Gas station without pumps

2014 November 12

Autodidacts (against and for)

Filed under: Uncategorized — gasstationwithoutpumps @ 22:05
Tags: , , , ,

Lately I’ve seen a lot of blog posts talking about autodidacts (people who learn things without teachers) as if they were some strange breed of alien being. For example, there is the post Ed tech promoters need to understand how most of us learn | The Hechinger Report, which includes the following paragraphs:

This is a very particular take on learning: the autodidact’s take. We shouldn’t mistake it for most people’s reality. Productive learning without guidance and support from others is rare. A pair of eminent researchers has gone so far as to call the very notion of self-directed learning “an urban legend in education.”

In a paper published in Educational Psychologist last year, Paul A. Kirschner of the Open University of the Netherlands and Jeroen J.G. van Merriënboer of Maastricht University challenge the popular assumption “that it is the learner who knows best and that she or he should be the controlling force in her or his learning.”

There are three problems with this premise, Kirschner and van Merriënboer write. The first is that novices, by definition, don’t yet know much about the subject they’re learning, and so are ill equipped to make effective choices about what and how to learn next. The second problem is that learners “often choose what they prefer, but what they prefer is not always what is best for them;” that is, they practice tasks that they enjoy or are already proficient at, instead of tackling the more difficult tasks that would actually enhance their expertise. And third, although learners like having some options, unlimited choices quickly become frustrating—as well as mentally taxing, constraining the very learning such freedom was supposed to liberate.

And yet, to paraphrase the economist Larry Summers: There are autodidacts. Look around. We all know at least one successfully self-taught expert, and the tech world is teeming with them. How’d they get that way?

While I do see a benefit to teaching (or I wouldn’t spend so much of my time teaching), I don’t think that the autodidacticism should be dismissed as “an urban legend in education”. In fact, the end goal of all my teaching is to turn out students who can continue to learn on their own, without needing the continuing crutch of having a teacher lead them. I’m not sure how successful I’ve been in a lot of cases—I see students for a 10-week class and then they disappear, giving me no clue whether they have developed new ways of learning that stay with them or they have just managed to fake it through my course and relapsed to expecting to be spoonfed immediately afterwards.

I think that Annie Murphy Paul has it wrong when she claims that few people can be autodidacts—she seems to be assuming that it is some sort of innate gift that one is born with (Carol Dweck’s hated “fixed mindset”). I am convinced that becoming an autodidact is something that most people are capable of. I recently read an account of one student who turned herself into an autodidact, and what prompted her to do it—How to become a programmer, or the art of Googling well | okepi:

He was the very picture of the competent hacker I held in my head, that I nursed a secret crush for. But most extraordinary, he threw something together using tools that he’d never used before. Yes, he did spend more time on Google than he did coding, but through sheer force of googling and a prior, general picture knowledge of how these things worked, he’d roped together a pretty sophisticated and working app. He knew where Twilio belonged in the grand hierarchy of things, knew exactly where to apply it, and so, even without knowledge prior, was able to figure things out.

And I despaired. How do you get so good that you can build something out of nothing?

The rest of the semester passed glumly, and without incident. Come winter, I began to panic again. Driven by the need to become employable, I tried my hand at a couple Code Academy website tutorials. Hm. Not bad. I made an attempt at my first website—pretty terrible, just one, static page full of boxes and awful colors, but it was something. Something I realized. Just like my code-god compatriot, when I didn’t understand something, all I needed to do … was google it.

To a large extent, the difference between the autodidact and the ordinary student is not one of competence, but of confidence. It is Carol Dweck’s “growth mindset”—the conviction that you can learn the material and are not doomed forever to learn only what someone predigests for you.  There are tremendous resources now available to everyone that can turn them into autodidacts: Wikipedia, for example, has thousands of excellent articles in all sorts of sciences (and the science articles suffer much less from point-of-view problems and vandalism than pop culture articles).  And, as “okepi” says, Google can find all sorts of answers for you (she goes on to much larger accomplishments later in her post).

I learn a lot of stuff on my own by reading Wikipedia articles, reading survey articles, reading research papers, googling stuff in StackExchange, going to weekly research seminars, even (sometimes) taking classes.  [The astute reader will have noticed that I did not include MOOCs or videos in that list—despite the claim that MOOCs are a godsend for autodidacts, I have found them profoundly unmotivating, and videos as a learning tool are just too bloody slow for my taste—I fall asleep before anything has been conveyed.]

There are some things for which teachers are essential—it is very hard to learn a foreign language well on your own, without a native (or near-native) speaker to help you hear the differences between what you say and how a native speaker would say it.  Theater is hard to do on your own (though a group of autodidacts could get together to learn to act).  Feedback on writing is very valuable, as is having an audience for public speaking. And there are times when it is useful to have the structure of a scheduled course to help with time management—to keep you on task to meet an external deadline when there are dozens of other things to do. But in a lot of cases, a textbook is all the structure that is needed, or an on-line tutorial document, or even just a particular problem that needs to be solved shaping what needs to be learned.  I learned those skills decades ago, and I think that my son learned them well by the time he was halfway through high school.

So I know how to be an autodidact, but how do I teach it to others?  That is a question I have no easy answers for. I try giving open-ended assignments, I try scaffolding by having students search for answers to specific questions, I try deliberately leaving material out of a lecture or a lab handout and telling students to go read about it in Wikipedia, and I try whatever else I can think of that will get students to learn on their own.  For some students something clicks, and they start doing more learning on their own—sometimes a lot more. For others, I’ve not found a secret sauce.

I particularly despair of those students who take copious notes in class and want to record my lectures (I have two of them this quarter)—they seem to have developed the attitude that I am the sole source of knowledge, and that if they just cram everything I say into their memories, they’re golden. But I’m not interested in hearing my words echo back to me—if I wanted that, I’d lecture to an empty classroom.  I’d much rather the students wrote down two or three keywords from my lecture, so that they could find what others had to say on the topic using Google and Wikipedia—or even looked up the topics I’m covering in the textbook (which does have an index). I’d rather that they thought about how to derive the algorithms we are learning in class, rather than trying to memorize what are really fairly arbitrary recursive definitions (and ones that are more easily derived than memorized).

Does anyone have any good techniques for converting note-takers into autodidacts?  Those are the techniques I need to learn (and I didn’t really see anything in Teach like a Champion that would help).

 

2014 November 3

Advising too many students

Filed under: Uncategorized — gasstationwithoutpumps @ 21:39
Tags: ,

I’m advising between 300 and 400 students this year, plus teaching two classes each quarter, meeting with 3 grad students, and being department vice chair. This makes my week a busy one—I don’t get any convenient large blocks of time for doing research, and  I probably spend about 6 hours a week in one-on-one meetings with undergraduates.

Because I have to meet with students a lot, and I have a lot of scheduled classes and meetings, I need to keep an appointment calendar. But I’m not willing to have students signing up on it directly—no one gets to put things on my calendar except me.

I’ve set up two open office hours a week, for which students can reserve a place in line by e-mail, or just show up and wait until those with reservations have all been served. I stay until all the students have had their time with me (I made the hours 4–5pm), which means I’m usually doing 4 hours a week, not 2, but I can leave as early as 5pm if there is no one waiting (which has happened, but not often).

I also allow students to make appointments at other times—but they have to send me their schedules, and I look for an opening on my calendar. Because some openings are more valuable to me than others, I try to give them a slot that will fit their openings but minimize disruption to my day (an optimization that never happens if others put appointments on my schedule). I never give the students multiple options when they are asking for times outside my allocated office hours. They tell me when they are available, and I ask them to come in the first of those slots that fits my schedule. If they don’t come then, I mark them on my calendar as a no-show, and wait for them to reschedule (but I’m less generous about giving up prime slots to no-shows).

Why do I have so many advisees this year? Simple: the bioengineering major has been growing rather rapidly recently, which has converted a reasonable load into an unmanageable one. Also we completely revamped the curriculum last year, so that it is effectively 4 different majors, with only about 30% overlap in courses. That means that there are 7 different curricula students could be following (the 3 old concentrations or the 4 new ones), and considerable confusion on the part of students about what their options are—they hear something about the new curriculum and assume it applies to the old one, or vice versa.

There is a staff adviser whom students are supposed to see before coming to me (the bioengineering advising is a full-time job for her), but I have to handle all the exceptions and all the “what-elective-should-I-take” questions.  I also have to sign the independent study requests and approve the senior thesis proposals. I like reading the thesis proposals and talking to students about what courses can help them learn what they want or need to learn—that is the rewarding part of the undergraduate director job.

One of the most useful questions I ask students is “what do you plan to do with your degree once you get it?” Somewhat surprisingly, many of them have never been asked that and never thought about it—they are so focused on the B.S. as a goal that they’ve never realized that the B.S. is not a goal: it is a means to an end, a stepping stone. Where they intend to go after that should be determining what electives they take, what concentration they choose, even what major they choose. My job is help guide them on their path, but if they don’t know where they’re going, I can’t help them get there.

Not all my interactions with students are that much fun, though. Just before the add/drop deadline and just before the declaration of major deadline, I get all the students who were too disorganized to do things in a timely fashion, who are also often those who’ve made a hash of scheduling their courses and are looking for exceptions so that they can graduate despite having missed some requirement that they should have fulfilled years ago. Dealing with these students is often a major pain—particularly since they are so late in making their requests that they often expect me to drop everything else so that they can make their deadlines.  Sorry, kids, a failure to plan on your part does not constitute an emergency on my part.  I’ll deal with them fairly and do what I reasonably can to help, but I’m not going to say “there, there, you don’t really have to take that tough course that you’ve been avoiding for so long that your financial aid has run out”.  Luckily, I don’t have the power to waive prerequisites—I can honestly tell the students that they have to convince the instructor to give them an add code, as only the instructor can waive the prereqs.

I avoid some of the problems with handling so many advisees by sending out e-mail to the entire list of majors and premajors occasionally, when something comes up that I think will be a common question or that may students would benefit from hearing. I also handle a lot of routine questions and approvals by e-mail.

Based on the load last year and this quarter, I can tell that I’ll be inundated in the spring quarter, when all the sophomores will have to declare their majors. My teaching load will be a lot heavier then also, as one of my two classes will have 6 or 12 hours of lab time a week (depending how many students will be taking it), with no TA. So I’ll need help. I’m going to try to get some other faculty to start advising in a couple of the concentrations, so that the load can be spread a bit.  I’ll still end up with the thesis proposals and the exceptions, but some of the major declaration and guidance for elective choosing can be done by others.

Update 2014 Nov 7: This week, I have gotten three other faculty to agreed to serve as faculty advisers for the students in Assistive Technology concentrations (the smallest part of the workload).  I will be looking to get some faculty advisers for the biomolecular concentration (the largest part of the workload).

2014 October 26

Critical thinking

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

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

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

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

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

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

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

http://www.cs.cmu.edu/~CompThink/

I weighed in with

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

CSProfMom responded with

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

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

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

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

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

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

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

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

 

 

2014 October 25

Grading based on a fixed “precent correct” scale is nonsense

Filed under: Uncategorized — gasstationwithoutpumps @ 10:12
Tags: , , , , , ,

On the hs2coll@yahoogroups.com mailing list for parents home-schooling high schoolers to prepare for college, parents occasionally discuss grading standards.  One parent commented that grading scales can vary a lot, with the example of an edX course in which 80% or higher was an A, while they were used to scales like those reported by Wikipedia, which gives

The most common grading scales for normal courses and honors/Advanced Placement courses are as follows:

“Normal” courses Honors/AP courses
Grade Percentage GPA Percentage GPA
A 90–100 3.67–4.00 93–100 4.5–5.0
B 80–89 2.67–3.33 85-92 3.5–4.49
C 70–79 1.67–2.33 77-84 2.5–3.49
D 60–69 1.0–1.33 70-76 2.0–2.49
E / F 0–59 0.0–0.99 0–69 0.0–1.99
​Because exams, quizzes, and homework assignments can vary in difficulty, there is no reason to suppose that 85% on one assessment has any meaningful relationship to 85% on another assessment.  At one extreme we have driving exams, which are often set up so that 85% right is barely passing—people are expected to get close to 100%.  At the other extreme, we have math competitions: the AMC 12 math exams have a median score around 63 out of 150, and the AMC 10 exams have 58 out of 150.  Getting 85% of the total points on the AMC 12 puts you in better than the top 1% of test takers.  (AMC statistics from http://amc-reg.maa.org/reports/generalreports.aspx ) The Putnam math prize exam is even tougher—the median score is often 0 or 1 out of 120, with top scores in the range 90 to 120. (Putnam statistics from  http://www.d.umn.edu/~jgallian/putnam.pdf) The point of the math competitions is to make meaningful distinctions among the top 1–5% of test takers in a relatively short time, so questions that the majority of test takers can answer are just time wasters.
I’ve never seen the point of having a fixed percentage correct ​used institution-wide for setting grades—the only point of such a standard is to tell teachers how hard to make their test questions.  Saying that 90% or 95% should represent an A merely says that tests questions must be easy enough that top students don’t have to work hard, and that distinctions among top students must be buried in the test-measurement noise.  Putting the pass level at 70% means that most of the test questions are being used to distinguish between different levels of failure, rather than different levels of success. My own quizzes and exams are intended to have a mean around 50% of possible points, with a wide spread to maximize the amount of information I get about student performance at all levels of performance, but I tend to err on the side of making the exams a little too tough (35% mean) rather than much too easy (85% mean), so I generally learn more about the top half of the class than the bottom half.
I’m ok with knowing more about the top half than the bottom half, but my exams also have a different problem: too often the distribution of results is bimodal, with a high correlation between the points earned on different questions. The questions are all measuring the same thing, which is good for measuring overall achievement, but which is not very useful for diagnosing what things individual students have learned or not learned.  This result is not very surprising, since I’m not interested in whether students know specific factoids, but in whether they can pull together the knowledge that they have to solve new problems.  Those who have developed that skill often can show it on many rather different problems, and those who haven’t struggle on any new problem.

Lior Pachter, in his blog post Time to end letter grades, points out that different faculty members have very different understandings of what letter grades mean, resulting in noticeably different distributions of grades for their classes. He looked at very large classes, where one would not expect enormous differences in the abilities of students from one class to another, so large differences in grading distributions are more likely due to differences in the meaning of the grades than in differences between the cohorts of students. He suggests that there be some sort of normalization applied, so that raw scores are translated in a professor- and course-specific way to a common scale that has a uniform meaning.  (That may be possible for large classes that are repeatedly taught, but is unlikely to work well in small courses, where year-to-year differences in student cohorts can be huge—I get large year-to-year variance in my intro grad class of about 20 students, with the top of the class some years being only at the performance level of  the median in other years.)  His approach at least recognizes that the raw scores themselves are meaningless out of context, unlike people who insist on “90% or better is an A”.

 People who design large exams professionally generally have training in psychometrics (or should, anyway).  Currently, the most popular approach to designing exams that need to be taken by many people is item-response theory (IRT), in which each question gets a number of parameters expressing how difficult the question is and (for the most common 3-parameter model) how good it is at distinguishing high-scoring from low-scoring people and how much to correct for guessing.  Fitting the 3-parameter model for each question on a test requires a lot of data (certainly more than could be gathered in any of my classes), but provides a lot of information about the usefulness of a question for different purposes.  Exams for go/no-go decisions, like driving exams, should have questions that are concentrated in difficulty near the decision threshold, and that distinguish well between those above and below the threshold.  Exams for ranking large numbers of people with no single threshold (like SAT exams for college admissions in many different colleges) should have questions whose difficulty is spread out over the range of thresholds.  IRT can be used for tuning a test (discarding questions that are too difficult, too easy, or that don’t distinguish well between high-performing and low-performing students), as well as for normalizing results to be on a uniform scale despite differences in question difficulty.  With enough data, IRT can be used to get uniform scale results from tests in which individuals don’t all get presented the same questions (as long as there is enough overlap in questions that the difficulty of the questions can be calibrated fairly), which permits adaptive testing that takes less testing time to get to the same level of precision.  Unfortunately, the model fitting for IRT is somewhat sensitive to outliers in the data, so very large sample sizes are needed for meaningful fitting, which means that IRT is not a particularly useful tool for classroom tests, though it is invaluable for large exams like the SAT and GRE.
The bottom line for me is that the conventional grading scales used in many schools (with 85% as a B, for example) are uninterpretable nonsense, that do nothing to convey useful information to teachers, students, parents, or any one else.  Without a solid understanding of the difficulty of a given assessment, the scores on it mean almost nothing.

2014 October 22

Banana Slug genome crowd funding

Filed under: Uncategorized — gasstationwithoutpumps @ 21:20
Tags: , , , , ,
T-shirt design from the first offering of the class.

T-shirt design from the first offering of the class. (click for high-res image)

A few years ago, I taught a Banana Slug Genomics course, based on some sequencing done for free as a training exercise for new technician.  I’ve mentioned the course occasionally on this blog:

The initial, donated sequencing runs did not produce enough date or high enough quality data to assemble the genome to an annotatable state, though we did get a lot of snippets and a reasonable estimate of the genome size (about 2.3GB total and about 1.2GB unique, so a lot of repeats).  All the class notes are in a wiki at https://banana-slug.soe.ucsc.edu/) and the genome size estimates are at https://banana-slug.soe.ucsc.edu/bioinformatic_tools:jellyfish.

I did manage to assemble the mitochondrion after the class ended (notes at https://banana-slug.soe.ucsc.edu/computer_resources:assemblies:mitochondrion), but I now think I made a serious error in doing the assembly, treating variants due to a heterogeneous mitochondrial population as repeats instead.  The mitochondrion was relatively easy, because it is much shorter than the nuclear genome (probably in the range 23kB to 36kB, depending on whether the repeats are real) and has many more copies in the DNA library, so coverage was high enough to assemble it—the hard part was just selecting the relevant reads out of the sea of nuclear reads.

Ariolimax dolichophallus at UCSC

Ariolimax dolichophallus at UCSC, from larger image at http://commons.wikipedia.org/wiki/File:Banana_slug_at_UCSC.jpg

The banana slug genomics class has not been taught since Spring 2011, because there was no new data, and we’d milked the small amount of sequence data we had for all that we could get for it.  I’ve played with the idea of trying to get more sequence data, but Ariolimax dolichophallus is not the sort of organism that funding agencies love: it isn’t a pathogen, it isn’t a crop, it isn’t an agricultural pest, and it isn’t a popular model organism for studying basic biology. Although it has some cool biology (only capable of moving forward, genital opening on the side of its head, penis as long as its body, sex for up to 24 hours, sometimes will gnaw off penis to separate after sex, …), funding agencies just don’t see why anyone should care about the UCSC mascot.

Obviously, if anyone is ever going to determine the genome of this terrestrial mollusk, it will UCSC, and the sequencing will be done because it is a cool thing to do, not for monetary gain.  Of course, there is a lot of teaching value in having new data on an organism that is not closely related to any of the already sequenced organisms—the students will have to do almost everything from scratch, for real, as there is no back-of-the-book to look up answers in.

At one point I considered asking alumni for donations to fund more sequence data, but our dean at the time didn’t like the idea (or perhaps the course) and squelched the plan, not allowing us to send any requests to alumni. When the University started getting interested in crowd funding, I started tentative feelers with development about getting the project going, but the development people I talked with all left the University, so the project fizzled.  I had a full teaching load, so did not push for adding starting a crowd-funding campaign and teaching a course based on it to my workload.

This fall, seemingly out of nowhere (but perhaps prompted by the DNA Day celebrations last spring or by the upcoming 50-year anniversary of UCSC), I was asked what it would take to actually get a complete draft genome of the slug—someone else was interested in pushing it forward!  I talked with other faculty, and we decided that we could make some progress for about $5k–10k, and that for $20k in sequencing we could probably create a draft genome with most of the genes annotated.  This is a lot cheaper than 5 years ago, when we did the first banana slug sequencing.

Although the top tentacles of the banana slug are called eyestalks and are light sensing, they do not have vertebrate-style eyes as shown in this cartoon.  Nor do they stick out quite that much.

Although the top tentacles of the banana slug are called eyestalks and are light sensing, they do not have vertebrate-style eyes as shown in this cartoon. Nor do they stick out quite that much.

And now there is a crowd funding campaign at http://proj.at/1rqVNj8 to raise $20k to do the project right!  They even put together this silly video to advertise the project:

Nader Pourmand will supervise students building the DNA library for sequencing during the winter, and Ed Green and I will teach the grad students in the spring how to assemble and annotate the genome.  Ed has much more experience at that than me, having worked with Neanderthal, Denisovan, polar bear, allligator, and other eukaryotic genomes, while I’ve only worked on tiny prokaryotic ones. (He’s also more famous and more photogenic, which is why he is in the advertising video.) We’re both taking on this class as overload this year (it will make my 6th course, in addition to my over-300-student advising load and administrative jobs), because we really like the project. Assuming that we get good data and can assemble the slug genome into big enough pieces to find genes, we’ll put up a genome browser for the slug.

I’m hoping that this time the class can do a better job of the Wiki, so that it is easier to find things on it and there is more background information.  I’d like to make the site be a comprehensive overview of banana-slug facts and research, as well as detailed lab notebook of the process we follow for constructing the genome.

Everyone, watch the video, visit the crowd funding site, read the info there (and as much of the Wiki as you can stomach), and tell your friends about the banana-slug-sequencing effort.  (Oh, and if you feel like donating, we’ll put the money to very good use.)

Update 30 Oct 2014: UCSC has put out a press release about the project.

Update 31 Oct 2014: It looks like they’ve made a better URL for the crowd-funding project: http://crowdfund.ucsc.edu/sluggenome

« Previous PageNext Page »

The Rubric Theme. Create a free website or blog at WordPress.com.

Follow

Get every new post delivered to your Inbox.

Join 284 other followers

%d bloggers like this: