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Do Subgroup Accountability Measures Affect School Ratings Systems?
The school accountability provisions of No Child Left Behind (NCLB) institutionalized a focus on the (test-based) performance of student subgroups, such as English language learners, racial and ethnic groups, and students eligible for free- and reduced-price lunch (FRL). The idea was to shine a spotlight on achievement gaps in the U.S., and to hold schools accountable for serving all students.
This was a laudable goal, and disaggregating data by student subgroups is a wise policy, as there is much to learn from such comparisons. Unfortunately, however, NCLB also institutionalized the poor measurement of school performance, and so-called subgroup accountability was not immune. The problem, which we’ve discussed here many times, is that test-based accountability systems in the U.S. tend to interpret how highly students score as a measure of school performance, when it is largely a function of factors out of schools' control, such as student background. In other words, schools (or subgroups of those students) may exhibit higher average scores or proficiency rates simply because their students entered the schools at higher levels, regardless of how effective the school may be in raising scores. Although NCLB’s successor, the Every Student Succeeds Act (ESSA), perpetuates many of these misinterpretations, it still represents some limited progress, as it encourages greater reliance on growth-based measures, which look at how quickly students progress while they attend a school, rather than how highly they score in any given year (see here for more on this).
Yet this evolution, slow though it may be, presents a somewhat unique challenge for the inclusion of subgroup-based measures in formal school accountability systems. That is, if we stipulate that growth model estimates are the best available test-based way to measure school (rather than student) performance, how should accountability systems apply these models to traditionally lower scoring student subgroups?
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Social And Emotional Skills In School: Pivoting From Accountability To Development
Our guest authors today are David Blazar and Matthew A. Kraft. Blazar is a Lecturer on Education and Postdoctoral Research Fellow at Harvard Graduate School of Education and Kraft is an Assistant Professor of Education and Economics at Brown University.
With the passage of the Every Student Succeeds Act (ESSA) in December 2015, Congress required that states select a nonacademic indicator with which to assess students’ success in school and, in turn, hold schools accountable. We believe that broadening what it means to be a successful student and school is good policy. Students learn and grow in multifaceted ways, only some of which are captured by standardized achievement tests. Measures such as students’ effort, initiative, and behavior also are key indicators for their long-term success (see here). Thus, by gathering data on students’ progress on a range of measures, both academic and what we refer to as “social and emotional” development, teachers and school leaders may be better equipped to help students improve in these areas.
In the months following the passage of ESSA, questions about use of social and emotional skills in accountability systems have dominated the debate. What measures should districts use? Is it appropriate to use these measures in high-stakes setting if they are susceptible to potential biases and can be easily coached or manipulated? Many others have written about this important topic before us (see, for example, here, here, here, and here). Like some of them, we agree that including measures of students’ social and emotional development in accountability systems, even with very small associated weights, could serve as a strong signal that schools and educators should value and attend to developing these skills in the classroom. We also recognize concerns about the use of measures that really were developed for research purposes rather than large-scale high-stakes testing with repeated administrations.
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A Few Reactions To The Final Teacher Preparation Accountability Regulations
The U.S. Department of Education (USED) has just released the long-anticipated final regulations for teacher preparation (TP) program accountability. These regulations will guide states, which are required to design their own systems for assessing TP program performance for full implementation in 2018-19. The earliest year in which stakes (namely, eligibility for federal grants) will be attached to the ratings is 2021-22.
Among the provisions receiving attention is the softening of the requirement regarding the use of test-based productivity measures, such as value-added and other growth models (see Goldhaber et al. 2013; Mihaly et al. 2013; Koedel et al. 2015). Specifically, the final regulations allow greater “flexibility” in how and how much these indicators must count toward final ratings. For the reasons that Cory Koedel and I laid out in this piece (and I will not reiterate here), this is a wise decision. Although it is possible that value-added estimates will eventually play a significant role in these TP program accountability systems, the USED timeline provides insufficient time for the requisite empirical groundwork.
Yet this does not resolve the issues facing those who must design these systems, since putting partial brakes on value-added for TP programs also puts increased focus on the other measures which might be used to gauge program performance. And, as is often the case with formal accountability systems, the non-test-based bench is not particularly deep.
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Building A Professional Network Of Rural Educators From Scratch
Our guest author today is Danette Parsley, Chief Program Officer at Education Northwest, where she leads initiatives like the Northwest Rural Innovation and Student Engagement Network. To learn more about this work, check out Designing Rural School Improvement Networks: Aspirations and Actualities and Generating Opportunity and Prosperity: The Promise of Rural Education Collaboratives.
Small rural schools draw from a deep well of assets to positively impact student experiences and outcomes. They tend to serve as central hubs within their communities, and their small size often facilitates close staff relationships, which in turn can enable moving innovative ideas into action. At the same time, rural schools face a number of challenges that differ from those of their urban and suburban counterparts.
First, it’s extremely difficult to draw high-quality teachers to geographically disconnected, rural communities—and, when they do come, it’s hard to get them to stay. Second, it’s a challenge to connect teachers across remote and rural communities so they can share instructional practices and professional development. One way to address the challenges facing rural schools, while leveraging their inherent assets, is to establish professional networks of teacher leaders aimed at providing support that helps their colleagues succeed and encourages them to stay.
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Economic Segregation In New York City Schools
Although student segregation by race and ethnicity is well documented in U.S. public schools, the body of evidence on the related outcome of economic school segregation (e.g., by income) is considerably smaller (Reardon and Owens 2014).
In general, economic segregation of students is increasing nationally over the past few decades, both between districts and between schools (Owens et al. 2014). It is inevitable that these aggregate trends vary widely by state, metropolitan area, and district. We were curious as to the situation in New York City, the nation’s largest district, but were unable to find any NYC-specific results, particularly results that included different types of segregation measures.
We therefore decided to take a quick look ourselves, using data from the NYC Department of Education. The very brief analysis below uses eligibility for subsidized lunch (free and reduced-price lunch, or FRL) as a (very) rough income proxy, and segregation is measured between district schools only (charters are not included) from 2002 to 2015. In the graph below, we characterize within-district, between-school segregation using two different and very common approaches, exposure and dissimilarity.
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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.
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An Alternative Income Measure Using Administrative Education Data
The relationship between family background and educational outcomes is well documented and the topic, rightfully, of endless debate and discussion. A students’ background is most often measured in terms of family income (even though it is actually the factors associated with income, such as health, early childhood education, etc., that are the direct causal agents).
Most education analyses rely on a single income/poverty indicator – i.e., whether or not students are eligible for federally-subsidized lunch (free/reduced-price lunch, or FRL). For instance, income-based achievement gaps are calculated by comparing test scores between students who are eligible for FRL and those who are not, while multivariate models almost always use FRL eligibility as a control variable. Similarly, schools and districts with relatively high FRL eligibility rates are characterized as “high poverty.” The primary advantages of FRL status are that it is simple and collected by virtually every school district in the nation (collecting actual income would not be feasible). Yet it is also a notoriously crude and noisy indicator. In addition to the fact that FRL eligibility is often called “poverty” even though the cutoff is by design 85 percent higher than the federal poverty line, FRL rates, like proficiency rates, mask a great deal of heterogeneity. Families of two students who are FRL eligible can have quite different incomes, as could two families of students who are not eligible. As a result, FRL-based estimates such as achievement gaps might differ quite a bit from those calculated using actual family income from surveys.
A new working paper by Michigan researchers Katherine Michelmore and Susan Dynarski presents a very clever means of obtaining a more accurate income/poverty proxy using the same administrative data that states and districts have been collecting for years.
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Contingent Faculty At U.S. Colleges And Universities
In a previous post, we discussed the prevalence of and trends in alternative employment arrangements, sometimes called “contingent work,” in the U.S. labor market. Contingent work is jobs with employment arrangements other than the “traditional” full-time model, including workers with temporary contracts, independent contractors, day laborers, and part-time employees.
Depending on how one defines this group of workers, who are a diverse group but tend to enjoy less job stability and lower compensation, they comprise anywhere between 10 and 40 percent of the U.S. workforce, and this share increased moderately between 2000 and 2010. Of course, how many contingents there are, and how this has changed over time, varies quite drastically by industry, as well as by occupation. For example, in 1990, around 28 percent of staffing services employees (sometimes called “temps”) worked in blue collar positions, while 42 percent had office jobs. By 2009, these proportions had reversed, with 41 percent of temps in blue collar jobs and 23 percent doing office work. This is a pretty striking change.
Another industry/occupation in which there has been significant short term change in the contingent work share is among faculty and instructors in higher education institutions.
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On Focus Groups, Elections, and Predictions
Focus groups, a method in which small groups of subjects are questioned by researchers, are widely used in politics, marketing, and other areas. In education policy, focus groups, particularly those comprised of teachers or administrators, are often used to design or shape policy. And, of course, during national election cycles, they are particularly widespread, and there are even television networks that broadcast focus groups as a way to gauge the public’s reaction to debates or other events.
There are good reasons for using focus groups. Analyzing surveys can provide information regarding declaratory behaviors and issues’ rankings at a given point in time, and correlations between these declarations and certain demographic and social variables of interest. Focus groups, on the other hand, can help map out the issues important to voters (which can inform survey question design), as well investigate what reactions certain presentations (verbal or symbolic) evoke (which can, for example, help frame messages in political or informational campaigns).
Both polling/surveys and focus groups provide insights that the other method alone could not. Neither of them, however, can answer questions about why certain patterns occur or how likely they are to occur in the future. That said, having heard some of the commentary about focus groups, and particularly having seen them being broadcast live and discussed on cable news stations, I feel strongly compelled to comment, as I do whenever data are used improperly or methodologies are misinterpreted.
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Thinking About Tests While Rethinking Test-Based Accountability
Earlier this week, per the late summer ritual, New York State released its testing results for the 2015-2016 school year. New York City (NYC), always the most closely watched set of results in the state, showed a 7.6 percentage point increase in its ELA proficiency rate, along with a 1.2 percentage point increase in its math rate. These increases were roughly equivalent to the statewide changes.
City officials were quick to pounce on the results, which were called “historic,” and “pure hard evidence” that the city’s new education policies are working. This interpretation, while standard in the U.S. education debate, is, of course, inappropriate for many reasons, all of which we’ve discussed here countless times and will not detail again (see here). Suffice it to say that even under the best of circumstances these changes in proficiency rates are only very tentative evidence that students improved their performance over time, to say nothing of whether that improvement was due to a specific policy or set of policies.
Still, the results represent good news. A larger proportion of NYC students are scoring proficient in math and ELA than did last year. Real improvement is slow and sustained, and this is improvement. In addition, the proficiency rate in NYC is now on par with the statewide rate, which is unprecedented. There are, however, a couple of additional issues with these results that are worth discussing quickly.
