The Pandemic Is Far From Over

While the rate of increase per day in the number of deaths is generally down, the COVID-19 pandemic is far from over. In general, more people are still dying each day in the US from this disease than the day before, as you can see from this data, which is taken from the CDC. The very tall bar on day 27 is when New York City finally added thousands of poor souls who had in fact died from this virus. (Day 27 means April 9, and Day 41 means April 30, which is today.)

Opening up the economy and encouraging everybody to go back to work, play, and school will mean a rebirth of exponential growth in deaths and in diagnosed cases after about 2 weeks, since this disease takes about that long to be noticed in those who have been exposed. And once everybody is back on the streets and in the stores and schools, the disease WILL spread exponentially. Opening wide right now, when we still can’t test or follow those who may be infected, would be a huge mistake.

us covid deaths per day

Only somebody as clueless as our current Grifter-In-Chief and his brainless acolytes could be recommending something so irresponsible, against the advice of every medical expert. Maybe they think that only the poor, the black, and the brown will get this disease. Wrong.

The shutdown, while painful, appears to have saved a LOT of lives so far

If you recall, the growth of the new corona virus disease in the US (and many other countries) at first looked to be exponential, meaning that the number of cases (and deaths) were rising at an alarming, fixed percent each and every single day.

Even if you slept through your high school or middle school math lessons on exponential growth, the story of the Shah and the chessboard filled with rice may have told you that the equation 2^x gets very, very hairy after a while. Pyramid schemes eventually run out of suckers people. Or perhaps you have seen a relatively modest credit-card bill get way out of hand as the bank applies 8 percent interest PER MONTH, which ends up multiplying your debt by a factor of 6 after just 2 years!

(If the total number of deaths were still increasing by 25 percent per day, as they were during the middle of March, and if that trend somehow continued without slowing down, then every single person residing inside America’s borders would be dead before the end of May. Not kidding! But it’s also not happening.)

However, judging by numbers released by the CDC and reported by my former colleague Ron Jenkins, I am quite confident that THE NUMBER OF CASES AND DEATHS FROM COVID-19 ARE NO LONGER following a fixed exponential curve. Or at least, the daily rate of increase has been going down. Which is good. But it’s still not zero.

Let me show you the data and fitted curves in a number of graphs, which often make complex things easier to visualize and understand.

My first graph is the total reported number of deaths so far in the US, compared to a best-fit exponential graph:

Deaths in US are not growing exponentially

During the first part of this pandemic, during the first 40 or so days, the data actually fit an exponential graph pretty well – that is, the red dotted line (the exponential curve of best fit) fit the actual cumulative number of deaths (in blue). And that’s not good. However, since about day 50 (last week) the data is WAY UNDER the red dots. To give you an idea of how much of a victory that is: find day 70, which is May 9, and follow the vertical line up until it meets the red dotted line. I’ll wait.

Did you find it? If this pandemic were still following exponential growth, now and into the future, at the same rate, we would have roughly a MILLION PEOPLE DEAD BY JUNE 9 in just the US, just from this disease, and 2 million the week after that, and 4 million the next week, then 8 million, then 16 million, and so on.

THAT AIN’T HAPPENIN’! YAY! HUZZAH!

As you can see — the blue and red graphs have diverged. Ignore the relatively high correlation value of 0.935 – it just ain’t so.

But what IS the curve of best fit? I don’t know, so I’ll let you look for yourself.

Is it linear?

Deaths in US are not growing in a linear fashion

This particular line of best doesn’t fit the data very well; however, if we start at day 36 or thereabouts, we could get a line that fits the data from there on pretty well, like so:

maybe this purple line

 

The purple line fits the blue dots quite well after about day 37 (about April 6), and the statistics algorithms quite agree. However, it still calls for over 80,000 Americans dead by May 8. I do not want the slope of that line to be positive! I want it to turn to the right and remain horizontal – meaning NOBODY ELSE DIES ANY MORE FROM THIS DISEASE.

Perhaps it’s not linear? Perhaps it’s one of those other types of equations you might remember from some algebra class, like a parabola, a cubic, or a quartic? Let’s take a look:

Deaths might be growing at a 2nd degree polynomial rate - still not good

This is a parabolic function, or a quadratic. The red dots do fit the data pretty well. Unfortunately, we want the blue dots NOT to fit that graph, because that would, once again, mean about a hundred thousand people dead by May 8. That’s better than a million, but I want the deaths to stop increasing at all. Like this piecewise function (which some of you studied). Note that the purple line cannot go back downwards, because generally speaking, dead people cannot be brought back to life.

maybe this purple line - nah, prefer horizontal

Well, does the data fit a cubic?

deaths fit a cubic very well

Unfortunately, this also fits pretty well. If it continues, we would still have about a hundred thousand dead by May 8, and the number would increase without limit (which, fortunately, is impossible).

How about a quartic (fourth-degree polynomial)? Let’s see:

4th degree polynomial is impossible - people do NOT come back to life

I admit that the actual data, in blue, fit the red calculated quartic red curve quite well, in fact, the best so far, and the number of deaths by Day 70 is the lowest so far. But it’s impossible: for the curve to go downwards like that would mean that you had ten thousand people who died, and who later came back to life. Nah, not happening.

What about logarithmic growth? That would actually be sweet – it’s a situation where a number rises quickly at first, but over time rises more and more slowly. Like this, in red:

logarithmic growth

I wish this described the real situation, but clearly, it does not.

One last option – a ‘power law’ where there is some fixed power of the date (in this case, the computer calculated it to be the date raised to the 5.377 power) which explains all of the deaths, like so:

no sign of a power law

I don’t think this fits the data very well, either. Fortunately. It’s too low from about day 38 to day 29, and is much too high from day 50 onwards. Otherwise we would be looking at about 230,000 dead by day 70 (May 8).

But saying that the entire number of deaths in the US is no longer following a single exponential curve doesn’t quite do the subject justice. Exponential growth (or decay) simply means that in any given time period, the quantity you are measuring is increasing (or decreasing) by a fixed percentage (or fraction). That’s all. And, as you can see, for the past week, the daily percentage of increase in the total number of deaths has been in the range of three to seven percent. However, during the first part of March, the rate of increase in deaths was enormous: 20 to 40 percent PER DAY. And the daily percent of increase in the number of cases was at times over A HUNDRED PERCENT!!! – which is off the chart below.

daily percentages of increases in covid 19 cases and deaths, USA, thru April 25

The situation is still not good! If we are stuck at a daily increase in the number of deaths as low as a 3%/day increase, then we are all dead within a year. Obviously, and fortunately, that’s probably not going to happen, but it’s a bit difficult to believe that the math works out that way.

But it does. Let me show you, using logs.

For simple round numbers, let’s say we have 50,000 poor souls who have died so far from this coronavirus in the USA right now, and that number of deaths is increasing at a rate of 3 percent per day. Let’s also say that the US has a population of about 330 million. The question is, when will we all be dead if that exponential growth keeps going on somehow? (Fortunately, it won’t.*) Here is the first equation, and then the steps I went through. Keep in mind that a growth of 3% per day means that you can multiply any day’s value by 1.03, or 103%, to get the next day’s value. Here goes:

in 10 months we are all dead

Sound unbelievable? To check that, let us take almost any calculator and try raising the expression 1.03 to the 300th power. I think you’ll get about 7098. Now take that and multiply it by the approximate number of people dead so far in the US, namely 50,000. You’ll get about 355,000,000 – well more than the total number of Americans.

So we still need to get that rate of increase in fatalities down, to basically zero. We are not there yet. With our current highly-incompetent national leadership, we might not.

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* what happens in cases like this is you get sort of an s-shaped curve, called the Logistic or logit curve, in which the total number levels off after a while. That’s shown below. Still not pleasant.

I have no idea how to model this sort of problem with a logistic curve; for one thing, one would need to know what the total ‘carrying capacity’ – or total number of dead — would be if current trends continue and we are unsuccessful at stopping this virus. The epidemiologists and statisticians who make models for this sort of thing know a lot more math, stats, biology, and so on than I do, but even they are working with a whole lot of unknowns, including the rate of infectiousness, what fraction of the people feel really sick, what fraction die, whether you get immunity if you are exposed, what is the effect of different viral loads, and much more. This virus has only been out for a few months…

logistic curve again

 

What’s the best approach – should we lock down harder, or let people start to go back to work? Some countries have had lockdowns, others have not. How will the future play out? I don’t know. I do know that before we can decide, we need to have fast, plentiful, and accurate tests, so we can quarantine just the people who are infected or are carriers, and let everybody else get back on with their lives. We are doing this lockdown simply because we have no other choice.

More on the “false positive” COVID-19 testing problem

I used my cell phone last night to go into the problem of faulty testing for COVID-19, based on a NYT article. As a result, I couldn’t make any nice tables. Let me remedy that and also look at a few more assumptions.

This table summarizes the testing results on a theoretical group of a million Americans tested, assuming that 5% of the population actually has coronavirus antibodies, and that the tests being given have a false negative rate of 10% and a false positive rate of 3%. Reminder: a ‘false negative’ result means that you are told that you don’t have any coronavirus antibodies but you actually do have them, and a ‘false positive’ result means that you are told that you DO have those antibodies, but you really do NOT. I have tried to highlight the numbers of people who get incorrect results in the color red.

Table A

Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 50,000 10% 45,000 5,000
Actually Negative 950,000 3% 28,500 921,500
Totals 1,000,000 73,500 926,500
Percent we assume are actually positive 5% Accuracy Rating 61.2% 99.5%

As you can see, using those assumptions, if you get a lab test result that says you are positive, that will only be correct in about 61% of the time. Which means that you need to take another test, or perhaps two more tests, to see whether they agree.

The next table assumes again a true 5% positive result for the population and a false negative rate of 10%, but a false positive rate of 14%.

Table B

Assume 5% really exposed, 14% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 50,000 10% 45,000 5,000
Actually Negative 950,000 14% 133,000 817,000
Totals 1,000,000 178,000 822,000
Percent we assume are actually positive 5% Accuracy Rating 25.3% 99.4%

Note that in this scenario, if you get a test result that says you are positive, that is only going to be correct one-quarter of the time (25.3%)! That is useless!

Now, let’s assume a lower percentage of the population actually has the COVID-19 antibodies, say, two percent. Here are the results if we assume a 3% false positive rate:

Table C

Assume 2% really exposed, 3% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 20,000 10% 18,000 2,000
Actually Negative 980,000 3% 29,400 950,600
Totals 1,000,000 47,400 952,600
Percent we assume are actually positive 2% Accuracy Rating 38.0% 99.8%

Notice that in this scenario, if you get a ‘positive’ result, it is likely to be correct only a little better than one-third of the time (38.0%).

And now let’s assume 2% actual exposure, 14% false positive, 10% false negative:

Table D

Assume 2% really exposed, 14% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 20,000 10% 45,000 2,000
Actually Negative 980,000 14% 137,200 842,800
Totals 1,000,000 182,200 844,800
Percent we assume are actually positive 2% Accuracy Rating 24.7% 99.8%

Once again, the chances of a ‘positive’ test result being accurate is only about one in four (24.7%), which means that this level of accuracy is not going to be useful to the public at large.

Final set of assumptions: 3% actual positive rate, and excellent tests with only 3% false positive and false negative rates:

Table E

Assume 3% really exposed, 3% false positive rate, 3% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 30,000 3% 45,000 900
Actually Negative 970,000 3% 29,100 940,900
Totals 1,000,000 74,100 941,800
Percent we assume are actually positive 3% Accuracy Rating 60.7% 99.9%

Once again, if you test positive in this scenario, that result is only going to be correct about 3/5 of the time (60.7%).

All is not lost, however. Suppose we re-test all the people who tested positive in this last group (that’s a bit over seventy-four thousand people, in Table E). Here are the results:

Table F

Assume 60.7% really exposed, 3% false positive rate, 3% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 45,000 3% 43,650 1,350
Actually Negative 29,100 3% 873 28,227
Totals 74,100 44,523 29,577
Percent we assume are actually positive 60.7% Accuracy Rating 98.0% 95.4%

Notice that 98% accuracy rating for positive results! Much better!

What about our earlier scenario, in table B, with a 5% overall exposure rating, 14% false positives, and 10% false negatives — what if we re-test all the folks who tested positive? Here are the results:

Table G

Assume 25.3% really exposed, 14% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 45,000 14% 38,700 6,300
Actually Negative 133,000 10% 13,300 119,700
Totals 178,000 52,000 126,000
Percent we assume are really positive 25.3% Accuracy Rating 74.4% 95.0%

This is still not very good: the re-test is going to be accurate only about three-quarters of the time (74.4%) that it says you really have been exposed, and would only clear you 95% of the time. So we would need to run yet another test on those who again tested positive in Table G. If we do it, the results are here:

Table H

Assume 74.4% really exposed, 14% false positive rate, 10% false negative
Group Total Error rate Test says they are Positive Test says they are Negative
Actually Positive 38,700 14% 33,282 5,418
Actually Negative 13,300 10% 1,330 11,970
Totals 52,000 34,612 17,388
Percent we assume are really positive 74.4% Accuracy Rating 96.2% 68.8%

This result is much better, but note that this requires THREE TESTS on each of these supposedly positive people to see if they are in fact positive. It also means that if they get a ‘negative’ result, that’s likely to be correct only about 2/3 of the time (68.8%).

So, no wonder that a lot of the testing results we are seeing are difficult to interpret! This is why science requires repeated measurements to separate the truth from fiction! And it also explains some of the snafus committed by our current federal leadership in insisting on not using tests offered from abroad.

 

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EDIT at 10:30 pm on 4/25/2020: I found a few minor mistakes and corrected them, and tried to format things more clearly.

People are Not Cattle!

This apparently did not occur to William Sanders.

He thought that statistical methods that are useful with farm animals could also be used to measure effectiveness of teachers.

I grew up on a farm, and as both a kid and a young man I had considerable experience handling cows, chickens, and sheep. (These are generic critter photos, not the actual animals we had.)

I also taught math and some science to kids like the ones shown below for over 30 years.

guy teaching  deal students

Caring for farm animals and teaching young people are not the same thing.

(Duh.)

As the saying goes: “Teaching isn’t rocket science. It’s much harder.”

I am quite sure that with careful measurements of different types of feed, medications, pasturage, and bedding, it is quite possible to figure out which mix of those elements might help or hinder the production of milk and cream from dairy cows. That’s because dairy or meat cattle (or chickens, or sheep, or pigs) are pretty simple creatures: all a farmer wants is for them to produce lots of high-quality milk, meat, wool, or eggs for the least cost to the farmer, and without getting in trouble.

William Sanders was well-known for his statistical work with dairy cows. His step into hubris and nuttiness was to translate this sort of mathematics to little humans. From Wikipedia:

“The model has prompted numerous federal lawsuits charging that the evaluation system, which is now tied to teacher pay and tenure in Tennessee, doesn’t take into account student-level variables such as growing up in poverty. In 2014, the American Statistical Association called its validity into question, and other critics have said TVAAS should not be the sole tool used to judge teachers.”

But there are several problems with this.

  • We  don’t have an easily-defined and nationally-agreed upon goal for education that we can actually measure. If you don’t believe this, try asking a random set of people what they think should be primary the goal of education, and listen to all the different ideas!
  • It’s certainly not just ‘higher test scores’ — the math whizzes who brought us “collateralization of debt-swap obligations in leveraged financings” surely had exceedingly high math test scores, but I submit that their character education (as in, ‘not defrauding the public’) was lacking. In their selfishness and hubris, they have succeeded in nearly bankrupting the world economy while buying themselves multiple mansions and yachts, yet causing misery to billions living in slums around the world and millions here in the US who lost their homes and are now sleeping in their cars.
  • Is our goal also to ‘educate’ our future generations for the lowest cost? Given the prices for the best private schools and private tutors, it is clear that the wealthy believe that THEIR children should be afforded excellent educations that include very small classes, sports, drama, music, free play and exploration, foreign languages, writing, literature, a deep understanding and competency in mathematics & all of the sciences, as well as a solid grounding in the social sciences (including history, civics, and character education). Those parents realize that a good education is expensive, so they ‘throw money at the problem’. Unfortunately, the wealthy don’t want to do the same for the children of the poor.
  • Reducing the goals of education to just a student’s scores on secretive tests in just two subjects, and claiming that it’s possible to tease out the effectiveness of ANY teacher, even those who teach neither English/Language Arts or Math, is madness.
  • Why? Study after study (not by Sanders, of course) has shown that the actual influence of any given teacher on a student is only from 1% of 14% of test scores. By far the greatest influence is from the student’s own family background, not the ability of a single teacher to raise test scores in April. (An effect which I have shown is chimerical — the effect one year is mostly likely completely different the next year!)
  • By comparison, a cow’s life is pretty simple. They eat whatever they are given (be that straw, shredded newspaper, cotton seeds, chicken poop mixed with sawdust, or even the dregs from squeezing out orange juice [no, I’m not making that up.]. Cows also poop, drink, pee, chew their cud, and sometimes they try to bully each other. If it’s a dairy cow, it gets milked twice a day, every day, at set times. If it’s a steer, he/it mostly sits around and eats (and poops and pees) until it’s time to send  them off to the slaughterhouse. That’s pretty much it.
  • Gary Rubinstein and I have dissected the value-added scores for New York City public school teachers that were computed and released by the New York Times. We both found that for any given teacher who taught the same subject matter and grade level in the very same school over the period of the NYT data, there was almost NO CORRELATION between their scores for one year to the next.
  • We also showed that teachers who were given scores in both math and reading (say, elementary teachers), there was almost no correlation between their scores in math and in reading.
  • Furthermore, with teachers who were given scores in a single subject (say, math) but at different grade levels (say, 6th and 7th grade math), you guessed it: extremely low correlation.
  • In other words, it seemed to act like a very, very expensive and complicated random-number generator.
  • People have much, much more complicated inputs, and much more complicated outputs. Someone should have written on William Sanders’ tombstone the phrase “People are not cattle.”

Interesting fact: Jason Kamras was considered to be the architect of Value-Added measurement for teachers in Washington, DC, implemented under the notorious and now-disgraced Michelle Rhee. However, when he left DC to become head of Richmond VA public schools, he did not bring it with him.

 

Not So Fast, Betsy DeVos!

I attended the official roll-out of the results of the 2019 National Assessment of Educational Progress (NAEP) a couple of days ago at the National Press Club here in DC on 14th Street NW, and listened to the current education secretary, Betsy Devos, slam public schools and their administrators as having accomplished nothing while spending tons of money. She and other speakers held up DC, Mississippi, and Florida as examples to follow. Devos basically advocated abandoning public schools altogether, in favor of giving each parent a “backpack full of cash” to do whatever they want with.

Some other education activists I know here in DC shared their thoughts with me, and I decided to look at the results for DC’s white, black, and Hispanic students over time as reported on the NAEP’s official site. (You can find them here, but be prepared to do quite a bit of work to get them and make sense out of them!)

I found that it is true that DC’s recent increases in scores on the NAEP for all students, and for black and Hispanic students, are higher than in other jurisdictions.

However, I also found that those increases were happening at a HIGHER rate BEFORE DC’s mayor was given total control of DC’s public schools; BEFORE the appointment of Michelle Rhee; and BEFORE the massive DC expansion of charter schools.

Here are two graphs (which I think show a lot more than a table does) which give ‘average scale scores’ for black students in math at grades 4 and 8 in DC, in all large US cities, and in the nation as a whole. I have drawn a vertical red line at the year 2008, separating the era before mayoral control of schools (when we had an elected school board) and the era afterwards (starting with appointed chancellor Michelle Rhee and including a massive expansion of the charter school sector). These results include both regular DC Public School students and the charter school sector, but not the private schools.

I asked Excel to produce linear correlations of the average scale scores for black students in DC starting in 1996 through 2007, and also for 2009 through 2019. It wasn’t obvious to my naked eye, but the improvement rates, or slopes of those lines, were TWICE AS HIGH before mayoral control. At the 4th grade level, the improvement rate was 2.69 points per year BEFORE mayoral control, but only 1.34 points per year afterwards.

Yes, that is a two-to-one ratio AGAINST mayoral control & massive charter expansion.

At the 8th grade level, same time span, the slope was 1.53 points per year before mayoral control, but 0.77 points per year afterwards.

Again, just about exactly a two-to-one ratio AGAINST the status quo that we have today.

pre and post Rhee, 4th grade NAEP, black students in DC, nation, large cities

pre and post Rhee, 8th grade NAEP, black students in DC, large cities, and nation

Charter schools do NOT get better NAEP test results than regular public schools

It is not easy to find comparisons between charter schools and regular public schools, partly because the charter schools are not required to be nearly as transparent or accountable as regular public schools. (Not in their finances, nor in requests for public records, nor for student or teacher disciplinary data, and much more.) At the state or district level, it has in the past been hard or impossible to find comparative data on the NAEP (National Assessment of Educational Progress).

We all have heard the propaganda that charter and voucher schools are so much better than regular public schools, because they supposedly get superior test scores and aren’t under the thumb of  those imaginary ‘teacher union thugs’.

However, NCES has released results where they actually do this comparison. Guess what: there is next to no difference between the scores of all US charter schools on the NAEP in both reading and math at either the 4th grade or 8th grade level! In fact, at the 12th grade, regular public schools seem to outscore the charter schools by a significant margin.

Take a look at the two graphs below, which I copied and pasted from the NCES website. The only change I made was to paint orange for the bar representing the charter schools. Note that there is no data available for private schools as a whole.

public vs charter vs catholic, naep, math

If you aren’t good at reading graphs, the one above says that on a 500-point scale, in 2017 (which was the last year for which we have results), at the 4th grade, regular public school students scored an average of 239 points in math, three points higher than charter school students (probably not a significant difference). At the 8th grade level, the two groups scored identically: 282 points. At the 12th grade, in 2015, regular public school students outscored charter school students by a score of 150 to 133 on a 300-point scale (I suspect that difference IS statistically significant). We have no results from private schools, but Catholic schools do have higher scores than the public or charter schools.

The next graph is for reading. At the 4th grade, charter school students in 2017 outscored regular public school students by a totally-insignificant 1 point (222 to 221 on a 500 point scale) and the same thing happened at the 8th grade level (266 to 265 on a 500 point scale). However, at the 12th grade, the regular public school students outscore their charter school counterparts by a score of 285 to 269, which I bet is significant.

charter vs public vs catholic, naep, reading, 2017

 

 

Why A New Generation of Teachers is Angry at Self-Styled Education ‘Reformers’

This is an excellent essay at Medium that I learned about from Peter Greene of Curmudgucation. I copy and paste it in its entirety in case you don’t like signing into Medium.

Why New Educators Resent “Reformers”

Let’s consider why so many young educators today are in open rebellion.

How did we lose patience with politicians and policymakers who dominated nearly every education reform debate for more than a generation?

Recall first that both political parties called us “a nation at risk,” fretted endlessly that we “leave no child behind,” and required us to compete in their “race to the top.”

They told us our problems could be solved if we “teach for America,” introduce “disruptive technology,” and ditch the textbook to become “real world,” 21st century, “college and career ready.”

They condemned community public schools for not letting parents “choose,” but promptly mandated a top-down “common core” curriculum. They flooded us with standardized tests guaranteeing “accountability.” They fetishized choice, chopped up high schools, and re-stigmatized racial integration.

They blamed students who lacked “grit,” teachers who sought tenure, and parents who knew too much. They declared school funding isn’t the problem, an elected school board is an obstacle, and philanthropists know best.

They told us the same public schools that once inspired great poetry, art, and music, put us on the moon, and initiated several civil rights movements needed to be split, gutted, or shuttered.

They invented new school names like “Green Renaissance College-Prep Academy for Character, the Arts, and Scientific Careers” and “Hope-Horizon Enterprise Charter Preparatory School for New STEM Futures.” They replaced the district superintendent with the “Chief Educational Officer.”

They published self-fulfilling prophecies connecting zip-coded school ratings, teacher performance scores, and real estate values. They viewed Brown v. Board as skin-deep and sentimental, instead of an essential mandate for democracy.

They implied “critical thinking” was possible without the Humanities, that STEM alone makes us vocationally relevant, and that “coding” should replace recess time. They cut teacher pay, lowered employment qualifications, and peddled the myth anyone can teach.

They celebrated school recycling programs that left consumption unquestioned, gave lip-service to “student-centered civic engagement” while stifling protest, and talked up “multiple intelligences” while defunding the arts.

They instructed critics to look past poverty, inequality, residential segregation, mass incarceration, homelessness, and college debt to focus on a few heartwarming (and yes, legitimate) stories of student resilience and pluck.

They expected us to believe that a lazy public-school teacher whose students fail to make “adequate yearly progress” was endemic but that an administrator bilking an online academy or for-profit charter school was “one bad apple.”

They designed education conferences on “data-driven instruction,” “rigorous assessment,” and “differentiated learning” but showed little patience for studies that correlate student performance with poverty, trauma, a school-to-prison pipeline, and the decimation of community schools.

They promised new classroom technology to bridge the “digital divide” between rich, poor, urban, and rural, while consolidating corporate headquarters in a few elite cities. They advertised now-debunked “value-added” standardized testing for stockholder gain as teacher salaries stagnated.

They preached “cooperative learning” while sending their own kids to private schools. They saw alma mater endowments balloon while donating little to the places most Americans earn degrees. They published op-eds to end affirmative action but still checked the legacy box on college applications.

They were legitimately surprised when thousands of teachers in the reddest, least unionized states walked out of class last year.

Meanwhile……

The No Child Left Behind generation continues to bear the fullest weight of this malpractice, paying a steep price for today’s parallel rise in ignorance and intolerance.

We are the children of the education reformer’s empty promises. We watched the few decide for the many how schools should operate. We saw celebrated new technologies outpace civic capacity and moral imagination. We have reason to doubt.

We are are the inheritors of “alternative facts” and “fake news.” We have watched democratic institutions crumble, conspiracies normalized, and authoritarianism mainstreamed. We have seen climate change denied at the highest levels of government.

We still see too many of our black brothers and sisters targeted by law enforcement. We watched as our neighbor’s promised DACA protections were rescinded and saw the deporters break down their doors. We see basic human rights for our LGBTQ peers refused in the name of “science.”

We have seen the “Southern strategy” deprive rural red state voters of educational opportunity before dividing, exploiting, and dog whistling. We hear climate science mocked and watch women’s freedom erode. We hear mental health discussed only after school shootings.

We’ve seen two endless wars and watched deployed family members and friends miss out on college. Even the battles we don’t see remind us that that bombs inevitably fall on schools. And we know war imposes a deadly opportunity tax on the youngest of civilians and female teachers.

Against this backdrop we recall how reformers caricatured our teachers as overpaid, summer-loving, and entitled. We resent how our hard-working mentors were demoralized and forced into resignation or early retirement.

Our collective experience is precisely why we aren’t ideologues. We know the issues are complex. And unlike the reformers, we don’t claim to have the answers. We simply believe that education can and must be more humane than this. We plan to make it so.

We learned most from the warrior educators who saw through the reform facade. Our heroes breathed life into institutions, energized our classrooms, reminded us what we are worth, and pointed us in new directions. We plan to become these educators too.

Some debate in Chevy Chase (DC) on significance of latest NAEP scores …

On a local DC list-serve for the region where I last taught (and also went to Junior High School), I posted this:

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Those of us with kids in Chevy Chase – DC, either now, in the future, or in the past, have seen many changes in education here in DC, especially since 2007, when the elected board of education was stripped of all powers under PERAA and Chancellor Rhee was appointed by Mayor Fenty.
[I personally went to Junior High School here at Deal back in the early 1960s, taught math in DCPS from 1978 to 2009, including 15 years at Deal (much to my surprise) and my own kids went K-12 in DCPS, graduating from Walls and Banneker, respectively]
Was mayoral control of schools in DC a success? Is the hype we have all heard about rising test scores for real?
We now have statistics from  NAEP* for about two decades, and we can compare scores for various subgroups before and after that 2007 milestone.
Did Black students make faster improvements after PERAA than beforehand? Nope. To contrary: their scores were inching up faster *before* 2007 than they have been doing since that time.
Did Hispanic students make faster improvements under the reformers? Nope, again.
How about students whose parent(s) didn’t graduate high school, and/or those who finished grade 12 but either never went to college or else didn’t earn a degree – surely they did better after Rhee, Henderson et al. took over? Again, no.
Then what group of students in Washington DC *did* make more progress on the NAEP after the Reformers took over?
You guessed it, I bet:
White students, and students with parents who earned a college degree.
Amazing.
Guy Brandenburg
*National Assessment of Educational Progress
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Another person contested my assessment and wrote the following:
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The NAEP is cross-sectional data, i.e. it does nothing to adjust for changes in composition of test-takers over time, which is why Steve Glazerman refers to comparisons of NAEP scores over time as “misNAEPery” [https://ggwash.org/view/ 31061/bad-advocacy-research- abounds-on-school-reform] and I have referred to the same thing as “jackaNAEPery” [https://www.urban.org/urban- wire/how-good-are-dcs-schools] .
There has been a dramatic, even shocking, compositional change since 2000 in births across the city, entering cohorts of students, and exit rates from DC schools and the city.
Most noticeably in NW, better educated parents are substantially more likely to have kids in DC, enroll them in DC public schools, and stay past 3rd grade.
Any analysis of test score change needs to grapple with that compositional change.
But more importantly, the compositional change itself is a policy outcome of note, which the DC Council and Mayor have an interest in promoting.
The only evidence one should accept must *at minimum* use longitudinal data on students to compute *learning* as opposed to static achievement, e.g. this analysis of 2008 school closures:
A lot of other things happened 1996-2008 of course, including a rapid expansion of charters, a shrinking proportion of DC residents attending private schools, etc.In 2008 alone, a lot of Catholic schools closed, and some converted to public charter schools.
During this time, we also had a voucher program that produced some gains early on, and then began to lower test scores relative to public options:
All of this is not to say DCPS and charter schools shouldn’t serve less advantaged students better than they do–obviously they should! But the evidence is nuanced, and DC has made huge gains across the board since the 1990’s that make attributing any changes to policy rather than shifting population composition problematic at best.
Interestingly, the NAEP data explorer [https://www. nationsreportcard.gov/ndecore/ xplore/nde]does not report scores for white 8th graders in 1990, 1992, and 1996, presumably because too few were tested. I.e. the means by race show a lot of  “‡ Reporting standards not met.
[I personally attended DCPS (Hyde, Hardy, and School Without Walls) 1976-1989, have 2 children currently in Deal and SWW.]
Austin Nichols
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I wrote a response to Nichols, but it hasn’t been posted yet, and might never be:
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My previous reply got lost somewhere in cyberspace.
If looking at long-term trends in the NAEP and TUDA is ‘misnaepery’ or ‘jacknaepery’, as Mr Austin would have us believe, then the entire NAEP bureaucracy has been doing just that. (In fact, an entire branch of the National Center for Education Statistics is devoted to, yes, Long Term Trends: https://nces.ed.gov/nationsreportcard/ltt/ )
It’s a laughable idea that we could just use the tests chosen by DCPS and later by OSSE and administered every year, to tell how good DC public or charter schools are, over time. First of all, the tests administered here have changed dramatically. Back in the 1990s it was the CTBS. Then it was the SAT-9, developed by a different company. Then it was the DC-CAS, again, a different vendor. Now we have the PARCC produced by yet another vendor. We also know that in the past there has been major fraud with these tests, committed by adults, in order to gain bonuses and keep their jobs. We also have no way of comparing DC with any other city or state using those tests, since only a handful of states even use the PARCC and for all I know, their cut scores and questions might be different from what we use here in DC.
The idea of measuring median student improvement from year to year might appear to have some merit, until you talk to students and teachers involved. You discover that many of the older students see no reason to take the tests seriously; they bubble in, or click on, answers as fast as possible, without reading the questions, in order to be free to leave the room and go do something else. Any results from that test are simply unreliable, and it is simply not possible to tell whether DC education policies have improved over time based on the PARCC, DC-CAS, SAT-9, or CTBS, no matter what sort of fancy statistical procedures are employed.
With the NAEP, on the other hand, there has never been any suggestion of impropriety, and the same agency has been devising, administering, and scoring these tests for decades. We have no other nation-wide test that has been systematically given to a random sample of students for any length of time.
Obviously the 4th or 8th graders who took the NAEP in 2017 were not the same ones who took it in 2015. (Duh!) However, we do in fact have a record of NAEP scores in every state and DC since the 1990s, and they are also broken down by lots of subgroups. Obviously DC is gentrifying rapidly, and there are more white students in DCPS than there were 10 or 20 years ago. If we trace the various subgroups (say, African-American students, or Hispanics, or students whose parents didn’t finish high school, or whatever group you like), you can watch the trends over time in each subgroup. However, Mr Austin does inadvertently raise one valid point: since the proportion of black students in DC is decreasing, and the proportion of white students with college-educated parents is rising, then the natural conclusion would be that this gentrification has *inflated* overall scores for 4th and 8th grade students in DC (and DCPS), especially since 2007. Which is more evidence that ‘reform’ is not working. Not evidence that we should throw the scores out and ignore them completely.
Those trends show something quite different from what Mayor Bowser keeps proclaiming. For one thing, if you look at the simple graphs that I made (and you can examine the numbers yourselves) you can see that any improvements overall in DC, or for any subgroups, began a decade before the ‘reformers’ took over DC schools. ( see https://bit.ly/2K3UyZ1 to begin poking around.) Secondly, for most of the subgroups, those improvements over time were greater before Rhee was anointed Chancellor. Only two groups had better rates of change AFTER Rhee: white students, and those with parents with college degrees – the ones that are inflating overall scores for DC and DCPS during the last decade.
I would note also that the previous writer’s salary is paid by one of the Reform organizations supported by billionaires Gates and Arnold. You can look at the funding page yourself ( page 3 at https://urbn.is/2II1YQQ ). I suspect that when ‘reform’ advocates say not to look at our one consistent source of educational data, it’s because they don’t like what the data is saying.
Guy Brandenburg

Mayoral Control of Schools in Washington DC Appears to have Benefitted Children of College Grads, But Nobody Else

The reason given for having the office of the Mayor (originally Adrian Fenty) take over the school system in Washington DC, and abolishing all the powers of the elected school board, was to help the poorest kids.

But that’s not how it worked out, according to official test results from the National Assessment of Educational Progress.

Using those stats, harvested for me by the parent of a former student of mine from the NAEP database, we see that children in DC whose parents did NOT finish college made lower gains after 2007 (the date of the changeover) than they did before that date. However, children of college graduates in DC made higher gains after 2007.

Amazing.

And yet another sign that the education ‘reform’ movement is a complete failure.

Here are my graphs and raw data. (Right-click to see them enlarged, if you have a PC – not sure what to do if you have an Apple product.)

annualized gains pre and post mayoral control, DC, 8th grade math, by parental education

The vertical orange line shows the date (June of 2007) when Michelle Rhee was appointed as the first Chancellor of DC Public Schools. The black, dashed line represents average scale scores on the 8th grade math NAEP for students who reported that their parent(s) graduated from college, and the other lines shows scores for kids whose parent(s) did or did not graduate high school, had some college courses. The thin, double blue line represents those students who were unsure of their parental education.

I asked Excel to calculate the annual rate of change pre- and post-mayoral control, and you can see the results in the last two columns. The boxes filled in with yellow are the ‘winners’, so to speak. Note that for the period 2000-2007, the annualized change in NAEP scale scores on the 8th grade NAEP math test in DC is 2.63, which means that on the average, that group of students (yeah, it’s a different group of students for each testing event) saw their scores rise by 2.63 points per year, or 5.26 points every two years. However, for the period 2007-2017, after mayoral control, that same group of students saw their gains cut nearly in half – it tumbled to 1.41 points per year. Kids whose parents did graduate from high school (but went no further) and those whose parents had some education after high school, also saw their rates of increase tumble drastically. Kids who were unsure of their parental education levels or who didn’t report it also saw a drop, but not so large: dropping from 2.08 down to 1.88 points per year.

The only group which saw their annualized scores increase after mayoral control were the children of college graduates: their rate went from 1.16 points/year to 2.60 points per year, which to me looks rather significant.

Ironic, huh?

And here are the results for reading:

annualized gains pre and post mayoral control, dc, 8th grade reading, by parental education

Once again, the results for students whose parents did NOT graduate from college (the first three lines of the table) tumbled dramatically after mayoral control. However, students whose parents did graduate from college (the fourth line) saw a dramatic increase. The last line, representing kids who didn’t know or didn’t report their parental education, saw a little uptick after mayoral control.

Remind me again why  we got rid of the elected school board and put the mayor in charge? Was it really to make sure that the ‘haves’ would get more and that the ‘have-nots’ would have less?

Let me point out the obvious: white parents in DC are overwhelmingly college-educated. Those in DC who did not graduate from high school, or who graduated from 12th grade and went no further, are overwhelmingly African-American or Hispanic. So our ‘reforms’ have had a disproportionately negative impact on black and hispanic students, and a positive one on white kids.

Was that really the intent all along?

What Do the Latest NAEP Results Tell Us About Education “Reform” in Washington, DC?

The usual gang of supporters of bipartisan education “reform” never tire of telling the world how wonderful education ‘reform’ has been in Washington, DC, what with the proliferation of charter schools, Congressional support for vouchers, a seriously handicapped teachers’ union, tremendous churn of teaching and administrative staff, tons of consultants, and direct mayoral control.

I’ve been among those saying that the results are NOT so wonderful. I have documented how virtually none of the promises came true that Chancellors Michelle Rhee and Kaya Henderson made about 8 years ago. They promised that the improvements in test scores, graduation rates and much more would go through the roof, but in fact, almost none of that came to pass. The recent scandals about truancy, absenteeism, phoney grades and illegitimate graduation rates have shown that much of their supposed successes have been purely fraudulent.

In addition, I showed recently that in fact, progress for a number of DC’s subgroups (blacks, whites, and Hispanics) on the NAEP 4th and 8th grade reading and math tests are further evidence of failure, since improvement rates per year BEFORE mayoral control cemented the rule of our ‘reformista’ Chancellors wee BETTER THAN they were AFTERWARDS.

I was asked by one of the members of DC’s now-powerless board of education to analyze changes over time for ALL of DC’s students as a group (not subdivided in any way) to compare pre- and post-‘reform’.

I made my own graphs using the data on the NAEP Data Explorer page, being careful to use the same vertical scale in each case, and starting at the lowest point, or nadir, of DC’s NAEP scores back in the 1990s. I asked Excel to calculate and draw the line of best fit for the data points. In each case, that ‘trend-line’ of linear correlation fit the data extraordinarily well. In fact, the R-values of linear correlation went from a low of 94% to a high of 99%. I didn’t use the graphs that the NAEP Data Explorer page provided, because they changed the vertical scale from situation to situation – so a rise of, say, 10 points over 20 years would look just about the same as a rise of, say, 60 points over 20 years. And they aren’t! So my vertical (y-axis scale) is 200 points in each case.

I also marked on the graphs where the dividing line was between the time when we had an elected school board (abolished in 2007) and the present, when we have direct mayoral control with essentially no checks or balances on his or her power.

So here are the graphs:

4th grade math, ANSS, all dc, 1996-20174th grade reading, ANSS, all DC, 1998-20178th grade math, ANSS, all DC, 1996-20178th grade reading, ANSS, all DC, 1998-2017

So do you see any miracles?

Me neither.

So what does all of this that mean?

  1. You need a good magnifying glass to see any significant differences in progress on the NAEP test scores for ‘all students’ in Washington, DC when comparing the two eras. The slopes of the dashed lines of best fit are essentially identical on the two sides of the purple line.
  2. Since the proportion of white inhabitants of DC and of students in DC’s publicly-funded schools have both increased markedly in the past 10 years, and the proportion of black residents and black students have decreased markedly, and this has skewed the graph in a positive direction after 2007.* That means that this data, and these graphs, are actually making the overall situation look more favorable to the reformistas.
  3. Anybody pretending that there are huge increases in national test scores after the reformistas took over education in DC, is blowing smoke in your eyes.

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*Why? When you remove low-scorers and add high-scorers (on anything) to a group, the overall average score will go up.

Here is a sports example: A football coach has been given a roster consisting of these players:

  • twenty big, strong, and bulky linesmen and backs and so on. Let’s pretend their average weight is 280 pounds.
  • twenty relatively small, but very fit, place-kickers (actually, they are soccer players looking for a fall sport) who weigh an average of 180 pounds each.

The team’s average weight is exactly 230 pounds (That’s (20*280 + 20*180) / 40) .

At noon,  the coach realizes there is no need for so many place-kickers, and she cuts 15 of the placekickers, leaving five of them. Their papers say that each one in fact weighs 180 pounds.

NOTHING ELSE CHANGES. In particular, none of the players gain or lose any weight during these fifteen minutes that the coach is making these changes.

At a quarter past noon, the average weight of the team has now increased markedly. It is now (20*280 + 5*180) / 25, or 260 pounds – it has gone up by 30 pounds simply by cutting 17 of its least-heavy players.

Is that coach a genius, or what, at bulking up her team?

Actually, although it’s not the direct result of what any Chancellor has done, this situation is somewhat similar to what’s happening in DC. Remember that white students in DC are the highest-scoring group of white students anywhere in the nation, because their parents overwhelmingly have graduate or professional degrees; DC’s white working class left town decades ago. So when relatively low-scoring African-American students (from working-class families) move to PG County, and white students and their relatively-highly-educated families move into DC from wherever, the averages will increase much as they did in my example with the imaginary football team.

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