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  • What Florida's School Grades Measure, And What They Don't

    Written on July 19, 2012

    A while back, I argued that Florida's school grading system, due mostly to its choice of measures, does a poor job of gauging school performance per se. The short version is that the ratings are, to a degree unsurpassed by most other states' systems, driven by absolute performance measures (how highly students score), rather than growth (whether students make progress). Since more advantaged students tend to score more highly on tests when they enter the school system, schools are largely being judged not on the quality of instruction they provide, but rather on the characteristics of the students they serve.

    New results were released a couple of weeks ago. This was highly anticipated, as the state had made controversial changes to the system, most notably the inclusion of non-native English speakers and special education students, which officials claimed they did to increase standards and expectations. In a limited sense, that's true - grades were, on average, lower this year. The problem is that the system uses the same measures as before (including a growth component that is largely redundant with proficiency). All that has changed is the students that are included in them. Thus, to whatever degree the system now reflects higher expectations, it is still for outcomes that schools mostly cannot control.

    I fully acknowledge the political and methodological difficulties in designing these systems, and I do think Florida's grades, though exceedingly crude, might be useful for some purposes. But they should not, in my view, be used for high-stakes decisions such as closure, and the public should understand that they don't tell you much about the actual effectiveness of schools. Let’s take a very quick look at the new round of ratings, this time using schools instead of districts (I looked at the latter in my previous post about last year's results).

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  • How Often Do Proficiency Rates And Average Scores Move In Different Directions?

    Written on July 17, 2012

    New York State is set to release its annual testing data today. Throughout the state, and especially in New York City, we will hear a lot about changes in school and district proficiency rates. The rates themselves have advantages – they are easy to understand, comparable across grades and reflect a standards-based goal. But they also suffer severe weaknesses, such as their sensitivity to where the bar is set and the fact that proficiency rates and the actual scores upon which they’re based can paint very different pictures of student performance, both in a given year as well as over time. I’ve discussed this latter issue before in the NYC context (and elsewhere), but I’d like to revisit it quickly.

    Proficiency rates can only tell you how many students scored above a certain line; they are completely uninformative as to how far above or below that line the scores might be. Consider a hypothetical example: A student who is rated as proficient in year one might make large gains in his or her score in year two, but this would not be reflected in the proficiency rate for his or her school – in both years, the student would just be coded as “proficient” (the same goes for large decreases that do not “cross the line”). As a result, across a group of students, the average score could go up or down while proficiency rates remained flat or moved in the opposite direction. Things are even messier when data are cross-sectional (as public data lmost always are), since you’re comparing two different groups of students (see this very recent NYC IBO report).

    Let’s take a rough look at how frequently rates and scores diverge in New York City.

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  • The Busy Intersection Of Test-Based Accountability And Public Perception

    Written on June 28, 2012

    Last year, the New York City Department of Education (NYCDOE) rolled out its annual testing results for the city’s students in a rather misleading manner. The press release touted the “significant progress” between 2010 and 2011 among city students, while, at a press conference, Mayor Michael Bloomberg called the results “dramatic." In reality, however, the increase in proficiency rates (1-3 percentage points) was very modest, and, more importantly, the focus on the rates hid the fact that actual scale scores were either flat or decreased in most grades. In contrast, one year earlier, when the city's proficiency rates dropped due to the state raising the cut scores, Mayor Bloomberg told reporters (correctly) that it was the actual scores that "really matter."

    Most recently, in announcing their 2011 graduation rates, the city did it again. The headline of the NYCDOE press release proclaims that “a record number of students graduated from high school in 2011." This may be technically true, but the actual increase in the rate (rather than the number of graduates) was 0.4 percentage points, which is basically flat (as several reporters correctly noted). In addition, the city's "college readiness rate" was similarly stagnant, falling slightly from 21.4 percent to 20.7 percent, while the graduation rate increase was higher both statewide and in New York State's four other large districts (the city makes these comparisons when they are favorable).*

    We've all become accustomed to this selective, exaggerated presentation of testing data, which is of course not at all limited to NYC. And it illustrates the obvious fact that test-based accountability plays out in multiple arenas, formal and informal, including the court of public opinion.

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  • Colorado's Questionable Use Of The Colorado Growth Model

    Written on June 25, 2012

    I have been writing critically about states’ school rating systems (e.g., OhioFloridaLouisiana), and I thought I would find one that is, at least in my (admittedly value-laden) opinion, more defensibly designed. It didn't quite turn out as I had hoped.

    One big starting point in my assessment is how heavily the systems weight absolute performance (how highly students score) versus growth (how quickly students improve). As I’ve argued many times, the former (absolute level) is a poor measure of school performance in a high-stakes accountability system. It does not address the fact that some schools, particularly those in more affluent areas, serve  students who, on average, enter the system at a higher-performing level. This amounts to holding schools accountable for outcomes they largely cannot control (see Doug Harris' excellent book for more on this in the teacher context). Thus, to whatever degree testing results can be used to judge actual school effectiveness, growth measures, while themselves highly imperfect, are to be preferred in a high-stakes context.

    There are a few states that assign more weight to growth than absolute performance (see this prior post on New York City’s system). One of them is Colorado's system, which uses the well-known “Colorado Growth Model” (CGM).*

    In my view, putting aside the inferential issues with the CGM (see the first footnote), the focus on growth in Colorado's system is in theory a good idea. But, looking at the data and documentation reveals a somewhat unsettling fact: There is a double standard of sorts, by which two schools with the same growth score can receive different ratings, and it's mostly their absolute performance levels determining whether this is the case.

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  • Louisiana's "School Performance Score" Doesn't Measure School Performance

    Written on June 18, 2012

    Louisiana’s "School Performance Score" (SPS) is the state’s primary accountability measure, and it determines whether schools are subject to high-stakes decisions, most notably state takeover. For elementary and middle schools, 90 percent of the SPS is based on testing outcomes. For secondary schools, it is 70 percent (and 30 percent graduation rates).*

    The SPS is largely calculated using absolute performance measures – specifically, the proportion of students falling into the state’s cutpoint-based categories (e.g., advanced, mastery, basic, etc.). This means that it is mostly measuring student performance, rather than school performance. That is, insofar as the SPS only tells you how high students score on the test, rather than how much they have improved, schools serving more advantaged populations will tend to do better (since their students tend to perform well when they entered the school) while those in impoverished neighborhoods will tend to do worse (even those whose students have made the largest testing gains).

    One rough way to assess this bias is to check the association between SPS and student characteristics, such as poverty. So let’s take a quick look.

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  • We Should Only Hold Schools Accountable For Outcomes They Can Control

    Written on May 29, 2012

    Let’s say we were trying to evaluate a teacher’s performance for this academic year, and part of that evaluation would use students’ test scores (if you object to using test scores this way, put that aside for a moment). We checked the data and reached two conclusions. First, we found that her students made fantastic progress this year. Second, we also saw that the students’ scores were still quite a bit lower than their peers’ in the district. Which measure should we use to evaluate this teacher?

    Would we consider judging her even partially based on the latter – students’ average scores? Of course not. Those students made huge progress, and the only reason their absolute performance levels are relatively low is because they were low at the beginning of the year. This teacher could not control the fact that she was assigned lower-scoring students. All she can do is make sure that they improve. That’s why no teacher evaluation system places any importance on students’ absolute performance, instead focusing on growth (and, of course, non-test measures). In fact, growth models control for absolute performance (prior year’s test scores) so it doesn't bias the results.

    If we would never judge teachers based on absolute performance, why are we judging schools that way? Why does virtually every school/district rating system place some emphasis – often the primary emphasis – on absolute performance?

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  • Herding FCATs

    Written on May 22, 2012

    About a week ago, Florida officials went into crisis mode after revealing that the proficiency rate on the state’s writing test (FCAT) dropped from 81 percent to 27 percent among fourth graders, with similarly large drops in the other two grades in which the test is administered (eighth and tenth). The panic was almost immediate. For one thing, performance on the writing FCAT is counted in the state’s school and district ratings. Many schools would end up with lower grades and could therefore face punitive measures.

    Understandably, a huge uproar was also heard from parents and community members. How could student performance decrease so dramatically? There was so much blame going around that it was difficult to keep track – the targets included the test itself, the phase-in of the state’s new writing standards, and test-based accountability in general.

    Despite all this heated back-and-forth, many people seem to have overlooked one very important, widely-applicable lesson here: That proficiency rates, which are not "scores," are often extremely sensitive to where you set the bar.

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  • Growth And Consequences In New York City's School Rating System

    Written on May 14, 2012

    In a New York Times article a couple of weeks ago, reporter Michael Winerip discusses New York City’s school report card grades, with a focus on an issue that I have raised many times – the role of absolute performance measures (i.e., how highly students scores) in these systems, versus that of growth measures (i.e., whether students are making progress).

    Winerip uses the example of two schools – P.S. 30 and P.S. 179 – one of which (P.S. 30) received an A on this year’s report card, while the other (P.S. 179) received an F. These two schools have somewhat similar student populations, at least so far as can be determined using standard education variables, and their students are very roughly comparable in terms of absolute performance (e.g., proficiency rates). The basic reason why one received an A and the other an F is that P.S. 179 received a very low growth score, and growth is heavily weighted in the NYC grade system (representing 60 out of 100 points for elementary and middle schools).

    I have argued previously that unadjusted absolute performance measures such as proficiency rates are inappropriate for test-based assessments of schools' effectiveness, given that they tell you almost nothing about the quality of instruction schools provide, and that growth measures are the better option, albeit one that also has its own issues (e.g., they are more unstable), and must be used responsibly. In this sense, the weighting of the NYC grading system is much more defensible than most of its counterparts across the nation, at least in my view.

    But the system is also an example of how details matter – each school’s growth portion is calculated using an unconventional, somewhat questionable approach, one that is, as yet, difficult to treat with a whole lot of confidence.

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  • The Weighting Game

    Written on May 9, 2012

    A while back, I noted that states and districts should exercise caution in assigning weights (importance) to the components of their teacher evaluation systems before they know what the other components will be. For example, most states that have mandated new evaluation systems have specified that growth model estimates count for a certain proportion (usually 40-50 percent) of teachers’ final scores (at least those in tested grades/subjects), but it’s critical to note that the actual importance of these components will depend in no small part on what else is included in the total evaluation, and how it's incorporated into the system.

    In slightly technical terms, this distinction is between nominal weights (the percentage assigned) and effective weights (the percentage that actually ends up being the case). Consider an extreme hypothetical example – let’s say a district implements an evaluation system in which half the final score is value-added and half is observations. But let’s also say that every teacher gets the same observation score. In this case, even though the assigned (nominal) weight for value-added is 50 percent, the actual importance (effective weight) will be 100 percent, since every teacher receives the same observation score, and so all the variation between teachers’ final scores will be determined by the value-added component.

    This issue of nominal/versus effective weights is very important, and, with exceptions, it gets almost no attention. And it’s not just important in teacher evaluations. It’s also relevant to states’ school/district grading systems. So, I think it would be useful to quickly illustrate this concept in the context of Florida’s new district grading system.

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  • There's No One Correct Way To Rate Schools

    Written on April 10, 2012

    Education Week reports on the growth of websites that attempt to provide parents with help in choosing schools, including rating schools according to testing results. The most prominent of these sites is GreatSchools.org. Its test-based school ratings could not be more simplistic – they are essentially just percentile rankings of schools’ proficiency rates as compared to all other schools in their states (the site also provides warnings about the data, along with a bunch of non-testing information).

    This is the kind of indicator that I have criticized when reviewing states’ school/district “grading systems." And it is indeed a poor measure, albeit one that is widely available and easy to understand. But it’s worth quickly discussing the fact that such criticism is conditional on how the ratings are employed - there is a difference between the use of testing data to rate schools for parents versus for high-stakes accountability purposes.

    In other words, the utility and proper interpretation of data vary by context, and there's no one "correct way" to rate schools. The optimal design might differ depending on the purpose for which the ratings will be used. In fact, the reasons why a measure is problematic in one context might very well be a source of strength in another.

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