We—and here am I tempted to specifically include you [on the list] alongside myself in this condemnation, but won’t because there’s always a small chance that some of you/us are exempt from these generalizations—in fact appear to take some pride in treating adjuncts as an inferior caste. It is the norm for adjuncts to be excluded from faculty meetings and to be deprived of any say in the management of departments. Instead of resisting the “adjunctification” of the professoriat by incorporating these colleagues—because they are colleagues—into the university and our respective departments, we tolerate them as useful proof of our Brahmin status. They are our untouchables.

I have noted this tendency for tenure-track faculty to treat instructors as lesser beings—I think it is partly out of fear, because they themselves could easily have ended up similarly poorly paid and without job security.  I have argued for providing “lecturer with security of employment” status (essentially the equivalent of tenure) to the top one or two instructors in the Jack Baskin School of Engineering—the ones who can pave their offices with the teaching awards they have been given by the graduating seniors.  The dean and the relevant department chairs are reluctant to do this, as it means that they would have to dedicate a faculty slot to someone who teaches more and better than them, when they could hire a new junior colleague who wouldn’t waste time on teaching, but dive directly into the task of writing research grants—the only activity they admire.  (The actual research is less important than the money, so only the grant writing matters to them.)

Ivan Evans asks

I have recently asked my colleagues at UCSD questions such as: How many adjunct/contingent/non-tenure track faculty are there in your department? Can you name them? Have you met any adjuncts for coffee or lunch on campus? Are they invited to the homes of ladder rank faculty? Do they have office space? Do they have any voting rights in your department? Should they? Do you know how they are evaluated? Should they be rewarded for publishing? Should ladder-rank faculty with poor teaching evaluations be assigned to courses ahead of adjunct colleague with excellent teaching evaluations? Should campus charters be changed to extend representation to adjuncts in the Senate?

Our small department has one full-time instructor (and, yes, I can name her, though I’m terrible at names). I rarely see her, as her classes are not nearby and her office is on a different floor.  She does not have voting rights in the department and does not come to faculty meetings.  I don’t know if this is because she doesn’t want to or because she is not invited.

We used to have another part-time instructor teaching 2 classes a year, while he worked as a postdoc at a university on the other side of the hill.  He has recently gotten a permanent job and no longer teaches for us. I saw him very rarely.

We also have an extremely good researcher and teacher who should be on our permanent faculty, except that he did his PhD with us, and the university has a disinclination to hire their own graduates.  I meet with him frequently—more often than with several of the ladder-rank faculty.  Usually we meet to talk about teaching, though sometimes to discuss his research and that of the undergrad students he is supervising.  I see him less often than I’d like, because his office is in a different building.  (Our department of 8.2 faculty is spread out over at least 4 buildings, and some of our grad students are in labs in another 3 buildings—it’s very hard to maintain cohesion when no one sees anyone else.) I believe he actually gets a pay cut when he teaches classes (the instructor salary is less than the researcher salary), though it does allow him to stretch out the grants that pay his researcher salary.  He is one of the best teachers in our department, and has developed or revamped several courses.  Our department tried to get him a status more in keeping with his contributions (the title “adjunct assistant professor”), but the dean shot it down, for reasons that no one in the department understands, since the title costs the dean nothing, and he gave them out like sugar candy in other departments to people rarely seen on campus.

The lecturers on our campus have a moderately powerful union, so are better treated than at most colleges, but they have little say in the running of university, and sometimes get jerked around by insensitive bureaucrats and department chairs.  I don’t know whether giving them voting rights or adding them to the Academic Senate would make any difference in their lives—I’d be in favor of including them, but I’ve no idea how other faculty in the department feel on the matter.  I suspect that Evans is right, and that they mostly don’t think about the contingent faculty at all.

There’s been much local coverage of two principal investigators switching from UCLA to USC, and taking with them an estimated 85 people from UCLA’s Laboratory of Neuro Imaging (LONI). The Los Angeles Times has run two stories about it, one of which received over 120 reader comments, and the story was Larry Mantle’s lead on his Airtalk show at KPCC, where he had one of the two departing faculty members as his guest.

But beyond a big win for the Trojans over the Bruins, why should the public care?

He goes on with an analysis of the importance and the cost of doing science research at UC, pointing out that the particular PIs lured away were very highly compensated:

I don’t know LONI’s equipment and infrastructure issues at UCLA, but the only publicized financial information was of the leaders’ salaries: over $1 million / year for Prof. Toga, over $420,000 for Prof. Thompson. A good number of highly qualified people will line up for jobs like these.

I had much the same reaction—those are not public university salaries.  A dozen top-notch assistant and associate professors could be hired for what those two were paid.  USC is welcome to sink their money into hiring big names—perhaps they could hire away all the athletic coaches from UCB and UCLA as well (please).

Chris got a lot of flak in the comments for his simile about the cost of doing research:

Public universities can’t fully support their grants because extramural funding doesn’t cover the full cost of research.  Labs burn money like a jet burns fuel, which is what they are supposed to do.  LONI spent $12 million a year, as a case in point. 

$12 million dollars for 85 people is only $141k per person.  Given the huge salaries of the PIs (not to mention the costs of their benefits), the expenditure per person for the other 83 was under $125k, and salaries were probably less than half that (given the necessary costs for reagents, equipment, travel, publication charges, benefits, …).  So most of the lab was making only modest salaries—only the PIs were raking in the dough.

Chris ended with 4 conclusions:

1. UCLA’s core problem is a funding shortage, not surplus bureaucracy. (UCLA is the wealthiest UC campus, so things only get worse from there). 
2. Public universities need to tell the truth about research funding.  This will include the facts that science loses money, that some portion of undergraduate tuition funds offset research costs, and that most funding doesn’t “produce” anything in the near-term, except findings [for] more research along with a great deal of useful failure.
3. Public universities need to explain why research like LONI’s should be to some large extent at public universities.  Why does it matter to the science, to the public impact, to the education of the next generation of scientists? Perhaps there is more openness and accountability at publics, and therefore more innovation. Perhaps scientists at public universities have a better feel for public needs and do more useful research.  Perhaps public universities uniquely have the necessary scale to train the thousands and millions of researchers in all fields to solve our ever-mounting problems.  We now need a new theory of public universities, before things get even worse.
4. Universities both private and public need to open up  discussion of spending priorities to their academic communities.  Given rising costs and shrinking revenues, choices have to be made. They  need to involve the faculty, from all disciplines, and students of all levels.  This is as true of USC as of UCLA, which has a poor record of consultation and can only buy a limited number of LONI-type labs with (in part) student tuition and non-STEM cross-subsidies.  Privates can now raise tuition only so much. Academic choices need to come from a bottom-up debate of a kind that higher ed has never had.

There was an excellent discussion in the comments to his post, mainly pushback on point 2, as STEM faculty see their overhead being spent on everything in the university except the proper indirect expenses that it is supposed to go for, while Chris looked at the overall expenditure on support for science research and the income from indirect costs.  I think that a lot of the discrepancy comes from the cost of new buildings for science labs, which are very, very expensive, but cannot be charged to grants.

I’m in agreement with Chris that UC has done a very poor job of making a good case for research in the public universities, talking about it mainly as a revenue stream or in PR terms as enhancing the image of the university.

In one of Chris’s comments, he restates his main point as a desire for greater budget transparency:

So my suggestion as always is that faculty across the disciplines stop being cynical about “byzantine accounting” and push for full data on funding flows—universities can’t even start the negotiations for ICR rates without this accounting to show federal agencies—let the chips fall where they may, and then have an involved discussion based on actual budgetary facts about what to do. My position is always that I do not want cuts to STEM research. I want research to be fully funded. The patchwork we have doesn’t work any more—for any field. Things will continue to deteriorate unless we can drop our longstanding mental habits get clarity on how our own institutions work .

I think that greater clarity in how funds flow around the university would be helpful—particularly to those of us at the underfunded campuses (UCB and UCL have long gotten far more than a fair share of state funds and tuition dollars, and even the “rebenching” now in progress has been carefully designed to perpetuate the inequity).  If the research grants are not paying what the research costs the University, then indirect costs need to be raised, or the state needs to provide explicit support for research to cover the costs, or the University has to make a much, much better case that undergrads benefit from the research and so their tuition should be used to cover the shortfall.

I think that the case for undergrad benefit is quite different on different campuses and even in different majors.

For example, in the bioengineering major at UCSC, all the undergrads do research, either individually with faculty, postdocs, and grad students, or as part of a group project supervised by faculty.  They use equipment and labs funded by research grants and get a lot of high-contact instruction in these projects that could not be duplicated without the money brought in from federal grants, grants from non-profits, and even industrial research contracts.  Some of the undergrad students in the bioengineering major are doing exceptional research, and all are being very well prepared for grad school.  I have no trouble asserting that these students are benefiting substantially from the active research programs in biomolecular engineering and molecular biology.

On the other hand, I’ve recently been visiting colleges to find a good place for my son to apply in computer science (see College tours around LA and UC Berkeley college tour), and neither UCB nor UCLA had much involvement of undergrads in computer science research. I’d have a hard time telling a student whose smallest class in their major had over 50 students and most upper division courses had 200, that research opportunities for 5–10% of the undergrad students were a good deal for them. (To be fair, this may be more discipline-specific than campus-specific, as the computer science department at UCSC also seems to have less involvement of undergrads in research than the other engineering departments.)

It is not clear to me whether student tuition is supporting the research mission or research grants are supporting student instruction.  I’ve seen arguments for both, and I don’t really believe any of the arguments are really solidly based on facts.  The UC budget is such a tangled web of inconsistencies that people can read anything into it that they want.

Although UC has certainly failed on budget transparency, I think that the bigger discussion that has been missing is Chris’s point 3, explaining why research is an essential part of the mission of some public universities.  Obviously it is not essential for all (neither the community college system nor the California State University system have research as a major part of their missions), but UC administration and faculty have never made a clear case to the public of the need for research in a public university.  I think that it is time to do so, but I don’t know that I can put together a clear case for it—certainly not to the point where I could say how much of student tuition should be going to support the research mission.

When it comes down to it, what I’m feeling right now is probably what every teacher feels at some point—the magical epiphany that “A few weeks ago, my students didn’t know [what programming was], and now they’re [running home to work on coding challenges] and [saying that they want to study computer science in college].”

There were two parts to this success story:

• a one-hour introduction to programming, which was really a thinly disguised pitch for the computer science course we’ll be offering for the first time next year. I had my students warm up with a Do Now asking them to identify some of the many ways they rely on coding in their everyday lives, without even realizing it. I used this PowerPoint as a basis for our discussion, which led into this (now semi-viral) video put out by Code.org, and finally some live coding in Python (the Word Smoosher was a big hit).
• a visit by Jeremy Keeshin, cofounder of CodeHS.com. Thirty-two (out of 74) juniors from across the academic spectrum signed up for an after-school workshop that Jeremy ran to introduce students to some of the basics of coding as well as the terrific online platform for learning coding that he has developed. The program is such that students are able to watch short tutorial videos and work on challenges at their own pace, and Jeremy and I mainly circulated to help students troubleshoot …

It sounds like a very good beginning—I hope that the programming course that they are offering next year goes well.

Read the full story at via Infinigons, etc.: Have an hour to fill with your students?.

Making science classes more “like that” is also the suggestion of a recent Scientific American blog post, To Attract More Girls to STEM, Bring More Storytelling to Science. Its authors, teachers at a STEM-focused high school, argue that the reason for the gender gap in the STEM fields is not a shortage of girls with ability, but the failure of our science curriculum to engage their interest and kindle their passion. The remedy they propose—telling the stories of science—could lend the STEM fields some of the allure traditionally left to the humanities.

While I agree that the shortage of women in STEM fields is not due to a shortage of girls with ability (the dominance of girls at middle school and high school science fairs is clear), I’m not convinced that a story-based approach is going to work. History of science is not science, and stories about scientists are not science. Replacing science instruction in middle and high school with stories and history would leave students less prepared to study and do real science, and more likely to choose a humanities field in college.

Note that there isn’t a gender gap in biology (at least not through grad school—there is still some gender gap in paid jobs), so the problem isn’t with “STEM” as a whole, but more specifically with the math and computation-based STEM fields.  Even among those fields, there are wide disparities, with math itself coming much closer to parity than physics or computer science.  Why?  Is it something about the field, about the way the field is taught, about the culture of the practitioners, or about the culture of the students currently majoring in those fields?

Making the science instruction more interesting is a good goal, but the suggestion of the SciAm blog post “How many engineering teachers include a fiction book like Kurt Vonnegut’s Player Piano in their syllabi?” seems to me to miss the point.  Replacing science and engineering with fiction reading will not result in more students studying engineering and science—it will result in students studying literature and thinking that they are studying science.

The basic idea—to use a more story-telling approach to teaching STEM—is a good one, but I think that the stories have to be intrinsic to the science and math, like Dan Meyer’s The Three Acts Of A Mathematical Story, not stories about science, which seems to be what both blogs are advocating.

I don’t know how successful approaches like “Storytelling Alice” have been—it is no longer available though the web page claims it was successful:

A study comparing middle school girls’ experiences with learning to program in Storytelling Alice and in a version of Alice without storytelling features (Generic Alice) showed that:

• Users of Storytelling Alice spent 42% more time programming than users of Generic Alice.
• Users of Storytelling Alice were more than three times as likely to sneak extra time to work on their programs as users of Generic Alice (51% of Storytelling Alice users vs. 16% of Generic Alice users snuck extra time to program).
• Despite the focus on making programming more fun, users of Storytelling Alice were just as successful at learning basic programming concepts as users of Generic Alice.

Of course, Alice is not the most fun programming environment for middle schoolers (I think that Scratch beats it hands down), so the storytelling component may just have made it a bit better.  Has anyone ever attempted a Storytelling Scratch class? (I wasn’t able to find any equivalent to Storytelling Alice using Scratch in a very brief web search.)

The newest version of Scratch (2.0) runs as a Flash program in the browser, and has some new media-related features (like being able to interact with the video from the computer’s camera).  My son has played with it a bit, but I’ve not had time to explore the new features.  The Flash-based Scratch means that no installation is necessary to run programs, but that Scratch will not run on iOS devices (like iPads), which could be a limitation at many schools.  I understand that an iPAD app or HTML5 implementation of Scratch is planned, now that Scratch 2.0 has been released.

A better approach than stories about science may be to have more hands-on science and engineering, where students learn the science and engineering in order to accomplish something, not just to pass a course and get into college.  So far, most attempts along those lines have favored stereotypically “boy” goals (robot sports, for example, and video games), and so have not served to shrink the gender gap.

The article accuses the ENCODE authors of several academic sins:

Oddly, ENCODE not only uses the wrong concept of functionality, it uses it wrongly and inconsistently.

Sadly, the authors of ENCODE decided to disregard evolutionary conservation as a criterion for identifying function.

Some of their comments are marvelously snarky:

According to Eric Lander, a Human Genome Project luminary, ENCODE is the “Google Maps of the human genome” (Durbin et al. 2010). We beg to differ, ENCODE is considerably worse than even Apple Maps.

The article provides solid reasoning for why the estimate that about 80% of the genome is functional is completely bogus, and provides more reasonable estimates:

Ward and Kellis (2012) confirmed that ~5% of the genome is interspecifically conserved, and by using intraspecific variation, found evidence of lineage-specific constraint suggesting that an additional 4% of the human genome is under selection (i.e., functional), bringing the total fraction of the genome that is certain to be functional to approximately 9%. The journal Science used this value to proclaim “No More Junk DNA” Hurtley 2012), thus, in effect rounding up 9% to 100%.

The ENCODE project produced a lot of good data, but some of the hype surrounding it irritated a lot of biologists and bioinformaticians, who are pleased to see the ENCODE hype so amusingly and accurately skewered.

• What it takes to be a bioinformatician
• The alternative “what it takes to be a bioinformatician”.

The first post is talking about a shift from “bioinformatics” to “computational biology”—that is, a shift from designing algorithms and data structures to answer biological questions to asking biological questions for which computational tools already exist.  It has quotes with some hype about job opportunities in bioinformatics, but it also has some counterpoints about more realistic views of the bioinformatics job market.  The tone of the piece overall is that bioinformatics is the best of all possible fields.

The second post has a less exalted view of bioinformatics, pointing out that most bioinformatics jobs are data wrangling.  They do say that even data wranglers can do research if they want to, which makes them better off than most wet-lab technicians.

Both posts stress the importance of programming, statistics, and knowing some biology.

• Nearly all those surveyed (93%) agree, “a candidate’s demonstrated capacity to think critically, communicate clearly, and solve complex problems is more important than their undergraduate major.”
• More than nine in ten of those surveyed say it is important that those they hire demonstrate ethical judgment and integrity; intercultural skills; and the capacity for continued new learning.
• More than three in four employers say they want colleges to place more emphasis on helping students develop five key learning outcomes, including: critical thinking, complex problem-solving, written and oral communication, and applied knowledge in real-world settings.
• Employers endorse several educational practices as potentially helpful in preparing college students for workplace success. These include practices that require students to a) conduct research and use evidence-based analysis; b) gain in-depth knowledge in the major and analytic, problem solving, and communication skills; and c) apply their learning in real-world settings.

(I guess they don’t care whether students learn proper punctuation, though, as the colon after “including” is incorrect.)

The Campaign for the Future of Higher Education uses this report as evidence that a “liberal education” is what students need, though it sounds more like what a good engineering school teaches than anything I’ve seen in the humanities.  In fact, the CFHE carefully omitted the major findings that are first in the summary of the report:

• Nearly all employers surveyed (95%) say they give hiring preference to college graduates with skills that will enable them to contribute to innovation in the workplace.
• More than nine in ten agree that “innovation is essential” to their organization’s continued success.

Not all innovation is engineering, nor does engineering education guarantee that the graduates will be innovators, but innovation is at the heart of engineering and of art, but is not so central in the humanities and sciences.

Of course, the definition of “liberal education” used in the report is one that few employers would say no to:

This approach to a college education provides both broad knowledge in a variety of areas of study and knowledge in a specific major or field of interest. It also helps students develop a sense of social responsibility, as well as intellectual and practical skills that span all areas of study, such as communication, analytical, and problem-solving skills, and a demonstrated ability to apply knowledge and skills in real-world settings.

Again, it sounds to me more like a good engineering school than a liberal-arts degree: particularly the parts about practical skills, analytical and problem-solving skills, and ability to apply knowledge and skills.  The areas that many engineering schools traditionally fall down on are the “sense of social responsibility” and communication skills.

But good engineering schools do include social responsibility and communications skills in the training that they provide. The engineering undergrad curricula that I’ve had a hand in helping design (computer engineering, bioinformatics, and bioengineering at UCSC) have all included both an ethics course and a technical writing course.  Communication is also a major part of engineering senior design projects and senior theses, and there are several social impacts and sustainability courses in the Jack Baskin School of Engineering at UCSC.

[Disclaimer: the ethics course was not part of our original design for the computer engineering curriculum, but was added later when the faculty was large enough to staff an engineering ethics course—the other two curricula included a bioethics course from the beginning.]

The report provides a detailed list of “selected learning outcomes”, sorted by how much more of each the employers wanted to see:

• Critical thinking and analytical reasoning skills
• The ability to analyze and solve complex problems
• The ability to effectively communicate orally
• The ability to effectively communicate in writing
• The ability to apply knowledge and skills to real-world settings
• The ability to locate, organize, and evaluate information from multiple sources
• The ability to innovate and be creative
• Teamwork skills and the ability to collaborate with others in diverse group settings
• The ability to connect choices and actions to ethical decisions
• Knowledge about science and technology
• The ability to work with numbers and understand statistics
• Proficiency in a language other than English
• Knowledge about global issues and developments and their implications for the future
• Knowledge about the role of the United States in the world
• Knowledge about cultural diversity in America and other countries
• Civic knowledge, civic participation, and community engagement
• Knowledge about democratic institutions and values

The priorities given by this list of learning outcomes seems to match the priorities of an engineering school pretty closely (though most engineering curricula put knowledge about science and technology, innovation, and teamwork higher on the list and oral communication lower), while most liberal arts curricula address the items nearer the bottom of the list.  This is not to say that those topics should be ignored:

While employers may not be clamoring for colleges to increase their emphasis on civic learning or in teaching about global issues, they widely agree that all students should receive civic education and learn about cultures outside the U.S. Fully 82% agree (27% strongly) that every student should take classes that build civic capacity, and learning about societies and cultures outside the United States (78% total agree; 26% strongly) is widely valued for all students. Additionally, four in five agree (32% strongly) that all students should acquire broad knowledge in the liberal arts and sciences, regardless of a student’s chosen field of study.

Some of the other standard educational practices in engineering also are highly supported in the report:

Employers express the greatest confidence in the following practices to help students succeed beyond graduation. Large majorities believe that colleges that set expectations for students to achieve these learning outcomes will do the most to prepare them for success:

• Develop research questions in their field and evidence-based analyses (83% will help a lot/fair amount)
• Complete a project prior to graduation that demonstrates their acquired knowledge and skills (79% will help a lot/fair amount)
• Complete an internship or community-based field project (78% will help a lot/fair amount)
• Develop the skills to conduct research collaboratively (74% will help a lot/fair amount)
• Acquire hands-on or direct experience with the methods of science (69% will help a lot/fair amount)
• Work through ethical issues and debates to form their own judgments about the issues at stake (66% will help a lot/fair amount)

Some other fashionable memes in the education world were not so popular with employers:

• Using new approaches that de-emphasize lectures in the classroom and instead have students listen to lectures online and devote classroom time to dialogue, debate, and problem solving in groups or alone, and with guidance from the instructor (59%)
• Expecting students to learn about the points of view of people in societies other than those of Western Europe or North America (47%)
  
• Expecting students to learn about cultural and ethnic diversity in the context of the United States (44%)
  
• Expecting students to explore challenges facing society, such as environmental sustainability, religious tolerance, or human rights (42%)

Something that we at UCSC have not been preparing students for is creating an electronic portfolio of their accomplishments:

Four in five (83%) employers say an electronic portfolio of student accomplishments would be very (43%) or fairly (40%) useful to them in ensuring applicants have the skills and knowledge to succeed in their company or organization.

Overall, I feel pretty good about the match between what the engineering curricula I’ve helped design teach and what employers are looking for in college graduates.  I think that both oral and written communication skills need to be given more emphasis across the curriculum (too many faculty are unwilling to take the time to provide feedback on written work), and that the bioengineering curriculum needs more design courses, but that the basic goals of the programs I’ve helped create are in good agreement with what employers are looking for.

Nick Falkner describes this use of passive well in The Blame Game: Things were done, mistakes were made:

The error is regretted? By whom? This is a delightful example of the passive voice, frequently used because people wish to avoid associating the problem with themselves.

But the whole point of a senior thesis is to show what a particular individual knows and has done (and presumably can do again).  The author must attribute every concept and action in the thesis to the right person: those ideas that come from the literature need to be properly cited, work done by others in the lab needs to be properly credited, and work done by the author of the thesis needs to be explicitly claimed.  (I’m aware of all the passive in the last sentence—see below for explanations of some acceptable uses of passive voice.)

Along with passive voice, students misuse the first-person plural, which has little role in a single-author work like a thesis. Almost the only time that “we” should appear in a thesis is shortly after a listing of who “we” are. It is ok to say, for example, “Alpha Beta and I ran alternating shifts for the 48-hour data collection period.  We collected samples every hour …”, but it is not ok to say “We collected samples every hour … “, if you did it alone, or (worse) if someone else in the lab did it and not you.  Saying “Samples were collected every hour …” sounds like you don’t know or are not willing to say who collected the samples (perhaps because it was done illegally?).

I am not going to prohibit students all use of the passive (as some writing instructors do, or used to do)—passive voice is sometimes useful. For example, passive can be used for improving the flow of a paragraph, since it allows flipping a sentence, which can strengthen the old info⇒new info flow heuristic.  This flipping of sentences is best shown with some schematic sentences: we start with

A creates B. C modifies B. D controls C.

and we can improve the flow by modifying to

A creates B. B is then modified by C, which is controlled by D.

Note that the second sentence of the above paragraph uses passive (“passive can be used …”) in order to connect better to the topic sentence.

Aside: The “we” in the middle of the paragraph above is not the multi-author “we”, which is as wrong for this single-author blog post as it would be in a thesis, but the “you-and-I” version of “we”, which is also acceptable in theses.

Students worry that if they avoid passive, then they’ll end up starting every sentence with “I”.  Certainly, starting every sentence identically would be a problem, but avoiding that problem is fairly easy, particularly if students talk about the goals and purposes of experiments, rather than just giving technician-level protocol dumps of what they did. Note that I did not use passive at all in this paragraph, and only this last sentence has “I” as a subject—forming gerunds is one good way to create alternative subjects for sentences.

Although my writing instructor’s despair about overuse of passive voice has been the theme of this post, that was not the point of Nick’s blog post—it was a plea to students (and others!) to take responsibility for their actions.  He wants people to be aware that actions have actors:

Responsibility doesn’t have to be a burden but it does give you a reason to exercise your agency, your capacity to act and to make change in the world. If all of your problems are in the passive voice, then “assignments are handed in late”, “the money ran out”, “mistakes were made” rather than “I didn’t start early enough or put enough time in or I was horribly ill and thought I could just push through”, “I spent all of my money too quickly” and “I made a mistake”.

His point is a good one (go read the whole article), but his equating passive voice with refusal of responsibility is the message I want to get to the thesis writers.  The whole goal of a thesis is to establish agency—that the writer of the thesis knows and has done certain things, so the writer should avoid using passive voice.  (I initially had written “passive voice should be avoided as much as possible”, but I didn’t trust that all my readers would get the joke—my apologies to those who would have.)

Enrollment is free, and all you need to do is log your miles over the summer.  You get one point for each mile you ride and 10 points for each day that you ride at least a mile. Once you’ve earned a few points you get entered into lotteries for small prizes.

I’m not quite sure what the point of it is for LAB and CBC (perhaps, like Bike Week, it is an attempt to increase daily biking).

So far only 6 people have signed up from Santa Cruz, making out points per capita astonishingly low (57th in California).

Bendix G15, opened up to show the insides. I don’t know whether either of these gentlemen is Huskey—perhaps the one at the teletype?

TIME magazine has a brief article about UCSC Professor Emeritus Harry Huskey, because he was just given their Fellow Award by the Computer History Museum (at age 97): Living History: Computing Pioneer Harry Huskey Is Honored at 97.  There are a couple of nice photos in the article, though not the one of the Bendix G15 he developed, the world’s first “personal” computer.

Harry retired from UCSC the year before I started, having taught there from 1967 to 1986.

The real gem of the article though is a clip from the time that Harry Huskey was on Groucho Marx’s radio show “You Bet Your Life”.

For example, today I found out a little about how nitrogenases (which usually fix nitrogen N₂ to make ammonia NH₃) produce H₂—I did a search with the student to try to find the stochiometry of the reaction, and we found a paper that explained not just the reactions but what mutations had been needed to turn off the normal control of the nitrogenase, so that it would continue to be active (and producing lots of hydrogen) even when there was no nitrogen to fix.  I couldn’t see any reason for the nitrogenase to be active when it wasn’t producing ammonia, and indeed it isn’t in the wild-type bacterium.

I also helped another student look at pitch detection algorithms (for finding pulse rate from video feeds), using cepstral analysis. He’d been using FFTs, which are not bad, but which can be confusing to interpret when there are higher harmonics present.  The cepstrum is often easier to find the fundamental from, and I’m curious whether it will help with the rather noisy data he has to work from.

A third student was having trouble with non-expression of a viral protein in an archaeal host system, and I suggested looking for the viral sequence in the CRISPR repeats of the archaeal genome, to see if the strain had been previously infected by this virus and so was chopping up the DNA or mRNA they were trying to express. I didn’t know before looking whether CRISPR systems would attack RNA or just viral DNA—the article I looked at suggested that it would attack RNA as well as DNA.  I also suggested that they look to see whether the desired mRNA was actually being expressed (using cDNA and PCR), to see whether the problem was a translation problem or a transcription/RNA-processing problem.

A fourth student had questions about whether he should include an electron micrograph from the literature to show the structure of the virus he was trying to express a protein from, so we brought up the paper on my computer (with a bit of a detour, since he had mis-spelled one of the author names).  The picture was worth including for the purposes of his thesis.  We also talked about whether a particular part of his thesis writing should be given more prominence and more generally about his paragraph and section structure.

Because this is our first cell phone, we wanted one with low initial cost and no long-term contracts.  What we ended up with was a $30 phone and an AT&T $2/day plan, that offers unlimited minutes and texts and no monthly charges, but charges $2 for each day that the phone is actually used. Since he does not expect to use the phone much, we’re more worried about time we’ve paid for expiring (the $25 we prepaid will expire in 3 months, unless we remember to add more money before then). One nice feature of the AT&T plan is the lack of roaming charges in the US, especially as he will be using the phone mainly while traveling.

Since he is planning to use the phone only for the school trip and for emergency calls (like letting us know that he’ll be late because of a flat tire on his bike), we are paying for the plan.  If his use changes and he starts using the phone recreationally, he’ll be responsible for paying for the plan (and he’d likely change to a different plan, since $2/day is expensive if you use the phone every day, though cheap if you use it rarely).

Data transfer is very expensive at 1¢/5KB ($2/MB, $2000/GB), but we got a cheap phone (with a keyboard) which will not tempt him into much downloading (we hope). This plan cannot have a data plan added to it—we’d have to switch to a $25/month with an extra $15/200MB or $25/GB. At those prices it might be worth looking for a cheaper phone company.

If he goes to college with this phone (possible, but not very likely), he’ll probably take an iPod Touch with WiFi as well, so that he can do web browsing—most college campuses have pretty good WiFi coverage for students, though the coverage off campus would be poor to non-existent.  It’s possible that by then he’d want a smartphone, even if the contracts cost $70/month ($30 for 3GB, $40 for voice).

At lot of parents of college students on the homeschool-to-college email list have been recommending that college students take a phone with texting capability to college with them, even if they haven’t been using one in high school.  Not only are emergency alerts used on many campuses sent more quickly with text messages, but a lot of social organization seems to happen by text messages, as students don’t seem to rely on face-to-face meeting to arrange their lives any more. Now that my son has a texting phone, we’ll see if it makes any difference in how he does things.

We started with the 8:15 p.m. SCMTD Highway 17 Express bus over the hill for $5. It’s a fairly comfortable bus, being a relatively new natural-gas powered bus with soft slightly reclining seats, air-conditioning on hot days, and free WiFi.  We took advantage of the soft seats to nap a bit, but did not try the WiFi, having no WiFi devices with us.

Then we took the VTA 181 Express from the Diridon train station to the Fremont BART station.  This bus costs $4 for adults, $1.75 for children under 18, and spends a big chunk of the trip on the freeway after a couple stops in downtown San Jose. The bus was packed, apparently with San Jose State students going home from evening classes. Unfortunately, the ride was on  a poorly maintained rattletrap bus with shocks that should have been replaced about 100,000 miles ago.  The seats were hard, the floors dirty, and knee room close to non-existent.  I’m guessing that either there is no money in Silicon Valley any more to maintain their bus fleet (or their roads) or that the money is concentrated in the hands of a few people who think that bus riders are so low-class that third-world quality is all that is needed. Perhaps VTA has been sinking all its money into expanding the light rail service and neglecting maintenance and replacement of its bus fleet.

The BART train ride  from Fremont to Berkeley ($4.35) was fine—the ride was fairly smooth and the cars clean, though it was clear that they were far from new.  It was a step up from the decrepit VTA bus, but not as comfortable as Caltrain or even the Highway 17 Express bus—and I usually find trains and light rail much more comfortable than buses.

Our connections were all excellent, and we got to Berkeley an hour earlier than Google Maps had suggested—the Highway 17 Express had made good time and we caught an hour earlier 181 than Google thought was possible.  (The usual schedule calls for the 181 to leave just before the Highway 17 Express arrives, since the transit agencies don’t think much about synchronizing between systems.)  We made it to our motel (Berkeley Travelodge) just over 3 hours after leaving our house, getting in before 11pm. Since the distance is 76 miles by car, we averaged about 23 mph, which is twice the speed of the public transit we took in LA.

The Travelodge had one of the smallest motel rooms we’ve stayed in (barely room for the 2 beds), and it smelled a little musty, but we slept well enough despite that.  The “continental breakfast” was also about the feeblest attempt at that we’ve seen—I had a cup of tea providing my own tea bag and a tiny sweet roll in a plastic package.  Our poor breakfast may have contributed to low blood sugar and less enthusiasm than we might have had with a decent breakfast.

In the morning we walked to Sproul Hall, the administrative building on Sproul Plaza where the information session was scheduled. The info session was so early in the day (9 a.m.) that the campus was nearly deserted as we walked across it to Sproul.  Even Sproul Plaza was nearly empty.

Because we are doing our college visits off-season, there were only 2 families at the information session: a father and son from New Jersey and us.  The admissions officer gave us his standard monologue, which was perhaps the least informative of the information sessions we’ve been to so far.  The video they showed us seemed more intended to recruit parents to donate to the college than to be helpful in deciding whether UCB was a good fit. It was difficult to ask questions, because the admissions officer giving the session maintained a continuous monolog (often about his family and career, rather than about UCB) that did not pause long enough for us to insert a question.  I did manage to ask one question about how home-schooling students were handled, but was told very little in response: there is no home-school supplementary form and that UCB does admit a lot of home schoolers. My son did ask a question about the difference between the engineering CS program and the letters and sciences CS program, which the admissions officer should have admitted he didn’t know, as the answer he gave was clearly incorrect, based on what we had read about the programs on the web.

The only substantive advice in the whole presentation was that the student essays should focus on achievements, not just activities, and that depth and duration of an activity are more important than breadth of different activities.  This was not news to us, but it was more clearly presented than at other colleges. The depth-rather-than-breadth focus is good for his admissions chances, as my son has two activities that he has been engaged in for a long time: theater for the past 12 years, science fair for the past 8, for both of which he has positive outcomes to talk about, though no obvious super-star status.  For example, he’s never won his category at state science fair, but he has gone to state 6 years running (a distinction shared by only about a dozen students), and he did make 3rd in his category one year.  Since all his science fair projects have been in the field he plans to major in, tying them into his application should not be too difficult for him.

The tour itself had more people on it than the information session, and was fairly ably presented by the student guide. It was not as sports-focused as the University of Colorado Boulder tour—perhaps not even as much as the UCLA one, though there was more mention of traditions surrounding football games than we were really comfortable with, and the tour guide referred to the sports terms in the first person (“we won …”), even though she was not on the team herself (she did play in the band that accompanies one of the teams).  As with the UCLA tour, we did get to see the interiors of a couple of buildings (the huge Valley Life Sciences Building and the Doe Memorial Library), but no classrooms.  The interiors we were shown looked more like museum entrances or film sets than like working parts of the university.  Because the dorms are at the uphill edge of campus, they were only pointed out to use from a distance. By the end of the tour (around 11:30 a.m.), Sproul Plaza was bustling, though the tour guide had felt obliged to apologize for how dead it was at 10 a.m.

Overall the Berkeley tour was perhaps the blandest and least distinctive of the tours we’ve had—it told us almost nothing about how well the school would fit my son’s needs.

My son had tried to set up an appointment with a CS faculty member at Berkeley, but he’d left it rather late, and the faculty member had said just to stop in during his open office hours (11–12) as he had no other time today.  The trek from Sproul Hall to Soda Hall is a fairly long one (½ mile)  for such a compact campus, and when we got there the professor was in a meeting (apparently with grad students).  We waited around for about 5–10 minutes, but it didn’t look like he was going to be free, so we left without meeting him.

We looked over Soda Hall, which is a nice new building.  We noted that it seemed awfully sterile: there were no conference posters, no announcements, nothing to break up the stretches of blank wall.  The faculty offices all seemed to be tucked away in lab pods behind closed doors—we saw no welcoming open doors as at Harvey Mudd. They had a big TV screen in the main lobby flashing up research posters and unidentified pictures, but none of the posters stayed on the screen long enough to read more than the title, and the resolution was too poor to read the poster even if it had been up long enough.  It looked like a movie set of a “futuristic” computer science department, rather than a real one.  The only lab we saw was the one where we waited for the faculty member—it had a huge bullpen of cubicles for grad students, a few conference rooms and offices, and a kitchen.  It looked like a department unto itself, and I wonder whether UCB is organized into independent fiefdoms that don’t talk to each other, the way so many large departments are.

We had lunch in a courtyard just down the hill from Soda Hall, that Google Maps currently identifies as “Northside Asian Ghetto”, which I doubt is any sort of official name.  There were several Asian restaurants (Korean, Chinese, Himalayan, Japanese (udon), Japanese (donburi), Vietnamese), and lots of students and faculty eating lunch.  I suspect that it is a favored hangout for CS students, though the few textbooks I saw were not likely texts for CS students. We saw a lot of students eating in groups, but we also saw a lot sitting by themselves.

Although we did not get a chance to talk with any faculty, we did get a chance to ask some CS undergrads about the program.  It seems that the classes are even bigger than at UCLA, with 400 or more in the lower division and 100–200 in the upper division.  There is more opportunity to do research, but students have to hustle a bit to find it—some of the students admitted that they didn’t know anything about research opportunities, as they had never tried to get involved, while one had done research his freshman year but had since dropped it.

I worked in one more visit, to the theater department to find out whether non-theater-majors had any hope of getting parts in productions.  The answer was a definite “yes”—they do open casting for all productions with no slots reserved for theater majors or minors.  The acting classes are the same way, though I note that Theater 10, 11, 110A, 110B, 111 all require an audition (a 1-minute monologue) to get into, and Theater 12, 162, 163 require an interview [ http://tdps.berkeley.edu/programs-courses/courses/class-auditions/ ].  The Acting Focus minor, which consists primarily of audition entry acting classes, looks like a pretty good fit for what he wants to do with theater in college, so that part of his education could be easily met at UCB.

Overall, UCB looked like a better fit than UCLA, but not really thrilling.  Even though UCB has only about 25,000 undergrads (smaller than the undergrad university I went to), it is big enough and the faculty distracted enough by grad students and research, that it would be easy for undergrads to get lost in the shuffle if they didn’t push themselves forward.

We took the BART, VTA 181, and Highway 17 Express back home, again getting excellent connections.  The VTA 181 bus this time didn’t rattle quite as much (the shocks were not completely gone), but it stank of stale urine.  VTA really needs to work on their cleaning and maintenance problems!

We’ve got one more visit to do this Spring (Stanford), then a few more to do in September (MIT, Olin College of Engineering, maybe CMU and U Washington).  He may apply to a few others without visiting them, visiting only if admitted.

Wilson’s Principle No. 1: It is far easier for scientists to acquire needed collaboration from mathematicians and statisticians than it is for mathematicians and statisticians to find scientists able to make use of their equations.

Wilson’s Principle No. 2: For every scientist, there exists a discipline for which his or her level of mathematical competence is enough to achieve excellence.

The first principle is probably true, but is more a sociological statement than one inherent to the disciplines: applied mathematicians and statisticians welcome collaborations with all sorts of scientists and are happy to learn about and work on real problems that come up elsewhere, while biologists (particularly old-school ones like Dr. Wilson) tend not to be interested in anything outside their own labs and those of their close collaborators and competitors.

The second principle is possibly also true, though much less so than in the past.  Biology used to be a major refuge for innumerate scientists, but modern biology requires a really strong foundation in statistics, far more than most biology students are trained in. The number of positions for innumerate scientists is rapidly shrinking, while the supply of innumerate biology PhDs is growing rapidly.  In the highly competitive job market for biology research, those who follow E. O. Wilson’s advice have a markedly smaller chance of getting the jobs they desire. Of course, Dr. Wilson seems to be unaware of the decades-long oversupply of biology researchers:

During my decades of teaching biology at Harvard, I watched sadly as bright undergraduates turned away from the possibility of a scientific career, fearing that, without strong math skills, they would fail. This mistaken assumption has deprived science of an immeasurable amount of sorely needed talent. It has created a hemorrhage of brain power we need to stanch.

An undergrad degree in biology (even from Harvard) has not gotten many students much more than low-level technician jobs for most of that time (admission to grad school is the better option, as biology PhDs have been able to get temporary postdoc positions at least).  Perhaps Dr. Wilson considers a dead-end job at little more than minimum wage a suitable scientific career—many others do not.

Dr. Wilson does make one unsubstantiated claim that I agree with:

The annals of theoretical biology are clogged with mathematical models that either can be safely ignored or, when tested, fail. Possibly no more than 10% have any lasting value. Only those linked solidly to knowledge of real living systems have much chance of being used.

Biology is a data-driven science, not a model-driven science (a distinction that physicists trying to jump into the field often miss).  Most of “mathematical biology” has been an attempt to apply physics-like models in places where they don’t really fit.  But there has been a big change in the past 10–15 years, as high-throughput experiments have become common in biology.  Now mathematics (mainly statistics) is needed to make any sense out of the experimental results, and biologists with inadequate training in statistics end up making ludicrously wrong conclusions from their experiments, often claiming high significance for random noise.  To understand the data requires more than Wilson’s “intuition”—it requires a solid understanding of the statistics of big data and multiple hypotheses, as humans are very good at perceiving patterns in random noise.

I was pointed to Dr. Wilson’s WSJ essay by Iddo Friedberg’s post Terrible advice from a great scientist, which has a somewhat different critique of the essay. He accuses Wilson of “not recognizing the generalization from an outlier cannot serve as a viable model, or even an argument to support his position.”  Iddo makes several other points, some of them the same as mine—go read his post! Of course, like me, Dr. Friedberg is a bioinformatician and so sees the central role of statistics in 21st century biology.  Perhaps the two of us are wrong, and innumerate biologists will again have glorious scientific careers, but I think the odds are against it.

Speaking as a college professor, one of the main values of the AP exams is providing a uniform external calibration for the level of high school classes.  Most high school teachers don’t have that much communication with other teachers, particularly not on matters like what level of performance should be expected of students at different levels.  The result has been an enormous grade inflation over the past few decades, so that “A” is the most common grade in many schools rather than a rare accolade.  (The problem is common in colleges also, perhaps even worse than in high schools, especially in the humanities.)

Having an external calibration (an A in this course is roughly the same as a 5 on the AP exam) is very useful for gauging the level of the course, for the teacher, for the students, for parents of the students, and for the colleges that might admit the student.  The AP exam scores, after all, are supposedly set to correlate with the grades the students would have gotten in a first-year course in college.  It used to be that 5, 4, and 3 correlated well with grades of A, B, and C, but grade inflation in the colleges appears to have advanced faster than on the APs, so perhaps 5 and high 4s correspond to an A, and low 4s and 3s to a B (depending on the college, of course, as grade inflation is far from uniform).

Of course, the AP test does not measure all the things that go on in an AP course, and a student can do well in the course and poorly on the exam or vice versa, but if A students in a course are consistently getting 1s on the test, it leads one to suspect that grade inflation has happened.  Similarly if C students are routinely getting 5s, one suspects that the course grading is ridiculously harsh.

Others pointed out the obvious thing, that the test is a 3-hour snapshot of how a student did on an arbitrary subset of the material on one day.  Seniors who have already been admitted to college (or decided not to attend) may have little incentive to do well, particularly if their college does not give credit for AP exams.    One teacher, who posts a lot of good stuff on the mailing list, asked me directly:

I am curious to know what portion of the grades in the courses you teach are determined by one, largely multiple choice exam?  I’ve never taken a worthwhile course where “passing” was ever determined in such a way.

He called it right on that one.  I’ve never given a multiple-choice exam in 31 years of being a professor—multiple-choice exams are very difficult to write well, and really only appropriate when there are enormous numbers of test takers to reduce the cost and variance of grading and amortize the large cost of making the exam.  For that matter, I give very few exams—most of my courses are graded on the basis of week-long or quarter-long projects, papers, and programs.  I’m not particularly interested in the things testable by multiple-choice tests (mainly memory and simple reasoning tasks), but in what students can do with a sustained effort.  My most recent course (Applied Circuits for Bioengineers) was graded mainly on the basis of their weekly design reports based on their lab work (about 5 pages of writing a week, and any mistakes in the schematics or explanations meant that they had to redo the writing).

I’m not going to defend the AP exams as great ways to evaluate learning, but they are better than the exams that most teachers write and rely on for grading, and they do have the advantage of uniformity across a large number of classrooms.  A 5 on an AP exam may not tell me a lot about a student’s capabilities, but I believe it tells me more than an A from teacher I’ve never met and who may have only taught a handful of AP students.

I agree that the goal of an AP course is not “college credit” or even “preparation for the AP exam,” but the learning that takes place.  But grades and  exams scores are used for selecting kids for college admission (as being better than a simple lottery or selection based solely on money or race), so it is better if the exams and grades are as meaningful as they can be made (at reasonable cost—a lot of state testing is providing very little useful data at enormously high cost in both money and lost time).  Because teachers have so little opportunity to calibrate their own grading, an external test at the right level provides very useful information.

I agree that a single sample of small number of students may not tell you much about the level of instruction in a class, but may be a warning that recalibration is needed. There are many possible reasons for the discrepancy (difference in content between course and exam, difference in level of expectations, student test-taking ability or attitude, random noise, …).  For a course labeled “AP”, the teacher has a responsibility to make sure that the content of the exam is covered in the course.  As a parent, I would also want the level of expectations in the course to be as high as in a first-year college course.  If the students are uniformly doing worse on the test than what the teacher expects, then some reflection on why the expectations are wrong is needed.

Elsewhere in the discussion, another teacher asked

What if, instead of grades, we could present colleges with better data about what we actually care about: skills that aren’t reportable by traditional methods.

When admissions officers are trying to select <6% of the students from 38,828 applicants (as Stanford did this year), it is difficult to process voluminous communications from individual teachers.  They rely on summary statistics (GPA and SAT scores, for example) to do crude filtering, then concentrate on student essays and letters of recommendation.  The very selective schools sometimes try to correct the GPAs based on grade inflation at the high school (there are databases of information about each high school being sold—I’ve no idea how accurate the information in the databases is, but some admissions offices use them).

The college faculty, who might care about the “skills that aren’t reportable by traditional methods”, are rarely part of the admissions decisions.

Public universities are usually forced to have a simple formula for most of their admissions, to be able to show the public that they are being scrupulously fair.  If they took into account “skills that aren’t reportable by traditional methods”, parents of rejected students would scream to their legislators to cut off funding to the university.  (An exception is always made for athletics, which is a sacred cow in the US.)

Later in the discussion, after more narrative transcripts were proposed and the value college admissions officers put on letters of recommendation had been introduced, I wrote about my experience with narrative evaluations.

I’ve been engaged in this debate for decades.  When I started teaching at UCSC, we had almost exclusively narrative evaluations,with optional grades in some classes.  We moved to optional grades in all classes, then grades plus narratives, and now to grades with optional narratives.  (Incidentally, in each vote I voted in favor of narratives with optional grades.) I have not noticed a difference in how easily students get into grad school, though one of the main arguments used against the narrative-only system was that it was hurting the chances for students getting into med schools, as their “pass” grades were getting converted to Cs by the big med schools, no matter what the narrative said.   I think that the real driver behind the switch from narratives to grades was the incredible workload of preparing narrative evaluations for large courses.  In many classes, the “narrative” was computer-generated from a list of grades, and not very informative.

I had to review narrative transcripts for honors review of graduating seniors, and I often found it very difficult to interpret the narratives.  It took a long time to read a narrative transcript, and often told me very little about the student. There was no controlled vocabulary, and the same word might be used by one instructor for a barely passing performance and by another for a truly excellent performance.  I could see why med schools could be frustrated by the difficulty of dealing with this format.  Nowadays the honors review in the school of engineering is based on GPA, with a well-defined grey region where student research projects can make a difference in the honors rating.  I understand that the honors review now takes only a fraction of the time it used to, despite large increases in the number of students reviewed, and the time is spent only on the cases where some thought is needed.

As a grad director, I read a lot of applicant files for admission to our program.  GRE scores and GPA do matter, but not very much, as the GRE tests essentially the same stuff as the SAT (nothing college level on the general GRE and there isn’t a subject GRE in our field) and college GPAs are often highly inflated, depending on the college.  We also get a lot of foreign applicants, whose grades come on a bewildering variety of different scales that are essentially uninterpretable.  We expect high GRE scores of all applicants, but decide between them based mainly on their personal statements and letters of recommendation. What we are looking for is strong evidence that the students can do research (not just coursework), and the best evidence is that the student has already done substantial research.  Many of our grad students come to us with multiple publications already—more than I had when I got my first faculty position.  Narrative transcripts would not be a good substitute for letters of recommendation from faculty that had supervised research—the signal we’re looking for would be buried in the noise of irrelevant comments for coursework that is not that important to us.

As a homeschooling parent of a high school junior who is likely to fit in best at a super-selective college like Harvey Mudd, MIT, or Stanford, I worry a lot about how to put together his transcript, school profile, and counselor letter to show that he really would fit in. There are mailing lists with 1000s of readers dedicated to parents worrying about how to get their home-schooled children into appropriate colleges (hs2coll@yahoogroups.com, for example).  I’m having to rely heavily on external validation of his coursework, much of which is not even accredited.  SAT 2 and AP exams form part of that validation, while college and university courses form another part.  Science fairs and contest exams (AMC12 and AIME in math, F=mA in physics) form yet another part, though his contest scores are not stellar enough to make him a shoo-in at the colleges where he would fit, since he does all his exams without prep. We are trying to get letters of recommendation from the university faculty (he’s been at the top of the classes), since those will be particularly informative for admissions officers.

I’ve written blog posts about how homeschoolers can get into the University of California, which is highly bureaucratic, but fairly straightforward:

http://gasstationwithoutpumps.wordpress.com/2011/10/07/satisfying-ucs-a-g-requirements-with-home-school/

http://gasstationwithoutpumps.wordpress.com/2012/06/14/ways-in-to-university-of-california/

http://gasstationwithoutpumps.wordpress.com/2013/03/29/admission-by-exam-at-uc/

In short, I like narrative feedback to students and their parents, but do not find narrative transcripts to be a particularly useful way to select students from a large pool (whether for honors or for admissions to the next level of education).  Small numbers of well-selected letters of recommendation are much more helpful.

I didn’t say this on the AP forum, but I will have to provide a narrative transcript of my son’s high-school curriculum, as it is pieced together from a variety of different sources. We’ll probably provide 2 formats for the transcript: a one-page one that lists courses and grades (for those courses that have grades) and a multi-page transcript that describes the content and level of each course in more detail.  Admissions officers in a hurry can glance at the one-page summary to see that he has taken all the expected courses, and those with more time or more interest can read the detailed descriptions to see that the courses were solid, even if not officially accredited.

Teachers participating in this discussion seemed to be in agreement that an AP class had several purposes, and that the most important ones were teaching the kids how to master college-level material and instilling both a knowledge of and a love of the subject.  Preparing for the exam was seen as a secondary goal at best—useful for calibrating the level of the course and with the possible college credit as a nice incentive to get students to study, but not the central focus of the teaching.  I found the discussion refreshing—teachers were talking about the goals of their courses and the value of different assessment techniques without falling into defensive postures or reciting meaningless eduspeak mantras.  There was a general feeling that the AP test was about as good as a 3-hour test could be and useful for calibrating course levels, but that a single data point was not sufficient for evaluating student performance. 

I did not see teachers blaming students or prior teachers for poor performance, nor talking about doing a lot of “test prep”, which seem to be the big problems with how standardized tests are handled in schools in discussions I’ve seen elsewhere.  The attitude among the AP teachers seems to be that if they teach a good course with student engagement in a lot of the right content, then the test will take care of itself.  Most of the discussion on the AP list is about lesson plans, text books, sources for lab supplies, debugging lab procedures or test questions, and (at this time of the year) review materials.  There are occasional complaints about administrators (mostly about ridiculously little teaching time for the course or overcrowded labs, but sometimes about administrators tying teacher evaluations to the single 3-hour test taken by the students), but most of the discussion focuses on the content and pedagogy.  I wish there were such discussion sites for the college-level courses that I teach.

This Town Ain’t Big Enough for the Ten of Us

The Broadway Playhouse

526 Broadway St, Santa Cruz, CA

Sunday, April 28, 2013, 7:00 pm.

$6

Santa Cruz Improv Fest Performance

The Broadway Playhouse

526 Broadway St, Santa Cruz, CA

Sunday, May 12, 2013, 7:00 pm.

$15

Information about their shows can usually be found at Upcoming Shows | Dinosaur Prom Improv.

Although I may be biased as a parent of a cast member, I find their shows to be pretty funny, and the laughter from the audience suggests that I am not alone in this assessment.

This is good news for me, as I have a low resting heart rate (around 52 bpm) and fit the other criteria.

I’m a little unclear on how they did the correction, as the fitness (measured with VO₂max) was very highly negatively correlated with heart rate (R=–0.34). They say they used “Cox proportional hazards regression analysis”, which seems to me to suffer the problem of all regression methods when the input variables are highly correlated. They do claim “Assumptions regarding the use of Cox proportional hazards were met by inspection of the log minus log function at the covariate mean.”

The are showing a huge difference between resting rates under 50 bpm and over 50 bpm, then small differences up to 80 bpm, and big increases for over 80 bpm and even bigger for over 90bpm.  I wish that they had divided the group into equally populated classes, rather than every 10 bpm, as the 51–60bpm group has almost 36% of the subjects (1003/2978), and I’m pretty sure the low and high end of that group are different.  I also wish that they had used the median group (61–70 bpm) as their baseline, rather than the unusual group having heart rates under 50 bpm.  The 51–60bpm group has only a 5% lower risk than the 61–70 bpm group, while the under 50bpm group has about a 30% lower risk.  It would be interesting to have analyzed the data to try to get a smooth curve of risk vs. heart rate, though that would have required more sophisticated modeling and the data may not be sufficient to produce a reasonable curve.

It looks like there is a chance my son will get to take the AP CS and AP Physics C: E&M exams this year, even though my first 5 attempts to find a place for him to take them failed. He needs to take the late exam for AP CS, since it conflicts with the Oregon Shakespeare Festival field trip and no one in the County offers Physics C—my attempts to get one of the high schools to offer the exam (which is at the same time as Physics B) all failed.  His consultant teacher is trying to arrange to be the proctor for him on the late AP CS and the late Physics C: E&M exams (it is now too late to register for the regular exams) through another high school in the same district.  I’m hopeful that she’ll be more successful in moving the bureaucracy than I was as an outsider.

Of course, he’ll probably never get any credit for taking the exams, since many of the schools he is applying to don’t do AP credit anyway, and he’ll have to retake physics at any of the schools he’s likely to choose.  But the exams will help validate that he has done rigorous work in physics, which should help him get into the colleges that would be a good fit for him. The AP CS exam is so low level that all it validates is that one has learned some Java syntax—but it might help with admissions offices also, as most will not be familiar with the new Art of Problem-Solving Java course.

In any case, we have to speed up a bit on the physics, despite the distractions, so here are the problems for Chapter 22 “Patterns of Fields in Space”: 22P15, 22p16, 22p18, 22P22, 22P23, 22P25, 22P29, 22P31, 22P33, 22P37.

I was going to complain about the Venn diagram as being useless here, as it did not include the number who had none of the conditions, thus not allowing the viewer to determine the probability of each condition separately, which is essential to making any real sense of the figure (are the conditions correlated?).

I did not complain on his blog for two reasons:

• He requires commenters  to sign in with a Google account, and I prefer to leave blog comments using my WordPress account, so that people can find my blog from the comments.
• I went back to the original source and found that the NY Times writer or artist had not been quite so cavalier with the data—there was another circle adjacent to the Venn diagram that included all those with none of the conditions.  (I was actually quite surprised to see that Jernigan had omitted an important part of the figure, as he is usually quite sensitive about probability distributions, so truncating a figure to omit one category seems out of character for him.)

I looked a bit at the pairwise comparisons on the last page of the NYTimes article, and decided that this way of presenting the data violated many of the principles of good data presentation.

First, it takes a huge amount of space to present just 3 numbers (the pairwise comparison shows percentages for conditions A&B, A&not B, B&not A).

Second, it is not possible to look at two different comparisons at the same time.

Third, the NYTimes Venn diagrams have rather distracting pointless animation, which is not visible in the static image I copied from Jernigan’s blog.

Fourth, the Venn diagram often implies a correlation (look how often these conditions co-occur!), when the probabilities of the conditions appear to be essentially independent in many cases.  For example, Alzheimer’s and high-blood pressure co-occur in 24% of the nursing home residents in the sample, but with probabilities of 46% for Alzheimer’s and 57% for high blood pressure, one would expect about 26% to have both if they were independent conditions.

The basic point of the original story is that people in assisted living facilities have very high probabilities of a debilitating medical condition (well, duh! that’s why they’re in assisted living, and not a lower-cost housing option) and that multiple conditions are common. One of their main points is that 9% of residents of assisted-living facilities have all three of dementia, heart disease, and high blood pressure, and that “treating these patients is extremely difficult because of complicated drug regimes and numerous side effects.”

Within the assisted living population the conditions seem to be nearly independent (though that is hard to tell from the Venn diagrams—they don’t give the sizes of all the parts in the 3-variable Venn diagram, and I did not click through all the pairs to check pairwise independence from the 2-variable Venn diagrams). But that near-independence may mean that multiple conditions are more common than a naive prediction based on independence in the overall population would suggest. To determine whether the conditions are correlated, one would have to look at the whole population at a given age, rather than just at the selected population in assisted living, since that selection probably under-represents those with no debilitating conditions. (I also wonder how “assisted-living facility” is defined, since I know that the definitions are quite different in California and Colorado, with a much looser definition in Colorado that would include many of the “independent-living” facilities in California.)

Doing a proper analysis of the data would require going back to the original study, which the byline-less NYTimes article only refers to vaguely as “the study, by the National Center for Health Statistics in 2010″.   I’m not interested enough to search for that study and see whether there is enough information to see whether any of the co-occurences are really surprising.

I think that there are two main noise sources:  the nanopore itself and the resistors used in converting the nanopore current to a voltage in the first stage of amplification.  Each of these noise sources has two types of white noise: thermal noise (dependent on temperature) and shot noise (independent of temperature).

Thermal noise

The RMS current for thermal noise is for a resistance of R at temperature T, with k_B being Boltzmann’s constant, 1.3896593E-23 J/°K, and being the bandwidth of the filter looking at the noise.  Normally, nanopore scientists don’t report the resistance of the nanopore, but the bias voltage and the DC current through the pore, but we can use Ohm’s law to rewrite the noise formula as .  A typical setup for a nanopore may have a voltage of 180mV and 60pA (for an open channel in 0.3M KCl), and a temperature of 25°C = 298.15°K, for , and noise of .

Shot noise

The RMS current for shot noise is , where q is the magnitude of the charge of the carriers (here 1.60217646E-19 C, since the charge carriers are K⁺ and Cl^-), and I is the DC current.  Again is the bandwidth of the filter looking at the noise.  For a current of 60pA, the shot noise would be , slightly more than the thermal noise.

Combined noise in nanopore

The combined noise in the nanopore from both shot noise and thermal noise should then be , which for 180mV and 60pA would be .  If a 1kHz low-pass filter is used, that makes an RMS noise level of 0.127pA, and with a 10kHz low-pass filter, 0.4pA.

Increasing the ionic concentration would provide a larger DC current (current proportional to concentration), and the noise current only grows with the square root of the DC current, so the signal to noise ratio also grows with the square root of the ionic concentration.

Amplifier noise

These noise levels are lower than what is actually observed, of course, because we haven’t taken into account noise generated in the first stage of amplification.

In the UCSC nanopore lab, they use an Axon Axopatch 200B Capacitor Feedback Patch Clamp Amplifier, which costs about $2000 used (if you have to ask the new price, you can’t afford it—you can get a quote, but there is no list price).  The web site claims “By introducing active cooling of components in the headstage to well below 0°C, the open-circuit noise in patch mode has been reduced to unprecedented levels, as low as <15 fA (RMS) at 0.1–1 kHz.”  But the lab uses a resistive headstage (so as to be able to make DC current measurements), with a 500MΩ feedback resistor in the current-to-voltage converter, which introduces thermal noise of about , larger than the nanopore noise. The shot noise for the resistor in the I-to-V converter should be the same as as the shot noise for the nanopore, since they have the same DC current. Combining the thermal and shot noises for both the nanopore and the resistor at 180mV and 60pA, I estimate , which would be 0.244pA at 1kHz, 0.547pA at 5kHz, and 0.774pA at 10KHz.  The noise levels observed in the signals are close to those levels, so there is not much that can be done to improve the amplifiers to get a better signal-to-noise ratio.

We could be interested in lower-cost amplifiers than the $2k lab instrument, but a very low noise off-the-shelf op amp (like the AD8432 ) has a noise level of about , which is much larger than what is observed in the lab.  Designs have been done at UCSC for much lower noise amplifiers (Gang Wang; Dunbar, W.B., “An integrated, low noise patch-clamp amplifier for biological nanopore applications,” Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE , vol., no., pp.2718,2721, Aug. 31 2010-Sept. 4 2010 doi: 10.1109/IEMBS.2010.5626570) claiming “input referred noise of the amplifier is 0.49 pA RMS over a 5 kHz bandwidth”, from simulation (I don’t know whether they have fabricated and tested the amp yet).

On Monday and Tuesday, we had the California State Science Fair, where he had a project in the math and software high school division, and I was judging in the math and software middle-school division.  He did not expect to win anything this year, as he had a fairly straightforward engineering project—the Arduino data logger that he wrote for my circuits class to use.  The project was well done for a high school student (comparable to some senior projects I’ve seen by college students), but not flashy in the way that science fair judges like. Indeed he did not win anything at state this year, but he was one of only 11 students who had been to state science fair 6 or more times—so he shows consistent quality and perseverance, even if he never wins the lottery that science fair judging often is.  The top math and software award at the high-school level this year went to a math project (not a software project), which is a bit unusual.  I did not read the poster for it in any detail, which I now regret, as it must have been pretty good to overcome the usual judging bias in favor of software.

The middle-school math and software category had a unanimous vote for the first-place project: an ambitious image-processing project with an interesting application and pretty good code (properly commented—a rarity at the middle-school level or even the high-school level).   The order of the next few projects was more strongly debated, but all of them were very good projects, and the order ended up depending more on the tastes and persuasive abilities of the judges than on the inherent merits of the projects.

Since we were down in Los Angeles for the science fair, we decided to extend the trip by 3 days to visit three colleges in the area: Caltech, UCLA, and Harvey Mudd.  [The science fair is right by USC, but that was not our list of colleges to visit—we've seen the campus often enough, and the academic program did not appeal.] Originally we had planned a west-to-east sweep (UCLA, Caltech, Harvey Mudd) to minimize the transit time, but Caltech was not doing tours on Thursday and Friday (preparing for their admitted-students yield event this weekend), so we changed the order to Caltech, UCLA, Harvey Mudd. To get from the science fair to Pasadena, we took a DASH bus, the red line (subway), and the gold line (light rail).  That used 2 different transit systems (LA DOT runs the DASH buses, and Metro runs the subway, the light rail, and all the other buses that we took on this trip).

I couldn’t find any reasonably priced motels or hotels near UCLA in my on-line searches, so we stayed one night in Pasadena and two nights in Claremont, with the UCLA tour sandwiched in between the 2-hour, 2-bus Pasadena-Westwood and 3-hour (bus, subway, train) Westwood-Claremont transits.  I had originally planned to take a taxi from UCLA to Claremont (a pretty expensive ride across Los Angeles), but my son wanted to include a Metrolink commuter rail link in the trip somewhere in our trip, so we ended up taking the Metro number 2 bus from UCLA to the red line, the red line to Union Station, and Metrolink to Claremont.  The subway and commuter rail portions were fairly pleasant, but the number 2 bus was so full that we felt guilty for having luggage—Metro probably needs to run more buses on that route during rush hour.

The LA transit system is usually maligned by the locals, who claim that it is so bad that they have to drive everywhere, but it seemed pretty reasonable to us—under-utilized, perhaps, but reasonably quick and with decent connections.  Of course, just about any local bus system will only provide about 10-mile-per-hour transportation, so bicycling is almost always faster, but that is an option that is seems very , very few people choose in Los Angeles.

OK, enough on transit, what about the 3 colleges?

At Caltech we had a very small tour group (just 3 prospective students) and a friendly, barefoot tour guide.  We were shown the Caltech “houses” and the guide talked a lot about Caltech traditions.  Some of the traditions (like the honor code) seem great, but a lot of the other traditions seemed to be based mainly on rivalry, competition, and mean-spirited pranks. The social activities mentioned (like the interhouse parties) seemed to be mainly competitive events also (which house could build the most elaborate set for their party).  We saw almost no students while on the tour, no classrooms, no professors—very little other than the houses and the outsides of buildings.  The campus seemed strangely deserted for a Wednesday afternoon in the middle of the term.

The Caltech campus does have some nice-looking buildings, and there are supposedly a lot of Nobel prize winners around, but we didn’t hear much about students actually interacting with the professors—the impression was that the professors mainly kept their heads down and did research with their postdocs and grad students. My son had tried to arrange meetings with a computer science faculty member by e-mail, but the first one he contacted suggested he talk to someone else, and that person said he was too busy, but that my son should just wander down the hall and stick his head in an open door.  We ended up not talking to any Caltech faculty or even seeing any from a distance.

The one academic message that we got from Caltech was “physics”.  They teach physics at Caltech—occasionally they give it a different name (math, chemistry, computer science, engineering, … ), but when you look at the research interests of the faculty, it is almost all physics in different flavors.  My son likes physics, and would probably do ok at Caltech, but he has other interests as well, and Caltech does not seem to provide instruction or opportunities in them.  He also likes doing applied work more than theory, and Caltech (according to the student tour guide and what we could glean from the web) is very theory-oriented.  Caltech does have some theater that he could participate in, but their entire “theater and visual arts” program apparently fits in a small 2-story house and a shed at the corner of campus, and there was no one around on a Wednesday afternoon to get any information from.

UCLA was in many ways the opposite of Caltech.  It is a large, bustling campus, crowded with students the whole time we were there. Students walked or hung out in groups (very little wheeled transportation, because of the number of hills and stairs).  There did not seem to be many quiet places on campus (unlike Caltech, where the entire campus seemed to be silent).

The tour group we were with for a 2-hour walking tour was large—probably 15 students plus accompanying family members.  The tour guide showed us many buildings (including the insides of a nice library), but no residences (which are a 20-minute walk away from the academic buildings), and she told us about admissions and other generic information.  The campus tours seem to be entirely student run (the campus tours office is in the student government building and staffed entirely by students), rather than part of the admissions office.  The tour was pretty good, for a large, generic tour, and UCLA does have some nice-looking buildings (and nice-looking students, but I’m not supposed to notice that).

We had arranged a meeting with a CS faculty member, who told us about his classes and research. Undergrad computer science at UCLA has huge classes (60–80 in upper-division courses, and three times that in lower-division courses). The faculty member told us that he does not allow undergrads into his grad courses and that few undergrads get research opportunities.  He did not have numbers, but estimating from what he said, it sounds like only about 5% of CS majors at UCLA get involved in faculty research—an appallingly small number.  It sounds like it is hard for an undergrad at UCLA to get a first-rate computer science education, because they are so focused on pumping through huge numbers of OK students.

UCLA does have a great reputation in theater, so we went over to the opposite side of campus to find out whether a non-theater major could ever get roles.  We did not talk to a theater faculty member nor an administrator, but to a friendly group of theater majors.  They basically said that non-majors had essentially no chance of getting a role (or even tech work) in any theater department production—even the theater minors only got theater-appreciation classes, not acting classes.  They did say that there were some non-departmental theater productions, but that they knew almost nothing about them.  In short, it sounded like what my son wants (a really advanced computer science education with the ability to do a fair amount of acting on the side) is not available at UCLA.

I had expected Harvey Mudd to be similar to Caltech.  They both have reputations for being very techie schools with impossibly high workloads, and Harvey Mudd was started by someone with close ties to Caltech.  They both have a similar-sounding common core requirement and both have a very pure form of honor code (tests are unproctored take-home exams, with students responsible for timing themselves as well as following directions about whether notes and books are permitted).  There were a number of observable differences, though, even on a one-day visit:

• Harvey Mudd has some of the ugliest buildings I’ve seen on any college campus.  The concrete block buildings with “warts” make UCSC’s cast concrete bunkers look stylish in contrast.  It is clear that Mudd has not been investing in the amenities wars—there is no luxury here.  The interior of the dorms look a lot like the concrete-block dorms I lived in back in the early 70s at Michigan State, but perhaps even more crowded.
• The campus is small.  Our walking tour showed us every building on campus, including a walk through the main academic building, showing us classrooms, faculty offices, and even the wood shop and machine shop (which Mudders can use 24/7 once they have passed the safety training). The class in which students have to make a hammer to specifications from a chunk of wood and a chunk of metal seems like a good, practical course.
• The campus is flat, so wheeled transportation is common (bikes, unicycles, skateboards, long boards, and freeline skates seemed the most popular).
• The density of students was between that of Caltech and UCLA.  There were plenty of students around, but it was never so crowded or so loud as to be claustrophobic. A lot of the students were wearing geek T-shirts and seemed likely to be the sorts of kids my son would get along well with.
• Faculty were clearly visible—one physics professor even kibbitzed the tour guide as he was giving the explanation of the physics core courses.
• The admissions office gave my son a ticket for a free meal at the dining hall (and a reduced-price ticket for me).  We had lunch there, and the food was pretty good for a dining hall—more important it included several things that my son would eat on a regular basis.  We also noticed that several of the faculty ate there.  I don’t know if Harvey Mudd encourages the faculty to eat with the students (free lunch might do the trick, or the unavailability of other options), but it was good to see faculty and students in the same hall, even if at different tables.  I also noticed that none of the students were eating alone—almost everyone was in a group of 2 to 10 students. For a group of geeks, that is a rather astonishing bit of social engineering—I wonder how they accomplished it.
• My son was also given a list of all the classes meeting at Harvey Mudd this semester and invited to sit in on any of them.  Unfortunately, we were there on a Friday, so few classes were meeting (mostly long labs).  We sat in on one of the “choice” labs for a while, and saw mainly one-on-one mentoring by the faculty member, which was good to know about, but not very exciting to watch.
• Harvey Mudd does have an 11-course humanities, social science, and arts (HSA) requirement, about half of which has to be done at Harvey Mudd, with the rest usually being done at the other Claremont colleges.  It would be possible for him to do a theater concentration (5 theater-related courses), by taking the one Harvey Mudd theater course (simply titled “Shakespeare”) and 4 courses at Pomona.  Most of the Mudders take a fair number of courses at the other Claremont colleges—usually PE courses and courses in their HSA concentration, and cross-registration seems to be fairly straight-forward, since the Claremont colleges share a common registration system.
• There is an aikido course at Scripps that my son could take for PE—he’s not done aikido since he was quite young, but thinks that he would enjoy picking it up again more than most PE options.
• My son had made an appointment with a computer science faculty member and we had a good conversation with him about the Harvey Mudd requirements and opportunities in computer science.  All the computer science students have to do research or development projects and most do more than one (the senior clinic plus one or more summer research projects).  There seems to be enough depth in courses and research in the fields my son is interested in that the lack of grad courses is not really important.  Even the required common-core first course in computer science has an option for students sufficiently advanced in CS, so that he would not have to repeat stuff he’s already done.
• The tour guide talked a lot about coöperation, mentoring, and group projects—concepts that were independently brought up by the admissions officer and by the CS faculty member.  The group projects don’t seem to be the one-person project forced on a group that most middle-school and high-school projects are, but projects big enough to benefit from multiple people working on them.  They do practice pair programming in most CS classes, which will be a new experience for my son.

Although I had expected Caltech and Harvey Mudd to be very similar schools from what I knew before the visits, I ended up with very different impressions of them.  Caltech seems to be a competitive school with a physics-centric, theoretical focus, while Harvey Mudd is a cooperative school with an applied engineering focus.  My son will probably apply to both, since getting in is largely a lottery (they both have about a 10% acceptance rate and his test scores are only average for either school), but I think that he’d end up much happier at Harvey Mudd.  UCLA looks much less attractive (other than financially), but he’ll probably apply to several of the UC schools as he is much more likely to get into them.

I have a confession to make: At this point in the semester (week 11), there’s a question I get that nearly drives me to despair. That question is:

Can we see more examples in class?

Why does this question bug me so much? It’s not because examples are bad. On the contrary, the research shows (and this is surely backed up by experience) that studying worked examples can be a highly effective strategy for learning a concept. So I ought to be happy to hear it, right?

The difficulty, of course, is that the students are asking to see examples, rather than working on the examples themselves—they are asking to be spoonfed mush rather than chewing for themselves.

I have found in my own learning that I can get a certain amount by reading, but that really understanding material requires me to work out problems for myself.  Sometimes this just means doing exercises from the textbook (a boring task which I have trouble forcing myself to do without the structure of a course), and sometimes it means struggling with making something work to solve a real problem. Real problems are both motivating and frustrating—just doing carefully drafted exercises that are designed to work out easily doesn’t always help much in applying ideas to the real world.

Talbert gets the point across well:

Of course at the beginning of a semester, students aren’t experts, and showing them examples is important. But what I also have to do is (1) teach students how to study examples and (2) set and adhere to an exit strategy for giving examples. My job is not to give more and more examples. Instead it’s to say: Rather than give you more examples, let me instead give you the tools to create and verify your own examples.  And then, at some point in the semester, formally withdraw from the role of chief example-giver and turn that responsibility over to the students.

This is the same idea as in my post Descaffolding, which was prompted by a post by Grant Wiggins, Autonomy and the need to back off by design as teachers.  It also fits in with Dan Meyer’s theme to “be less helpful”.

Given how frequently teachers and teacher leaders discuss it, I think that over-scaffolding is a common problem for many teachers.  We all want to help the struggling student succeed, but too often we make them incapable of succeeding without us.  If they always outsource their thinking, they’ll never develop their own skills.

To use analogies from other fields: overscaffolding is like showing the students only great literature and telling them about writing process, but never having them struggle through 5 to 10 drafts of a piece of writing, or teaching art by showing only cast bronzes and mosaics, but never having them do a sketch or sculpt in clay.  Showing or telling students how to do something is often necessary (students can’t be expected to guess non-obvious methods), but it needs to be followed by students doing things for themselves.

A lot of us put a lot of time into polishing our presentations so that the students see the cleanest, most elegant way of doing a proof or solving a problem, but never see the debugging and refinement process that creates such elegant results.  I’ve never been guilty of the over-polished lecture: I give my lectures as extemporaneous performances that are never the same twice.  For one course, I did not even prepare any lectures, but had the students give me problems from the homework that they wanted to see how to do, a process I called live-action math.  That approach required a thorough understanding of the material and a confidence that I could do any of the problems in front of an audience without prior prep.

Not all my classes are so extreme, but when I give examples I always try to make them examples of problem solving (as opposed to examples of solved problems).  In the circuits course last quarter I probably did about the right number of examples in class and got the students involved in solving them, but I did not give the students enough simple problems to practice on.  I was withdrawing the supports too quickly and trying to have them jump from the material in the reading (which they weren’t doing) directly to design problems. Next year I’ll assign some more routine exercises (though I’ve always hated the drill work) to help them build their skills.

So too many examples is not a big problem in my teaching style. The bigger teaching difficulty I have is in not doing debugging for the students.  In labs and programming courses I can find student problems much more quickly than they can, and I have to restrain myself from just pointing out the (to me) obvious problem. I can think of several times in the circuits lab last quarter when I glanced at a breadboard that students had asked for help with and just asked them “where’s the connection to ground for this component?” or “why are all these nodes shorted together?”  That was not quite the right approach—it got them unstuck and left them some of the debugging still to do (that is, it was better than just moving the wires around for them), but did not help them develop the skills needed to see the problem at a glance themselves.

Some other approaches, like “Show me your schematic—I can’t debug without a clear schematic of what you are trying to build,” were probably more effective—there were a couple of students who kept trying to build without a clear schematic and being unable to debug the resulting mess.  I probably walked away from them 3 or 4 times during the quarter, telling them I’d help once they had proper schematics to debug from.

It might be better for me to go through a checklist with the students—for example, having them check that each component has the right number of connections and check the breadboard against the schematic to see if the wiring is the same.  Occasionally I’d still have to step in to correct a misunderstanding (particularly at the beginning when some students don’t understand how the holes of the breadboard are connected together underneath and put components in sideways), but by stepping them through a process I think I could eventually get more of them debugging on their own.

After all, the point of the programming assignments and labs to teach students how to debug, not just to get them to produce working programs or circuits.  It is much harder to teach a student how to debug than to demonstrate debugging—I’m still working on better ways to do that.  I think that what I did in the circuits course worked for some students (they were debugging pretty independently by the end of the quarter), but others were still relying too much on help even at the end of the quarter.

A big chunk of learning how to teach is figuring out how to withdraw the initial support without students failing.  Suddenly yanking it out from under them will make many collapse, but being too slow to remove support will leave them still leaning on the crutch when they should be running on their own.

The list has now been whittled down to 40 applicants on the short list—our department chair had to identify a specific reason why each of the other 80 was not included before we could go on to the next stage of the process (I looked at many of the rejected ones, but nowhere near 80 of them).  I spent today trying to read the folders for the 40 remaining applicants.  The folders, luckily, are all electronic these days, so I did not have to camp out in the department office during the week, the way we used to for faculty hiring, but even just downloading them to my laptop took over 15 minutes (I had to click “download” separately for each one—there is no way to request all the files on the short list).

I looked mainly at the CV (had the person published a reasonable amount for the time since PhD, and were the publications on topics we were interested in) and at the proposed future research (was the person proposing work that matched my idea of protein engineering, which covers a lot of things, but not everything that has the word “protein” in it somewhere).

I managed to shrink the list of 40 down to 10, but I don’t know that my list of 10 is going to match up with anyone else on the committee.  Most of the rest of the committee will be looking mainly at “science” rather than “engineering” and there were some pure molecular bio projects that might excite them, even though there is little chance of the person doing any engineering with the ideas in the next decade.  I may have been the only committee member to look at the teaching statements (though most of them were so wishy-washy that they weren’t worth reading—only a few had bothered to propose a new course or look at our curriculum and see where they might contribute).  I didn’t pay much attention to the recommendation letters—I think that those will have been given very high weight by other members of the committee, and I was pressed for time.

We have to get the list down to 6 to interview before we can interview anyone, which is going to be tough, as the candidates are all quite different from each other along many different dimensions—there is no simple linear ranking.  We’re about 3–4 months behind everyone else in the recruiting process (thanks to foot dragging by our dean), and I’m afraid that some of the best candidates will get offers elsewhere before we even get to interview them.  The one good thing about the awful job market for assistant professors right now is that it is actually fairly unlikely that more than one or two of our top candidates will be getting competing offers, so we should still be able to hire someone even with the bureaucratic delays.

Unfortunately, I’ll be missing the committee meeting where the reduction to 6 candidates will occur—I’m flying down to LA tomorrow to judge in the California State Science Fair Monday and Tuesday).  I’ve sent the rest of the committee my shorter list, and asked them to send me names of any of their favorites that I did not include, so that I can look at them again and see whether I missed someone we should be interviewing.

…
5. I don’t know a single instructor of writing who enjoys grading.

6. At the same time, the only way, and I mean the only way, to develop a relationship with one’s students is to read and respond to their work. Automated grading is supposed to “free” the instructor for other tasks, except there is no more important task. Grading writing, while time-consuming and occasionally unpleasant, is simply the price of doing business.

7. The only motivations for even experimenting [with], let alone embracing, automated grading of student writing are business-related.

…
12. The second most misguided statement in the New York Times article covering the EdX announcement is this from Anant Argawal, “There is a huge value in learning with instant feedback. Students are telling us they learn much better with instant feedback.” This statement is misguided because instant feedback immediately followed by additional student attempts is actually antithetical to everything we know about the writing process. Good writing is almost always the product of reflection and revision. The feedback must be processed, and only then can it be implemented. Writing is not a video game.

…

14. The most misguided statement in the Times article is from Daphne Koller, the founder of Coursera: “It allows students to get immediate feedback on their work, so that learning turns into a game, with students naturally gravitating toward resubmitting the work until they get it right.”

15. I’m sorry, that’s not misguided, it’s just silly.

…22. The purpose of writing is to communicate with an audience. In good conscience, we cannot ask students to write something that will not be read. If we cross this threshold, we may as well simply give up on education. I know that I won’t be involved. Let the software “talk” to software. Leave me out of it.

I pulled out the points that resonated most for me, but I recommend reading the whole of John Warner’s post.

Lecture Course

Undergraduate Supplemental Sheet
Information to accompany Request for Course Approval
Sponsoring Agency: Biomolecular Engineering
Course #: 101
Catalog Title: Applied Circuits for Bioengineers

Please answer all of the following questions using a separate sheet for your response.
1. Are you proposing a revision to an existing course? If so give the name, number, and GE designations (if applicable) currently held.

This is not a revision to any existing course.A prototype version of the course was run as BME 194 Group Tutorial in Winter 2013. Notes on the design and daily running of that prototype can be found at https://gasstationwithoutpumps.wordpress.com/circuits-course-table-of-contents

2. In concrete, substantive terms explain how the course will proceed. List the major topics to be covered, preferably by week.

The Applied Circuits course is centered around the labs in the accompanying lab course.  Concepts are taught as needed for the labs, with design and analysis exercises in the lecture course cementing the understanding. The recurring theme throughout the course is voltage dividers: for change of voltage, for current-to-voltage conversion, for high-pass and low-pass RC filters, in Wheatstone bridges, and as feedback circuits in op amp circuits.  The intent of this course is to provide substantial design experience for bioengineering students early in their studies, and to serve both as as bridge course to entice students into the bioelectronics concentration and as a terminal electronics course for those students focussing on other areas.

1. Basic DC circuit concept review: voltage current, resistance, Kirchhoff’s Laws, Ohm’s Law, voltage divider, notion of a transducer.
   The first week should cover all the concepts needed to do the thermistor lab successfully.
2. Models of thermistor resistance as a function of temperature. Voltage and current sources, AC vs DC, DC blocking by capacitors, RC time constant, complex numbers, sine waves, RMS voltage, phasors. The second week should cover all the concepts needed to do the electret microphone lab successfully.
3. Low-pass and high-pass RC filters as voltage dividers, Bode plots. Concepts necessary for properly understanding digitized signals: quantized time, quantized voltage, sampling frequency, Nyquist frequency, aliasing.
4. Amplifier basics: op amps, AC coupling, gain computation, DC bias for single-power-supply offsets, bias source with unity-gain amplifier.  In the lab, students will design, build, and test a low-gain amplifier (around 5–10 V/V) for audio signals from an electret microphone. We’ll also include a simple current-amplifier model of a bipolar transistor, so that they can increase the current capability of their amplfier.
5. Op amps with feedback that has complex impedance (frequency-dependent feedback), RC time constants, parallel capacitors, hysteresis, square-wave oscillator using Schmitt triggers, capacitance-output sensors, capacitance-to-frequency conversion.   Topics are selected to support students designing a capacitive touch sensor in the accompanying lab.
6. Phototransistors and FETs for the tinkering lab and for the class-D amplifier lab. In preparation for the lab in which students model a pair of electrodes as R+(C||R), we will need a variety of both electronics and electrochemistry concepts: variation of parameters with frequency, impedance of capacitors, magnitude of impedance, series and parallel circuits, limitations of R+(C||R) model, and at least a vague understanding of half-cell potentials for the electrode reactions: Ag → Ag⁺ + e^-, Ag⁺ + Cl^- → AgCl, Fe + 2 Cl^-→ FeCl₂ + 2 e^-.
7. Differential signals, twisted-pair wiring to reduce noise, strain gauge bridges, instrumentation amplifier, DC coupling, multi-stage amplifiers.
   Topics are selected to support the design of a 2-stage amplifier for a piezoresistive pressure sensor in the lab.
8. System design, comparators, more on FETs. Students will design a class-D power amplifier to implement in the lab.
9. A little electrophysiology: action potentials, electromyograms, electrocardiograms. Topics are chosen so that students can design a simple 3-wire electrocardiogram (EKG) in the lab.There will also be a bit more development of simple (single-pole) filters.
10. The last week will be review and special topics requested by the students.

3. Systemwide Senate Regulation 760 specifies that 1 academic credit corresponds to 3 hours of work per week for the student in a 10-week quarter. Please briefly explain how the course will lead to sufficient work with reference to e.g., lectures, sections, amount of homework, field trips, etc. [Please note that if significant changes are proposed to the format of the course after its initial approval, you will need to submit new course approval paperwork to answer this question in light of the new course format.]

The combination of BME101 and BME101L is 7 units (21 hours per week).  The time will be spent approximately as follows:

• 3.5 hours lecture/discussion
• 3.5 hours reading background and circuits text
• 3 hours read lab handouts and doing pre-lab design activities
• 6 hours lab
• 5 hours writing design reports for lab

4. Include a complete reading list or its equivalent in other media.

No existing book covers all the material.  For the prototype run of the course, we relied heavily on Wikipedia articles, which turned out to be too dense for many of the students.  Other alternatives (such as Op amps for everyone by Ron Mancini http://www.e-booksdirectory.com/details.php?ebook=1469 Chapters 1–6 and Op Amp Applications Handbook by Analog Devices http://www.analog.com/library/analogDialogue/archives/39-05/op_amp_applications_handbook.html Sections 1-1 and 1-4) were also much too advanced.

In future we will most likely use the free on-line text All about Circuits as the primary text, with material not covered there (such as the various sensors) coming mainly from Wikipedia and the datasheets for the components.

5. State the basis on which evaluation of individual students’ achievements in this course will be made by the instructor (e.g., class participation, examinations, papers, projects).

Students will be evaluated primarily on design reports with some in-class or take-home quizzes to ensure that they do the needed reading on theoretical concepts.

6. List other UCSC courses covering similar material, if known.

EE 101 covers some of the same circuit material, but without the focus on sensors and without instrumentation amps.  It covers linear circuit theory in much more depth and focuses on mathematical analysis of complicated linear circuits, rather than on design with simple circuits.  The expectation for bioengineering students is that those in the bioelectronics track would take BME 101 before taking EE101, and that those in other tracks would take BME 101 as a terminal electronics course providing substantial engineering design.  The extra material in BME 101 would prepare the bioengineering students better for EE 101.

Physics 160 offers a similar level of practical electronics, but focuses on physics applications, rather than on bioengineering applications, and is only offered in alternate years.

7. List expected resource requirements including course support and specialized facilities or equipment for divisional review. (This information must also be reported to the scheduling office each quarter the course is offered.)

The lecture part of the course needs no special equipment—a standard media-equipped classroom with a whiteboard, screen, and data projector should suffice. Having a portable laptop-connected oscilloscope would make demos much easier to do, but is not essential.

The lecture course is not really separable from the associated lab course,whose equipment needs are described on the supplemental sheet for that course.

The course requires a faculty member (simultaneously teaching the co-requisite Applied Circuits lab course) and a teaching assistant or undergraduate group tutor for discussion sections and assistance in grading.  The same TA/group tutor should be used for both the lecture and the lab courses.

8. If applicable, justify any pre-requisites or enrollment restrictions proposed for this course. For pre-requisites sponsored by other departments/programs, please provide evidence of consultation.

Students will be required to have single-variable calculus and a physics electricity and magnetism course. Both are standard prerequisites for any circuits course. Although DC circuits can be analyzed without calculus, differentiation and integration are fundamental to AC analysis. Students should have already been introduced to the ideas of capacitors and inductors and to serial and parallel circuits.

The prerequisite courses are already required courses for biology majors and bioengineering majors, so no additional impact on the courses is expected.

9. Proposals for new or revised Disciplinary Communication courses will be considered within the context of the approved DC plan for the relevant major(s). If applicable, please complete and submit the new proposal form (http://reg.ucsc.edu/forms/DC_statement_form.doc or http://reg.ucsc.edu/forms/DC_statement_form.pdf) or the revisions to approved plans form (http://reg.ucsc.edu/forms/DC_approval_revision.doc or http://reg.ucsc.edu/forms/DC_approval_revision.pdf).

This course is not expected to contribute to any major’s disciplinary communication requirement, though students will get extensive writing practice in the design reports (writing between 50 and 100 pages during the quarter).

10. If you are requesting a GE designation for the proposed course, please justify your request making reference to the attached guidelines.

No General Education code is proposed for this course, as all relevant codes will have already been satisfied by the prerequisites.

11. If this is a new course and you requesting a new GE, do you think an old GE designation(s) is also appropriate? (CEP would like to maintain as many old GE offerings as is possible for the time being.)

No General Education code is proposed for this course, as all relevant codes (old or new) will have already been satisfied by the prerequisites.

Lab course

Undergraduate Supplemental Sheet
Information to accompany Request for Course Approval
Sponsoring Agency Biomolecular Engineering
Course # 101L
Catalog Title Applied Circuits Lab

Please answer all of the following questions using a separate sheet for your response.
1. Are you proposing a revision to an existing course? If so give the name, number, and GE designations (if applicable) currently held.

This is not a revision to any existing course. A prototype version of the course was run as BME 194F Group Tutorial in Winter 2013. Notes on the design and daily running of that prototype can be found at https://gasstationwithoutpumps.wordpress.com/circuits-course-table-of-contents

2. In concrete, substantive terms explain how the course will proceed. List the major topics to be covered, preferably by week.

The course is a lab course paired with BME 101, Applied Circuits for Bioengineers.  The labs have been designed to be relevant to bioengineers and to have as much design as is feasible in a first circuits course.  The labs are the core of the course, with lecture/discussion classes to support them. There will be six hours of lab a week, split into 2 3-hour sessions. Lab assignments will generally take two lab sessions, with data collection in the first lab session, and data analysis and design between lab sessions.   Some of the more straightforward labs will need only a single session.  Except for the first intro lab, these labs have been used in the prototype run of the class as 3-hour labs.  Most did not fit in one 3-hour lab session and would benefit from being split into two separate lab sessions with data analysis and design between the sessions.

1. Intro to parts, tools, and lab equipment (single session)
2. Thermistor
3. Microphone
4. Sampling and aliasing (single session)
5. Audio amp
6. Hysteresis oscillator and soldering lab
7. FET and phototransistor
8. Electrode modeling
9. Pressure sensor and instrumentation amp (soldered)
10. Class-D power amplifier
11. EKG (instrumentation amp with filters, soldered)

1. Intro to parts, tools, and lab equipment
   Students will learn about the test equipment by having them use the multimeters to measure other multimeters. What is the resistance of a multimeter that is measuring voltage? of one that is measuring current? what current or voltage is used for the resistance measurement? Students will be  issued their parts and tool kits, learn to use the wire strippers and make twisted-wire cables for the power supplies to use all quarter.  They will learn to set the current limits on the power supplies and  measure voltages and currents for resistor loads around 500Ω.  This lab will not require a written lab report.
   Lab skills developed: wire strippers, multimeter for measuring voltage and current, setting bench power supply
   Equipment needed: multimeter, power supply
2. Thermistor lab
   The thermistor lab will have two lab sessions involving the use of a Vishay BC Components NTCLE413E2103F520L thermistor or equivalent.
For the first lab session, the students will use a bench multimeter to measure the resistance of the thermistor, dunking it in various water baths (with thermometers in them to measure the temperature). They should fit a simple curve to this data based on standard thermistor models. A class period will be spent on learning both the model and how to do model fitting with gnuplot, and there will be a between-lab exercise where they derive the formula for maximizing | dV/dT | in a voltage divider that converts the resistance to a voltage.
   For the scond lab session, they will add a series resistor to make a voltage divider. They have to choose a value to get as large and linear a voltage response as possible at some specified “most-interesting” temperature (perhaps body temperature, perhaps room temperature, perhaps DNA melting temperature).  They will then measure and plot the voltage output for another set of water baths. If they do it right, they should get a much more linear response than for their resistance measurements. 
Finally, they will hook up the voltage divider to an Arduino analog input and record a time series of a water bath cooling off (perhaps adding an ice cube to warm water to get a fast temperature change), and plot temperature as a function of time.
   Lab skills developed: use of multimeter for measuring resistance and voltage, use of Arduino with data-acquisition program to record a time series, fitting a model to data points, simple breadboarding.Equipment needed: multimeter, power supply, thermistor, selection of various resistors, breadboard, clip leads, thermoses for water baths, secondary containment tubs to avoid water spills in the electronics lab. Arduino boards will be part of the student-purchased lab kit. All uses of the Arduino board assume connection via USB cable to a desktop or laptop computer that has the data logger software that we will provide.
3. Electret microphone
   First, we will have the students measure and plot the DC current vs. voltage for the microphone. The microphone is normally operated with a 3V drop across it, but can stand up to 10V, so they should be able to set the Agilent E3631A  bench power supply to various values from 0V to 10V and get the voltage and current readings directly from the bench supply, which has 4-place accuracy for both voltage and current. Ideally, they should see that the current is nearly constant as voltage is varied—nothing like a resistor.  They will follow up the hand measurements with automated measurements using the Arduino to measure the voltage across the mic and current through it for voltages up to about 4v.  The FET in the microphone shows a typical exponential I vs. V characteristic below threshold, and a gradually increasing current as voltage increases in the saturation region.  We’ll do plotting and model fitting in the data analysis class between the two labs.
   Second, we will have them do current-to-voltage conversion with a 5v power supply and a resistor to get a 1.5v DC output from the microphone and hook up the output of the microphone to the input of the oscilloscope. Input can be whistling, talking, iPod earpiece, … . They should learn the difference between AC-coupled and DC-coupled inputs to the scope, and how to set the horizontal and vertical scales of the scope. They will also design and wire their own DC blocking RC filter (going down to about 1Hz), and confirm that it has a similar effect to the AC coupling on the scope. Fourth, they will play sine waves from the function generator through a loudspeaker next to the mic, observe the voltage output with the scope, and measure the AC voltage with a multimeter, perhaps plotting output voltage as a function of frequency. Note: the specs for the electret mic show a fairly flat response from 50Hz to 3kHz, so most of what the students will see here is the poor response of a cheap speaker at low frequencies.
   EE concepts: current sources, AC vs DC, DC blocking by capacitors, RC time constant, sine waves, RMS voltage, properties varying with frequency.Lab skills: power supply, oscilloscope, function generator, RMS AC voltage measurement.Equipment needed: multimeter, oscilloscope, function generator, power supply, electret microphone, small loudspeaker, selection of various resistors, breadboard, clip leads.
4. Sampling and Aliasing
   Students will use the data logger software on the Arduino to sample sine waves from a function generator at different sampling rates.  They will need to design a high-pass RC filter to shift the DC voltage from centered at 0 to centered at 2.5v in the middle of the Arduino A-to-D converter range.  They will also design a low-pass filter (with corner frequency below the Nyquist frequency) to see the effect of filtering on the aliasing.
   EE concepts: quantized time, quantized voltage, sampling frequency, Nyquist frequency, aliasing, RC filters.
   Equipment needed:  function generator, Arduino board, computer.
5. Audio amplifier
   Students will use an op amp to build a simple non-inverting audio amplifier for an electret microphone, setting the gain to around 6 or 7. The amplifier will need a a high-pass filter to provide DC level shifting at the input to the amplifier. Note that we are using single-power-supply op amps, so they will have to design a bias voltage supply as well. The output of the amplifier will be recorded on the Arduino (providing another example of signal aliasing).
   The second half of the lab will add a single bipolar transistor to increase the current and make a class A output stage for the amplifier, as the op amp does not provide enough current to drive the 8Ω loudspeaker loudly.
   EE concepts: op amp, DC bias, bias source with unity-gain amplifier, AC coupling, gain computation.
   Lab skills: complicated breadboarding (enough wires to have problems with messy wiring). If we add the Arduino recording, we could get into interesting problems with buffer overrun if their sampling rate is higher than the Arduino’s USB link can handle.
   Equipment needed: breadboard, op amp chip, assorted resistors and capacitors, electret microphone, Arduino board, optional loudspeaker.
6. Hysteresis and capacitive touch sensor
   For the first half of the lab, students will characterize a Schmitt trigger chip, determining V_IL, V_IH, V_OL, and V_OH. Using these properties, they will design an RC oscillator circuit with a specified period or pulse width (say 10μs), and measure the frequency and pulse width of the oscillator.
   For the second half of the lab, the students will build a relaxation oscillator whose frequency is dependent on the parasitic capacitance of a touch plate, which the students can make from Al foil and plastic food wrap. In addition to breadboarding, students will wire this circuit by soldering wires and components on a PC board designed for the oscillator circuit. Students will have to measure the frequency of the oscillator with and without the plate being touched. We will provide a simple Arduino program that is sensitive to changes in the pulse width of the oscillator and that turns an LED on or off, to turn the frequency change into an on/off switch.  Students will treat the oscillator board as a 4-terminal component, and examine the effect of adding resistors or capacitors between different terminals.
   EE concepts: frequency-dependent feedback, oscillator, RC time constants, parallel capacitors.
   Lab skills: soldering, frequency measurement with digital scope.
   Equipment needed: Power supply, multimeter, Arduino, clip leads, amplifier prototyping board, oscilloscope.
7. Phototransistor and FET
   First half: characterize phototransistor in ambient light and shaded.  Characterize nFET and pFET.
   Second half: students will “tinker” with the components they have to produce a light-sensitive, noise-making toy.
   EE concepts: phototransistors, FETs.
   Equipment needed: breadboard, phototransistor, power FETs, loudspeaker, hysteresis oscillator from previous lab, oscilloscope.
8. Electrode measurements
   First, we will have the students attempt to measure the resistance of a saline solution using a pair of stainless steel electrodes and a multimeter. This should fail, as the multimeter gradually charges the capacitance of the electrode/electrolyte interface.Second, the students will use a function generator driving a voltage divider with a load resistor in the range 10–100Ω. The students will measure the RMS voltage across the resistor and across the electrodes for different frequencies from 3Hz to 300kHz (the range of the AC measurements for the Agilent 34401A Multimeter). They will plot the magnitude of the impedance of the electrodes as a function of frequency and fit an R2+(R1||C1) model to the data, most likely using gnuplot. There will be a prelab exercise to set up plotting of the model and do a little hand tweaking of parameters to help them understand what each parameter changes about the curve.Third, the students will repeat the measurements and fits for different concentrations of NaCl, from 0.01M to 1M. Seeing what parameters change a lot and what parameters change only slightly should help them understand the physical basis for the electrical model.Fourth, students will make Ag/AgCl electrodes from fine silver wire. The two standard methods for this involve either soaking in chlorine bleach or electroplating. To reduce chemical hazards, we will use the electroplating method. As a prelab exercise, students will calculate the area of their electrodes and the recommended electroplating current.  In the lab, they will adjust the voltage on the bench supplies until they get the desired plating current.Fifth, the students will measure and plot the resistance of a pair of Ag/AgCl electrodes as a function of frequency (as with the stainless steel electrodes).Sixth, if there is time, students will measure the potential between a stainless steel electrode and an Ag/AgCl electrode.EE concepts: magnitude of impedance, series and parallel circuits, variation of parameters with frequency, limitations of R+(C||R) model.Electrochemistry concepts: At least a vague understanding of half-cell potentials, current density, Ag → Ag⁺ + e^-, Ag⁺ + Cl^- → AgCl, Fe + 2 Cl^-→ FeCl₂ + 2 e^-.Lab skills: bench power supply, function generator, multimeter, fitting functions of complex numbers, handling liquids in proximity of electronic equipment.Equipment needed: multimeter, function generator, power supply, stainless steel electrode pairs, silver wires, frame for mounting silver wire, resistors, breadboard, clip leads, NaCl solutions in different concentrations, beakers for salt water, secondary containment tubs to avoid salt water spills in the electronics lab.
9. Pressure sensor and instrumentation amplifier
   Students will design an instrumentation amplifier with a gain of 300 or 500 to amplify the differential strain-gauge signal from a medical-grade pressure sensor (the Freescale MPX2300DT1), to make a signal large enough to be read with the Arduino A/D converter. The circuit will be soldered on the instrumentation amp/op amp protoboard. The sensor calibration will be checked with water depth in a small reservoir. Note: the pressure sensor comes in a package that exposes the wire bonds and is too delicate for student assembly by novice solderers. We will make a sensor module that protects the sensor and mounts the sensor side to a 3/4″ PVC male-threaded plug, so that it can be easily incorporated into a reservoir, and mounts the electronic side on a PC board with screw terminals for connecting to student circuits.  This sensor is currently being prototyped, and if it turns out to be too fragile, we will use a Freescale MPX2050GP, which has a sturdier package, but is slightly less sensitive and more expensive. (It also isn’t made of medical-grade plastics, but that is not important for this lab.) Note that we are deliberately notusing pressure sensors with integrated amplifiers, as the pedagogical point of this lab is to learn about instrumentation amplifiers.EE concepts: differential signals, twisted-pair wiring, strain gauge bridges, instrumentation amplifier, DC coupling, gain.Equipment needed: Power supply, amplifier prototyping board, oscilloscope, pressure sensor mounted in PVC plug with breakout board for easy connection, water reservoir made of PVC pipe, secondary containment tub to avoid water spills in electronics lab.
10. Class-D power amplifier
11. Electrocardiogram (EKG)
    Students will design and solder an instrumentation amplifier with a gain of 2000 and bandpass of about 0.1Hz to 100Hz. The amplifier will be used with 3 disposable EKG electrodes to display EKG signals on the oscilloscope and record them on the Arduino.Equipment needed: Instrumentation amplifier protoboard, EKG electrodes, alligator clips, Arduino, oscilloscope.

3. Systemwide Senate Regulation 760 specifies that 1 academic credit corresponds to 3 hours of work per week for the student in a 10-week quarter. Please briefly explain how the course will lead to sufficient work with reference to e.g., lectures, sections, amount of homework, field trips, etc. [Please note that if significant changes are proposed to the format of the course after its initial approval, you will need to submit new course approval paperwork to answer this question in light of the new course format.]

The combination of BME101 and BME101L is 7 units (21 hours per week).  The time will be spent approximately as follows:

• 3.5 hours lecture/discussion
• 3.5 hours reading background and circuits text
• 3 hours read lab handouts and doing pre-lab design activities
• 6 hours lab
• 5 hours writing design reports for lab

4. Include a complete reading list or its equivalent in other media.

Lab handouts: there is a 5- to 10-page handout for each week’s labs, giving background material and design goals for the lab, usually with a pre-lab design exercise.  The handouts from the prototype run of the course can be found at http://users.soe.ucsc.edu/~karplus/bme194/w13/#labs
Data sheets: Students will be required to find and read data sheets for each of the components that they use in the lab.  All components are current commodity components, and so have data sheets easily found on the web.  Other readings are associated with the lecture course.

5. State the basis on which evaluation of individual students’ achievements in this course will be made by the instructor (e.g., class participation, examinations, papers, projects).

Students will be evaluated on in-lab demonstrations of skills (including functional designs) and on the weekly lab write-ups.

6. List other UCSC courses covering similar material, if known.

CMPE 167/L (sensors and sensing technologies) covers some of the same sensors and design methods, but at a more advanced level.  BME 101L would be excellent preparation for the CMPE 167/L course.

Physics 160 offers a similar level of practical electronics, but focuses on physics applications, rather than on bioengineering applications, and is only offered in alternate years.

7. List expected resource requirements including course support and specialized facilities or equipment for divisional review. (This information must also be reported to the scheduling office each quarter the course is offered.)

The course will need the equipment of a standard analog electronics teaching lab: power supply, multimeter, function generator,  oscilloscope,  computer, and soldering irons. The equipment in Baskin Engineering 150 (commonly used for EE 101L) is ideally suited for this lab. There are 12 stations in the lab, providing a capacity of 24 students since they work in pairs rather than as individuals.  The only things missing from the lab stations are soldering irons and circuit board holders (such as the Panavise Jr.), a cost of about $45 per station.  Given that a cohort of bioengineering students is currently about 35–40 students, two lab sections would have to be offered each year.

In addition, a few special-purpose setups will be needed for some of the labs, but all this equipment has already been constructed for the prototype run of the course.

There are a number of consumable parts used for the labs (integrated circuits, resistors, capacitors, PC boards, wire, and so forth), but these are easily covered by standard School of Engineering lab fees.  The currently approved lab fee is about $131, but may need some adjustment to change exactly what tools and parts are included, particularly if the students are required to buy their own soldering irons (a $20 increase).

The course requires a faculty member (simultaneously teaching the co-requisite Applied Circuits course) and a teaching assistant (for providing help in the labs and for evaluating student lab demonstrations). Because the lab is such a core part of the combined course, it requires faculty presence in the lab, not just coverage by TAs or group tutors.

8. If applicable, justify any pre-requisites or enrollment restrictions proposed for this course. For pre-requisites sponsored by other departments/programs, please provide evidence of consultation.

Students will be required to have single-variable calculus and a physics electricity and magnetism course. Both are standard prerequisites for any circuits course. Most of the labs can be done without calculus, but it is essential for the accompanying lecture course.

9. Proposals for new or revised Disciplinary Communication courses will be considered within the context of the approved DC plan for the relevant major(s). If applicable, please complete and submit the new proposal form (http://reg.ucsc.edu/forms/DC_statement_form.doc or http://reg.ucsc.edu/forms/DC_statement_form.pdf) or the revisions to approved plans form (http://reg.ucsc.edu/forms/DC_approval_revision.doc or http://reg.ucsc.edu/forms/DC_approval_revision.pdf).

This course is not expected to contribute to any major’s disciplinary communication requirement, though students will get extensive writing practice in the design reports (writing between 50 and 100 pages during the quarter).

10. If you are requesting a GE designation for the proposed course, please justify your request making reference to the attached guidelines.

No General Education code is proposed for this course, as all relevant codes will have already been satisfied by the prerequisites.

11. If this is a new course and you requesting a new GE, do you think an old GE designation(s) is also appropriate? (CEP would like to maintain as many old GE offerings as is possible for the time being.)

No General Education code is proposed for this course, as all relevant codes (old or new) will have already been satisfied by the prerequisites.

• Teen Production: Ferris Bueller’s Day Off (2013 June 17–2013 July 7)
• Actors Conservatory with Theatre Témoin: WEST meets London’s West End – Devising An Original Play (2013 July 8–2013 July 19)
• Teen Conservatory w/Shakespeare Santa Cruz; Scene Study Intensive (2013 August 5–2013 August 17)

That’s almost 7 weeks of acting (July 4 is a holiday), at 5–7 hours a day, 5–6 days a week (the teen production is longer, but less intense than the two conservatories).  The acting classes will cost $2265 (plus tickets to see the shows), or a bit less than $10/hour for the classes.

All three of the classes are scheduled to be in the West End Studio Theatre, which I’ve not seen since they did some remodeling in the fall (all his productions during the school year have been in the Broadway Playhouse, an older, smaller space). The West End Studio Theatre is much more flexible, as it is basically just warehouse space, and they can set up the stage and audience seating in various configurations—they even did arena seating for Hunger Games last year.

The collaborations with Shakespeare Santa Cruz have been going on for several years, but the one with Theatre Témoin is new.  I think that it will be good for my son to learn the more physical style of acting that they will be teaching, as he tends to focus more on the verbal part of acting than on his physical acting (not exclusively, of course).

The computer continued to work that day, but the next time I tried to restart it, it wouldn’t boot up.  I took it down to the local computer shop on Monday, where I found out that the extended warranty had expired 6 months ago, so I had to pay labor as well as materials for replacing the disk, which ended up costing me $376 (as long as they were replacing the drive, I had them upgrade from a 500GB drive to a 1TB drive, since I was running out of disk space).  When I finally got the computer back on Saturday, I spent most of the day restoring the system from my backup drive.  Now I need to replace the backup drive, since Time Machine complains that the old drive does not have enough space to do a full backup.  It looks like that will cost me another $100–$150 for a 1–2TB backup drive.  I don’t have many choices of drive, since I need a Firewire 800 interface (my old MacBook Pro does not have USB 3 or Thunderbolt). So my moment’s carelessness cost me the use of my laptop for a week and about $500.

After having confirmed that the magnetometer was ok, we did a rough calculation of how strong the field from the coil should be, to see whether we ought to be detecting. (I know, we should have done that first.)  We were running about 33mA through a coil of 5 turns with a diameter of about 4.4cm. Using the formula , with I=0.033A, R=0.022m, and N=5, I computed that the magnetic field right at the center of the coil (z=0m) should be 4.7µT,  at 1cm (about as close as I could get the magnetometer) it should be 3.6µT, and at 2cm (where the measurements were being attempted) the field should be about 1.9µT.  The magnetometer has a resolution of 0.1µT per count, but the noise level was high enough that counts of 20 (2µT) would have been barely detectable. I suspect that a lot of the noise was because we had not immobilized the magnetometer.  According to the World Magnetic model, as displayed in Wikipedia, we should have about 49µT at a 60° inclination due to Earth’s field, so changes in orientation of 1° in the magnetometer would causes changes of about 0.9µT.

We’ll repeat the experiment (without having a strong magnet near the laptop!) using more current (say 300mA), more turns (40), and a smaller radius (a diameter of 1.25cm). With those values, we should be able to get a field of  1.2mT at the center of the coil, 180µT at 1cm, 32µT at 2cm, and 11µT at 3cm. We’ll also immobilize the magnetometer in my plastic-jawed Panavise, and make measurements by subtracting the field with the current off from the field with the current on. We may even double the signal by subtracting the field with the current in one direction from the field with the current in the opposite direction.

I occasionally answer biology questions on the forum also, though my biology has an even shakier foundation: one freshman bio course, one junior-level biochem class (without the prerequisite general and o. chem), one graduate protein structure class—again, I have to rely heavily on things I’ve heard from colleagues or seen on the internet.  I feel like a real imposter answering bio questions on the AP Bio teachers’ forum, since everyone else on the forum has had far more courses in biology than I ever will. I doubt that I have the knowledge to teach even an 8th-grade life science course, much less an AP bio course.

While I’m always willing to share what I know, I frequently have gaps in my understanding that I’m not even aware of.

Of course, I’ve gotten used to teaching things I’ve had to teach myself—several of the courses I’ve created have been in subjects where I had had no formal instruction:

• applied circuits for bioengineers (2013)
• technical writing (1987–1999)
• digital typography course (1996–1998). Just this month I met an alumnus of that course, who got into graphical design, then web design and programming as a result of that course—he regrets that he did not take any other computer courses in college.
• bicycle transportation engineering (1997)
• bioinformatics: models and algorithms (our core grad bioinformatics course, 1998–present)
• protein structure prediction (1996–2011)
• banana slug genomics (2010, 2011)
• how to be a grad student (1990–present)
• resource-efficient programming (2004)

Other courses I’ve created after only one prior course:

• VLSI design (1982–2000)
• digital synthesis of music (1989 and 1991)

For that matter, my first faculty position was a joint appointment between an EE and a CS department, teaching mainly EE courses, based on having a CS PhD and having taken 3 EE courses (digital logic, microprocessors, and VLSI design).

Of course, I’ve also taught several courses designed by others, often with little prior training in the field.  I find that more difficult than teaching a course that I’ve designed myself, even if it takes me six months or more to teach myself the material before designing a course (as with the circuits course).

Because so much of what I’ve taught is material that I’ve had to teach myself, I tend to take a different approach to teaching than many other faculty.  I see my role as trying to provide guidance for students to learn the material faster than I did, with less time chasing down blind alleys, not to just dump some pre-digested knowledge into their heads for them to memorize and regurgitate. I don’t teach them as I’ve been taught, but as how I wish I had been taught.  I tend to pose them problems to guide their learning, rather than giving them information, then expecting them to repeat it back to me. (I’m self-taught in pedagogy also, but that is normal for university faculty.)

I want them to learn skills (not facts) that can serve them as a basis for further learning—for example, in the circuits course, I wanted the students to be able to design and build simple amplifier circuits and to be able to write design reports.  I didn’t care so much whether they could work book problems as that they acquired the mental attitudes of engineers—that they could design and build things, that data sheets are worth consulting, that precise and accurate recording of what was designed and measured is essential, that often you have to check things for yourself (not blindly trusting the data sheets or simple models), that consistency and sanity checks are an important part of any problem solving, that breaking a problem into subproblems is an essential element of design in any engineering field, and so forth.  (I think they got some of that, but it takes more than 10 weeks for the attitudes to really become part of their worldview.)

I think that the flattery on the AP Bio teachers’ forum was to soften me up to mentor a bright high school student that the teacher knew.  I’m willing to serve as a mentor for smart and motivated kids interested in bioinformatics, but not in other branches of biology—I just don’t know enough in those fields to guide anyone.  Even in bioinformatics, I don’t find it easy to guide students below a certain level of training—I have a few programming projects I could use student help on, but I don’t have many ideas for students who aren’t already expert programmers.

I have one pending request from a high school student wanting to do computational protein work in my lab this summer—something I don’t really do any more.  I have no idea what to tell her—10 years ago, I had an active lab that I could have worked her into, but with the repeated failure of grant requests and my subsequent disillusionment with the whole grant rat race, I no longer have a lab. I’m now working more as a consultant on other people’s research (helping out with statistics, signal processing, genome assembly, and other things I’m self-taught in) and putting most of my time into teaching and creating new courses.

Stanford offered admission to 2,210 students via electronic notification today, producing – at 5.69 percent – the lowest admit rate in University history.

…

On Thursday, several peer institutions also reported historically low admit rates. Harvard, Yale, Columbia and Princeton admitted 5.8, 6.72, 6.89 and 7.29 percent of applicants respectively.

via Stanford Daily | Class of 2017 admit rate marks record low at 5.7 percent.

Another site gives the MIT rate for the class of 2017 as 8.2% (1548/18989), another record low.

Since many of the schools that would be a good fit for my son are highly selective, and it seems to be almost a lottery who gets in, it looks like he’ll need to apply to just about every college that might be a good fit, plus a couple of poorer-fit “safety schools” to raise his expected number of acceptances to more than 1. Ideally, I think that he’ll want an expected number of acceptances around 3, in order to have some choice and to keep the probability of zero acceptances low enough.  It looks like that means about a dozen super selective schools (like MIT), a handful of selective schools (like UC Berkeley), and a couple of less selective schools (Cal Poly?).  That means a lot of college application essay writing for him this summer!

I’ll have to put in some time this summer putting together the “school profile”, “guidance counselor,” and “home school supplement” parts of the application, to try to make it clear to the admissions officers what his education has been.

Early applicants seem to be accepted at much higher rate than the overall pool (14–30%, so 2–3 times higher).  Is this because the earlier applicants are better? because they have to commit sooner? because they are offered less financial aid? because they have to commit without seeing the financial aid terms?

If the early admissions process is still only a 1-in-5 lottery, I can’t see betting everything on one school—but does the process allow multiple early admissions, or do you have to commit to one school?  It seems that some programs (like MIT and Caltech) are non-restrictive early action: a student can apply to multiple early-action programs.  Others, like Yale and Stanford are restrictive single-choice early action—you don’t have to accept their offer, but you can’t apply to other early-action programs. Still others are early decision: you may only apply to one program and are committed to accepting whatever offer they make if they accept you.  (Note: I did not check the programs at the schools—the information comes from Wikipedia, and certainly needs to be checked with a more authoritative source—it may all be different next fall anyway, as admissions offices have gone back and forth on the worth of early-admissions programs.)

The College Board has a reasonably informative discussion on the advantages and disadvantages of early decision and early action programs.

It seems to me that my son should apply early for the non-restrictive early action programs that look like a good fit, but not for single-choice early action or early decision programs, unless he really falls in love with just one school (which seems unlikely at the moment).  If nothing else, having some applications due for an early deadline and some for a later one will help him spread out the workload of preparing all the applications.

Giggle-inducing Scientific Terminology. Uranus, excited state, naked singularity, panspermia, ram pressure, Trojans, black hole, galactic bulge, hadron, space probe, parsecs, and 21-centimeter emission, to name a few. These are not “dirty words.” They are official scientific terms and we will need to use them in class.

The post as a whole mocks the anti-science attitude of the Dietrich, Idaho parents who protested a 10th grade biology teacher using the word “vagina” in the unit about reproduction. [http://www.washingtontimes.com/news/2013/mar/28/idaho-teacher-under-investigation-way-he-teaches-h/]

I guess that Idaho is racing Kansas to become the most anti-science state in the United States.

1. 1 year World History, 1 year US history  (world history at home in 10th grade, US history at AFE in 11th)
2. 4 years English (9th grade English didn’t happen, so we had to overload up in 10th and 11th grade)
3. 3 years Math (Art of Problem Solving Precalculus and Calculus, Mathematical Problem Solving at UCSC, Applied Discrete Math at UCSC)
4. 2–3 years science (Physiology in 9th, calculus-based physics in 10th and 11th, probably chemistry next year in 12th)
5. 2 years foreign language (Spanish, through Spanish 3 at Cabrillo College, possibly through Spanish 4 next year)
6. 1 year visual and performing arts (9th grade drama class, continuing theater classes at WEST performing arts)
7. 1 year elective (various computer science and robotics projects, including the Art of Problem Solving Java course)

Unfortunately, only a few of these courses are officially “UC-approved”, so the University needs to have them validated some other way (see my previous post on a–g courses).  The English (requirement b) is validated by his SAT score on the reading section, math (requirement c) is validated by his SAT 2 and AP Calculus BC scores, the science (requirement d) will be validated by his AP Physics scores and the UC-approved physiology course, the foreign language (requirement e) by his community college Spanish courses, the arts (requirement f) by his high school drama course, and the elective (requirement g) by either the extra math courses at UCSC or his video editing course in 9th grade.

The only courses that are difficult to validate in this way are the history courses, as he is reluctant to take the SAT 2 tests in World History and US History—they are not his strongest subjects.  It might be good for him to take a practice test in them, to see whether he’d do well enough to get UC-level approval (the barrier is fairly low—only 540 on the World history and 550 on the US history).

If he doesn’t meet the letter of the rules for the a–g requirements, even though he exceeds them in spirit, there are other ways into UC: admission by exception and admission by exam.

The admission by exception is a pretty sleazy way into UC, used mainly to recruit athletes with substandard academic records to the big UC schools, and not much used as the poorer but more virtuous smaller campuses.  Since my son is a computer programmer and an actor (and maybe a mathematician and scientist), but not an athlete,  musician, or relative of a powerful politician, there is practically no chance of his getting admission by exception.

That leaves admission by exam, which seems at first glance like the most promising method, since he has no trouble with exams:

To qualify by examination, you must achieve a minimum UC Score total — calculated according to the instructions below — of 410 (425 for nonresidents). In addition, you must earn a minimum UC Score of 63 on each component of the ACT or SAT Reasoning Test and on each SAT Subject Test.

You may not use an SAT Subject Test to meet these requirements if you have completed a transferable college course in that subject with a grade of C or better.

via Admission by exam | UC Admissions.

With the SAT test and one SAT 2 test, he is a little short on the number of points he needs, but even a crummy job on another SAT 2 test (540 or better) will put him well over the threshold.

The last sentence in the quote is a little confusing though.  If he takes the SAT2 in Spanish, which I think he should for admission elsewhere, will that not count for admission by exam? (Probably not, since he took community college Spanish and has not yet taken the SAT 2 exam in Spanish.) After taking the SAT Math 2 test, he took some college math courses at UCSC.  Does his taking those courses suddenly invalidate his SAT2 Math score?  Or would only a college precalc class invalidate the SAT2 Math level 2?  What, exactly, does “in that subject” mean in this context?  Does the timing of the completion of the course and the taking of the exam matter? Or just the completion of the course and the admission to UC?

He’ll probably take a SAT 2 in physics, but not take any transferable college physics next year, so that would be one SAT2 he could count regardless of whimsical bureaucratic interpretation of the vague wording of the requirement.  He’ll need at least one more SAT2, besides math level 2 and physics, since some colleges require 3 SAT 2s.  I think he should take 2 more: US History and Spanish with listening (though we’ll have to buy a “portable CD player with earphones” for the listening test—a minor hassle and about $15 extra expense).  The US History would leave him just one course short of validating all the a–g requirements (still no validation for World History), as well as providing an unassailable set of admission by exam scores.

It may take some convincing to get him to take the US History and Spanish SAT 2s, though as he has little interest in history and he has not done Spanish in almost a year now.  I’d recommend an intensive Spanish summer experience for him before the November SAT2s (an immersion program in Latin America, for example), but he is planning on either 5 or 7 weeks of intensive theater this summer, so I don’t think that there is room to fit an immersion program in Spanish into his schedule.

The first half of the two-hour session was spent as a large group.  Each of the judges who was there (in their new role of coach) introduced themselves briefly, then we went around the room having each student introduce their poster briefly (about 1 minute each).  Then students asked questions about the state competition—about what they could expect, about poster design, about what judges wanted to see, and so forth.  Since I was the only one there who had judged at state, I ended up answering a lot of the questions, but others got in good comments also.

One message that I think we got out this year was that at the state science fair, students tend to use bigger poster boards than is common at the County Science Fair, so that they can put more content on the poster and still use a large enough font to be readable.  (A lot of the posters had tiny fonts suitable only for close reading.)  The construction techniques for two of the larger posters there were shown.  One was just two ordinary science fair tri-fold boards stacked with PVC pipe glued on the back as a stiffener.  It is quite sturdy, but a bit unwieldy even when folded, since it is still 5′–6′ long.  The other was my son’s foam-core board, which is just as big, but folds up small enough to be carried like a suitcase and be checked as luggage (not small enough to be carry-on though.  I’ve provided detailed construction instructions for this design in a previous blog post (though that post shows the previous carrier box, not the new one that fits the board and surrounds it on all 6 sides).

After the group discussion we broke up into one-on-one sessions with the judges circulating around answering questions for whoever had questions for them.  I ended up doing some coaching for two of the students I had judged, plus one who was doing a bioinformatics project.  I also provided less detailed advice to several other students who had questions.  I got a chance to meet some of the students who I had not seen at the county science fair—I think that we have some potential winners at the state fair this year.

Based on the conversations at the coaching session, I think that we’ll see some changes to this year’s projects before state. But even if we don’t, next year’s projects are likely to be stronger, as these students share what they heard with their teachers and fellow students, as well as improving their own projects for next year.

The coaching session worked well enough that I think we should do it again next year—perhaps lengthening it to 2.5 or 3 hours, with the first 30–45 minutes for a group session and the rest of the time for 1-on-1 coaching.  We could also have used another 4 or 5 judges there, so that students with individual questions did not have to wait to get them answered.

We call for schools, colleges, and educational assessment programs to stop using computer scoring of student essays written during high-stakes tests.

Every year hundreds of thousands of students write essays for large-scale standardized tests. The scores are used in life-changing decisions. Students are accepted into, placed within, and rejected from educational programs. Graduates are hired or not hired. Teachers are qualified, evaluated, promoted, and fired. Learning institutions are compared, accredited, and punished. Yet in a major disservice to all involved, more and more of these essays are scored not by human readers but by machines.

Let’s face the realities of automatic essay scoring. Computers cannot “read.” They cannot measure the essentials of effective written communication: accuracy, reasoning, adequacy of evidence, good sense, ethical stance, convincing argument, meaningful organization, clarity, and veracity, among others. Independent and industry studies show that by its nature computerized essay rating is

• trivial, rating essays only on surface features such as word size, topic vocabulary, and essay length
• reductive, handling extended prose written only at a grade-school level
• inaccurate, missing much error in student writing and finding much error where it does not exist
• undiagnostic, correlating hardly at all with subsequent writing performance
• unfair, discriminating against minority groups and second-language writers
• secretive, with testing companies blocking independent research into their products

…

The basic premise of the petition is good: computers can’t score those aspects of writing that people actually care about, and so should not be used for scoring any essays that matter. Of course, some of the alternatives are just about as bad (like the MOOCs that use peer grading by incompetent “peers”), but I signed the petition anyway.

The bottom line is that assessment of writing is difficult, and so is expensive. If you can’t afford to pay for proper evaluation by well-trained human readers, then you can’t afford to use essays as part of an assessment. There are no shortcuts.

They have 15 different camps this summer, ranging in length from 1 week to 3 weeks (with two having a 1-week/2-week option), and ranging from ages 4–7 for the youngest group to ages 14–21 for the oldest.  Most of the summer camps are done by grade range (grades 1–4, 4–10, 6–12, …), but the youngest and oldest groups poke out of the normal K–12 grade range, and use age ranges instead.

The grade and age ranges are usually fairly large (6 or 7 years) and the classes accommodate kids with a wide range of acting experience, from first time on stage to 40 previous productions.  The teen productions often have many of the same kids in them (the theater-obsessed kids), but there are usually a couple of first-timers also.  I’m always pleased to see how well the kids incorporate newcomers, make them welcome, and get them to perform at a fairly high level.

There is no auditioning at WEST—the teachers assign parts after observing the kids for a few classes and getting input from them about how big and what sort of part they want (some prefer having a lot of lines, others prefer having less to memorize, some like mainly verbal roles, others prefer primarily physical roles, …).

Casting is often quite complex, with one actor playing several roles, or multiple actors playing the same role.  In some of the larger classes they do two casts, with the leads in one casts having bit parts in the other cast.  One particularly memorable production was of Hitchhikers’ Guide to the Galaxy,  which had only one cast, but almost every role was played by 2 or 3 actors and almost everyone had multiple roles, using costuming to maintain continuity.  I doubt that it would have worked in a more serious play, but Hitchhikers’ Guide is surreal enough and comical enough that it worked quite well.

This year, there are three summer acting camps that my son is eligible for:

• Ferris Bueller’s Day Off (a 3-week comic production)
• Conservatory with Theatre Témoin (1-week or 2-week physical theater and devised theater)
• Shakespeare Conservatory with Shakespeare Santa Cruz (1-week or 2-week Shakespeare intensive)

(He’s also eligible for the tech class, but he’s done that one before and decided he’d much rather be on stage than doing tech work.)

He’s decided he wants to do the comic production and the Shakespeare conservatory, but he’s not decided about the physical theater yet.  Personally, I think that the physical theater would teach him the most, as his acting has almost always centered around the lines and the verbal delivery (even his improv work tends more toward the verbal than the physical).  He’s worked with the Shakespeare Santa Cruz people several times now, but not with Theatre Témoin, whose collaboration with WEST is new this year.

But summer theater is at least as much about recreation as it is about improving skills, so I leave the decisions up to him.  He may think that 7 weeks of intensive theater work is too much for this summer, as it won’t leave much time for recreational programming.  (It would give him a good excuse for not working on college application essays for those weeks, though.)

Last week I authorized CruzIO to contact AT&T to check the line to the house.  This is the last test to be done, because if AT&T finds no problem on the wiring to the house, they charge $75 for the service call.  (There is no charge if they do find a problem.)

This morning when CruzIO called to check on the status of the lines, they found out I was still waiting for AT&T to check the lines, and they discovered that someone at CruzIO had canceled the request to AT&T—a mistake they apologized for and promised to fix.  They then got AT&T out within a couple of hours.

The AT&T person found that the signal at the house was terrible, but at the top of the telephone pole was fine, so he replaced the “drop” (the line from the telephone pole to the house) and the box where the interior and exterior wiring meet. (Both the drop and the box were probably 50 years old or more.)

Now the internet access is working better than it ever did before.  We’re getting 6–6.8Mbps download and 0.83–0.86Mbps upload, which is what we’ve been paying for, but not getting in the past (previous download results topped out at about 4Mbps), so apparently the drop has been the limiting factor on our internet access for quite some time.

[Update: 2013 March 26.  CruzIO checked on us again and uncapped the speed limits.  We're now getting about 10Mbps download and 0.83Mbps upload.]

I’ve been thinking about that and see a few problems:

• The block diagram the students came up with this fall (with my guidance) calls for 3 power supplies, which makes the amplifier a bit impractical to power from a battery or wall-wart power supply.
• The students used an external triangle-wave generator.
• The protoboard would have to be fairly large to accommodate the FETs and inductor and enough wiring space, which would probably result in a larger board than 5cm × 5cm, and so $2/board rather than $1/board.

I’ve not given much thought yet to the power-supply problem, but I did think a little about the triangle-wave generator.  Since they only need to generate triangle waves at a single frequency (in the 60kHZ–100kHz range), it should be possible to use a hysteresis oscillator with an integrator rather than a simple RC timing circuit.  Something like the following might work:

The slew rate of the MCP6004 op amps is only 0.6 V/µs, but the triangle wave only swings from V_IL to V_IH, or about 1V, so the minimum period is about 3.3µs, for a maximum frequency of 300kHz. If we use another op amp to amplify the signal to make it swing almost 5v, instead of 1V, the maximum frequency would be only 60kHz.

I might want to try this circuit out this week, to see if it is worth having the students play with next year.

Since this is a milestone on my blog, I should probably report a few statistics:

1,023 Posts, 20 Categories, 1,305 Tags

211,322 Views, 2,878 Comments, but about 40% of those comments are from me (either adding notes to a post or responding to another commenter, so I’m really getting only about 8 comments for every 1000 views.

The big categories are

Because WordPress.com still does not support displaying posts in chronological order (only reverse chronological), and the theme I use does not include next/previous links (the one major complaint I have with the theme), I’ve had to create and manually maintain a couple of table of contents pages:

Circuits course

Homeschool Physics

The major tags are

There are probably a number of posts that should have the “home school” tag but don’t.

The major referrers are

Search Engines  (almost all Google)	84,867
Various Yahoo mail servers	1,879
Google Reader	1,468
WordPress.com	1,401
Facebook	915
computinged.wordpress.com	762
santacruzsentinel.com	486
blogger.com	470
mail.live.com	443
Twitter	432
blog.mrmeyer.com	359
google.ca	283
engineerblogs.org	283
larkolicio.us	208

Since I have neither Facebook nor Twitter accounts, the number of referrals from those social media sites are surprisingly large, but searches and e-mail referrals are clearly a far more common way to get to my blog. The coming loss of Google Reader may end up hurting my readership numbers, though I suppose that most Google Reader users will switch over to a different RSS reader. I’ll have to choose one soon myself (I’m thinking of The Old Reader, NewsBlur, or NetVibes, though I understand that NewsBlur has stopped giving out free accounts for now, because they got too many new users with the demise of Google Reader).

Here are some of my all-time most popular posts (some of them are definitely not among my favorite posts):

I seem to have 20 posts with over 1000 views. I wonder what the blogging equivalent of the h-index is. Probably something like the largest h such that there are h posts with ≥50h views.

Python has many advantages over Java as a first text-based language, but it is hard for me to articulate precisely which differences are the important ones.

One big difference is that Python does not require any declaration of variables. Objects are strongly typed, but names can be attached to any type of object—there is no static typing of variables. Python follows the Smalltalk tradition of “duck typing” (“If it walks like a duck and quacks like a duck, then it is a duck”). That means that operations and functions can be performed on any object that supports the necessary calls—there is no need for a complex class inheritance hierarchy.

Java has a lot of machinery that is really only useful in very large projects (where it may be essential), and this machinery interferes with the initial learning of programming concepts.

Python provides machinery that is particularly useful in small, rapid prototyping projects, which is much closer to the sorts of programming that beginners should start with. Python is in several ways much cleaner than Java (no distinction between primitive types and objects, for example), but there is a price to pay—Python can’t do much compile time optimization or error checking, because the types of objects are not known until the statements are executed. There is no enforcement of information hiding, just programmer conventions, so partitioning a large project into independent modules written by different programmers is more difficult to achieve than in statically typed languages with specified interfaces like Java.

As an example of the support for rapid prototyping, I find the “yield” statement in Python, which permits the easy creation of generator functions, a particularly useful feature for separating input parsing from processing, without having to load everything into memory at once, as is usually taught in early Java courses. Callbacks in Java are far more complicated to program.

Here is a simple example of breaking a file into space-separated words and putting the words into a hash table that counts how often they appear, then prints a list of words sorted by decreasing counts:

def readword(file_object):
    '''This generator yields one word at a time from a file-like object, using the white-space separation defined by split() to define the words.
    '''
    for line in file_object:
        words=line.strip().split()
        for word in words:
             yield word

import sys
count = dict()
for word in readword(sys.stdin):
     count[word] = count.get(word,0) +1
word_list = sorted(count.keys(), key=lambda w:count[w], reverse=True)
for word in word_list:
    print( "{:5d} {}".format(count[word], word) )

Note: there is a slightly better way using Counter instead of dict, and there are slightly more efficient ways to do the sorting—this example was chosen for minimal explanation, not because it was the most Pythonic way to write the code. Note: I typed this directly into the e-mail without testing it, but I then cut-and-pasted it into a file—it seems to work correctly, though I might prefer it if if the sort function used count and then alphabetic ordering to break ties. That can be done with one change:

word_list = sorted(count.keys(), key=lambda w:(-count[w],w))

Doing the same task in Java is certainly possible, but requires more setup, and changing the sort key is probably more effort.

Caveat: my main programming languages are Python and C++ so my knowledge of Java is a bit limited.

Bottom-line: I recommend starting kids with Scratch, then moving to Python when Scratch gets too limiting, and moving to Java only once they need to transition to an environment that requires Java (university courses that assume it, large multi-programmer projects, job, … ). It might be better for a student to learn C before picking up Java, as the need for compile-time type checking is more obvious in C, which is very close to the machine. Most of the objects-first approach to teaching programming can be better taught in Python than in either C or Java. For that matter, it might be better to include a radically different language (like Scheme) before teaching Java.

The approach I used with my son was more haphazard, and he started with various Logo and Lego languages, added Scratch and C before Scheme and then Python.  He’s been programming for about 6 years now, and has only picked up Java this year, through the Art of Problem Solving Java course, which is the only Java-after-Python course I could find for him—most Java courses would have been far too slow-paced for him.  It was still a bit low-level for him, but he found ways to challenge himself by stretching the assigned problems into more complicated ones.  His recreational programming is mostly in Python, but he does some JavaScript for web pages, and he has done a little C++ for Arduino programming (mostly the interrupt routines for the Data Logger code he wrote for me).  I think that his next steps should be more CS theory (he’s just finished an Applied Discrete Math course, and the AoPS programming course covers the basics of data structures, so he’s ready for some serious algorithm analysis), computer architecture (he’s started learning about interrupts on the Arduino, but has not had assembly language yet), and parallel programming (he’s done a little multi-threaded programming with queues for communication for the Data Logger, but has not had much parallel processing theory—Python relies pretty heavily on the global interpreter lock to avoid a lot of race conditions).

My hypothesis is that a science blog for a science student can serve the same purpose that a portfolio serves for an artist or a set of articles serves for a writer.  Your blog can be your record of accomplishments.

Not only can your blog document your work, your blog can show that you can write, that you can spell (not a skill to take for granted), and can give you a chance to describe what you’ve done.

She describes her first job interview and what she is doing to avoid similar embarrassment for her students.  She has students in one class keep a professional lab notebook and bring it to interviews—showing that they can keep a proper lab notebook and providing documentation to support their assertion of knowing various protocols.

Student blogging is another approach she is experimenting with.  She encourages the students to use blogs as an on-line notebook (much like I’ve been doing on this blog for the circuits course), and to include the URL for the blog in resumes and cover letters for jobs.  If interviewers are interested, they can check out a few posts on the blog to see if the student can write coherently (a very important skill that can not be automatically assumed of college graduates) and, if there are search boxes and appropriate tags on the posts, whether the students know the protocols and equipment that the job requires.

In a subsequent post, The ten commandments of student science blogging, she talks about the guidelines she gives students for their blogs, to keep them from accidentally doing unprofessional things that would hurt, rather than, help their chances of getting a job.

The biggest problem I see with her recommendations is that the only audience she has identified for the student is a mysterious “job interviewer” whom the students have never met.  Writing for an unknown, difficult-to-imagine audience is hard. Writing for an imagined expert (an interviewer or professor) almost always brings out the worst writing, with inflated diction, misused jargon, and awkward ungrammatical sentences.  When writing to show that they know something to someone who knows it better, students stumble over nearly every sentence—leaving out important concepts and tossing in irrelevant minor points in a vain attempt to impress.

I think it might benefit the students to be given a more specific audience—one that they can picture writing to directly and actually informing of something new.  For an online lab notebook, it could be students at other schools (“look at the cool stuff we get to do here!”) or future students in the same lab (“never use the pink labels in the freezer—the glue on them cracks in the cold and the labels fall off”), both of whom are imaginable audiences.

The advice I gave in my circuits course is the standard advice I give to students: Write to students taking the course next year.  Assume they know what you knew coming into the course, but explain to them anything that you didn’t already know.  Make the report detailed enough that a student reading it could duplicate your work without having access to the original assignment—though they might have to looks a few things up on the web or in text books. (Provide pointers to appropriate readings, when possible.)  Explain not just what you did, but why, and provide warnings to help your reader avoid mistakes that you made.

Most of the students in the circuits course got this idea, and the reports were mostly coherent and directed at the right audience, though they were a little light on pointers to appropriate reading.

One thing that Sandra Porter doesn’t mention in her “ten commandments”, but which I had to really rant about in my course: “Get the details right!”  Sandra mentions spelling and punctuation, which are markers for attention to details, but the accuracy of the content is far more important. I can forgive an occasional typo (though failure to run text through a spell checker indicates a level of sloppiness that would disturb me as a job interviewer), but the main engineering content needs to be checked and double-checked, both for consistency with the lab notebook notes and for general sanity (recompute the corner frequency from the RC values in the schematic—is that what was intended?).

If you are giving a circuit schematic, every wire must be correctly connected, every component must have the correct value, and pin numbers should be correct.  The students  in the circuits course had incredible difficulty with checking their own and each other’s work for accuracy, and obvious errors (like power-ground shorts) occurred on most of the assignment first drafts.  For a biotech student, the equivalent would be getting the wrong reagent in a protocol, putting ice in autoclave, or replacing µg with mg.

The rate of errors in schematics did not drop much over the quarter, though I felt it should have.  Other writing problems (like poor audience assessment, overuse of passive, or misuse of “would”) were generally fixed after being pointed out, but the sloppiness in the circuit diagrams continued to be a problem all quarter.  By “sloppiness” I don’t mean poor drawing skills, as most of the students used CircuitLab to draw neat schematics, but semantic errors that changed the meaning of the circuits.

If anyone has ideas for improving student attention to details in schematics, I’d appreciate hearing them.

Front of T-shirt

Back of T-shirt. The silkscreen is intended as a white silkscreen over a black T-shirt.

Because the applied circuits course did not have a final exam, the students asked if we could get together at a bar for a beer during the exam time instead (which my son quipped should be considered a “bar exam”).  Because we had some underage students in the class, I chose Caffe Pergolesi as a site (they serve beer but also coffee, hot chocolate, and coffee-house snacks).  The café was surprisingly crowded for 4 p.m. on a Tuesday (probably due to it being the first day of exam week), and I had to sit out on the deck, because there were no tables available inside.  At first I was a bit worried that no one would show up (a common problem for parties I’ve tried to have in the past, so I’ve stopped attempting to have parties), especially when no one was there by 4:10.  But the students started trickling in and we eventually had all the students in the class—even those who had sent e-mail saying they couldn’t make it.

I showed the students the T-shirt design, modified according to their suggestions the day before, and they approved it.  I still need to check with the screen printer that the SVG files I have will work—I think that the back is ok (it is a single black rectangle for the T-shirt with a single path for the white layer on top), but I’m worried about the front. The text, slug, and small thought bubbles should be fine, but the black images on the large thought bubble are currently objects on top of the white thought bubble, and I’ve not figured out how to get Inkscape to make them cuts through the thought bubble to the black T-shirt underneath.  The Inkscape “path difference” operation, which worked for the back of the T-shirt doesn’t do the right thing with these images.  So far I’ve gotten 7 orders for T-shirts from the class (including one for me and one for my son), and I’m hoping for another 5 or 6 to amortize the setup costs.  I think that we’ll have about $90 in setup plus $12/shirt, so 7 shirts would be about $25 each and 12 shirts would be about $20 each (long sleeve shirts a couple of bucks more).

I used the time to get feedback from the class about how it should be modified in future, starting from a handout I’d given them the day before.  Here are some of my notes from the discussion.  If I’ve missed anything, I hope that students will send me e-mail.

• Parts and tools to eliminate:  velcro cable ties (unused), long-nose pliers (low quality and not used), thermometers (change to lab equipment), LEDs (not used).
• Parts and tools to add: inductor for class D amplifier, soldering iron.  A soldering station like the one I have (and similar to the ones they used in the lab this year) would add $20 to the cost of the course.
• It may be worthwhile upgrading the screwdriver set, as the under $2 set was really low-quality and some of the screwdrivers failed (blade slipped in handle, so that screwdriver did not turn with handle).
• I had been worried about the high price for the large assortment of resistors ($13.35 for 1120 resistors, 10 each of 112 sizes), but the students liked that they always had whatever resistor size they needed, and were contemptuous of the approach used in EE101 of providing students with only about 20 resistors of the precise sizes that the faculty had decided the students would use.
• One student suggested having a protoboard for designing the class D amplifier, since that is something they might want to keep.  I’ll have to think about that, as it doesn’t strike me as an immediate win, though I can see wanting to keep the power amplifier.  One problem is that the class-D amplifier is not as generic a project as the instrumentation amplifiers, so it is harder to come up with a general-purpose protoboard. Also, most students ended up having to do a lot of experimenting to get the biasing to work out for the power FETs, which could be difficult on a PC board.  The class-D amplifier also needs a bit more space than the two instrumentation amp projects, so a PC board for it would have to be bigger ($2/board instead of $1/board).  Having the same protoboard for both the pressure-sensor lab and the EKG lab meant that time spent learning how to use the protoboard was amortized over two projects, which would not be the case for a special-purpose power-amp board.
• One student suggested adding a voltmeter for home use, but the problem there is that voltmeters that can read AC voltage correctly for 100kHz signals are mostly in the $100-and-up range.  The $5 voltmeters that could be put in a kit for everyone to buy are not useful for some of the labs.
• Students suggested that the first quiz should be given as homework instead of a quiz—a good idea, since the questions were too hard for the students as a quiz, and having time to think about them and discuss them with each other would lead to more learning.
• The students do not think that adding a textbook to the class would help, but being directed to the All about Circuits readings more often (including the worksheets) might help.  They generally found the Wikipedia articles too detailed and too broad to be helpful in learning the material.  They got fairly good at at searching the web for keywords and finding lecture powerpoints from other courses that were relevant.  No one found a steady source of good material though—the searches tended to find different sources for each topic.  The students reported being able to find data sheets fairly easily and consulting them fairly often, so at least one of the goals of the course was met.
• One student reported that soldering the instrumentation amp for the pressure sensor lab seemed a bit pointless to some, as they don’t buy the pressure sensors to connect to, so a permanent board is not much use. The benefits (soldering practice and less noise pickup from long wires) may not justify the extra effort of soldering.
• We discussed re-ordering the labs, moving the electrode measuring and modeling lab later, and the sampling and aliasing lab earlier.  A possible new order is
  1. Thermistor
  2. Sampling and aliasing
  3. Microphone
  4. Audio amp
  5. Hysteresis oscillator
  6. FET and phototransistor
  7. Electrode modeling
  8. Pressure sensor
  9. Class-D power amplifier
  10. EKG

  That order could cause some difficulty for the sampling lab, which needs RC filter design (hence complex impedance), so maybe swapping the mic and sampling labs would be better.

• We also discussed the idea of having 2 labs a week (both Tuesday and Thursday), with a data analysis day in between (to teach gnuplot scripting and fitting models).  None of the students had done model fitting (other than straight lines) in any other course, so this is a skill worth spending a bit more time on in class.  Having 2 105-minute labs a week (the standard TTh time slot) would probably not be enough, as that is barely more than the 3-hour lab weekly lab this quarter, and the setup time would probably eliminate any gains.  I’d probably have to schedule 2 time slots per lab (say 10–1:45, 2–5:45, or 6–9:45).  If the course grows to full size, I would be spending 8–12 hours in the lab on Tuesdays and Thursdays, without break.
• If I do have more lab time next year, I could start a little slower, using the first week to have students learn to identify all the parts, mark the capacitor bags with the capacitor sizes, learn to use the ohmmeter and power supplies, … .  Some of the later labs would have no more time than this year, but some of them needed no extra time.
• Students would like several explanations to come earlier in the course relative to the labs—FETs before the microphone lab, PN junctions and phototransistors before the tinkering lab, block diagrams earlier in the course, … .  I agree, and moving the first labs a week later could help with that.  I’ll be doing a day-by-day topic planner before resubmitting the course approval paperwork.  One problem with teaching block diagrams earlier is that—like outlining in writing—they’re really only useful once the complexity of the design gets high enough that subdividing the problem is useful.
• The students were pretty pleased with the data logger software that my son wrote.  The biggest complaint was about the logger freezing when recording a long run at high sampling rate (a known problem).  I believe that he is developing a fix for that problem, which will generally result in faster live charts.  Students also like the idea of being able to produce eps, pdf, png, or svg output directly from the data logger, so that they didn’t feel the need to make screenshots.  Providing starter gnuplot scripts (which they could then add to in order to do model fitting) was also attractive to them.  There was one request for icon-based executables (avoiding the command line), but I actually prefer for engineering students to have to learn to use command-line tools—I was shocked that they had gotten to their senior year and had not learned how to use command lines.
• Students thought that the current prereqs for the course were fine—they did not see a need to add a programming prereq, unless the course was changed in a major way to include Arduino programming (which I’m not tempted to do, as there are already courses on campus covering that).  They did think that the course needed to remain an upper-division course, but that sophomores might be able to handle it by the Spring (which is when it will be scheduled in future).
• Some students thought that the course could be reduced to 9 labs (from 10)—mainly to reduce the number of reports written.  I think that we could achieve that by putting the microphone lab and audio amp lab together and having 3 lab sessions with only one report.  We might be able to combine the hysteresis lab and the tinkering (FET and phototransistor) lab into one report also.
• The students really liked the undergrad group tutor we had—saying that he was the best TA they’d ever had.  I believe that he is graduating this year (as are all the students in the course), so I don’t know whether we’ll be able to get as good an assistant next year.
• Students liked having learned gnuplot, though they initially struggled with it and hated it.  Once they got past the initial learning, they found it useful for senior theses and courses other than the circuits course.
• Overall, students thought that the class had met most of the learning goals I had set for it, and several of them wished the course had been available to them earlier—some of them might even have opted for the bioelectronics track (they were all biomolecular track), had they taken this course early enough (and if EE would accept it as prereq to the other upper-division courses needed for bioelectronics).  I’m certainly going to try to convince the EE faculty that this course can serve as more than adequate preparation for courses like signals and systems (better than the existing circuits course).

The students in the class gave me two bottles of wine as a thank-you for the course—that is a first for me in 30 years of being a professor.  Most often students are glad to have survived my courses, but don’t generally appreciate them until several years later.

The student appreciation certainly isn’t because I’ve been grading leniently—the class is mostly in the B- to B+ range, and some had to go through 2 or 3 drafts of the lab reports to get to even that level. There may be one or two A- grades (I still have the last 2 lab reports to grade, so I don’t know yet—I’m hopeful, but I’m not going to give out As unless the work justifies them).

I think that the recognized that I was genuinely interested not just in the material but in getting them to do real engineering design and to think like engineers.  Several have taken to heart the “try it and see” mantra and have learned to appreciate the value of “sanity checks”.  I think that the value of a UC education lies mainly in these high-contact “artisanal” courses, not in the mega-lectures and cookbook labs that they have mostly been suffering through.  (To be fair, many of them are working on senior theses in various faculty labs, so they have had high-contact educational experiences—just not structured as a required course.)