Education

The ‘Snob’ Debate: Making High School Matter For Non-College-Bound Students

Our guest author today is James R. Stone, professor and director of the National Research Center for Career & Technical Education at the University of Louisville.

The current debate about “college for all” centers on a recent speech made by President Obama in Troy, MI, in which he argued that all young people should get at least some post-high school education or training. Republican presidential primary candidate Rick Santorum, in a misreading of Obama’s remarks, responded with a focus on four-year degrees alone—suggesting, among other things, that four-year college degrees are overrated and that the president’s emphasis on college devalued working people without such degrees. The political chatter around this particular back-and-forth continues, but the issue of “college for all” has rightly raised some serious issues about the content and direction of U.S. education policy both at the high school and post-secondary levels.

Statistics seem to show that the college-educated  graduates of four-year institutions earn more money and experience less unemployment than their non-college-educated peers. This has fueled the argument is that college is the surest path—perhaps the only path—into the middle class. But the argument confuses correlation with causality. What if every U.S. citizen obtained a community college or university degree? Would that really do anything to alter wage rates at Starbucks, or increase salaries for home healthcare aides (an occupation projected to enjoy the highest demand over the next decade)? Of course not.

Why Stop In Early Childhood? Two-Generation Strategies To Improve Educational Outcomes

In education research, it is now widely accepted that ages 0 to 5 are crucial years for child development. In addition, there is a growing body of evidence demonstrating that children perform better behaviorally and academically in families with stable employment and rising incomes, families with stable employment and those where parents themselves are improving their own educational levels.

Although it’s clear that increasing parents’ human capital protects and enhances the investments made in their children, "few programs have addressed the postsecondary education and training needs of low-income parents" (p. 2) through comprehensive, family-(child- and parent-) centered strategies.*

I learned about some remarkable exceptions at a recent New America Foundation discussion on innovations in child care and early learning. Four providers from around the country were asked to describe their programs, all largely focused on helping parents achieve the kind of economic stability needed to support their children’s educational attainment.**

Revisiting The "5-10 Percent Solution"

In a post over a year ago, I discussed the common argument that dismissing the “bottom 5-10 percent" of teachers would increase U.S. test scores to the level of high-performing nations. This argument is based on a calculation by economist Eric Hanushek, which suggests that dismissing the lowest-scoring teachers based on their math value-added scores would, over a period of around ten years  (when the first cohort of students would have gone through the schooling system without the “bottom” teachers), increase U.S. math scores dramatically – perhaps to the level of high-performing nations such as Canada or Finland.*

This argument is, to say the least, controversial, and it invokes the full spectrum of reactions. In my opinion, it's best seen as a policy-relevant illustration of the wide variation in test-based teacher effects, one that might suggest a potential of a course of action but can't really tell us how it will turn out in practice. To highlight this point, I want to take a look at one issue mentioned in that previous post – that is, how the instability of value-added scores over time (which Hanushek’s simulation doesn’t address directly) might affect the projected benefits of this type of intervention, and how this is turn might modulate one's view of the huge projected benefits.

One (admittedly crude) way to do this is to use the newly-released New York City value-added data, and look at 2010 outcomes for the “bottom 10 percent” of math teachers in 2009.

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.