# Gas station without pumps

## 2011 July 17

### Skills at the center

Filed under: Uncategorized — gasstationwithoutpumps @ 12:23
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This month, a number of the teacher bloggers whose posts I read are participating in a virtual convention, started by Riley Lark.  He introduced a theme for bloggers and is collecting pointers to their posts at the “Convention Center“.  The prompt boils down to “What is at the center of your classroom?”

The current dogma is that classrooms should be “student-centered” (as opposed to “teacher-centered”, which is decried as ultimately evil) and predictably many of the posts reinforce that message.

I’ve been thinking about my courses and I can’t honestly say that they are centered on people at all.  I’m not a “people person”: I have extreme difficulty remembering names or what people look like, and I don’t enjoy small talk or listening to stories about relatives or acquaintances, which seems to be a major pleasure for some people.  So claiming that my courses are either “student-centered” or “teacher-centered” just seems wrong to me—neither is the center of the course.

Mostly I’ve taught adults (grad students and college seniors), though some classes have been for college freshmen and I’ve done a few after-school and summer things for middle-schoolers.  This focus on older students means that development and growth in the traditional sense are not a major goal of my courses, the way they might be for an elementary school teacher. The students coming out of my classes are not transformed in fundamental ways.  Many of them have had 30, 40, or even 50 years to form their personalities and their approaches to life and learning—I may be able to make some small changes in the details, but a 10-week class is not going to turn their lives around.

But I can’t honestly put the content or the curriculum at the center either.  I’ve created and taught too many different courses on a wide range of different topics (VLSI design, technical writing, digital synthesis of music, genome assembly, desktop publishing, bioinformatics, protein design, digital logic, applied discrete math, resource-efficient programming, bicycle transportation engineering, …). Although the content is important to me and I can’t teach a subject unless I know it cold, there are some common threads that run through many of my classes that transcends the specific content of the course.

Looking for those common threads in my 29 years of teaching, I see that I’m mainly interested in students developing skills.  The specific skills vary slightly between courses, but tend to be problem-solving, designing, or writing skills.  I’m not particularly interested in how many facts students learn or how quickly they can recall them, but in how well they apply what they know to new problems.

Different courses have different mixes of skills.  I was trained in mathematics and computer science, so two of the skills that come up again and again are math problem solving and computer programming.  Lectures are often devoted to introducing a skill or particular tools and techniques that the students need learn, followed by students practicing that skill and my providing feedback on their work.

For example, I want students in my genome assembly classes  to apply simple combinatorics to problems like estimating the genome size and figuring out how much sequencing is needed to get the contig lengths they need for finding genes. I don’t want to teach them a formula, but a way of looking at the data and doing back-of-the-envelope calculations (or writing a small program) to estimate what they need to know.

In my bioinformatics class, I want students to be able to write a Python program quickly to find over- and under-represented DNA palindromes in a genome (one of many ways to look for biologically relevant signals).  The specific tasks are not that important (I could replace the DNA palindrome exercise with one that looked for a set of known transcription-factor binding sites, for example), but the skills in writing programs and applying Bayesian probability to biological problems are.

The senior design projects emphasize writing, oral presentation, and debugging of designs (with some time management and group management thrown in, though I’m uncomfortable teaching those, since they are not skills I have mastered).  The bioinformatics course emphasizes programming and applying statistics.  The applied discrete math course emphasized just mathematical problem solving. Many of my grad courses combine various research skills with practice in written and oral presentation.

For senior design and project-based research classes, I end up meeting weekly with individual students (or teams),  working to help them learn to debug their designs or research protocols, as well as providing feedback on written and verbal presentations.  In one senior design course, I made each team start each meeting with a 2-minute summary of what their project was about, so that by the end of the quarter, everyone in the class could give a coherent 2-minute précis of their project without faltering.  It’s a little skill, but a useful one for job and grad school interviews, which most of the students were about to do—we’ve also introduced the 2-minute talk as an annual requirement for all our grad students.

Undoubtedly, an education professor observing my classes could analyze them for adherence to dogma, deciding that they are “student-centered” (for the time spent on feedback on student work) or “teacher-centered” (for the lecture time spent teaching tools), depending what slice of the course they choose to view.  But I think they’d be missing the point—the courses are about developing skills, which only happens with a combination of instruction, practice, and feedback on the practice.

So, my hope is that after one of my courses, students have acquired or improved some of the essential skills that will serve them well in future work.  My assessments of students are primarily intended to determine how well they have developed these skills.  This is not a quick, cheap thing to do.  There are no standardized multiple-choice tests or clicker questions.  To find out if someone can write a 20-page paper or design and implement a program to solve a problem, there is no substitute for having them write the paper or the program, and no substitute for my reading the whole thing closely.  (I have occasionally had a TA to off-load grading to, but it doesn’t help all that much, as they rarely provide a sufficiently detailed critique of writing or programming skills—sometimes because they lack these skills themselves.)

Because I’ve been part of this particular community of teacher bloggers, I’ve done a lot of thinking about standards-based grading (SBG). Unfortunately, that approach to assessment does not seem to work well for courses centered on skills that are not easily decomposed into reductionist standards.  The lack of cheap reassessment undercuts one of the main stays of the SBG approach.

My courses take a lot of my time and a lot of student time, so I can’t recommend my approach to teachers who teach a huge lecture class, who teach many classes at once, or whose students have many other courses, and I’m very afraid that this sort of intensive teaching will be thrown out in the massive budget cuts sweeping through higher education.

1. It’s interesting that you use the word “skill” to mean something synthesized, metacognitive, and critical (hope I’m not paraphrasing incorrectly). I often hear/read the word “skill” used slightly pejoratively, to indicate following instructions thoughtlessly. I don’t know why. Recently, I’ve also been coming across the acronym “KAS” a lot — “knowledge, attitudes, skills” — as if they should be identified and assessed separately. Maybe it’s to encourage people to notice the (typically less visible) knowledge and attitudes, but I can’t help thinking that splitting them up is destructive, and leads to strange situations like instructors who try to assess knowledge and attitudes without having the students do anything substantive (because that would be a skill).

The Institute for Critical Thinking also uses the word skill to refer to thinking, and your post reminded me of this quote. In interviews where they asked American college faculty how they taught critical thinking, they found that “Most faculty answered open-ended questions with vague answers, rather than clear and precise answers. In many of their answers there were internal ‘tensions’ and in some cases outright contradictions. The magic talisman were phases like ‘constructivism’, ‘Bloom’s Taxonomy’, ‘process-based’, ‘inquiry-based’, ‘beyond recall’, ‘active learning’, ‘meaning-centered’ and such like–phrases that under probing questions the majority of interviewees were unable to intelligibly explain in terms of critical thinking.”

Comment by Mylène — 2011 July 18 @ 13:26

• A “skill” is the ability to do something. It can be a early-learned one (like addition or tying shoelaces) or a high-level one (designing and implementing computer programs, writing poetry, editing a documentary, … ). Because I teach at the graduate and senior college level, I’m mainly interested in the higher-level skills, though certain low-level skills are essential (grammar, punctuation, algebra, … ).

The high-level skills are usually synthesis skills in my classes, since I teach engineering. In a science class, they may tend more towards analysis than synthesis. Knowledge is important mainly for the enabling effect it has on the skills—I don’t care how much someone knows if they can’t use the knowledge. I don’t do much assessment of knowledge (except indirectly through skills assessments) and I don’t assess attitude. I don’t down grade people for being jerks or drifters, and I don’t give them brownie points for being nice. I have found that competent people tend to have good attitudes, but I’ve met a few very nice incompetents.

You say that they asked “American college faculty”, but all the answers seem to be ones that would come only from education faculty—they don’t sound like anything my colleagues in the engineering school would say (with perhaps one or two exceptions). Indeed, all the faculty interviewed were involved in teacher training programs—the results are probably not generally applicable to college faculty.

Comment by gasstationwithoutpumps — 2011 July 18 @ 15:53

• *laugh* Absolutely right about the education faculty. Typo on my part (also, it’s the “Foundation for Critical Thinking”, not the “Institute,” apparently I was asleep at the wheel). I suspect that “student-centered” belongs in that list of ideas — many of them potentially helpful, but often ill-defined.

Comment by Mylène — 2011 July 19 @ 11:04

2. Excellent post. This pretty much sums up my approach to teaching also (I am in computer science). We are also struggling with assessment.

Comment by BKM — 2011 July 19 @ 05:23

3. “I’m not particularly interested in how many facts students learn or how quickly they can recall them, but in how well they apply what they know to new problems.”

But students cannot apply knowledge to new problems if they don’t possess that knowledge. It sounds as if you’re in agreement with cognitive scientist Daniel Willingham, who stresses the importance of learning “facts”, or “inflexible knowledge”, as the basis for higher order learning, or “expertise”. One of my beefs with K-12 education is that they often diminish the importance of acquiring inflexible knowledge.

“… before expertise is achieved, there is a large middle ground where students must acquire inflexible knowledge. Acquiring and working (including practice) with inflexible knowledge are vital steps in the educational process.

… the educational establishment has a bias against inflexible knowledge, leading schools to skip over a vital step in the learning process. They often push to teach critical thinking skills before students have sufficient knowledge of the topics about which they are supposed to be thinking critically.”

http://educationquicktakes.blogspot.com/2011/05/difference-between-rote-knowledge-and.html

Comment by Grace — 2011 July 20 @ 03:50

• I don’t see how you reached the conclusion that I’m ‘in agreement with cognitive scientist Daniel Willingham, who stresses the importance of learning “facts”, or “inflexible knowledge”, as the basis for higher order learning, or “expertise”.’

In fact, I believe more in the opposite order of things: that problem-solving and debugging skills are fundamental and facts are learned by repeated use of them in solving problems. Rote memorization was what I was worst at in school and hated the most—history classes were particularly difficult for me, as I can’t remember dates and names, and biology (over 40 years ago) was almost pure memory work. Trivial Pursuit strikes me as torture, not recreation. One of the attractions of math and computer science for me was that very little fact learning was required, but could be replaced by problem solving.

Over the years, I’ve come to realize that other people do not see math the way I do, and that they try to memorize huge piles of formulas (like the trig identities), rather than just a few key ones (like $e^{i\omega}=\cos(\omega) + i \sin(\omega)$) and derive the rest as needed. When I teach my grad students, at lot of what I do is trying to get them to think rather than memorize.

I have learned (and forgotten) many facts and factoids over the years, and it certainly helps to have odd bits of knowledge around to tinker with, but the joy is in the tinkering, not in the accumulation of factoids.

Comment by gasstationwithoutpumps — 2011 July 20 @ 07:06

• Willingham stresses the importance of learning “inflexible knowledge”, not “rote facts”. And that inflexible knowledge is fundamental in problem solving. I also just cannot see how a person bereft of knowledge can even begin to build problem solving skills. Interesting how your views are so different.

Comment by Grace — 2011 July 21 @ 05:28

• I think it may be in the nature of what we teach. Computer science, unlike history or biology, is not founded on a vast storehouse of knowledge. Yes, there are facts to be learned – you end a Java statement with a semi-colon, Richard Stallman is the guy behind GNU, HTML is used in web applications, etc, etc. These facts are not the center of the discipline though, and they tend to rapidly change. The center of the discipline is the reasoning process, the ability to abstract away from the factoids and see the commonalities. It is the ability to think in a very precise manner. The fuzzy notion of “critical thinking” that is pushed in the education world won’t cut it in computer science because the computer is so unforgiving. We have to teach students how to think precisely because in most cases, they have never had to do it before.

As an example, a stack is a very useful data structure that we usually teach in the second or third programming course. On the face of it, there isn’t much to a stack. It is a list, and the main thing you have to *know* (in the Willingham sense) is that you always push and pop from the top of the list. Many students memorize this and stop at that point. But if they stop there, they haven’t really learned much about stacks. The key thing about stacks is that they are incredibly useful in a wide range of situations. You can maintain states of things on a stack, for example, giving you the ability to move to an earlier saved state. Even that idea is kind of an abstraction.The key skill that I want my students to take away with regards to stacks is the ability to recognize situations in which a stack would be a useful construct. You can’t memorize your way through that skill. It requires the ability to see a pattern in a problem that may be a bit amorphous.

The other thing about factual knowledge in computer science is that it tends to change fast. A good software developer is one who can see the underlying abstraction behind the facts, so he or she can discard the actual *facts* quickly. Different programming languages will have different syntax for loops, or to declare pointers, for example. A poor software developer, who has memorized his or her way through a language without understanding the underlying abstraction, will have trouble moving to a new language. A good developer knows that a loop is a loop, and the syntax isn’t as important as knowing when to use a loop. Thus he or she can switch languages and environments quickly.

There is an argument to be made for the idea that software developers should be able to learn new fact-based knowledge quickly. This is in fact a key skill for working developers, and one at which many developers fail. If you are working in a healthcare company, you will need to learn vast amounts of factual knowledge about the way that hospitals operate, and this knowledge needs to be acquired quickly. If you then move to a financial company, you will have to quickly discard all those healthcare facts, and learn the ways of the financial world. However, your core computer science skills should still be there. You should still know when to use a stack, whether the data is HL7 messages or prices.

Whew! This is a long post.

Comment by BKM — 2011 July 21 @ 06:04

• Very well put! It isn’t that facts don’t matter, but that they aren’t the core. Bioinformatics has an even faster learn-and-discard pace than computer science, as different data characteristics become important. Currently for genome assembly you need to know a lot about what sorts of data you get from Illumina sequencing machines, as the read lengths, read pairing, and error models drive the choice of algorithms. A couple of years ago, SOLiD machines and 454 machines with completely different error characteristics dominated. In two more years, it may be different again, and in 10 years it certainly will be.

Similarly RNAseq experiments are replacing DNA microarrays for a lot of measurement of RNA levels, and they have totally different error models, needing different programs to determine what is or isn’t statistically significant. Previously one needed a lot of knowledge of how DNA microarrays worked in order to model the errors and avoid spurious results. Now one needs to learn about next-generation sequencing and mapping, which have totally different sorts of errors.

The flexible skill of being able to develop new algorithms as the characteristics of the data change is core—the inflexible knowledge of specific technologies has about a 4-year half-life.

Comment by gasstationwithoutpumps — 2011 July 21 @ 09:34

4. […] class time are the primary assessments, and little or no class time is used for assessment.  (See Skills at the Center for more discussion of teaching based on skills rather than on testable […]

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