Archive for April 8th, 2007

If you have nothing but tail ends, what better to make than soup–especially when there’s snow on the ground? If you have, say, about 3 1/2 quarts of rich, gelatinous, homemade beef stock left, apply what I call the “what would taste good?” principle–but remember, most of the ingredients you must have on hand. Here’s an example.

You ask yourself what soup you could make with that stock that would taste good, and for some reason, you think of cheese tortellini. Ask yourself what you don’t have, and you come up with two things: Beef and tortellini. (If the list of things you don’t have is longer than the list of things you have, you’re cheating, so think of something else–and yes, you could leave out the beef, but your stock won’t be quite so rich.)

Run to the store and get a small package of stew beef and cheese tortellini. When you get home, you’re ready to throw it together, along with whatever else you may have at home.

First, get the stock out of the refrigerator and pop it into the microwave, and set it to nuke for seven minutes, long enough to melt all the grease and get the stock hot. While the stock is being nuked, cut the beef into smaller cubes (they’re always too big for soup) and toss them into the bottom of a pot. Measure a quart of the stock into a glass degreaser (you know, with the spout rising from the bottom), and let it sit for a minute, until all the fat rises to the top. Pour the degreased stock into the pot with the beef, and the fat back into the remaining stock and stick it back in the refrigerator. Open a can of diced tomatoes (canned tomatoes in the three essential forms–sauce, diced, and crushed–should be a permanent part of your pantry) and toss it in with the juice, then add some basil and some oregano. Bring it to a boil, then simmer it until the beef is tender.

So what do you have lurking in the freezer? The tail end of a bag of frozen sweet corn, and the tail end of a bag of frozen peas. What’s that, you have diced prosciutto and roasted red peppers in the refrigerator? Great. Add the tortellini, the peas and the corn, dice some roasted red peppers and add them, along with some prosciutto (just not too much–it can easily overpower everything else). Simmer just until the pasta is done, add salt and pepper to taste, and there you have it. Memorable soup from what you had left!

Originally published September 17 2006:

Some eight years ago, I attended a series of presentations (not by choice) given by the ed school diversity police. At one, we got the party line on “learning styles/modalities,” presented with no evidence to back it up because like contrastive rhetoric, there is no evidence to back it up.

A particularly grumpy faculty member–who also happened to be a Dean at the time–asked the presenter what I, and no doubt many others, were thinking. He said, “Other than the fact that you have no evidence to support this, so what? We have material to cover. We barely have enough time as it is. We certainly don’t have time to present the material in each style just to make it easier for some of the students. So what do you want us to do with this information?”

Another one of these presentations was given by the feminonsense police, and covered how men are “goal-oriented,” and women are “process-oriented.” She and her co-feminuts, along with a few cooperative feminized males, presented a “role play” that began with a normal, goal-oriented meeting (of men) where the problem was addressed, a solution was agreed upon, and men were assigned to implement the solution. The next “role play” was feminuts having a meeting with no goal or purpose, other than to make each other feel good, and even though it was ostensibly to address the same problem as the first meeting “role play,” the feminuts ended the meeting without ever addressing a solution. Finally, there was the final, two-part “role play,” in which both sexes took part. In the first of the two-parter, the feminuts chose to shut up and sit there like lumps when the men insisted on having a meeting with a goal and purpose, and tackling the problem. In the second part of the two-parter, the men acquiesced to the “process oriented” meeting, the “issues” were discussed, feelings about the “issues” were shared, no solution was ever mentioned much less discussed, and nothing was accomplished (of course). The second of the two-parter was presented as how men could be more “sensitive” to women in meetings. When confronted with the fact that the “sensitive” meeting was unproductive, the feminuts accused the questioner of being patriarchal, and avoided the issue.

Ignore the man behind the curtain!

Both of these presentations illustrate why “being sensitive to our differences” (codename: diversity) is destructive to education.

As far as “learning styles” go, the unnamed Dean said it best at the time. Unless you have very little material to cover, in which case you shouldn’t be teaching the class in the first place, you don’t have the time to screw with, or worry about, such nonsense–especially when it is motivated by no evidence at all.

As far as “goal-oriented” v. “process-oriented” goes, education is, by definition, goal-oriented. “Process-oriented” approaches rarely produce a result.I’m hedging, since I do not know of one single case in which a “process-oriented” approach has resulted in a solution They are, by definition, unproductive–given that solving a problem of some kind is the goal of education, and if women truly are “process oriented” (and I’m not accepting that, given that there are so many logical women in the world, and have always been), then it is one purpose of education to teach them to be goal-oriented thinkers.

This “diversity” obsession is particularly destructive when it rears its inefficient, navel-gazing, narcissistic head in math education.

As knowledge systems go, math is the prototypical, linear system. Each skill builds upon others, so mastering a skill requires that one has already mastered previous skills. Math is essentially Aristotelian in nature, however patriarchal and serial raping and penis waving that may be.

Fifty percent of the reason for teaching any math skill, then, is because mastery of that skill will be required for the mastery of other skills down the road. While little Johnny may be a macaroni art learner or little Michelle may be a crayon and poster board project learner, allowing (worse, encouraging) little Johnny to solve the math problem by gluing macaroni to a toilet paper tube is counter-productive to fifty percent of the reason for covering the skill in class (and presenting Johnny with the problem). While Michelle’s crayon and poster board project may be very cute and creative, she learns no useful skill from doing it, and her failure to master the skill will handicap her later down the road. Educrats will then point to evil patriarchal traditionalist math teachers, Michelle’s sex, Michelle’s parents, Michelle’s socioeconomic status, the lack of technology in the classroom, or conservatives in general and blame them for “disadvantaging” poor little Michelle–when their own nutty educration methods are responsible. (For the latest example of fuzzy-headed, illogical educrat whining, see here.)

Repeat after me: There is no such thing as “mindless” drilling, or “mindless” rote memorization. Nothing about memorization or drilling is “mindless.” Rote memorization gives us domain knowledge, with which we can build other skills. Drilling is learning. Both teach discipline, both strengthen connections (there’s your neuroscience reference), and both build the skills necessary to solve problems.

When you can point to anyone in the real world solving a real-world problem by creating macaroni art, then by all means, object. I have a hard time trying to think of an example of anyone taking a complex problem and solving it “holistically,” or by sitting around in a matriarchal, vagina monologues-emulating, “process oriented” meeting, much less by making a cute, creative, crayon and poster board project. But please, let me know if you can think of any examples.

Educrats are fond of throwing around the phrase, “problem-solving skills,” yet seem to believe that every problem is unique, and unrelated to every other problem–as, indeed, you must believe if you think that macaroni art is, or ever can be, a problem-solving skill. We can see an example of this in this nonsense from the NEA:

A student well versed in algebra might do the following to set up the problem: p = pigs, c = chickens. p + c = 70 (heads) 4p + 2c = 200 (pigs have 4 legs and chickens have 2 legs). These two equations may be used to solve the problem. Students might solve this problem by “guessing and checking,” or drawing pictures. Some methods of solving problems might be considered more “efficient.” That may be true, but the correct answer can be found using multiple methods. Children think about mathematics in different ways depending on their prior experiences at home and school. By allowing students to think flexibly about numbers, we encourage them to “own” the math forever, instead of “borrowing” until class is over.

Allowing multiple methods encourages failure–because, again, math is wholly linear, and skills build upon other skills. Allowing students to “own” math means not teaching them math at all.

The linearity of math means that there is exactly one method, and only one method, for any given skill:Yes, I realize that one may approach a conditional bottom-up or top-down, or that one may calculate a problem with different series of steps, or put steps in different orders. that symbol manipulation which must be mastered not only to solve the current problem, but to master other skills down the road. It makes no difference if little Johnny would rather glue macaroni on toilet paper tubes. It makes no difference if little Michelle is a crayon project-oriented learner. Only one method accomplishes the entire reason for teaching the skill in the first place.

But teaching math has an even more basic function than math itself, and always has: Learning math is learning that step-by-step, logical approach to problem-solving, an approach whose applications far exceed the scope of mathematics. Problem-solving is its own knowledge system, and math is the best way to learn that knowledge system. Math teaches us to take a complex problem and simplify it by dissembling it. Math teaches us to take a complex problem and by writing equivalent statements, clarify it and the path to its solution. Math teaches us the progression of logical steps (remember all those proofs in geometry?) Math is coldly and unforgivingly logical–”close to the right answer” is an absurdity in math, where there is the right answer and there is every other, equally wrong, answer–and gives us problem-solving skills we will use throughout our lives.

Mathematics has, for this reason, been a cornerstone of education since the Greeks. Crayon and poster board projects accomplish nothing other than allowing Michelle to get an A without having mastered the content.

And doing all those cute projects ensures that little Johnny and little Michelle will go through life devoid of those invaluable problem-solving skills, that Aristotelian logic, and that they will be crippled for the rest of their lives. Is making them feel more comfortable by letting them glue macaroni to cardboard tubes really worth that?

First, we had overpopulation, then global cooling and the coming ice age, then nuclear winter, then smoking, and currently, the most popular Chicken Littlisms are global warming climate change, transfats, red meat, and on alternating days, obesity and malnutrition. The latest is going to be

[drum roll]

Zits.

Follow the links.

While I was collecting NAEP data, I thought I’d see if the explosion in education spending from NCLB has any relationship to the percentage of students proficient or above in math and reading, so I downloaded the most recent data (8th graders, 2005, aggregated by state). First, though, I was curious to see if there was a statistically significant difference between the percentage proficient and above in math and the percentage proficient and above in reading, so I ran ANOVA:

ANOVA: % Proficient or above in math and reading
Source of Variation SS df MS F P-value F crit
Between Groups 48.5898039 1 48.5898039 0.94313513 0.33381604 3.93614278
Within Groups 5151.9451 100 51.519451
Total 5200.5349 101        

Interestingly, there is not. In order to disprove the null hypothesis, p must be 0.05 or lower, and as you can see, p is 0.33381604 (alternatively, you can see if the value of the F is greater than the critical value of F, and it is not) . So we cannot claim from these data that there is any statistically significant difference between the percentage proficient or above in math and the percentage proficient or above in reading.

Let’s turn, then, to per pupil spending, and see if it (our independent variable) has any effect on the percentage proficient in math or the percentage proficient in reading (our dependent variables). If we just eyeball the data it seems dubious that there is an effect:

State Per Pupil Spending Proficient or Above Math Proficient or Above Reading
Alaska $16,665 28.7 26.4
District of Columbia $16,344 6.9 11.7
Tennessee $6,460 20.6 26.2
Mississippi $6,387 13.5 18.5

These are the two states that spend the most per pupil, and the two states that spend the least. Note that in reading, Tennessee scores almost as high as Alaska, and note the vast difference between the two top spending states, Alaska and DC (yes, I know DC is not a state, but it’s included as a state for the purposes of aggregating the data). Mississippi, which spends the least per pupil, ranks pretty low compared to both Alaska and Tennessee, but higher in both math and reading than DC, the second-highest per pupil spender. But to see if per pupil spending has a statistically significant effect on the percentage proficient or above in math and reading, we need to run regressions, first on math:

Regression Output: Per Pupil Spending (x) and % Proficient or Above (math)
Regression Statistics
Multiple R 0.03949115
R Square 0.001559551
Adjusted R Square -0.018816785
Standard Error 7.760173608
Observations 51
ANOVA df SS MS F Significance F
Regression 1 4.609102257 4.609102257 0.076537358 0.783209568
Residual 49 2950.794427 60.22029443
Total 50 2955.403529      
  Coefficients Standard Error t Stat P-value  
Intercept 27.08558202 4.728833956 5.727750703 6.13097E-07
Per Pupil Spending 0.000141012 0.000509707 0.27665386 0.783209568  

The value of p is 0.783209568, not 0.05 or lower, so per pupil spending has no statistically significant effect on the percentage of students proficient or above in math (note that the correlation coefficient between the two variables is only 0.03949115). So how about reading?

Regression Output: Per Pupil Spending (x) and % Proficient or Above (reading)
Regression Statistics
Multiple R 0.048217771
R Square 0.002324953
Adjusted R Square -0.018035762
Standard Error 6.687537468
Observations 51
ANOVA df SS MS F Significance F
Regression 1 5.106856808 5.106856808 0.114188199 0.736868904
Residual 49 2191.434712 44.72315738
Total 50 2196.541569      
  Coefficients Standard Error t Stat P-value  
Intercept 28.39898531 4.075199326 6.968735277 7.41626E-09
Per Pupil Spending 0.000148431 0.000439253 0.337917444 0.736868904  

The value of p is 0.736868904 so per pupil spending has no statistically significant effect on the percentage of students proficient or above in reading (and note that the correlation coefficient between the two variables is only 0.048217771). So these data–the most recent NAEP data, for 8th graders, aggregated by state), support no statistically significant relationship between per pupil spending and math or reading proficiency.

However, one of the data tables I downloaded for these analyses included the mean NEAP math scores by parents’ level of education, aggregated by state (again, 8th graders for 2005). It seemed almost a waste of time to run ANOVA on the scores for all four levels of education, so I ran ANOVA on adjacent education levels, first no high school diploma and only a high school diploma:

ANOVA: Parental Ed and NAEP mean math score (No HS diploma, HS diploma)
Source of Variation SS df MS F P-value F crit
Between Groups 1304.6544 1 1304.6544 24.7627 2.7727E-06 3.9381
Within Groups 5163.2512 98 52.6862
Total 6467.9056 99        

The p-value is 2.7727*10-6, so the NAEP math scores for students whose parents who have no high school diploma and those whose parents have only a high school diploma are statistically significant. And indeed, we see a statistically significant difference between all adjacent groups:

ANOVA: Parental Ed and NAEP mean math score (HS diploma, Some college)
Source of Variation SS df MS F P-value F crit
Between Groups 4080.6544 1 4080.6544 77.0904 5.3483E-14 3.9381
Within Groups 5187.4712 98 52.9334
Total 9268.1256 99        

ANOVA: Parental Ed and NAEP mean math score (Some college, College grad)
Source of Variation SS df MS F P-value F crit
Between Groups 1429.5961 1 1429.5961 23.0638 5.6337E-06 3.9381
Within Groups 6074.4778 98 61.9845
Total 7504.0739 99        

So the level of parental education has a statistically significant effect on childrens’ math scores even between adjacent groups. I find this somewhat surprising. I would have expected a statistically significant difference between the math scores of students whose parents who had never completed high school and those whose parents graduated from college, but these data indicate that the level of parental education has a significant effect even when comparing students whose parents went to college but did not graduate and those whose parents graduated from college.

While we see no significant effect of funding on scores, we do see a strong effect of parental education on scores. Interesting.

Yesterday, I looked at the Department of Education data on doctorates awarded from 1971 up to 2004. I was going to look at the aggregated data by discipline, but, well, first, I don’t know if you’ve actually downloaded data from the Dept of Ed, but whoever sets up the Excel files doesn’t have a clue that Excel is for analyzing data, and not making something look like a dot matrix printout. In between each column of data you have a column that’s there just to put in a | so there will be a little line. Complete idiocy. Oh, and I was starving and had to eat.

When I opened the XLS file this morning, another problem (again related to idiocy) arose: The almost haphazard way disciplines had been aggregated. There were things that should not have been aggregated ("Foreign languages and literatures, linguistics" was one), which I could do nothing about, since there was no way to disaggregate the data. There were things that weren’t aggregated but should have been, which I dealt with. There were disciplines that, well, I had to guess at, such as "Security and protective services," which I called forensics (I’m not sure that’s what it is).

The disciplines I ended up with are: Agriculture and natural resources, Biological/biomedical/health, Business, Education, Engineering-related, Forensics, Public administration and social services, Humanities, Information sciences, Legal, Math/sciences, Phys Ed, Social sciences, and Theology and religious vocations. They’re not perfect, but they’re the best I could do with the mess the Dept of Ed provided (Architecture is included in Engineering-related, for example). Anyway, here are the data, sorted by the difference in the number of doctorates awarded:

Doctorates conferred by degree-granting institutions, by discipline: Selected years, 1970-71 through 2003-04
Discipline 1970-71 1975-76 1980-81 1985-86 1990-91 1995-96 2000-01 2003-04 Delta
Biological/biomedical/health 5,199 4,858 5,526 5,649 6,753 7,945 8,322 10,788 5,589
Social sciences 5,927 7,492 6,945 6,855 7,173 8,276 9,375 8,967 3,040
Engineering-related 3,724 2,956 2,701 3,529 5,465 6,572 5,757 6,154 2,430
Humanities 4,243 4,669 3,774 3,728 4,159 5,392 5,645 5,713 1,470
Education 6,041 7,202 7,279 6,610 6,189 6,246 6,284 7,088 1,047
Theology and religious vocations 312 1,022 1,273 1,185 1,076 1,517 1,461 1,304 992
Information sciences 167 323 334 412 745 929 828 964 797
Business 774 906 808 923 1,185 1,366 1,180 1,481 707
Public administration and social services 174 292 362 382 430 499 574 649 475
Phys Ed 2 15 42 39 28 104 177 222 220
Agriculture and natural resources 1,086 928 1,067 1,158 1,185 1,259 1,127 1,185 99
Legal 20 76 60 54 90 91 286 119 99
Forensics 1 9 21 21 28 38 44 54 53
Math/sciences 5,523 4,244 3,833 4,263 5,226 5,670 4,908 4,875 -648

And here are the top five plotted:

Note that the only disciplines that awarded fewer doctorates in 2004 than 1971 are math and the sciences. Also note that these are doctorate degrees, and with a few exceptions (some of the doctorates awarded in Biological/biomedical/health, for example), doctorates are research degrees, and have one primary job market: The university. Therefore, you can’t interpret these data in terms of recent market trends (the boom in IT degrees won’t be reflected here, because that boom is in Bachelor and Master degrees, and the big increase in biomedical doctorates has something to do with the boom in biotech, but because of the way these data were aggregated, we don’t know how many of those biomedical doctorates are going into the private sector, and how many are going to the university).

These trends do affect the job market, of course: The university faculty job market. That’s the point. And given that the retirements are underway in most departments, if these trends continue, it’s not going to be a great job market for those who are just now starting PhD programs.