• Do Value-Added Models "Control For Poverty?"

    There is some controversy over the fact that Florida’s recently-announced value-added model (one of a class often called “covariate adjustment models”), which will be used to determine merit pay bonuses and other high-stakes decisions, doesn’t include a direct measure of poverty.

    Personally, I support adding a direct income proxy to these models, if for no other reason than to avoid this type of debate (and to facilitate the disaggregation of results for instructional purposes). It does bear pointing out, however, that the measure that’s almost always used as a proxy for income/poverty – students’ eligibility for free/reduced-price lunch – is terrible as a poverty (or income) gauge. It tells you only whether a student’s family has earnings below (or above) a given threshold (usually 185 percent of the poverty line), and this masks most of the variation among both eligible and non-eligible students. For example, families with incomes of $5,000 and $20,000 might both be coded as eligible, while families earning $40,000 and $400,000 are both coded as not eligible. A lot of hugely important information gets ignored this way, especially when the vast majority of students are (or are not) eligible, as is the case in many schools and districts.

    That said, it’s not quite accurate to assert that Florida and similar models “don’t control for poverty." The model may not include a direct income measure, but it does control for prior achievement (a student’s test score in the previous year[s]). And a student’s test score is probably a better proxy for income than whether or not they’re eligible for free/reduced-price lunch.

    Even more importantly, however, the key issue about bias is not whether the models “control for poverty," but rather whether they control for the range of factors – school and non-school – that are known to affect student test score growth, independent of teachers’ performance. Income is only one part of this issue, which is relevant to all teachers, regardless of the characteristics of the students that they teach.

  • Public Schools Create Citizens In A Democratic Society

    Our guest author today is Jeffrey Mirel, Professor of Education and History at the University of Michigan.  His book, Patriotic Pluralism: Americanization Education and European Immigrants, published in 2010 by Harvard University Press, is available in bookstores and online.

    How do you get people who hate each other learn to resolve their differences democratically? How do you get them to believe in ballots not bullets?

    What if the answer is “public schools” and the evidence for it is in our own history during the first half of the twentieth century?

    In the years spanning about 1890-1930, two institutions—public schools and the foreign language press—helped generate this trust among the massive wave of eastern and southern European immigrants who came to the U.S. during that time. This is not a traditional “melting pot” story but rather an examination of a dynamic educational process.

  • Interpreting Achievement Gaps In New Jersey And Beyond

    ** Also posted here on "Valerie Strauss' Answer Sheet" in the Washington Post

    A recent statement by the New Jersey Department of Education (NJDOE) attempts to provide an empirical justification for that state’s focus on the achievement gap – the difference in testing performance between subgroups, usually defined in terms of race or income.

    Achievement gaps, which receive a great deal of public attention, are very useful in that they demonstrate the differences between student subgroups at any given point in time. This is significant, policy-relevant information, as it tells us something about the inequality of educational outcomes between the groups, which does not come through when looking at overall average scores.

    Although paying attention to achievement gaps is an important priority, the NJDOE statement on the issue actually speaks directly to the fact, which is well-established and quite obvious, that one must exercise caution when interpreting these gaps, particularly over time, as measures of student performance.

  • If Newspapers Are Going To Publish Teachers' Value-Added Scores, They Need To Publish Error Margins Too

    It seems as though New York City newspapers are going to receive the value-added scores of the city’s public school teachers, and publish them in an online database, as was the case in Los Angeles.*

    In my opinion, the publication will not only serve no useful purpose educationally, but it is also a grossly unfair infringement on the privacy of teachers. I have also argued previously that putting the estimates online may serve to bias future results by exacerbating the non-random assignment of students to teachers (parents requesting [or not requesting] specific teachers based on published ratings), though it's worth noting that the city is now using a different model.

    That said, I don’t think there’s any way to avoid publication, given that about a dozen newspapers will receive the data, and it’s unlikely that every one of them will decline to do so. So, in addition to expressing my firm opposition, I would offer what I consider to be an absolutely necessary suggestion: If newspapers are going to publish the estimates, they need to publish the error margins too.

  • Guessing About NAEP Results

    Every two years, the release of data from the National Assessment of Educational Progress (NAEP) generates a wave of research and commentary trying to explain short- and long-term trends. For instance, there have been a bunch of recent attempts to “explain” an increase in aggregate NAEP scores during the late 1990s and 2000s. Some analyses postulate that the accountability provisions of NCLB were responsible, while more recent arguments have focused on the “effect” (or lack thereof) of newer market-based reforms – for example, looking to NAEP data to “prove” or “disprove” the idea that changes in teacher personnel and other policies have (or have not) generated “gains” in student test scores.

    The basic idea here is that, for every increase or decrease in cross-sectional NAEP scores over a given period of time (both for all students and especially for subgroups such as minority and low-income students), there must be “something” in our education system that explains it. In many (but not all) cases, these discussions consist of little more than speculation. Discernible trends in NAEP test score data are almost certainly due to a combination of factors, and it’s unlikely that one policy or set of policies is dominant enough to be identified as “the one." Now, there’s nothing necessarily wrong with speculation, so long as it is clearly identified as such, and conclusions presented accordingly. But I find it curious that some people involved with these speculative arguments seem a bit too willing to assume that schooling factors – rather than changes in cohorts’ circumstances outside of school – are the primary driver of NAEP trends.

    So, let me try a little bit of illustrative speculation of my own: I might argue that changes in the economic conditions of American schoolchildren and their families are the most compelling explanation for changes in NAEP.

  • A Case For Value-Added In Low-Stakes Contexts

    Most of the controversy surrounding value-added and other test-based models of teacher productivity centers on the high-stakes use of these estimates. This is unfortunate – no matter what you think about these methods in the high-stakes context, they have a great deal of potential to improve instruction.

    When supporters of value-added and other growth models talk about low-stakes applications, they tend to assert that the data will inspire and motivate teachers who are completely unaware that they’re not raising test scores. In other words, confronted with the value-added evidence that their performance is subpar (at least as far as tests are an indication), teachers will rethink their approach. I don’t find this very compelling. Value-added data will not help teachers – even those who believe in its utility – unless they know why their students’ performance appears to be comparatively low. It’s rather like telling a baseball player they’re not getting hits, or telling a chef that the food is bad – it’s not constructive.

    Granted, a big problem is that value-added models are not actually designed to tell us why teachers get different results – i.e., whether certain instructional practices are associated with better student performance. But the data can be made useful in this context; the key is to present the information to teachers in the right way, and rely on their expertise to use it effectively.

  • A Big Open Question: Do Value-Added Estimates Match Up With Teachers' Opinions Of Their Colleagues?

    A recent article about the implementation of new teacher evaluations in Tennessee details some of the complicated issues with which state officials, teachers and administrators are dealing in adapting to the new system. One of these issues is somewhat technical – whether the various components of evaluations, most notably principal observations and test-based productivity measures (e.g., value-added) – tend to “match up." That is, whether teachers who score high on one measure tend to do similarly well on the other (see here for more on this issue).

    In discussing this type of validation exercise, the article notes:

    If they don't match up, the system's usefulness and reliability could come into question, and it could lose credibility among educators.
    Value-added and other test-based measures of teacher productivity may have a credibility problem among many (but definitely not all) teachers, but I don’t think it’s due to – or can be helped much by – whether or not these estimates match up with observations or other measures being incorporated into states’ new systems. I’m all for this type of research (see here and here), but I’ve never seen what I think would be an extremely useful study for addressing the credibility issue among teachers: One that looked at the relationship between value-added estimates and teachers’ opinions of each other.
  • A Look Inside Principals' Decisions To Dismiss Teachers

    Despite all the heated talk about how to identify and dismiss low-performing teachers, there’s relatively little research on how administrators choose whom to dismiss, whether various dismissal options might actually serve to improve performance, and other aspects in this area. A paper by economist Brian Jacob, released as working paper in 2010 and published late last year in the journal Education Evaluation and Policy Analysis, helps address at least one of these voids, by providing one of the few recent glimpses into administrators’ actual dismissal decisions.

    Jacob exploits a change in Chicago Public Schools (CPS) personnel policy that took effect for the 2004-05 school year, one which strengthened principals’ ability to dismiss probationary teachers, allowing non-renewal for any reason, with minimal documentation. He was able to link these personnel records to student test scores, teacher and school characteristics and other variables, in order to examine the characteristics that principals might be considering, directly or indirectly, in deciding who would and would not be dismissed.

    Jacob’s findings are intriguing, suggesting a more complicated situation than is sometimes acknowledged in the ongoing debate over teacher dismissal policy.

  • Fundamental Flaws In The IFF Report On D.C. Schools

    A new report, commissioned by the District of Columbia Mayor Vincent Gray and conducted by the Chicago-based consulting organization IFF, was supposed to provide guidance on how the District might act and invest strategically in school improvement, including optimizing the distribution of students across schools, many of which are either over- or under-enrolled.

    Needless to say, this is a monumental task. Not only does it entail the identification of high- and low-performing schools, but plans for improving them as well. Even the most rigorous efforts to achieve these goals, especially in a large city like D.C., would be to some degree speculative and error-prone.

    This is not a rigorous effort. IFF’s final report is polished and attractive, with lovely maps and color-coded tables presenting a lot of summary statistics. But there’s no emperor underneath those clothes. The report's data and analysis are so deeply flawed that its (rather non-specific) recommendations should not be taken seriously.

  • The Perilous Conflation Of Student And School Performance

    Unlike many of my colleagues and friends, I personally support the use of standardized testing results in education policy, even, with caution and in a limited role, in high-stakes decisions. That said, I also think that the focus on test scores has gone way too far and their use is being implemented unwisely, in many cases to a degree at which I believe the policies will not only fail to generate improvement, but may even risk harm.

    In addition, of course, tests have a very productive low-stakes role to play on the ground – for example, when teachers and administrators use the results for diagnosis and to inform instruction.

    Frankly, I would be a lot more comfortable with the role of testing data – whether in policy, on the ground, or in our public discourse – but for the relentless flow of misinterpretation from both supporters and opponents. In my experience (which I acknowledge may not be representative of reality), by far the most common mistake is the conflation of student and school performance, as measured by testing results.

    Consider the following three stylized arguments, which you can hear in some form almost every week: