The annual release of state testing data makes the news in every state, but Florida is one of those places where it is to some degree a national story.*
Well, it’s getting to be that time of year again. Last week, the state released its writing exam (FCAT 2.0 Writing) results for 2013 (as well as the math and reading results for third graders only). The Florida Department of Education (FLDOE) press release noted: “With significant gains in writing scores, Florida’s teachers and students continue to show that higher expectations and support at home and in the classroom enable every child to succeed.” This interpretation of the data was generally repeated without scrutiny in the press coverage of the results.
Putting aside the fact that the press release incorrectly calls the year-to-year changes “gains” (they are actually comparisons of two different groups of students; see here), the FLDOE’s presentation of the FCAT Writing results, though common, is, at best, incomplete and, at worst, misleading. Moreover, the important issues in this case are applicable in all states, and unusually easy to illustrate using the simple data released to the public.
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In his State of the City address last month, New York City Mayor Michael Bloomberg made some brief comments about the upcoming adoption of new assessments aligned with the Common Core State Standards (CCSS), including the following statement:
But no matter where the definition of proficiency is arbitrarily set on the new tests, I expect that our students’ progress will continue outpacing the rest of the State’s[,] the only meaningful measurement of progress we have.
On the surface, this may seem like just a little bit of healthy bravado. But there are a few things about this single sentence that struck me, and it also helps to illustrate an important point about the relationship between standards and testing results. Read More »
Our guest authors today are Morgan Polikoff and Andrew McEachin. Morgan is Assistant Professor in the Rossier School of Education at the University of Southern California. Andrew is an Institute of Education Science postdoctoral fellow at the University of Virginia.
In a previous post, we described some of the problems with the Senate’s Harkin-Enzi plan for reauthorizing the No Child Left Behind Act, based on our own analyses, which yielded three main findings. First, selecting the bottom 5% of schools for intervention based on changes in California’s composite achievement index resulted in remarkably unstable rankings. Second, identifying the bottom 5% based on schools’ lowest performing subgroup overwhelmingly targeted those serving larger numbers of special education students. Third and finally, we found evidence that middle and high schools were more likely to be identified than elementary schools, and smaller schools more likely than larger schools.
None of these findings was especially surprising (see here and here, for instance), and could easily have been anticipated. Thus, we argued that policymakers need to pay more attention to the vast (and rapidly expanding) literature on accountability system design. Read More »
In October of last year, the education advocacy group ConnCAN published a report called “The Roadmap to Closing the Gap” in Connecticut. This report says that the state must close its large achievement gaps by 2020 – that is, within eight years – and they use to data to argue that this goal is “both possible and achievable.”
There is value in compiling data and disaggregating them by district and school. And ConnCAN, to its credit, doesn’t use this analysis as a blatant vehicle to showcase its entire policy agenda, as advocacy organizations often do. But I am compelled to comment on this report, mostly as a springboard to a larger point about expectations.
However, first things first – a couple of very quick points about the analysis. There are 60-70 pages of district-by-district data in this report, all of it portrayed as a “roadmap” to closing Connecticut’s achievement gap. But it doesn’t measure gaps and won’t close them. Read More »
Let’s try a super-simple thought experiment with data. Suppose we have an inner-city middle school serving grades 6-8. Students in all three grades take the state exam annually (in this case, we’ll say that it’s at the very beginning of the year). Now, for the sake of this illustration, let’s avail ourselves of the magic of hypotheticals and assume away many of the sources of error that make year-to-year changes in public testing data unreliable.
First, we’ll say that this school reports test scores instead of proficiency rates, and that the scores are comparable between grades. Second, every year, our school welcomes a new cohort of sixth graders that is the exact same size and has the exact same average score as preceding cohorts – 30 out of 100, well below the state average of 65. Third and finally, there is no mobility at this school. Every student who enters sixth grade stays there for three years, and goes to high school upon completion of eighth grade. No new students are admitted mid-year.
Okay, here’s where it gets interesting: Suppose this school is phenomenally effective in boosting its students’ scores. In fact, each year, every single student gains 20 points. It is the highest growth rate in the state. Believe it or not, using the metrics we commonly use to judge schoolwide “growth” or “gains,” this school would still look completely ineffective. Take a look at the figure below. Read More »
In 1998, the National Institutes of Health (NIH) lowered the threshold at which people are classified as “overweight.” Literally overnight, about 25 million Americans previously considered as having a healthy weight were now overweight. If, the next day, you saw a newspaper headline that said “number of overweight Americans increases,” you would probably find that a little misleading. America’s “overweight” population didn’t really increase; the definition changed.
Fast forward to November 2012, during which Kentucky became the first state to release results from new assessments that were aligned with the Common Core Standards (CCS). This led to headlines such as, “Scores Drop on Kentucky’s Common Core-Aligned Tests” and “Challenges Seen as Kentucky’s Test Scores Drop As Expected.” Yet, these descriptions unintentionally misrepresent what happened. It’s not quite accurate – or at least highly imprecise – to say that test scores “dropped,” just as it would have been wrong to say that the number of overweight Americans increased overnight in 1998 (actually, they’re not even scores, they’re proficiency rates). Rather, the state adopted different tests, with different content, a different design, and different standards by which students are deemed “proficient.”
Over the next 2-3 years, a large group of states will also release results from their new CCS-aligned tests. It is important for parents, teachers, administrators, and other stakeholders to understand what the results mean. Most of them will rely on newspapers and blogs, and so one exceedingly simple step that might help out is some polite, constructive language-policing.
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Whatever one thinks of the heavy reliance on standardized tests in U.S. public education, one of the things on which there is wide agreement is that cheating must be prevented, and investigated when there’s evidence it might have occurred.
For anyone familiar with test-based accountability, recent cheating scandals in Atlanta, Washington, D.C., Philadelphia and elsewhere are unlikely to have been surprising. There has always been cheating, and it can take many forms, ranging from explicit answer-changing to subtle coaching on test day. One cannot say with any certainty how widespread cheating is, but there is every reason to believe that high-stakes testing increases the likelihood that it will happen. The first step toward addressing that problem is to recognize it.
A district, state or nation that is unable or unwilling to acknowledge the possibility of cheating, do everything possible to prevent it, and face up to it when evidence suggests it has occurred, is ill-equipped to rely on test-based accountability policies. Read More »
The new breed of school rating systems, some of which are still getting off the ground, will co-exist with federal proficiency targets in many states, and they are (or will be) used for a variety of purposes, including closure, resource allocation and informing parents and the public (see our posts on the systems in IN, FL, OH, CO, NYC).*
The approach that most states are using, in part due to the “ESEA flexibility” guidelines set by the U.S. Department of Education, is to combine different types of measures, often very crudely, into a single grade or categorical rating for each school. Administrators and media coverage usually characterize these ratings as measures of school performance – low-rated schools are called “low performing,” while those receiving top ratings are characterized as “high performing.” That’s not accurate – or, at best, it’s only partially true.
Some of the indicators that comprise the ratings, such as proficiency rates, are best interpreted as (imperfectly) describing student performance on tests, whereas other measures, such as growth model estimates, make some attempt to isolate schools’ contribution to that performance. Both might have a role to play in accountability systems, but they’re more or less appropriate depending on how you’re trying to use them.
So, here’s my question: Why do we insist on throwing them all together into a single rating for each school? To illustrate why I think this question needs to be addressed, let’s take a quick look at four highly-simplified situations in which one might use ratings. Read More »
When I point out that raw changes in state proficiency rates or NAEP scores are not valid evidence that a policy or set of policies is “working,” I often get the following response: “Oh Matt, we can’t have a randomized trial or peer-reviewed article for everything. We have to make decisions and conclusions based on imperfect information sometimes.”
This statement is obviously true. In this case, however, it’s also a straw man. There’s a huge middle ground between the highest-quality research and the kind of speculation that often drives our education debate. I’m not saying we always need experiments or highly complex analyses to guide policy decisions (though, in general, these are always preferred and sometimes required). The point, rather, is that we shouldn’t draw conclusions based on evidence that doesn’t support those conclusions.
This, unfortunately, happens all the time. In fact, many of the more prominent advocates in education today make their cases based largely on raw changes in outcomes immediately after (or sometimes even before) their preferred policies were implemented (also see here, here, here, here, here, and here). In order to illustrate the monumental assumptions upon which these and similar claims ride, I thought it might be fun to break them down quickly, in a highly simplified fashion. So, here are the four “requirements” that must be met in order to attribute raw test score changes to a specific policy (note that most of this can be applied not only to claims that policies are working, but also to claims that they’re not working because scores or rates are flat):
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As states’ continue to finalize their applications for ESEA/NCLB “flexibility” (or “waivers”), controversy has arisen in some places over how these plans set proficiency goals, both overall and for demographic subgroups (see our previous post about the situation in Virginia).
One of the underlying rationales for allowing states to establish new targets (called “annual measurable objectives,” or AMOs) is that the “100 percent” proficiency goals of NCLB were unrealistic. Accordingly, some (but not all) of the new plans have set 2017-18 absolute proficiency goals that are considerably below 100 percent, and/or lower for some subgroups relative to others. This shift has generated pushback from advocates, most recently in Florida, who believe that lowering state targets is tantamount to encouraging or accepting failure.
I acknowledge the central role of goals in any accountability system, but I would like to humbly suggest that this controversy, over where and how states set proficiency targets for 2017-18, may be misguided. There are four reasons why I think this is the case (and one silver lining if it is). Read More »
The State of Indiana has received a great deal of attention for its education reform efforts, and they recently announced the details, as well as the first round of results, of their new “A-F” school grading system. As in many other states, for elementary and middle schools, the grades are based entirely on math and reading test scores.
It is probably the most rudimentary scoring system I’ve seen yet – almost painfully so. Such simplicity carries both potential advantages (easier for stakeholders to understand) and disadvantages (school performance is complex and not always amenable to rudimentary calculation).
In addition, unlike the other systems that I have reviewed here, this one does not rely on explicit “weights,” (i.e., specific percentages are not assigned to each component). Rather, there’s a rubric that combines absolute performance (passage rates) and proportions drawn from growth models (a few other states use similar schemes, but I haven’t reviewed any of them).
On the whole, though, it’s a somewhat simplistic variation on the general approach most other states are taking — but with a few twists. Read More »
** Also reprinted here in the Washington Post
In the education community, many proclaim themselves to be “completely data-driven.” Data Driven Decision Making (DDDM) has been a buzz phrase for a while now, and continues to be a badge many wear with pride. And yet, every time I hear it, I cringe.
Let me explain. During my first year in graduate school, I was taught that excessive attention to quantitative data impedes – rather than aids – in-depth understanding of social phenomena. In other words, explanations cannot simply be cranked out of statistical analyses, without the need for a precursor theory of some kind – a.k.a. “variable sociology” – and the attempt to do so constitutes a major obstacle to the advancement of knowledge.
I am no longer in graduate school, so part of me says: Okay, I know what data-driven means in education. But then, at times, I still think: No, really, what does “data-driven” mean even in this context? Read More »
** Reprinted here in the Washington Post
I’ve written many times about how absolute performance levels – how highly students score – are not by themselves valid indicators of school quality, since, most basically, they don’t account for the fact that students enter the schooling system at different levels. One of the most blatant (and common) manifestations of this mistake is when people use NAEP results to determine the quality of a state’s schools.
For instance, you’ll often hear that Massachusetts has the “best” schools in the U.S. and Mississippi the “worst,” with both claims based solely on average scores on the NAEP (though, technically, Massachusetts public school students’ scores are statistically tied with at least one other state on two of the four main NAEP exams, while Mississippi’s rankings vary a bit by grade/subject, and its scores are also not statistically different from several other states’).
But we all know that these two states are very different in terms of basic characteristics such as income, parental education, etc. Any assessment of educational quality, whether at the state or local level, is necessarily complicated, and ignoring differences between students precludes any meaningful comparisons of school effectiveness. Schooling quality is important, but it cannot be assessed by sorting and ranking raw test scores in a spreadsheet.
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It is a gross understatement to say that the No Child Left Behind (NCLB) law is, was – and will continue to be – a controversial piece of legislation. Although opinion tends toward the negative, there are certain features, such as a focus on student subgroup data, that many people support. And it’s difficult to make generalizations about whether the law’s impact on U.S. public education was “good” or “bad” by some absolute standard.
The one thing I would say about NCLB is that it has helped to institutionalize the improper interpretation of testing data.
Most of the attention to the methodological shortcomings of the law focuses on “adequate yearly progress” (AYP) – the crude requirement that all schools must make “adequate progress” toward the goal of 100 percent proficiency by 2014. And AYP is indeed an inept measure. But the problems are actually much deeper than AYP.
Rather, it’s the underlying methods and assumptions of NCLB (including AYP) that have had a persistent, negative impact on the way we interpret testing data. Read More »
New York City has just released the new round of results from its school rating system (they’re called “progress reports”). It relies considerably more on student growth (60 out of 100 points) than absolute performance (25 points), and there are efforts to partially adjust most of the measures via peer group comparisons.*
All of this indicates that the city’s system is more focused on school rather than student test-based performance, compared with many other systems around the U.S.
The ratings are high-stakes. Schools receiving low grades – a D or F in any given year, or a C for three consecutive years – enter a review process by which they might be closed. The number of schools meeting these criteria jumped considerably this year.
There is plenty of controversy to go around about the NYC ratings, much of it pertaining to two important features of the system. They’re worth discussing briefly, as they are also applicable to systems in other states. Read More »
