Shanker Blog

A Look At The Education Programs Of The Gates Foundation

Our guest author today is Ken Libby, a graduate student studying educational foundations, policy and practice at the University of Colorado at Boulder.

The Bill & Melinda Gates Foundation is the largest philanthropic organization involved in public education. Their flexible capital allows the foundation to change course, experiment and take on tasks that would be problematic for other organizations.

Although the foundation’s education programs have been the subject of both praise and controversy, one area in which they deserve a great deal of credit is transparency. Unlike most other foundations, which provide a bare minimum, time-lagged account of their activities, Gates not only provides a description of each grant on its annually-filed IRS 990-PF forms, but it also maintains a continually updated list of grants posted on the foundation’s website. This nearly real-time outlet provides the public with information about grants months before the foundation is required to do so.

The purpose of this post is to provide descriptive information about programmatic support and changes between 2008 and 2010. These are the three years for which information is currently available.

Apprenticeships: A Rigorous And Tested Training Model For Workers And Management

Our guest author today is Robert I. Lerman, Institute Fellow at the Urban Institute and Professor of Economics at American University. Professor Lerman conducts research and policy analyses on employment, income support and youth development, especially as they affect low-income populations. He served on the National Academy of Sciences panel examining the U.S. post-secondary education and training system for the workplace.

In a recent Washington Post article, Peter Whoriskey points out the striking paradox of serious worker shortages at a time of high unemployment.  His analysis is one of many indicating the difficulties faced by manufacturing firms in hiring enough workers with adequate occupational skills.  As a result, many firms are having serious problems meeting the demand for their products, putting on long shifts, and turning down orders.

The article cites a survey of manufacturers indicating that as many as 600,000 jobs are going unfilled.  The skilled jobs going begging include machinists, welders, and machine operators -- jobs that pay good wages.  So what happened?

Ready, Aim, Hire: Predicting The Future Performance Of Teacher Candidates

In a previous post, I discussed the idea of “attracting the best candidates” to teaching by reviewing the research on the association between pre-service characteristics and future performance (usually defined in terms of teachers’ estimated effect on test scores once they get into the classroom). In general, this body of work indicates that, while far from futile, it’s extremely difficult to predict who will be an “effective” teacher based on their paper traits, including those that are typically used to define “top candidates," such as the selectivity of the undergraduate institutions they attend, certification test scores and GPA (see here, here, here and here, for examples).

There is some very limited evidence that other, “non-traditional” measures might help. For example, a working paper, released last year, found a statistically discernible, fairly strong association between first-year math value-added and an index constructed from surveys administered to Teach for America candidates. There was, however, no association in reading (note that the sample was small), and no relationships in either subject found during these teachers’ second years.*

A recently-published paper – which appears in the peer-reviewed journal Education Finance and Policy, originally released as working paper in 2008 –  represents another step forward in this area. The analysis, presented by the respected quartet of Jonah Rockoff, Brian Jacob, Thomas Kane, and Douglas Staiger (RJKS), attempts to look beyond the set of characteristics that researchers are typically constrained (by data availability) to examine.

In short, the results do reveal some meaningful, potentially policy-relevant associations between pre-service characteristics and future outcomes. From a more general perspective, however, they are also a testament to the difficulties inherent in predicting who will be a good teacher based on observable traits.

Reign Of Error: The Publication Of Teacher Data Reports In New York City

Late last week and over the weekend, New York City newspapers, including the New York Times and Wall Street Journal, published the value-added scores (teacher data reports) for thousands of the city’s teachers. Prior to this release, I and others argued that the newspapers should present margins of error along with the estimates. To their credit, both papers did so.

In the Times’ version, for example, each individual teacher’s value-added score (converted to a percentile rank) is presented graphically, for math and reading, in both 2010 and over a teacher’s “career” (averaged across previous years), along with the margins of error. In addition, both papers provided descriptions and warnings about the imprecision in the results. So, while the decision to publish was still, in my personal view, a terrible mistake, the papers at least make a good faith attempt to highlight the imprecision.

That said, they also published data from the city that use teachers’ value-added scores to label them as one of five categories: low, below average, average, above average or high. The Times did this only at the school level (i.e., the percent of each school’s teachers that are “above average” or “high”), while the Journal actually labeled each individual teacher. Presumably, most people who view the databases, particularly the Journal's, will rely heavily on these categorical ratings, as they are easier to understand than percentile ranks surrounded by error margins. The inherent problems with these ratings are what I’d like to discuss, as they illustrate important concepts about estimation error and what can be done about it.

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.