Gas station without pumps

2017 February 5

Units matter

Filed under: Circuits course — gasstationwithoutpumps @ 11:37
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I was a little surprised by how many students had trouble with the following homework question, which was intended to be an easy point for them:

Estimate C2(touching) − C2(not touching), the capacitance of a finger touch on the packing-tape and foil sensor, by estimating the area of your finger that comes in contact with the tape, and assume that the tape is 2mil tape (0.002” thick) made of polypropylene (look up the dielectric constant of polypropylene on line). Warning: an inch is not a meter, and the area of your finger tip touching a plate is not a square meter—watch your units in your calculations!

Remember that capacitance can be computed with the formula C = \frac{\epsilon_r\epsilon_0 A}{d}~,
where \epsilon_r is the dielectric constant,  \epsilon_0=8.854187817E-12 F/m is the permittivity of free space, A is the area, and d is the distance between the plates.

The problem is part of their preparation for making a capacitance touch sensor in lab—estimating about how much capacitance they are trying to sense.

There is a fairly wide range of different correct answers to this question, depending on how large an area is estimated for a finger touch. I considered any area from 0.5 (cm)2 to 4 (cm)2 reasonable, and might have accepted numbers outside that range with written justification from the students.  Some students have no notion of area, apparently, trying to use something like the length of their finger times the thickness of the tape for A.

People did not have trouble looking up the relative dielectric constant of polypropylene (about 2.2)—it might have helped that I mentioned that plastics were generally around 2.2 when we discussed capacitors a week or so ago.

What people had trouble with was the arithmetic with units, a subject that is supposed to have been covered repeatedly since pre-algebra in 7th grade. Students wanted to give me area in meters or cm (not square meters), or thought that inches, cm, and m could all be mixed in the same formula without any conversions.  Many students didn’t bother writing down the units in their formula, and just used raw numbers—this was a good way to forget to do the conversions into consistent units.  This despite the warning in the question to watch out for units!

A lot of students thought that 1 (cm)2 was 0.01 m2, rather than 1E-4 m2. Others made conversion errors from inches to meters (getting the thickness of the tape wrong by factors of 10 to 1000).

A number of students either left units entirely off their answer (no credit) or had the units way off (some students reported capacitances in the farad range, rather than a few tens of picofarads).

A couple of students forgot what the floating-point notation 8.854187817E-12 meant, even though we had covered that earlier in the quarter, and they could easily have looked up the constant on the web to figure out the meaning if they forgot.  I wish high-school teachers would cover this standard way of writing numbers, as most engineering and science faculty assume students already know how to read floating-point notation.

Many students left their answers in “scientific” notation (numbers like 3.3 10-11 F) instead of using more readable engineering notation (33pF). I didn’t take off anything for that, if the answer was correct, but I think that many students need a lot more practice with metric prefixes, so that they get in the habit of using them.

On the plus side, it seems that about a third of the class did get this question right, so there is some hope that students helping each other will spread the understanding to more students.  (Unfortunately, the collaborations that are naturally forming seem to be good students together and clueless students together, which doesn’t help the bottom half of the class much.)

2016 November 11

Overvaluing innovation

Filed under: Uncategorized — gasstationwithoutpumps @ 10:43
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Mark Guzdial, in We overvalue innovation and entrepreneurship: Shifting the focus to Maintenance over Fads, points out

We increasingly teach computer science to prepare students to be innovators and create new things (e.g., join startups), when the reality is that most computer science graduates are going to spend the majority of their time maintaining existing systems. (See the papers by Beth Simon and Andy Begel tracking new hires at Microsoft.)  Few who do enter the startup world will create successful software and successful companies, so it’s unlikely that those students who aim to create startups will have a lifelong career in startups. In terms of impact and importance, keeping large, legacy systems running is a much greater social contribution than creating yet another app or game, when so few of those startup efforts are successful.

His post was triggered by a Freakonomics podcast In Praise of Maintenance, which includes Lee Vinsel (of Stevens Institute of Technology) saying

VINSEL: The value of engineering is much, much more than just innovation and new things.  Focusing on taking care of the world rather than just creating the new nifty thing that’s going to solve all of our problems.  If you look at what engineers do, out in the world, like 70–80 percent of them spend most of their time just keeping things going. And so, this comes down to engineering education too, when we’re forcing entrepreneurship and innovation as the message, is that we’re just kind of skewing reality for young people and we’re not giving them a real picture and we’re also not valuing the work that they’re probably going to do in their life. That just seems to me to be kind of a bad idea.

It also includes Martin Casado, a general partner with the venture capital firm Andreessen Horowitz, saying

CASADO: Large public companies in mature markets tend to invest primarily on maintenance. And often they don’t have the additional capital you need to do large innovation. So for example between say 2011 and 2015 growth companies, companies that are in fast-growing areas, spent two times more than legacy companies on research and development. So as companies mature , the majority of their investment and their spend is kind of maintaining existing technologies and so forth. And this is largely because of the pressure from the public markets.

The idea is that well-established companies don’t innovate—they maintain.  When they need innovation, they buy a startup company that looks promising.  Venture capitalists invest in highly speculative innovations, while the stock market invests in stable companies that mainly do maintenance rather than innovation.

Steven Dubner, the podcast author, says

Not often, but once in awhile, I take the time to marvel at the fact that so many people do so much work behind the scenes to keep the world humming. Whether it’s the internet, the roads, the electricity grid, you name it. Of course it’s easy to point out the failures—they’re visible, whereas the bulk of maintenance is practically invisible. But, in praise of maintenance, let me just say this: it’s necessary work; it’s hard work; and for people like me, who are always in a hurry to make the next new thing, it can be really unappealing work.

Although the podcast was talking mainly about infrastructure maintenance (both civil engineering and cyber infrastructure), I like Mark Guzdial’s approach of looking at engineering education, which has started stressing entrepreneurship.

Two decades ago, entrepreneurship was a minor add-on to engineering education.  A few engineers were expected to form startups, but they were mostly on their own—it was a path only for highly motivated individuals, not seen as a dominant form of employment. Now every engineering school seems to push entrepreneurship at its students, as if working for someone else is some sort of failure.

For faculty, this push is often a “do-as-I-say-not-as-I-do” admonition:

The fraction of start-up owners among recent graduates is 6.4% for all universities and colleges and 5.2% for top-rated schools. These fractions are several times higher than the fraction of start-up owners among faculty, which is 1.3% for all schools and 1.6% for top-rated schools. Indeed, start-ups by recent graduates outnumber start-ups by faculty by a factor of 24.3 among all colleges and universities and by a factor of 11.7 when looking only at “top-rated schools”. []
Now 6.4% of graduates owning start-ups is a pretty large number of students, so there is reason to make entrepreneurship instruction widely available, but apparently 94.6% of students are not going to be owners of start-ups, so there needs to be more emphasis on the sort of maintenance work that is the bread-and-butter of any industry.
(Before someone calls me on it, I’m aware that my 94.6% figure is bogus—the 6.4% figure was based on current owners of start-ups, not eventual owners of start-ups.  I suspect that the number of eventual entrepreneurs may be double or even triple the reported figure, which still leaves over 80% of the students never owning start-ups.)
So the traditional engineering education, which prepared students about equally for new design and for maintenance of existing systems, is still much needed.  How should we be shaping our curricula to meet both sets of needs? How do we get the message to students that innovation is only a small part of the real job, particularly when the media is putting so much emphasis on “innovation” and “disruption”?

2015 November 21

Am I benevolently sexist?

Filed under: Uncategorized — gasstationwithoutpumps @ 16:09
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In her blog, xykademiqz just posted Benevolently Sexist, which I excerpt part of here:

For probably several years now he has been spearheading this notion, backed by research but not in the literal form he seems to espouse, that we need to pitch our field as the haven for those people who want to help others and that we need to do it specifically so that we would attract more women students.

On the other hand, there are several things that are sexist about this attitude. First, it assumes that, deep down, all women want to be nurses, and that one has to appeal to a smart woman’s inner nurse in order to bring her—nay, trick her!—into the physical sciences. It also assumes that while men are naturally geeks, women could not possibly be real geeks or like the physical sciences for the same reasons as men, or for any reasons unrelated to their inner nurse.

I don’t know what one has to do to get this through people’s skulls: There are women geeks. Honestly, they exist. *raises hand to be counted* There are women who like and are very good at math, physics, chemistry, computer science; who play video games; who like science fiction and fantasy.

Go read the whole post, and the comments attached to—they are thought-provoking.

I’m a little uncomfortable responding to the post, because I have also held the view that we could get more women into engineering if we emphasized some of the useful and helpful things engineers can do, rather than just assuming that people would sign up for the coolness of the math and programming.  Am I, then, benevolently sexist?

I have no evidence that emphasizing “helping” would make any difference to the abysmal gender balance in engineering, but it is one of the few suggestions I’ve seen that might help, and as fadsklfhlfja said, it would be a good thing to do even if it had no effect on the gender balance, so I’m comfortable recommending that engineering programs pay more attention to how they can help people.

Bioinformatics and bioengineering, my current fields, attract more women than other engineering fields at our university (though still not to parity, unlike biology, for example). The worst gender balance among undergrads here is in electrical engineering, and the next worse is in computer game design (despite an almost equal gender balance on the faculty for the department that runs the game-design major).  The EE ratio may be explainable by math phobia (though I think it has more to do with the way the EE courses are taught), but the game design ratio seems most explainable by the “usefulness” theory, as game design has all the coolness and employability factors one might want, except that.

I have no interest in tricking anyone into pursuing engineering—I only want the ones who will pursue engineering diligently (and preferably passionately). If anything, I’d like to send away the students who are just in the field because their parents think they ought to be.  But I think that a lot of students go through high school with really bad stereotypes of what engineers are (Dilbert, for example) and spreading a more accurate and honest message about engineering would go a long way towards improving gender balance.

We have a couple of concentrations in bioengineering that are very close to other majors that have bad gender balances:

  • the Assistive Technology: Motor concentration is very close to the Robotics Engineering major.  There are a few extra bio courses and a corresponding shortage of upper-division tech courses, but the cores are quite similar.  The main difference is that assistive technology stresses the application of robotics to helping people with movement disabilities.  Once this concentration has existed long enough for statistics to be meaningful, I’d be interested in comparing the gender balances in the concentration with gender balances in robotics engineering.
  • the Bioelectronics concentration is close to the Electrical Engineering major.  Again there are chemistry and bio courses that the EE students don’t take, and a corresponding shortage of some of the more esoteric upper-division EE courses.  The application is interfacing biological systems to computers.  Again, I’d like to see how the gender balances compare in a few years, when there have been enough students through the concentration for the statistics to be meaningful.

From what I’ve seen of the statistics so far, the bioengineering program here is doing a reasonable job at retaining women and under-represented minority students, but recruitment is still a problem—the ratios for our majors (juniors and seniors) are essentially the same as for our proposed majors (freshmen and sophomores), so we need to get better at attracting women and minority students to the field. If putting more emphasis on how the engineering we do helps people has any positive effect on recruitment, we should definitely do it.

2014 November 16

Good enough for what?

Filed under: Uncategorized — gasstationwithoutpumps @ 11:05
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A blog post by Nick Falkner, Thoughts on the colonising effect of education, ended with the

I had a discussion once with a remote colleague who said that he was worried the graduates of his own institution weren’t his first choice to supervise for PhDs as they weren’t good enough. I wonder whose fault he thought that was?

Nick’s implied message was that it was the duty of the professors to make the undergrads they taught be good enough to go on for PhDs.  But I’m not sure he’s right here.

We do not need huge numbers of new PhDs—some, but not nearly as many as are being graduated from BS programs. Only about 10% of undergrads (or less) should be going on for PhDs, so the majority of graduates from any institution should not be “first choice to supervise for PhDs”. We should be bringing up as PhDs those most likely to be productive researchers and university faculty, and encouraging other students to find productive lives outside of academia (there is a world outside academia, though many professors prefer to ignore it).

If most of the undergrads graduating are top candidates for PhD programs, then perhaps the criteria for PhD candidates are wrong—or the undergraduate program is too small and selective, so that students who would benefit from it are being excluded.

I’m an engineering professor, and in most engineering fields the working degrees are the BS and the MS—the PhD is reserved for cutting-edge research that is not expected to result in products any time soon and for university teaching. I would consider myself a failure as an engineering professor if none of my students went on to become working engineers, but all went into academia.

I expect many of the best students not to be well-suited for PhD degrees—they want to go out into the real world and solve real problems (sometimes to make money, sometimes to save the world, sometimes just for the joy of solving problems).  The best PhD candidates are often not the best engineering students, because a PhD candidate has to be willing to work on an esoteric problem for a really long time with no promise of success, while good engineering often calls for quick prototyping and rapid development, dropping unproductive projects quickly, before they cost too much—not long-term projects that may never pay off.

So, while I certainly want some of my undergrad students to go into academia and to be top choices for PhD programs, I’m happy if most of them are not suited for PhDs, as long as they have acquired an engineer’s problem-solving mindset, enough skills to get them started in a job, and a lifelong habit of picking up new knowledge and skills.

2014 October 26

Critical thinking

In a comment exchange on the Cost of College post Trying to teach the enigmatic and increasingly popular skill of critical thinking, CSProfMom and I were discussing the relationship between critical thinking and the various modes of thinking used in CS and engineering.  I’ll pull out some of the highlights of the discussion to set the stage for this post, but I’m leaving some things out, so go read the original post and comments.

Grace, the author of the post, presented some of the definitions of critical thinking she had found, and CSProfMom replied with

I do not like these definitions of critical thinking because they are only based on verbal reasoning. Mathematical and computational problem solving are utterly ignored; yet I think more critical thinking goes on in those areas than in fields like literary analysis.

Grace defended the definitions, and CSProfMom responded with CMU’s definition of computational thinking:

Computational thinking means creating and making use of different levels of abstraction, to understand and solve problems more effectively.

Computational thinking means thinking algorithmically and with the ability to apply mathematical concepts such as induction to develop more efficient, fair, and secure solutions.

Computational thinking means understanding the consequences of scale, not only for reasons of efficiency but also for economic and social reasons.

I weighed in with

I think that CSProfMom’s point is that “critical thinking” is generally defined rather narrowly and generically, and so misses the important thinking styles that are crucial to some fields. “Computational thinking” is one that is missing. One I see students not getting in most of their college classes is “engineering thinking” or “systems thinking”—dividing difficult problems into simpler subproblems with clearly defined interactions between the subproblems. Although one can argue that these specific modes of thinking are somehow subsumed in “critical thinking”, classes that purport to develop critical thinking skills don’t generally develop these important modes of thinking.

CSProfMom responded with

I think there is a lot of overlap between “computational thinking”, “mathematical thinking”, and “systems thinking”. Abstraction and decomposition are key skills in all three. Your description “dividing difficult problems into simpler subproblems with clearly defined interactions” is absolutely critical in computer science. Maybe computational thinking is simply systems thinking + algorithms?

In any case, because the “critical thinking” term does not include this idea of systems thinking, we see students arrive into our engineering/CS programs utterly unable to think in this manner. I believe that is a major reason why we see the terrible attrition rates in these programs.

The rest of this post will be an expansion on the comment I left in response to this.

There are several different terms floating around in our discussion, and I’d like to pull them out for closer examination:

critical thinking
This seems to be a subset of the medieval trivium (grammar, logic, and rhetoric), leaving out the grammar and being a bit light on the rhetoric. It doesn’t even cover modern mathematical logic, but only the simplest Aristotelian logic. The Wikipedia article on the trivium even points to the critical thinking article, which collects nine conflicting definitions of critical thinking, none of which include the other concepts that I list below, except in the vaguest ways.
mathematical thinking
Mathematical thinking is about setting up formal systems of rules and examining their properties very closely. Proofs are a major component of mathematical thinking, which has a much more formal and unforgiving definition of proof than other fields. Computation has created a lot of new formal systems to study, and has been a fruitful area recently for mathematicians, just as physics was in previous centuries. Theoretical computer science is basically a branch of mathematics, involving primarily mathematical thinking.
scientific thinking
Scientific thinking studies the real world, constructing models of how it functions and testing the models empirically.  Different branches of science differ in how much they are model-driven and how much they are data-driven. Physics is highly model-driven, with the models often coming out 40 or 50 years in advance of the data (see Higgs boson).  Biology is highly data-driven often with non-quantitative  verbal stories as the models.  The key concept throughout science is empirical validation of predictive models.
engineering thinking
Engineering is about designing new things.  An engineering question is more of the form “how can I make this work?” rather than the science question “how does this work?”  I’ve talked about the distinction between science and engineering in one of my early blog posts, so I won’t belabor the point here.  Although scientists and engineers often wander back and forth between scientific and engineering thinking, the two are really distinctly different modes of thought.
systems thinking
Systems thinking is an important component of engineering thinking, consisting of dividing difficult problems into simpler subproblems with clearly defined interactions between the subproblems.  But systems thinking cuts across many fields, including mathematical thinking and scientific thinking. 
Computer programming is one of the best subjects to teach systems thinking in, because computer languages provide formal (though still inadequate) ways of representing the modules that encapsulate the subproblems and the interactions between them.  Electrical engineers try to do the same thing with their block diagrams, but these formalize a little less of the interactions, relying on English-language descriptions that are often omitted or poorly written. 
Unfortunately, many of the lower-level computer science classes have the faculty or textbook authors do all the systems thinking for the students, so that the students never learn to do it themselves. The scaffolding put in place to help the students find good solutions is where all the systems thinking happened, and descaffolding so that students have to learn to subdivide difficult problems into easier ones is an essential, but often missing, component of teaching programming.
The “multiple levels of abstraction” mentioned in the CMU definition of computational thinking is really about systems thinking, as each subproblem gets broken down into still smaller problems. 
algorithmic thinking
Algorithmic thinking is coming up with very precise recipes for doing things—not just flailing around trying things, but having very specific methods that can be shown to work (and work efficiently). Algorithmic thinking is really a branch of mathematical thinking, interested in provably correct manipulations in formal rule systems.  Originally it was applied to computing numerical functions, first manually and later by machine, but now has been expanded to cover many different types of data that can be represented in computers.  This is the second part of the CMU definition of computational thinking.
computational thinking
I don’t like the CMU definition of computational thinking, as they seem to have picked up definitions of mathematical, systems, and algorithmic thinking, and missed the computational part entirely. Computational thinking, to me, involves using computation to solve problems (data analysis, algorithmic solution of symbolic problems, numerical simulation, …) and may not involve much systems thinking or algorithmic thinking—someone else may have done that for you to enable you to use a computational tool.  Using Google to search for information is computational thinking, albeit at a rather low level.
statistical thinking
Statistical thinking is distinct from all of the above, though it is often important in scientific thinking.  Statistical thinking involves reasoning about data that comes from random processes, or that can be modeled as having been corrupted by random noise.  Notions of probability, sample size, statistical significance, multiple-hypothesis correction, correlation, and causation are all part of statistical thinking, which has applications to decision making in all aspects of life.

Obviously, there are overlaps and intersections between these different modes of thought (proofs done with the aid of a computer are a combination of mathematical and computational thinking, for example), but there are important differences also.  For example, Engineering thinking is not just systems thinking, but includes attention to fine details in the edge conditions (a hallmark of mathematical thinking), making allowances for variations in manufacturing (statistical thinking), testing how the device works in the real world (scientific thinking), and, very often these days, figuring out how to use cheap microcontrollers to do tasks that traditionally were done with more expensive analog devices (computational thinking).

The UCSC general education requirements (see my blog post on general education) recognize mathematical reasoning, scientific inquiry, and statistical reasoning as distinct parts of general education, adding textual analysis and cross-cultural analysis to cover what is often lumped under “critical thinking”.  They did not include anything that guarantees exposure to systems thinking, and they tossed in a few other things, some of which seem to me to be more politically expedient choices or fashion following than fundamental modes of thinking, but a general education system is always a compromise between different beliefs about what a university education should mean.  I think they did a better job of defining the basis for their general education system than most universities manage.

There have been a lot of calls for more education in “critical thinking” lately.  I’m not really happy with these calls, because teaching only a weakened version of the medieval trivium instead of more powerful modern forms of thinking does not educate students properly.



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