Blog Talk Radio: The Great Education Reform Debate V

The time is 8pm Pacific.


"My job, Arne, is to make Americans."

A snippet from my favorite response to Arne Duncan's thank fuck you to teachers.
Arne, I don’t want my students to be like the Chinese. If I did, I’d have moved there a long time ago. I don’t make data points in ELA, Math or any other subject. I don’t make goal setters, essential-question verbalizers, educational inputs, lifelong learners, lifelong objective makers, coherent planners, or jargon-based drones worthy of a “quality review” like widgets on an assembly line.

My job, Arne, is to make Americans.
Mr. D’s Neighborhood

Thursday Cartoon Fun: Gory Pictures Edition

“America can do whatever we set our mind to.”

Celebrating death? Death is nothing to celebrate, but we seem to do it well. We do killing real good, too. Poverty? We just haven't set our minds to it, apparently.
Perhaps the only thing more disturbing than the celebrations unleashed in the wake of bin Laden’s demise was the cynical way in which the president suggested that his killing proved “America can do whatever we set our mind to.” If this is, indeed, the lesson of bin Laden’s death, then this only suggests we clearly don’t want to diminish, let alone end, child poverty, excess mortality rates in communities of color, rape and sexual assault of women (including the many thousands who have been victimized in the U.S. military), or food insecurity for millions of families; because we aren’t addressing any of those things with nearly the aplomb as that put to warfare and the killing of our adversaries.

We are, if the president is serious here, a nation that has narrowly constricted its marketable talents to the deployment of violence. We can’t manufacture much of anything, but we can kill you. We can’t fix our schools, or build adequate levees to protect a city like New Orleans from floodwaters. But we can kill you. We can’t reduce infant mortality to anywhere near the level of other industrialized nations with which we like to compare ourselves. But we can kill you. We can’t break the power of Wall Street bankers, or jail any of those bankers and money managers who helped orchestrate the global financial collapse. But we can kill you. We can’t protect LGBT youth from bullying in schools, or ensure equal opportunity for all in the labor market, regardless of race, gender, sexuality or any other factor. But we can kill you. Booyah, bitches.
Tim Wise


Wednesday Cartoon Fun: Shocked! Edition

American Mathematical Association Accuses Mathematicians Of Incorrectly using VAM

Value-Added Models

In the past two decades, a group of statisticians has focused on addressing the first of these four problems. This was natural. Mathematicians routinely create models for complicated systems that are similar to a large collection of students and teachers with many factors affecting individual outcomes over time.

Here’s a typical, although simplified, example, called the “split-plot design”. You want to test
fertilizer on a number of different varieties of some crop. You have many plots, each divided
into subplots. After assigning particular varieties to each subplot and randomly assigning levels of fertilizer to each whole plot, you can then sit back and watch how the plants grow as you apply the fertilizer. The task is to determine the effect of the fertilizer on growth, distinguishing it from the effects from the different varieties. Statisticians have developed standard mathematical tools (mixed models) to do this.

Does this situation sound familiar? Varieties, plots, fertilizer…students, classrooms, teachers? Dozens of similar situations arise in many areas, from agriculture to MRI analysis, always with the same basic ingredients—a mixture of fixed and random effects—and it is therefore not surprising that statisticians suggested using mixed models to analyze test data and determine “teacher effects”.This is often explained to the public by analogy.

One cannot accurately measure the quality of a teacher merely by looking at the scores on a single test at the end of a school year. If one teacher starts with all poorly prepared students, while another starts with all excellent, we would be misled by scores from a single test given to each class. To account for such differences, we might use two tests, comparing scores from the end of one year to the next. The focus is on how much the scores increase rather than the scores themselves. That’s the basic idea behind “value-added”.

But value-added models (VAMs) are much more than merely comparing successive test scores.
Given many scores (say, grades 3–8) for many students with many teachers at many schools, one creates a mixed model for this complicated situation. The model is supposed to take into account all the factors that might influence test results—past history of the student, socioeconomic status, and so forth. The aim is to predict, based on all these past factors, the growth in test scores for students taught by a particular teacher. The actual change represents this more sophisticated “value added”—good when it’s larger than expected; bad when it’s smaller.

The best-known VAM, devised by William Sanders, is a mixed model (actually, several models), which is based on Henderson’s mixed-model equations, although mixed models originate much earlier [Sanders 1997]. One calculates (a huge computational effort!) the best linear unbiased predictors for the effects of teachers on scores. The precise details are unimportant here, but the process is similar to all mathematical modeling, with underlying assumptions and a number of choices in the model’s construction.


When value-added models were first conceived, even their most ardent supporters cautioned
about their use [Sanders 1995, abstract]. They were a new tool that allowed us to make sense of mountains of data, using mathematics in the same way it was used to understand the growth of crops or the effects of a drug. But that tool was based on a statistical model, and inferences about individual teachers might not be valid, either because of faulty assumptions or because of normal (and expected) variation.

Such cautions were qualified, however, and one can see the roots of the modern embrace of VAMs in two juxtaposed quotes from William Sanders, the father of the value-added movement, which appeared in an article in Teacher Magazine in the year 2000. The article’s author reiterates the familiar cautions about VAMs, yet in the next paragraph seems to forget them:
Sanders has always said that scores for individual teachers should not be released publicly. “That would be totally inappropriate,” he says. “This is about trying to improve our schools, not embarrassing teachers. If their scores were made available, it would create chaos because most parents would be trying to get their kids into the same classroom.”

Still, Sanders says, it’s critical that ineffective teachers be identified. “The evidence is overwhelming,” he says, “that if any child catches two very weak teachers in a row, unless there is a major intervention, that kid never recovers from it. And that’s something that as a society we can’t ignore” [Hill 2000].
John Ewing


Monday Bonus Cartoon Fun: 72 What? Edition

A Picture Worth A Thousand Words

President Barack Obama and Vice President Joe Biden, along with with members of the national security team, receive an update on the mission against Osama bin Laden in the Situation Room of the White House, May 1, 2011. Please note: a classified document seen in this photograph has been obscured. (Official White House Photo by Pete Souza)This official White House photograph is being made available only for publication by news organizations and/or for personal use printing by the subject(s) of the photograph. The photograph may not be manipulated in any way and may not be used in commercial or political materials, advertisements, emails, products, promotions that in any way suggests approval or endorsement of the President, the First Family, or the White House.

Monday Cartoon Fun: Toward The Teaching Of Lessons

Song Of The Day: He's Gone

Osama bin Laden. Shot in the head, buried at sea. Nothing's gonna bring him back.

Rat in a drain ditch, caught on a limb, you know better but I know him.
Like I told you, what I said, Steal your face right off your head.

Now he's gone, now he's gone, Lord he's gone, he's gone.
Like a steam locomotive, rollin' down the track
He's gone, gone, nothin's gonna bring him back...He's gone.

Nine mile skid on a ten mile ride, hot as a pistol but cool inside.
Cat on a tin roof, dogs in a pile,
Nothin' left to do but smile, smile, smile!!!!

Now he's gone, now he's gone Lord he's gone, he's gone.
Like a steam locomotive, rollin' down the track
He's gone, gone, nothin's gonna bring him back...He's gone.

Goin' where the wind don't blow so strange,
Maybe off on some high cold mountain chain.
Lost one round but the price wasn't anything,
A knife in the back and more of the same.

Same old, rat in a drain ditch, caught on a limb,
You know better but I know him.
Like I told you, what I said,
Steal your face right off your head.

Now he's gone, now he's gone Lord he's gone, he's gone.
Like a steam locomotive, rollin' down the track
He's gone, gone, nothin's gonna bring him back...He's gone.

Ooh, nothin's gonna bring him back.


Sunday Bonus Cartoon Fun: Proof Edition

Sunday Cartoon Fun: Scrappy Edition

The Blame Game

. . . .WHEN we don’t get the results we want in our military endeavors, we don’t blame the soldiers. We don’t say, “It’s these lazy soldiers and their bloated benefits plans! That’s why we haven’t done better in Afghanistan!” No, if the results aren’t there, we blame the planners. We blame the generals, the secretary of defense, the Joint Chiefs of Staff. No one contemplates blaming the men and women fighting every day in the trenches for little pay and scant recognition.

And yet in education we do just that. When we don’t like the way our students score on international standardized tests, we blame the teachers. When we don’t like the way particular schools perform, we blame the teachers and restrict their resources.
Schools Matter

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