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2016 October 29

iGEM age rules

Filed under: Uncategorized — gasstationwithoutpumps @ 05:12
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I’m currently attending the iGEM synthetic biology competition’s “Jamboree”, where all the teams present their results.  It is a big conference (about 3000 attendees this year, representing about 300 teams), so a bit overwhelming.

One thing that surprised me was that the UCSC team of 17 undergraduates was listed as an “overgrad” team, rather than an “undergrad” one.    It turns out that the classification has nothing to do with student status or education:

There are 3 sections in iGEM 2016:

  1. Undergraduate: all student team members are age 23 or younger on March 31, 2016
  2. Overgraduate: one or more student team members are older than 23 on March 31, 2016
  3. High School: all student team members are high school students on March 31, 2016; includes students who graduate from high school spring 2016

http://2016.igem.org/Requirements/Sections

The age constraint seems like a very strange way to divide undergraduates from graduate students.  It does not work well in countries where there is mandatory military or civil service before college, and it does not work well for minorities and the poor in the USA.  I think that the problem is that the people defining the sections have a very narrow view of what it means to be an undergraduate—one that is colored by their teaching at elite private universities in the US.

The Common Data Set that each college in the US has to publish provides information about the percent of undergrad students age 25 and older at different institutions (question F1 on the form).  For example, UCSC reports  4% of undergrads are 25 and older, UCLA reports 5.1%, UC Berkeley and University of Illinois report 6%, Cal Poly reports 3%, San Jose State reports 20%, while Stanford and  MIT report only 1%.  I have not looked at many colleges, but there seems to be a clear trend that elite private schools are much less likely to be familiar with older undergraduates than public schools, and that higher status public schools have (like UC) have fewer older students than good, but slightly lesser status schools like San Jose State.  (I’ve not found a site that allows rapid summaries of the Common Data Set across many institutions—colleges are required to report the information, but no one seems to be making it accessible other that by one-college-at-a-time lookup—if someone knows of a good site for exploring the data, please let me know.)

Looking at the schools most attended by minorities and poor students in the US—the average age of a community college student is 29 [http://www.aacc.nche.edu/AboutCC/Trends/Pages/studentsatcommunitycolleges.aspx].

By using age as a cutoff, iGEM is being quite elitist—their definition of “undergraduate” only matches the demographics at elite private schools.

I asked about the reasons for the age cutoff, and it seems like some teams were complaining about having to compete against teams that had 35-year-old students on them, and that this was somehow unfair.  I find this mystifying.  How is it that a student who worked in a warehouse or tending bar for 15 years before finally being able to afford college has an unfair advantage over a student whose parents had the money to send them to college immediately?

I’m a bit more sensitive about re-entry students than many college professors, perhaps because of my mother.  Her college education was interrupted by World War II, and she did not get an opportunity to go back to college until her 50s. I am very grateful to the US system of community colleges that allowed her to return to college at that age and earn an AA degree. Being told that she would not have qualified as a “real undergrad” is personally offensive.

Coming up with a simple rule that can be applied uniformly around the world to distinguish undergraduate from graduate students is not easy, but I think that a simple age cutoff is one of the poorer choices that could have been made.  Years of education since age 5 (to avoid cultural differences in when schooling starts) might be a better choice.  Certainly the reasons given for the age criterion (to make the competition fair to undergrads) reveals a real misunderstanding of who undergraduates are outside the elite US colleges.

2016 September 29

GRE Analytic Writing favors bullshitters

Filed under: Uncategorized — gasstationwithoutpumps @ 22:33
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My son recently took the GRE exam to apply for grad school in computer science.  The test has changed since I took it in 1973, but it still looks a lot like the SAT exam, which has also changed since I took it in 1970.  The multiple-choice section is still primarily 9th and 10th grade material, so it is a bit surprising that only 5.5% of CS students, 11.4% of physics students, and 15.3% of math students get 170, the highest possible score, on the quantitative reasoning section. [All data in this post from https://www.ets.org/s/gre/pdf/gre_guide_table4.pdf]

The “quantitative reasoning” questions are primarily algebra and reading simple graphs, so the banking and finance students do best with 15.5% getting 170. The scores would be more valuable for STEM grad school admissions if they included some college-level math (calculus, ODEs, combinatorics, statistics, … ), but the general GRE has always been based on an extremely low math level.

The verbal scores are perhaps less surprising, with philosophy being the only major with over 3% getting a 170 (5.1%), and with some of the education and business majors doing the worst—except for computer science, where 8% get the lowest scores (130–134), with the next worst major being accounting with 2.7% having 130–134.  I wonder how much of the difference here is due to the number of native and non-native speakers, as computer science certainly attracts a lot more foreign students than most majors.

I was most interested in looking at the “Analytical Writing” scores, since I’ve not seen much correlation between them and the quality of student writing on the grad school applications I’ve reviewed over the last decade.  I was interested in two things: the mean score and the fraction that got 4.5 or better (the fraction getting 5.5 or better is too small to be worth looking at).  Again computer science and electrical engineering stand out as having extremely low means and small fractions of students having 4.5 or better.  I have not found any analyses of the GRE scores that separate native speakers of English from non-native ones—I wonder how much of the effect we see here is due to being native speakers and how much is due to curricular differences.

Here is the table of all the broad categories in the order that ETS provided them:

Subject

Mean writing

%ile ≥4.5

Computer and Information Sciences

3.1

8.8

Electrical and Electronics

3.1

6.7

ENGINEERING

3.3

12.6

Civil

3.3

13.2

Industrial

3.3

9.8

Mechanical

3.3

12.3

PHYSICAL SCIENCES

3.4

17.3

Accounting

3.4

12.3

Banking and Finance

3.4

10.7

Natural Sciences ─ Other

3.5

14.8

Materials

3.5

19.4

BUSINESS

3.5

15.2

Other

3.5

14.7

Agriculture, Natural Res. & Conservation

3.6

18.0

Mathematical Sciences

3.6

21.0

Chemical

3.6

21.6

Early Childhood

3.6

16.0

Student Counseling and Personnel Srvcs

3.6

17.3

Business Admin and Management

3.6

17.8

Health and Medical Sciences

3.7

19.0

Chemistry

3.7

23.8

Other

3.7

23.1

Other

3.7

21.3

Arts ─ Performance and Studio

3.7

24.3

Administration

3.7

21.9

Elementary

3.7

21.3

Special

3.7

19.5

Other

3.7

23.7

LIFE SCIENCES

3.8

21.3

Biological & Biomedical Sciences

3.8

26.0

Earth, Atmospheric, and Marine Sciences

3.8

25.4

Physics and Astronomy

3.8

26.8

Economics

3.8

27.8

Sociology

3.8

28.2

EDUCATION

3.8

23.9

Curriculum and Instruction

3.8

21.4

Evaluation and Research

3.8

23.6

SOCIAL SCIENCES

3.9

29.1

Psychology

3.9

26.6

Higher

3.9

29.7

Anthropology and Archaeology

4.0

34.7

Foreign Languages and Literatures

4.0

37.2

Secondary

4.0

33.9

Political Science

4.1

42.9

ARTS AND HUMANITIES

4.1

40.8

Arts ─ History, Theory, and Criticism

4.1

38.5

History

4.1

40.4

Other

4.1

38.6

English Language and Literature

4.2

45.2

Philosophy

4.3

52.7

 OTHER

Architecture and Environmental Design

3.4

13.1

Communications and Journalism

3.7

23.3

Family and Consumer Sciences

3.7

20.7

Library and Archival Sciences

4.0

34.3

Public Administration

3.8

23.7

Religion and Theology

4.2

46.5

Social Work

3.6

16.7

The table is more interesting in sorted order (say by %ile ≥4.5 on Analytical Writing):

Subject

Mean writing

%ile ≥4.5

Electrical and Electronics

3.1

6.7

Computer and Information Sciences

3.1

8.8

Industrial

3.3

9.8

Banking and Finance

3.4

10.7

Mechanical

3.3

12.3

Accounting

3.4

12.3

ENGINEERING

3.3

12.6

Architecture and Environmental Design

3.4

13.1

Civil

3.3

13.2

Other

3.5

14.7

Natural Sciences ─ Other

3.5

14.8

BUSINESS

3.5

15.2

Early Childhood

3.6

16.0

Social Work

3.6

16.7

PHYSICAL SCIENCES

3.4

17.3

Student Counseling and Personnel Srvcs

3.6

17.3

Business Admin and Management

3.6

17.8

Agriculture, Natural Res. & Conservation

3.6

18.0

Health and Medical Sciences

3.7

19.0

Materials

3.5

19.4

Special

3.7

19.5

Family and Consumer Sciences

3.7

20.7

Mathematical Sciences

3.6

21.0

Other

3.7

21.3

Elementary

3.7

21.3

LIFE SCIENCES

3.8

21.3

Curriculum and Instruction

3.8

21.4

Chemical

3.6

21.6

Administration

3.7

21.9

Other

3.7

23.1

Communications and Journalism

3.7

23.3

Evaluation and Research

3.8

23.6

Other

3.7

23.7

Public Administration

3.8

23.7

Chemistry

3.7

23.8

EDUCATION

3.8

23.9

Arts ─ Performance and Studio

3.7

24.3

Earth, Atmospheric, and Marine Sciences

3.8

25.4

Biological & Biomedical Sciences

3.8

26.0

Psychology

3.9

26.6

Physics and Astronomy

3.8

26.8

Economics

3.8

27.8

Sociology

3.8

28.2

SOCIAL SCIENCES

3.9

29.1

Higher

3.9

29.7

Secondary

4.0

33.9

Library and Archival Sciences

4.0

34.3

Anthropology and Archaeology

4.0

34.7

Foreign Languages and Literatures

4.0

37.2

Arts ─ History, Theory, and Criticism

4.1

38.5

Other

4.1

38.6

History

4.1

40.4

ARTS AND HUMANITIES

4.1

40.8

Political Science

4.1

42.9

English Language and Literature

4.2

45.2

Religion and Theology

4.2

46.5

Philosophy

4.3

52.7

Note that all the fields that call for precise, mathematical reasoning do poorly on this test, but those which call for fuzzy, emotional arguments with no mathematical foundation do well—the test is designed to favor con men. I believe that this is partly baked into the prompts (see the pool of issue topics and, to a lesser extent, the pool of argument topics), partly the result of having the writing being done entirely without access to facts (benefitting those who BS over those who prefer reasoning supported with well-sourced facts), and partly the result of having graders who are easily swayed by con men.

I believe that most of the graders are trained in the humanities, and so are more swayed by familiar vocabulary and rhetoric.  If ETS had science and engineering professors doing the grading (which they would have a hard time getting at the low rates they pay the graders), I think that the writing scores would come out quite different.

Of course, there are curricular differences, and science and engineering faculty are mostly not paying enough attention to their students’ writing (and I can well believe that CS and EE are the worst at that). But I don’t think that even engineering students who do very, very good engineering writing will necessarily score well on the GRE analytical writing test, which seems to favor rapid writing in only one style.

I will continue to give relatively little weight to Analytical Writing GRE scores in graduate admissions. The untimed essays that the students write for the applications are much closer to the sort of writing that they will be expected to do in grad school, and so much more indicative of whether their writing skills are adequate to the job. I will continue to interpret low GRE scores as a warning sign to look more closely at the essays for signs that the students are not up to the task of writing a thesis, but high GRE writing scores are not a strong recommendation—I don’t want grad students who are good at bull-shitting.

2016 September 4

The Great Mistake by Christopher Newfield

Filed under: Uncategorized — gasstationwithoutpumps @ 20:42
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Johns Hopkins University Press has announced pre-orders for Chris Newfield’s new book, The Great Mistake:

The Great Mistake

How We Wrecked Public Universities and How We Can Fix Them

Christopher Newfield

In The Great Mistake, Newfield asks how we can fix higher education, given the damage done by private-sector models. The current accepted wisdom—that to succeed, universities should be more like businesses—is dead wrong. Newfield combines firsthand experience with expert analysis to show that private funding and private-sector methods cannot replace public funding or improve efficiency, arguing that business-minded practices have increased costs and gravely damaged the university’s value to society.

The book should ship in October 2016.

I’ve been reading his blog Remaking the University for quite some time, and I’ve found that he has intelligent things to say about how public universities are funded. I’m not sure I’d want to read a 448-page book on the subject with very few illustrations (2 halftones, 33 charts), but people who are interested in what has happened to make public universities so unaffordable in the past decade or two should read at least some of his writing.

2016 August 11

Email to professors

Filed under: Uncategorized — gasstationwithoutpumps @ 10:37
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This is the time of year when many semester-based colleges are starting classes again, so there are the usual spate of blog posts from faculty trying to orient the new students.  On perennial theme is on how to communicate with faculty, since so many students seem clueless about it.  (Two years ago, I plugged the book Say This, NOT That to your Professor, which I still recommend.)

Today, I happened to see the post How to Email Your Professor (without being annoying AF), in which Laura Portwood-Stacer provides a template and explanations:

10 Elements of an Effective, Non-Annoying Email

Here’s a template you can follow in constructing your email to a professor. Each element is explained further below.

Dear [1] Professor [2] Last-Name [3],

This is a line that recognizes our common humanity [4].

I’m in your Class Name, Section Number that meets on This Day [5]. This is the question I have or the help I need [6]. I’ve looked in the syllabus and at my notes from class and online and I asked someone else from the class [7], and I think This Is The Answer [8], but I’m still not sure. This is the action I would like you to take [9].

Signing off with a Thank You is always a good idea [10],
Favorite Student

Element #1: Salutation …

Element #2: Honorific 

Element #3: Name …

Element #4: Meaningless Nicety…

Element #5: Reminder of how they know you …

Element #6: The real reason for your email …

Elements #7 and 8: This is where you prove you’re a wonderful person …

Element #9: Super polite restatement of your request …

Element #10: Sign-off …

The hidden Element #11: The follow-up …

I don’t think that Ms. Portwood-Stacer is a professor, as her advice seems more appropriate for freelance writers than for students.  It isn’t bad advice, but I’d recommend something slightly different.

First, I don’t care much whether students include elements #1, #2, and #3, though I agree with her that “Hey!” is offensive. I don’t mind students using my first name, and I tell them so, but I agree that it is probably safer to use “Professor X” if you don’t know the person’s preferences.  In a formal business letter, the proper salutation is important, but in an e-mail without CCs it can be omitted.  (In an email with CCs, it is important to indicate who is being addressed.)

I disagree strongly about #4. I read a lot of email every day, and don’t want to have to wade through meaningless noise.  Skip the chitchat and get to the point—don’t waste my time.

Along the same lines, move #6 to the front. Ask your question or make your request directly, don’t bury the lede. After you’ve made a clear request, then provide the background information: who you are and what you’ve already done to try to get an answer. Make this more complete—if you are asking for something in my role as undergraduate director, for example, I need to know your major, your concentration, and which year’s catalog you are following.

The “thank you” at the end is nice, but a followup thank you message after my reply is appreciated more—the extra trouble taken makes the thanks seem more sincere.

One missed point—provide your full name and your nickname if you go by that in class right at the beginning of the message: This is Ridiculous Name Overly-Hyphenated, who goes by “Rid Overly” in class. I have to read my university e-mail with Google, which does an absolutely horrendous job of showing me who messages are from (there are probably 40 people it identifies to me as just “David”).

Use the official University e-mail address, as FERPA rules require me not to discuss your academic record with anyone but you (unless you’ve given explicit permission otherwise). We’ve had incidents of people pretending to be students to get information they had no right to, so I’m trying to be careful to respond only to the official email addresses. Remember to edit your campus directory entry, so that your email is associated with your real name, and not just your userid (I have no idea who “alkim345” is).

So rewriting her example for a classroom question:

This is Ridiculous Name Overly-Hyphenated, who goes by “Rid Overly” in Class Number. 

This is the question I have or the help I need.

I’ve looked in the syllabus and at my notes from class and online and I asked someone else from the class, and I think This Is The Answer, but I’m still not sure.

This is the action I would like you to take.

Thank you.

For an advising question:

This is Ridiculous Name Overly-Hyphenated, who goes by “Rid Overly”. 

This is the question I have or the help I need.

I’m a bioengineering major in the bioelectronics concentration, following the 2013–14 curriculum. I plan to graduate in Spring 2017.

I’ve looked at the curriculum charts, in the online catalog, and at the online advising web pages; I asked the professional advising staff; and I was directed to ask you.

This is the action I would like you to take.

Thank you.

If you need to meet with me, which is not needed for a lot of routine things, but is sometimes quite useful, add

May I come to your office hours next week at 3:15 p.m.?

Technically, you don’t need an appointment for open office hours, but those who have reserved slots ahead of time take priority over those who drop in. If you can’t make scheduled office hours and want to meet in person, say something like

I have a conflict during your office hours, but am free at the following five times …, would any of those times work for you?

2016 March 14

History of the CS enrollment roller coaster

Filed under: Uncategorized — gasstationwithoutpumps @ 10:55
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I just read a report by Eric Roberts, A History of Capacity Challenges in Computer Sciencewhich I was pointed to by a guest post on Mark Guzdial’s blog.  The report discusses the two previous rapid increases in CS enrollment and BS degree production (peaking in 1986 and 2004), comparing them to the current rapid growth.  He makes the argument that the current growth is more like the 1986 peak (triggered by the introduction of the PC) and less like the 2004 peak from the dot-com bubble.

Number of CS bachelor's degrees per year, which trails total enrollment by a couple  of years.  [Copied from http://cs.stanford.edu/people/eroberts/CSCapacity/images/BSDegrees-1975-2014.png]

Number of CS bachelor’s degrees per year, which trails total enrollment by a couple of years. [Copied from http://cs.stanford.edu/people/eroberts/CSCapacity/images/BSDegrees-1975-2014.png%5D

The crash in enrollments after then 1986 peak was a “capacity crash”—that is, it was not triggered by a loss of interest by the students nor by a lack of interest from industry, but by deliberate university policies to make CS unwelcoming to limit the demand.

The rapid growth of enrollment in CS poses a problem that academia is ill-equipped to handle, for two reasons:

  1. The rate of growth is much faster than the rate at which universities respond.  Faculty growth in any department is generally limited by bureaucratic processes to a maximum of about 10% a year and generally only allocated after a 2–5-year delay, but enrollment growth has been 15–20% a year for several years in a row.
  2. The PhDs to fill CS faculty positions are not available.  This is an unfamiliar problem for academic administrators, because most of the rest of academia has a huge buffer of under-employed PhDs (the “postdoc holding tank” in life sciences) that can be tapped to fill any new positions.  But in CS, and in some other engineering fields, the existence of attractive industrial jobs with more resources, better working conditions, and higher pay than academia means that there aren’t many people waiting for an academic position to open—in the earlier spurts of enrollment growth, there were as many as 7 faculty job openings per qualified candidate, and the current market seems to have 4 faculty job openings per qualified candidate.

Eric Roberts makes the case that we can’t know for sure whether the current rapid enrollment growth is like the dot-com bubble, but if we don’t do something to address capacity very quickly, we will trigger a capacity crash like the 1986 one.

I think that one point he missed, which is affecting UCSC strongly, is that the enrollment growth in this round is much broader than in previous ones—many fields of engineering are seeing rapid growth in enrollment, and all the departments in the Baskin School of Engineering at UCSC are seeing capacity problems.  The problem is most acute for Computer Science, but it is not a single-department problem as it was in the two previous peaks. (Note: Eric Roberts presents a slide which shows that engineering as a whole is not in crisis, while CS is, but I think that a lot depends on which engineering fields you look at—there is a glut of petroleum engineers, but shortages of electrical, robotics, and computer engineers.  UCSC is unusual in having mainly the fields in which there is high demand, due to deliberate planning to grow only those programs, rather than having the full complement of traditional engineering programs.)

He shows the Bureau of Labor Statistics estimates of jobs in various fields for the next few years:

Note that computing and engineering jobs make up huge fraction of the job market, despite the relatively small proportion of engineering and CS faculty in most universities. Figure copied from http://cs.stanford.edu/people/eroberts/CSCapacity/images/BLSJobGrowth.png

Note that computing and engineering jobs make up huge fraction of the job market, despite the relatively small proportion of engineering and CS faculty in most universities.
Figure copied from http://cs.stanford.edu/people/eroberts/CSCapacity/images/BLSJobGrowth.png

Unfortunately, Eric Roberts’s paper does not offer solutions, merely historical perspective, but even that is valuable, as there are relatively few CS faculty who remember the enrollment problems of the early 1980s—too many faculty left for industry or took early retirement.  He points out another interesting challenge for newer CS faculty learning about the enrollment problems:

Much of the early history lies beyond the Google “event horizon.” In putting together this history, I was interested to discover that several relevant articles I remembered from the early 1980s were invisible online because they predate digital archiving for the journals in which they appear. Looking for evidence about faculty shortages in the 1980s becomes much harder when none of the references from, for example, The Chronicle of Higher Education, show up in Google searches.

I highly recommend reading the full report by Eric Roberts, as I’ve only touched on a couple of the highlights here.

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