2016 April 10
2015 December 21
In On Chasing Dreams, or Not: A Post Devoid of Coherence, xykademiqz muses about whether to encourage grad students to pursue professorial positions:
Chances of not succeeding at anything competitive are much greater than of succeeding. It’s heartbreaking to fail. But is it always the best idea to discourage the people from even trying? Should we as PIs at good but non-elite schools actively try to stomp out every inclination of all our progeny to even dream of a professorship because the chances are really slim? I try to let my students know as much as I can about what the job is; for most, that’s enough to turn them off. But there are some in whom I see the fire and the combination of skills and determination, and I think they could, with coaching and some luck, make it. Should I tell them to forget it just because, even for them, the odds of not succeeding are high?
In my current field (bioinformatics), there are still reasonable chances of students getting PhD-requiring jobs (in industry or in academia), though only a few of our students will go on to get tenure-track positions in research universities. Based on the alumni information at https://www.soe.ucsc.edu/departments/biomolecular-engineering/programs/msphd-bme-bioinformatics/alumni, about ¼ of our PhD alumni have gone on to become professors (and several are still postdocs, so the number who eventually become professors may be as high as ⅓). Success probabilities of 25% are still quite good (grant funding odds have gotten way lower than that), and many of those who don’t become professors never had any intention of becoming professors, so the odds of success for those actually seeking professorial positions from our department may be as high as 40%.
It is not necessarily the best researchers who became professors, because the attractions of startups, national labs, and industrial labs can be high for those who are solely research-focussed. Instead, it is those who seek both research freedom and teaching opportunities that have been attracted to professorial jobs. In some cases, the jobs are primarily teaching, with only modest research opportunities, while in other cases the jobs are mainly research.
I’m not directly supervising any PhD students now, since I’ve stopped pursuing research grants, but I do still advise students in their first year as grad students, in an informal way. I encourage them to try teaching, and if they like it, to consider getting more practice and more training through programs like the Institute for Scientists and Educators. But I don’t assume that everyone is going to end up teaching nor that it represents the best outcome for most students.
If I were in a field where the opportunities were much more limited (like biomedical research or physics), or if I were at an institution that was not at the top of the field for the subject, I’d have a harder time figuring out how to advise students.
2015 November 23
Last Spring I got a small grant from the Academic Senate to create a new “Disciplinary Communications” course for the bioengineering majors (a $7,000 “partial course relief” for 2015–16). Most of the effort of creating the course happened last year, as we needed to offer the course in Spring 2015, but the money comes for this year. I’m not actually taking any course relief this year, though my load is lighter than last year, since I’m not doing two overload courses this year. The money (as all our course relief money) is being spent on hiring a lecturer—paying part of the salary of the lecturer teaching the new writing course.
But I felt that I ought to be doing something this year on improving “disciplinary communications” for bioengineers, in order to have something to report at the end of the year for the grant. Since the new course was designed last year, the main effort this year will be on tweaking that course and other courses our students do that involve writing. Rather than work just with the instructor of that new course, I thought it would be useful to gather all the faculty who teach writing to engineering students, to discuss (according to the message I sent out):
- course design
- teaching techniques
- grading techniques
- use of TAs or graders
- creation of a “Professional Learning Community” to meet on a regular (quarterly?) basis
There was no set agenda for the meeting—just a chance to meet and talk about what we do. We had a pretty good turnout: 3 ladder-rank faculty, 4 writing instructors, and 1 staff person who teaches writing to a small group of minority students.
After self-introductions we had a wide-ranging conversation about assignments people gave, challenges they faced, approaches to making assignments work better, and so forth. We did not talk much about TAs and graders, course design, or grading techniques, concentrating more on assignments and teaching techniques.
I’m a lousy note-taker, so I don’t have good notes of what was discussed, but I remember a few things. I’ll present them here mainly as they apply to me, since that is what I remember best.
None of the ladder-rank faculty are teaching courses where writing is the primary content of the course, but improving student writing is a secondary goal of their courses. In my case, I’m (thankfully) not teaching either the technical writing for bioengineers course nor the senior thesis writing course this year, but I do provide a fair amount of writing feedback both in the Bioinformatics: Models and Algorithms course and in the Applied Electronics course. In the bioinformatics course, there are a couple of writing assignments, but most of the feedback is on in-program documentation. In the Applied Electronics course, there is a weekly design report due, which is centered on the graphics (block diagrams, schematics, and fits of models to measured data). Other courses include assignments to write abstracts, write proposals, write standard operating procedures, and other assignments typical of both academic and industrial writing tasks.
One aspect of teaching writing that I’ve never had much luck with is peer editing—another of the ladder-rank faculty brought this topic up as one of the challenges that help was needed on. A couple of the writing instructors agreed that peer editing was hard, because the students had no notion of “editing” as an activity. What they suggested was having a set of specific questions for the peer editors to answer—questions relevant to the piece they were editing, like “what is the research question? Is there a summary of results? Is the approach clear?” for editing an abstract. Without specific guidance, students tend to fall back on the if-you-can’t-say-anything-nice-don’t-say-anything meme, and provide useless “looks good to me” comments. One technique that the faculty member who raised the issue has tried (with mixed success) is getting students to rewrite another student’s abstract in their own words. Although this often pointed out problems in the original writing, it sometimes just reflected the inability of the editing student to write coherently.
One idea that seemed to come as a bit of surprise to some of the writing instructors was creating the figures and figure captions of a document first, and then writing the paper around the figures. This is a common approach in some research groups in our department, and one that some students will have to face. One of the writing instructors pointed out that the poster assignment (used in two of the courses) is good preparation for this.
We all pretty much agreed that there was no place in the writing instruction students were getting about good presentation of data and generation of figures. I mentioned that one of our junior faculty is interested in creating a course centered on scientific graphics, but it wasn’t clear whether he’d get to teach it next year or not. I felt that students in my Applied Electronics course got a lot of instruction and got pretty good at displaying data (at least the scatter diagrams and fit models for that course), but that they really struggled with the notion of block diagrams and organizing problems into subproblems. One of the writing instructors, who saw the students mostly after they had had the applied electronics course, saw more problems with data presentation than with block diagrams. This may be because of different expectations of the block diagram, or it may be that the data representations her students needed were not among the few types covered in Applied Electronics.
Another form of writing that a lot of students were not getting adequate feedback in was lab notebooks. Unfortunately, the different disciplines have such different expectations of the content of a lab notebook that it is hard to provide any sort of standardized assignment. A couple of the instructors who teach Writing 2 classes, mainly to STEM students, do include an observational-field-notebook assignment, which at least gets across the idea of taking notes as you go, and not trying to reconstruct what you did at the end of the day (a flaw I’ve seen in several of the Applied Electronics labs) or the end of the quarter (a flaw I’ve seen in some senior theses).
We did discuss the strategy of setting high expectations on the first assignment by giving detailed feedback on that assignment, with reduced checking on subsequent assignments. This helps keep the grading down to an almost sane level, and the students still benefit from the practice, even if not everything they do is checked. I’ve certainly noticed on the bioinformatics assignments that by the 4th or 5th assignment I only need to spot-check the internal documentation, or check it for students who are struggling with the concepts of the assignment, as the better students generally are routinely producing decent documentation by then.
We discussed various things we could do that would be generally helpful, and I ended up with two action items:
- Create a shared Google Drive folder where we can put assignments and examples of student work (access limited to faculty involved in the group).
- Organize another meeting for next quarter. People were pleased enough by the meeting to want to meet again.
I don’t think that anyone will make any radical changes to how they teach as a result of the meeting, but I think that several of us came away with the nugget of an idea for a small improvement we could make. It was also very refreshing to have a discussion of teaching techniques—something we professors don’t often get a chance to engage in meaningfully. Most attempts to foster such discussions are way too broad (like the Academic Senate teaching forums) in an attempt to include everyone, or way too bureaucratic (like the attempts of the administration to push assessing “program learning outcomes”). Today’s informal discussion seemed to me to be focused enough to be productive, yet broad enough to involve many different courses. I’m looking forward to doing it again next quarter.
2015 September 1
I was complaining recently about the dearth of teaching blogs in my field(s), and serendipitously almost immediately afterwards, I read a post by lexnederbragt Active learning strategies for bioinformatics teaching:
The more I read about how active learning techniques improve student learning, the more I am inclined to try out such techniques in my own teaching and training.
I attended the third week of Titus Brown’s “NGS Analysis Workshop”. This third week entailed, as one of the participants put it, ‘the bleeding edge of bioinformatics analysis taught by Software Carpentry instructors’ and was a unique opportunity to both learn different analysis techniques, try out new instruction material, as well as experience different instructors and their way of teaching. …
I demonstrated some of my teaching and was asked by one of the students for references for the different active learning approaches I used. Rather then just emailing her, I decided to put these in this blog post.
It is good to see someone blogging about teaching bioinformatics—there aren’t many of us doing it, and most of us are more focused on research than on our pedagogical techniques. For that matter, in my bioinformatics courses, I’ve only been making minor tweaks to my teaching techniques—increasing wait time after asking questions, randomizing cold calls better, being more aware of the buildup of clutter on the whiteboard, … . Where I’ve been focusing my pedagogic attention is on my applied electronics course and (to a lesser extent) the freshman design seminar.
I’ll be starting my main bioinformatics course in just over 3 weeks, a first-quarter graduate course that is also taken by seniors doing a BS in bioinformatics. This will be the 14th time I’ve taught the course (every year since 2001, except for one year when I took a full-year sabbatical). Although the course has evolved somewhat over that time, it is difficult for me to make major changes to something I’ve taught so often—I’ve already knocked off most of the rough edges, so major changes will always seem inferior, even if they would end up being better after a year or two of tweaking. I think that major changes in the course would require a change of instructor—something that will have to be planned for, as I’ll be retiring in a few years.
My main goals in this core bioinformatics course are to teach some stochastic modeling (particularly the importance of good null models), dynamic programming (via Smith-Waterman alignment), hidden Markov models, and some Python programming. The course is pretty intense (the Python programming assignments take up a lot of time), but I think it sets the students up well for the subsequent course in computational genomics (which I do not teach) and for general bioinformatics programming in their research labs. I don’t cover de Bruijn graphs or assembly in this course—those are covered in subsequent courses, though both the exercises Lex mentions seem useful for a course that covers genome assembly.
The live-coding approach that Lex mentions in his blog seems more appropriate for an undergrad course than for a grad course. I do use that approach for teaching gnuplot in my applied electronics course, though I’ve had trouble getting students to bring their data sets and laptops to class to work on their own plots for the gnuplot classes—I’ll have to emphasize that expectation next spring.
It might be possible to use a live-coding approach near the beginning of the quarter in the bioinformatics course—on the first assignment when I’m trying to get students to learn the “yield” statement for make generators for input parsing. I’ve been thinking that a partial worked example would help students get started on the first program, so I could try live coding half the assignment, and having them finish it for their first homework.
One of the really nice things about Python is how easily one can create input handlers that spit out one item at a time and how cleanly one can interface them to one-pass algorithms. Way too many of the students have only done programming in a paradigm that reads all input, does all processing, and prints all output. Although there are some bioinformatics programs that need to work that way, most bioinformatics tasks involve too much data for that paradigm, and programs need to process data on the fly, without storing it all. Getting students to cleanly separate I/O from processing while processing only one item at time is the primary goal of the first two “warmup” Python programs in the course.
One thing I will have to demonstrate in doing the live coding is writing the docstring before writing any of the code for a routine. Students (and professional programmers) have a tendency to code first and document later, which often turns into code-first-think-later, resulting in unreadable, undebuggable code. I should probably make a bigger point of document-first coding in the gnuplot instruction also, though the level of commenting needed in gnuplot is not huge (plot scripts tend to be fairly simple programs).
2015 August 30
There is a new blog, intended for K–12 math teachers, that is dedicated to “trying to get a little bit better at questioning”: https://betterqs.wordpress.com/
I read a number of math-teacher blogs, even though I’ve not taught a math course since Spring 2003 (Honors Applied Discrete Math), because a lot of the teaching discussion is relevant to what I do teach. I also read some physics teacher blogs, for the same reason.
It would be nice if there were blogs discussing precisely the same courses and teaching challenges that I face, but I don’t know if there is anyone else in the world who teaches the same eclectic mix of courses that I do. Last year I taught a first-year grad course on bioinformatics, a how-to-be-a-grad-student course, a freshman design seminar for bioengineers, a senior thesis writing course, a grad course on assembling the banana-slug genome (co-taught with another faculty member), and a lecture/lab course on applied electronics. Over the decades I’ve been a professor, I’ve created and taught courses on an even wider range than that, including bicycle transportation engineering, desktop publishing, VLSI design, technical writing, digital synthesis of music, and most of the core computer engineering courses. At the moment, I don’t see myself creating any more new courses before I retire, unless I can hand off some of the existing courses to younger faculty.
The “better at questioning” theme of betterqs.wordpress.com is an interesting one for a teacher blog, as it focuses on one rather narrow aspect of teaching, but is open to a diversity of different subjects, different age ranges for the students, and different teaching styles. I’ve considered joining that blog as a contributor (it is open to any teacher, I believe), but I’m not sure how much I have to say about asking questions that is relevant to the math teachers who are the main audience.
I have much less time with students than K–12 teachers do (35 hours for a standard course, 95 for my intense Applied Electronics lecture+lab course), so I don’t have the luxury of slowly developing a classroom culture—I fully expect some students to still be uncomfortable with the way I teach even at the end of the course, though I attempt to get them to buy into the main purposes of the course within the first few hours of class time.
My goal in lecture classes is not to ask questions, but to get students to ask me questions—I’d rather that they figured out what they needed to know, rather than me trying to guess what holes they have based on what they get wrong on questions. I’m also not very interested in what students can do in 30 seconds—I want to know what they can do if they have adequate time to think and to look things up, so in-class questions don’t tell me much about what students need. I rely on week-long homework and papers to do that.
I mainly use in-class questions to keep students engaged in the class—asking for the next step in a derivation, for example—rather than to test their knowledge or understanding. Since engagement is my goal, I don’t generally ask students who raise their hands, but do cold calling—selecting students randomly after asking the question.
Questions in the lab are a different matter. There I’m either trying to understand what the student is attempting (“What is the corner frequency you were trying to get?”) or prompting them to learn to do debugging (“Where is your circuit schematic?” “Have you compared your wiring to your schematic?” “What voltage did you expect to see there?”).