It’s been interesting as we’ve visited a zillion colleges this summer, which schools are helping students learn to navigate the unknown, and which are turning out editors for textbook companies :)

Since President Obama’s announcement of the Educate to Innovate program in November 2009, an encouraging number of technology and media companies, non-profit organizations and government agencies have been working in concert to strengthen the nation’s approach to science education. But the reality is that the lion’s share of transformation must come from within: from school systems, in the case of K-12 education, and from the academy, in the case of higher education.

A position paper recently issued by the Nature Publishing Group illustrates this point in the context of higher education. A significant majority, 77 percent, of the 450 faculty surveyed for the paper consider their educational responsibilities to be equally as important as research responsibilities. Only 6 percent consider research more important than education. Yet when asked to appoint a hypothetical candidate to an open tenure position in their department, the majority chose a star researcher with poor teaching skills over both a star teacher with little research background and a candidate equally skilled, though not notable, in both teaching and research.

The ripple effects of this mindset in the academy are damaging to the goals of universities.

We're not quite ready to abandon classroom learning in favor of on-line education.

It’s an interesting study, and likely to spur a whole new slew of interest in on-line courses, but it’s really not clear what it means. I’m sure there are a zillion ways I could pull together data showing an education advantage of X over Y, for a zillion Xs and Ys, but one would have to be very careful in the interpretation. I’m willing to bet that most of my colleagues here at UMM would teach better in English than Chinese, and most faculty in China would teach more effectively in Chinese than English, but that hardly means one is a better teaching language than the other. Context is everything, and it’s not clear (at least in the survey study) what the contexts are.

A few possible issues:

• 12 years is an eternity in the history of the web and web-based teaching. Are the studies from 12 years ago even talking about the same things as those now?
• What are we actually comparing? Face-to-face courses have all kinds of variance, and their effectiveness changes with the instructor, the students in a particular running of the course, and external events. Presumably on-line courses will as well. Are we comparing the best to the best? The median to the median? I can easily imagine that an on-line course of a few dozen people can be a vastly better experience than a huge lecture hall of 800 students, even if the latter is still called “face-to-face” instruction. Similarly, one person struggling to manage 150 on-line students is not likely to look good compared to an energetic classroom discussion section of 12 people. The meta-survey doesn’t make it easy to see clearly what the comparisons are in the individual surveys, and I suspect that they probably vary widely, ranging from the pretty reasonable to apples-vs-kumquats.
• How much of this is simply a function of novelty, both in faculty putting a lot of effort into a cool new thing, and students being impressed by the shiny new toy?

Etc., etc., etc.

I think the question isn’t, and can’t ever be, whether on-line is better than the classroom. In the end it’s about finding a way for a particular instructor and a particular student (or group of students) to work well together, and that’s going to depend on an awful lot of things and almost certainly change over time as teachers, students, and the world changes. On-line education and classroom education augmented with on-line components are clearly going to be an important (and probably increasing) part of that, but there will probably always be circumstances where a group of people are better served by some face time than by an on-line experience.

This study also looks at courses as isolated experiences. At a residential university like ours, the courses are crucial, but hardly the whole picture. Students learn a ton from simply living together, eating, doing laundry, volunteering, going to the movies, dating, being in clubs, and generally making all sorts of vital transitions as they move from 18 to 22 (give or take). Look at the important differences between someone’s who’s 16 and someone who’s 26, and an awful lot of that has nothing do to with courses. A good university experience can play a critical role both in and beyond the classroom, and a heck of a lot of that is tied up in physical presence.

• Models of Computing Systems
• Fuzzy logic and fuzzy sets
• Refactoring

I’ve taught Refactoring several times and have a pretty good handle on that. Fuzzy Logic I’ve taught once before and am pretty comfortable with. The Systems course, however, is one I’ve never taught before and am still struggling with on a number of levels, including the textbook.

Any suggestions and ideas on any of these would most certainly be appreciated. I’ll say a little more about each course below the fold for those who want all the gory details.

Models of Computing Systems This is one of our three core courses (the others being Algorithms and Computability, and Software Design and Development), is a 5 credit course (4 hours of lecture and a 2 hour lab each week), and is intended to expose students to computing systems using a layer model that includes as major topics

• Assembly language and a quickie overview of basic architecture
• Operating systems basics, with an emphasis on processes, process management, and threading/concurrancey
• Computer networks
• System administration, including the installation, configuration, and management of common tools like web servers

I’m planning to use x86 assembly for the first item, and the sysadmin work will happen on Linux boxes. I’m planning (still in a vague way at the moment) to try to use computer security issues to motivate/illustrate a number of key concepts in this course. Things like file system and disk structure can be pretty abstract, for example, but I’m thinking that doing a lab where we see how those decisions lead to lots of “erased” data being recoverable might make it seem more “real” and significant.

I realize that no book is going to cover all these things, and on-line resources plus lecture can provide the necessary background for several of these. This course has typically using a “standard” OS book like Silberschatz, et al, but this is large and expensive and really only addresses one of the four topics. If anyone knows of a good book that would touch meaningfully on more than one of these areas, though, that would be really helpful.

Fuzzy logic This is a 2 credit elective course, and should provide a reasonable background in the theoretical definitions and concepts in fuzzy logic, as well as giving the students a chance to apply those ideas. (My current plan is to write robot race car drivers using fuzzy notions of concepts like fast, slow, near, and straight.)

I’m probably more interested in solid coverage of the theoretical material than the applications side. The students will benefit from a good introduction and reference on the mathematical material, and I can probably handle the motivation and application side in class pretty well.

When I last taught this (Spring, 2006) I used An Introduction to Fuzzy Logic for Practical Applications by Kazuo Tanaka, translated by T. Niimura. That book actually worked quite decently. There were some predictable translation issues, but nothing that we couldn’t manage.

Refactoring This is also a 2 credit elective course. I’ve taught this course several times, and I’m likely to again use a combination of Fowler’s Refactoring and Kerievsky’s Refactoring to Patterns. Fowler is the “standard” in the field; the first five chapters of his book are absolutely classic material on the realities of software development and should be read by pretty much anyone who aspires to write good software. Kerievsky’s book builds on Fowler and does a really nice job of demystifying design patterns, converting them from magical insights codified by geniuses into things you could discover on your own through careful refactoring. When I last taught it I think I had Kerievsky as the required text and Fowler as the optional book. Given that together they still add up to less than $100, I’m tempted to require them both. We have lots of copies of Fowler in the lab, though, so I might just require Kerievsky again. Ideas/thoughts on this would definitely be appreciated.

One of the problems we’ve run into in this course in the past has been attempting refactorings on code with limited automated testing. Without good tests you lose your courage to refactor mercilessly, or you have false courage and end up breaking things without realizing it until (sometimes much) later. With only 2 credits to work with, however, you don’t want to spend two weeks writing unit tests for a system before you can start refactoring it, especially when you don’t really understand what the units are and what they’re supposed to be doing. This time I’m planning to use BDD tools like Cucumber, RSpec, and JBehave this time to more cheaply write high level acceptance/functional tests that exercise the key parts of the system in meaningful ways without getting bogged down in a bunch of poorly understood unit tests. We’ll see how that goes.

Wrap-up

So, there they be. Any thoughts, ideas, or suggestions would be greatly appreciated, especially on the Models of Computing Systems course.

Teaching kindergarteners is like herding kittens

Timna Wyckoff (one of our biologists and mother of a kindergartener) arranged to have all the local kindergarten kids comes to the science building for 90 minutes to learn a little bit about science. They were divided up into groups of about twelve, and each group spent about 30 minutes at three of the six stations we’d set up.

I talked with them about their experience using computers at school (mostly “playing games”) and how the computer did things like draw pictures on the screen. (We determined that it wasn’t elves or fairies or tiny mice with little glasses and hats that took coffee breaks when you turned the computer off.) We then talked about how computers are machines, like their fridge or a car, and let them look inside a couple of old boxes destined for the scrap heap. This led to a bit on how computers are general purpose machines instead of single purpose machines (“Can you drive your fridge to the store?”), and how what the do is determined by the program they run. It turns out that computers are in fact machines specifically designed to follow lists of instructions, and programs are lists of instructions created by computer scientists that tell the computer how to do certain things (like draw dinosaurs on the screen). We then headed into a semi-tangential (but concrete for 5 and 6 year olds) discussion of recipes as a instructions, and people as machines for following those instructions. Finally, if and as time allowed (and it varied quite a bit across my three groups), they all got numbers, stood in a line, and pretended they were a computer running through the bubble sort algorithm. (Yeah, bubble sort. Don’t shoot me – it’s easy to run through with little kids.)

I spent a total of 90 minutes doing this three times, plus some setup at the beginning and tear down at the end, and I’m exhausted. If nothing else, this reinforced my belief that a good teacher of young kids is a real treasure. These are bright, enthusiastic kids, but they don’t always focus real well, and my short morning is enough to send me scurrying back to teaching adults. (To be honest, my students don’t always focus well, but they’re much less likely to distract everyone around them in the process.)

This was my first time doing this, and my little script was an amalgam of lots of ideas from KK, Timna, and WeatherGrrrl, and various students and alum responding to my request for ideas on Twitter. Many thanks to all of them for their ideas and feedback!

photo credit: brianwallace

All OK? You’ve been soooooo quiet.

Many, many thanks for asking. It’s nice to be missed :-).

And yes, all the big things are OK, even if some of the details are a little stressful at the moment.

My wife and son are still amazing people, UMM is still home to some incredibly cool folks at all levels, and institutional corn dogs at the Student Activities Fair remain one of my favorite start-of-the-school-year rituals.

On the other hand, I’m still scrambling with some school stuff like syllabi and planning — I’m seriously crap at almost all of the mechanics of teaching, which makes my career choice unfortunate at times. (At least I like my job, though, which puts me way ahead of lots of folks.)

Computer Science at UMM is also really swamped because a search failed and we’ve got 3 faculty doing what 5 faculty (actually 6 people, two of which were half time) were doing four months ago. We canceled a couple of things, and moved a few other things around, so I think it will all work out fine, but it’s a challenge.

And on top of that, it turns out that I have arthritis in my neck that is causing my vertebrae to poke at my spinal cord in ways that express themselves annoyingly in my left arm. I got a steroid shot in my neck last week up in Fergus Falls that was (I think) helping. Unfortunately I pulled an almost-all-nighter night before last doing course prep, and may have undone much of that progress. Damn. It would be useful if I remembered that I’m no longer 18 at key moments… On the neat side, though, I’ve got the MRI images, so I have lots of cool pictures of my spine which I can give to Len Keeler‘s physics course on medical imaging. So there will be a bunch of physics students marveling at what a complete mess my back is, and getting college credit for their trouble!

So in short, everything big is all still fine. Some of the details are a pain, however, and that tends to draw energy from all the “optional” pieces of my life (blogging, Flickr, people who aren’t standing in my office door looking confused or unhappy or just glad to see me again). Hence a fair amount of Twitter, but very little that moves any closer to the paragraph form.

Sorry, and thanks for asking. Hopefully things will settle down in the next week or two. If you can’t find me in my office, I might be taking a nap on the couch in the computer science lounge.

Thus it is with very mixed feelings that I am considering changing my FYS topic for next year when I return from sabbatical. I’ve taught this for a long time and feel like I’m running out of steam on it. I also continue to struggle with lifting the subject from being about “entertainment” to being about human life and culture; I’ve found it difficult to convey my belief in the vitality of the subject. Another issue I’ve struggled with has been critical thinking. FYS replaced a course called Inquiry that had critical thinking as one of its core elements; I always thought that was very valuable, but never really felt like I included that in a consistent way in my roots music course.

So I’m considering changing topics.

In particular I’m thinking of something like “Climate change: Global crisis, or a tempest in a teapot?”. I think this is one of the (if not the) major questions of our age, and that it can be damnedly difficult to make sense of all the contradictory things said on the subject. My vision is for the class to be an exercise in critical thinking, using climate change as the underlying source of questions and material.

In a one semester, two credit course it’s clear that there’s only so much that we’re going to be able to address, so they’re not going to become experts on the subject (just as I would never claim to be one). Hopefully, however, they’d have a better understanding both of this subject, and of how to approach complex subjects like this in the future.

Thoughts?

Thanks in advance.

This wonderful little video (produced by some U of M Twin Cities mathematicians) has apparently been viewed over 1 million times now, which is a lot more views than it would ever get in class. I frequently run into faculty that are very intent on holding on to their teaching ideas and techniques, and certainly not sharing them openly with the world. They see those ideas as “their property”, to be guarded and controlled as much as possible. It’s a weird attitude, because almost none of them will ever see any money from those ideas, and the potential for wider viewing and usage is just so much greater if they open up (as in this case).

A good video like this takes a lot of time to produce, but faculty often put in huge hours on their lectures, labs, and demonstrations. Get it out there!