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"From my experience, education reporters are smart, knowledgeable, and attentive to detail. That said, the bulk of the stories about testing data - in big cities and suburbs, in this year and in previous years - could be better. Listen, I know it's unreasonable to expect every reporter and editor to address every little detail when they try to write accessible copy about complicated issues, such as test data interpretation. Moreover, I fully acknowledge that some of the errors to which I object - such as calling proficiency rates "scores" - are well within tolerable limits, and that news stories need not interpret data in the same way as researchers. Nevertheless, no matter what you think about the role of test scores in our public discourse, it is in everyone's interest that the coverage of them be reliable. And there are a few mostly easy suggestions that I think would help a great deal. Below are five such recommendations. They are of course not meant to be an exhaustive list, but rather a quick compilation of points, all of which I've discussed in previous posts, and all of which might also be useful to non-journalists."

Charter schools were developed, in part, to serve as an R&D engine for traditional public schools, resulting in a wide variety of school strategies and outcomes. In this paper, we collect unparalleled data on the inner-workings of 35 charter schools and correlate these data with credible estimates of each school's effectiveness. We find that traditionally collected input measures -- class size, per pupil expenditure, the fraction of teachers with no certification, and the fraction of teachers with an advanced degree -- are not correlated with school effectiveness. In stark contrast, we show that an index of five policies suggested by over forty years of qualitative research -- frequent teacher feedback, the use of data to guide instruction, high-dosage tutoring, increased instructional time, and high expectations -- explains approximately 50 percent of the variation in school effectiveness. Our results are robust to controls for three alternative theories of schooling: a model emphasizing the provision of wrap-around services, a model focused on teacher selection and retention, and the "No Excuses'' model of education. We conclude by showing that our index provides similar results in a separate sample of charter schools.

Data can tell educators and students where they have been successful and where they need help, but too much data can make those same educators lose focus of what they are supposed to be doing, which is educating students and creating a safe and nurturing environment.

This week, the Wall Street Journal reported that the NY Board of Regents approved the state's sharing of student and teacher information with a new national database, to be funded by the Gates Foundation, and designed by News Corp's Wireless Generation. Other states that have already agreed to share this data, according to the NY State Education Department, include Colorado, Delaware, Georgia, Illinois, Kentucky, North Carolina, Louisiana and Massachusetts. All this confidential student and teacher data will be held by a private limited corporation, called the Shared Learning Collaborative LLC, with even less accountability,  which in July was awarded $76.5 million by the Gates Foundation, to be spent over 7 months. According to an earlier NYT story,  $44 million of this funding will go straight into the pockets of Wireless Generation, owned by Murdoch's News Corp and run by Joel Klein.

The state's private-school voucher program in New Orleans -- the test case for Gov. Bobby Jindal's new statewide voucher push -- has yet to produce enough raw data to show whether it is really boosting student achievement. The governor's office is backing the voucher idea with figures that appear to show impressive test results for New Orleans students who get state aid to pay private school tuition. But in truth, limited test-score data and the lack of comparable public school numbers make the program's effectiveness almost impossible to judge, according to some of the country's leading number-crunchers in the education field. At best, state data offer only a snapshot of how those students are doing, and even then results are mixed.

We use several data sets to consider the effect of teaching practices on student beliefs, as well as on organization of firms and institutions. In cross-country data, we show that teaching practices (such as copying from the board versus working on projects together) are strongly related to various dimensions of social capital, from beliefs in cooperation to institutional outcomes. We then use micro-data to investigate the influence of teaching practices on student beliefs about cooperation both with each other and with teachers, and students' involvement in civic life. A two-stage least square strategy provides evidence that teaching practices have an independent sizeable effect on student social capital. The relationship between teaching practices and student test performance is nonlinear. The evidence supports the idea that progressive education promotes social capital.

Last week, I asked a question that had been on my mind: How can you tell the difference between good and bad education research? Colleagues in the Teacher Leaders Network and I have previously written about the importance of having a certain amount of healthy skepticism about research in the field, and I've written about the importance of being data-informed instead of being data-driven. Even then, though, we need to be careful about which data is informing us, and how it is being interpreted. In addition, I've compiled additional resources at The Best Resources For Understanding How To Interpret Education Research. Today, two experienced education researchers have provided guest responses -- Matthew Di Carlo. from the Albert Shanker Institute and P. L. Thomas from Furman University. I'm also publishing comments from two readers.

This report presents data from the School District Finance Survey (F-33) of the Common Core of Data (CCD) survey system for school year (SY) 2008-09 (fiscal year [FY] 2009). The F-33 is a district-level financial survey that consists of data submitted annually to the National Center for Education Statistics (NCES) and the Governments Division of the U.S. Census Bureau (Census) by state education agencies (SEAs) in the 50 states and the District of Columbia.

In short, there are different ways to measure the gender gap, and their "accuracy" is not about the statistics as much as how they're interpreted. The gap is 75-80 cents on the male dollar if you're making no claims that the difference is attributable solely to discrimination. When you account for the underlying factors - and you must do so to interpret the data in this manner - you get a somewhat different picture of the extent of the problem (problem though it still is). Now, think about how easily this all applies to test data in education. We are inundated every day with average scores and rates - for schools, districts, states, subgroups of students, etc. These data are frequently compared between groups and institutions in much the same way as wages are compared between men and women.

According to the new Recovery School District (RSD) superintendent, John White, "Five years ago, there were 62 percent of the youngsters attending failing schools. There are now only 18 percent of those youngsters who attend failing schools …so what exists, works."1 What a stupendous claim! If true, it would signify extraordinary student progress that the RSD has made since Katrina. Conversely, considering that the RSD and its proponents are so adept at manipulating data and misleading the public to support their cause, Research on Reforms (ROR) decided to investigate these claims more closely. The data for this commentary were all obtained from the 2009 and 2010 School Performance Scores (SPS) and student enrollment data from the website of the Louisiana Department of Education (LDOE).2"

In a series of posts inspired by one of Jeff Henig's recent guest posts for Rick Hess Straight Up, I'd like to explore the concept of data-driven instruction over the next few days, why it's become so suddenly all the rage in low-achieving schools, and its promises and pitfalls. I intend on doing this especially by relating my experiences with data-driven instruction, exploring the culture and systems that bore it, and soliciting feedback from other educators who have similar and divergent perspectives.

I've already beaten the issue of the various flaws, misrepresentations and outright data abuse in the Third Way middle class report into the ground on this blog. And it's really about time for that to end. Time to move on. But here is one simple illustration which draws on the same NAEP data compiled and aggregated in the Middle Class report. For anyone reading this post who has not already read my others on the problems with the definition of "Middle Class," and related data abuse & misuse please start there

"Here's a quick data driven post on Governor's state aid cuts - or aid changes. So far, I've been able to compile data from a few states which make it relatively easy to access and download data on district by district runs of state aid (and one state that does not, but I have good sources of assistance). Here, I compare changes in state aid to K-12 public school districts in Ohio, Pennsylvania and New York."

In several previous posts I have addressed the common argument among charter advocacy organizations (notably, not necessarily those out there doing the hard work of actually running a real charter school - but the pundits who claim to speak on their behalf) that charter schools do more, with less while serving comparable student populations. This argument appears to be a central theme of current policy proposals in Connecticut, which, among other things, would substantially increase funding for urban charter schools while doing little to provide additional support for high need traditional public school districts. For more on that point, see here. I've posted some specific information on Connecticut charter schools in previous posts, but have not addressed them more broadly. Here, I provide a run-down of simple descriptive data, widely available through two major credible sources.

Value-added has been getting a lot of media attention lately but, unfortunately, most stories are missing the point.  In Gotham Schools I read about a teacher who got a low score but it was because her score was based on students who were not assigned to her.  In The New York Times I read about three teachers who were rated in the single digits, but it was because they had high performing students and a few of their scores went down.  In The Washington Post I read about a teacher who was fired for getting low value-added on her IMPACT report, but it was because her students had inflated pretest scores because it is possible that the teachers from the year before cheated. Each of these stories makes it sound like there are very fixable flaws in value-added.  Get the student data more accurate, make some kind of curve for teachers of high performing students, get better test security so cheating can't affect the next year's teacher's score.  But the flaws in value-added go WAY beyond that, which is what I've been trying to show in my posts - not just some exceptional scenarios, but how it affects the majority of teachers.

The average scores of graduating teacher-candidates on state-required licensing exams are uniformly higher, often significantly, than the passing scores states set for such exams, according to an Education Week analysis of preliminary data from a half-dozen states. The pattern appears across subjects, grade levels, and test instruments supplied by a variety of vendors, the new data show, raising questions about the rigor and utility of current licensing tests.

Any reader of my blog knows already that I'm a skeptic of the usefulness of Value-added models for guiding high stakes decisions regarding personnel in schools. As I've explained on previous occasions, while statistical models of large numbers of data points - like lots of teachers or lots of schools - might provide us with some useful information on the extent of variation in student outcomes across schools or teachers and might reveal for us some useful patterns - it's generally not a useful exercise to try to say anything about any one single point within the data set. Yes, teacher "effectiveness" estimates tend to be based on the many student points across students taught by that teacher, but are still highly unstable. Unstable to the point, where even as a researcher hoping to find value in this information, I've become skeptical.

New York State's educational community has come together in an unprecedented show of support for the broad education reforms detailed in the State's Race to the Top application.  Thanks to the leadership of the Governor, the State legislature, and the Board of Regents, New York State passed new legislation in May 2010 that will usher in a new era of educational excellence in the State and ensure that we are able to fully execute the innovative, coherent reform agenda outlined in our Race to the Top application. The new laws: (1) establish a new teacher and principal evaluation system that makes student achievement data a substantial component of how educators are assessed and supported; (2) raise our charter school cap from 200 to 460; (3) enable school districts to enter contracts with Educational Partnership Organizations for the management of their persistently lowest‐achieving schools and schools under registration review; and (4) appropriate more than $20 million to the State Education Department to implement its P‐20 longitudinal data system.

There have now been several stories in the New York news media about New York City's charter schools' "gains" on this year's state tests (see here, here, here, here and here). All of them trumpeted the 3-7 percentage point increase in proficiency among the city's charter students, compared with the 2-3 point increase among their counterparts in regular public schools. The consensus: Charters performed fantastically well this year. In fact, the NY Daily News asserted that the "clear lesson" from the data is that "public school administrators must gain the flexibility enjoyed by charter leaders," and "adopt [their] single-minded focus on achievement." For his part, Mayor Michael Bloomberg claimed that the scores are evidence that the city should expand its charter sector. All of this reflects a fundamental misunderstanding of how to interpret testing data, one that is frankly a little frightening to find among experienced reporters and elected officials.

After spending more than $80 million on a controversial online student achievement database, the NYC Department of Education's portal is about to become obsolete as the state rolls out its own nearly-identical system as part of a federal education grant, DNAinfo.com New York has learned. The city is quietly making the transition from its $81 million data system - known as ARIS, or "Achievement Reporting and Innovation System" - to a new statewide database being developed with federal education funding, according to officials and city and state documents.