. And, making things worse, virtually all of these will be schools that serve the most disadvantaged students. If that’s true, it’s difficult to think of anything more ill-advised than closing these schools, or even labeling them as “low performing.” Let’s take a quick, illustrative look at this possibility using the “bottom five percent” criterion, and data from Colorado in 2013-14 (note that this simple analysis is similar to what I did in this post, but this one is a little more specific; also see Glazerman and Potamites 2011; Ladd and Lauen 2010; and especially Chingos and West 2015).