Now a position of just the sort he was originally looking for is available to him in a sunny paradise. He says, "I have quite a decision to make.... it's hard to convince myself to leave the secure confines of [Big Company]. Now I see why their turnover rate is so low."

I had a hard time offering any advice. When I was growing up, my dad work for Ford Motor Company in an assembly plant, and he faced insecurity about the continuance of his job several times. I don't know how much this experience affected my outlook on jobs, but in any case my personality is one that tends to value security over big risk/big gain opportunities.

Now I hold a job with greater job security than anyone who works for a big corporation. An older colleague is fond of saying Real men don't accept tenure. I first heard him say that when I was in grad school, and I remember not getting it at all. What's not to like about tenure?

After a decade with tenure, I understand better now what he means. I always thought that the security provided by having tenure would promote taking risks, even if only of the intellectual sort. But too much security is just as likely to stunt growth and inhibit taking risks. I sometimes have to make a conscious effort to push myself out of my comfort zone. Intellectually, I feel free to try new things, but pushing myself out of a comfortable nest here into a new wnvironment -- well, that's another matter. What are the opportunity costs in that?

I love what Paul Graham says about young CS students and grads having the ability to take entrepreneurial risk, and how taking those risks may well be the safer choice in the long run. It's kind of like investing in stocks instead of bonds, I think. I encourage all of my students to give entrepreneurship a thought, and I encourage even more the ones whom I think have a significant chance to do something big. There is probably a bit of wistfulness in my encouragement, not having done that myself, but I don't think I'm simply projecting my own feelings. I really do believe that taking some employment risk, especially while young, is good for many CS grads.

But when faced with a concrete case -- a particular student having to make a particular decision -- I don't feel quite so cocksure in saying "go for it with abandon". This is not abstract theory; his job and home and fiancee are all in play. He will have to make this decision on his own, and I'd hate to push him toward something that isn't right for him from my cushy, secure seat in the tower. I feel a need to stay abstract in my advice and leave him to sort things out. Fortunately, he is a bright, level-headed guy, and I'm sure he'll do fine whichever way he chooses. I wish him luck.

A scientific genius is not a person who does what no one else can do; he or she is someone who does what it takes many others to do.

For those of us who are not geniuses, the lesson is that we can still accomplish great things -- if we take part in the right sort of collaboration and be curious, inquisitive, and open to big ideas. I think this applies not only to inventions but also to ideas for start-ups and insight to class projects.

(So go to class. You'll find people there.)

But being in a group is not a path to easy accomplishment, as people who have tried to write a book in a group know:

Talking about a "group-book" is a lot of fun. Actually putting one together, maybe less fun.

The ongoing ChiliPLoP working group of which I am a member is another datapoint for Mitzenmacher's claim. Doing more than brainstorming ideas in a groups takes all the same effort, coordination, and individual and collective responsibility as any other sort of work.

(As an aside, I love Stigler's Law as quoted in the Gladwell article linked above! Self-reference can be a joy, especially with the twist engendered by this one.)

I sometimes talk about lecture (say, here) as not being the optimal way for students to learn. That doesn't mean that I don't think it lecture has value at all. I still lecture, though I prefer to punctuate my disquisition with occasional problem breaks, during which students try out some idea. Without those breaks, the active learning they afford, and the feedback they can give students about where they stand, I sometimes wonder how much value being in class with me for seventy-five minutes has.

It turns out that there is probably value even in "just listening". Mark Guzdial recently described work by a psychology grad student that explains the relationship between learning and reading text, hearing narration, and viewing images. Most people learn more efficiently when they hear an explanation while looking at text, code, or diagrams. If they read the same explanation while looking at the text, code, or diagrams, it will take them longer to learn the material to the same depth.

So, coming to class and hearing a good lecture can be a good investment of time. It jump-starts the brain. Of course, the student still needs to read and work through problems at home later, too. Reading and solving problems give the mind an opportunity to rehearse and to process material more deeply. The result of listening to lecture followed by intense study can be a powerful form of learning.

I encourage students to take advantage of all their modalities. Augmenting lecture with opportunities for practice and feedback gives them a strong combination of learning styles in class. Then, as often as I can, I provide students with written notes that contain both my explanations and the in-class exercises we did. This allows them to recall their in-class experience as much as possible. On the occasion when students really must miss class, they can get a flavor of what happened in class, but reading the notes is a poor substitute for experiencing the class live. Then, I assign readings from a text or other sources that supplements the material with cover in class with a different presentation. Finally, I ask students to do a significant amount of project work, which gives them the chance to learn how to do while exercising the knowledge of the material in ways that make connections in their minds. I hope that this multi-faceted approach maximizes student opportunities to learn deeply and come to appreciate what they learn.

I notice a common rhetorical device in many academic arguments. It goes like this. One person makes a claim and offers some evidence. Often, the claim involves doing something new or seeing something in a new way. The next person rebuts the argument with a claim that the old way of doing or seeing things is more "fundamental" -- it is the foundation on which other ways of doing and seeing are built. Oftentimes, the rebuttal comes with no particular supporting evidence, with the claimant relying on many in the discussion to accept the claim prima facie. We might call this The Fundamental Imperative.

This device is standard issue in the CS curriculum discussions about object-oriented programming and structured programming in first-year courses. I recently noticed its use on the SIGCSE mailing list, in a discussion of what mathematics courses should be required as part of a CS major. After several folks observed that calculus was being de-emphasized in some CS majors, in favor of more discrete mathematics, one frequent poster declared:

(In a word, computer science is no longer to be considered a hard science.)

If we know [the applicants'] school well we may decide to treat them as having solid and relevant math backgrounds, but we will no longer automatically make that assumption.

Often, the conversation ends there; folks don't want to argue against what is accepted as basic, fundamental, good, and true. But someone in this thread had the courage to call out the emperor:

If you want good physicists, then hire people who have calculus. If you want good computer scientists, then hire people who have discrete structures, theory of computation, and program verification.

I don't believe that people who are doing computer science are not doing "hard science" just because it is not physics. The world is bigger than that.

...

You say "solid and relevant" when you really should be saying "relevant". The math that CS majors take is solid. It may not be immediately relevant to problems [at your company]. That doesn't mean it is not "solid" or "hard science".

I sent this poster a private "thank you". For some reason, people who drop the The Fundamental Imperative into an argument seem to think that it is true absolutely, regardless of context. Sure, there may be students who would benefit from learning to program using a "back to the basics" approach, and there may be CS students for whom calculus will be an essential skill in their professional toolkits. But that's probably not true of all students, and it may well be that the world has changed enough that most students would benefit from different preparation.

"The Fundamental Imperative" is a nice formal name for this technique, but I tend to think of it as "if it was good enough for me...", because so often it comes down to old fogies like me projecting our experience onto the future. Both parties in such discussions would do well not to fall victim to their own storytelling.

In his recent bestseller The Black Swan: The Impact of the Highly Improbable, Nassim Nicholas Taleb uses the term narrative fallacy to describe man's penchant for creating a story after the fact, perhaps subconsciously, in order to explain why something happened -- to impute a cause for an event we did not expect. This fallacy derives from our habit of imposing patterns on data. Many view this as a weakness, but I think it is a strength as well. It is good when we use it to communicate ideas and to push us into backing up our stories with empirical investigation. It is bad when we let our stories become unexamined truth and when we use the stories to take actions that are not warranted or well-founded.

Of late, I've been thinking of the narrative fallacy in its broadest sense, telling ourselves stories that justify what we see or want to see. My entry on a response to the Onward! submission by my ChiliPLoP group was one trigger. Those of us who believe strongly that we could and perhaps should be doing something different in computer science education construct stories about what is wrong and what could be better; we're like anyone else. That one OOPSLA reviewer shed a critical light on our story, questioning its foundation. That is good! It forces us to re-examine our story, to consider to what extent it is narrative fallacy and to what extent it matches reality. In the best case, we now know more about how to tell the story better and what evidence might be useful in persuading others. In the worst, we may learn that our story is a crock. But that's a pretty good worst case, because it gets us back on the path to truth, if indeed we have fallen off.

A second trigger was finding a reference in Mark Guzdial's blog to a short piece on universal programming literacy at Ken Perlin's blog. "Universal programming literacy" is Perlin's term for something I've discussed here occasionally over the last year, the idea that all people might want or need to write computer programs. Perlin agrees but uses this article to consider whether it's a good idea to pursue the possibility that all children learn to program. It's wise to consider the soundness of your own ideas every once in a while. While Perlin may not be able to construct as challenging a counterargument as our OOPSLA reviewer did, he at least is able to begin exploring the truth of his axioms and the soundness of his own arguments. And the beauty of blogging is that readers can comment, which opens the door to other thinkers who might not be entirely sympathetic to the arguments. (I know...)

It is essential to expose our ideas to the light of scrutiny. It is perhaps even more important to expose the stories we construct subconsciously to explain the world around us, because they are most prone to being self-serving or simply convenient screens to protect our psyches. Once we have exposed the story, we must adopt a stance of skepticism and really listen to what we hear. This is the mindset of the scientist, but it can be hard to take on when our cherished beliefs are on the line.

[earlier]

Them: These reports can't be combined. Please make separate ones.

Me: They will be identical.

Them: That's okay.

Me: Fine.

[later]

Them: These reports are identical. That's a problem.

Me: That's what you asked me to do.

Them: They can't be identical. Please make them different.

Me: But the committee in charge told us long ago to combine the tasks, so we did. Most of the resulting document really is one item, so we have a common report.

Them: But the reports can't be the same. Please make them different.

Me: But they aren't different.

Them: If they aren't different, the board will be unhappy.

Me: But if I make reports that differ artificially, they'll see that, too. And if they differ only a little, then it will be hard to tell where they differ.

Them: If they aren't different, the board will be unhappy. Please make them different.

Me: But...

Them: Please.

~~~~~

This is the sort of situation that makes me wonder (1) if the board really understands how universities work and (2) what I am doing in The Office.

I found it interesting that the most negative reviewer recommended the paper for acceptance. This reviewer was clearly engaged by the idea of our paper and ended up writing the most thorough, thoughtful review, challenging many of our assumptions along the way. I'd love to have the chance to engage this person in conversation at the conference. For now, I'll have to settle for pointing out some of the more colorful and interesting bits of the review.

In at least one regard, this reviewer holds the traditional view about university education. When it comes to the "significant body of knowledge that is more or less standard and that everyone in the field should acquire at some point in time", "the current lecture plus problem sets approach is a substantially more efficient and thorough way to do this."

Agreed. But isn't it more efficient to give the students a book to read? A full prof or even a TA standing in a big room is an expensive way to demonstrate standard bodies of knowledge. Lecture made more sense when books and other written material were scarce and expensive. Most evidence on learning is that lecture is actually much less effective than we professors (and the students who do well in lecture courses) tend to think.

The reviewer does offer one alternative to lecture: "setting up a competition based on mastery of these skills". Actually, this approach is consistent with the spirit of our paper's studio-based, apprenticeship-based, and project-based. Small teams working to improve their skills in order to win a competition could well inhabit the studio. Our paper tended to overemphasize the softer collaboration of an idyllic large-scale team.

This comment fascinated me:

Another issue is that this approach, in comparison with standard approaches, emphasizes work over thinking. In comparison with doing, for example, graph theory or computational complexity proofs, software development has a much lower ratio of thought to work. An undergraduate education should maximize this ratio.

Because I write a blog called Knowing and Doing, you might imagine that I think highly of the interplay between working and thinking. The reviewer has a point: an education centered on projects in a studio must be certain to engage students with the deep theoretical material of the discipline, because it is that material which provides the foundation for everything we do and which enables us to do and create new things. I am skeptical of the notion that an undergrad education should maximize the ratio of thinking to doing, because thinking unfettered by doing tends to drift off into an ether of unreality. However, I do agree that we must try to achieve an appropriate balance between thinking and doing, and that a project-based approach will tend to list toward doing.

One comment by the reviewer reveals that he or she is a researcher, not a practitioner:

In my undergraduate education I tried to avoid any course that involved significant software development (once I had obtained a basic mastery of programming). I believe this is generally appropriate for undergraduates.

Imagine the product of an English department saying, "In my undergraduate education I tried to avoid any course that involved significant composition (once I had obtained a basic mastery of grammar and syntax). I believe this is generally appropriate for undergraduates." I doubt this person would make much of a writer. He or she might be well prepared, though, to teach lit-crit theory at a university.

Most of my students go into industry, and I encourage them to take as many courses as they can in which they will build serious pieces of software with intellectual content. The mixture of thinking and doing stretches them and keeps them honest.

An education system that produces both practitioners and theoreticians must walk a strange line. One of the goals of our paper was to argue that a studio approach could do a better job of producing both researchers and practitioners than our current system, which often seems to do only a middling job by trying to cater to both audiences.

I agree wholeheartedly, though, with this observation:

A great strength of the American system is that it keeps people's options open until very late, maximizing the ability of society to recognize and obtain the benefits of placing able people in positions where they can be maximally productive. In my view this is worth the lack of focus.

My colleagues and I need to sharpen our focus so that we can communicate more effectively the notion that a system based on apprenticeship and projects in a studio can, in fact, help learners develop as researchers and as practitioners better than a traditional classroom approach.

The reviewer's closing comment expresses rather starkly the challenge we face in advocating a new approach to undergraduate education:

In summary, the paper advocates a return to an archaic system that was abandoned in the sciences for good reason, namely the inefficiency and ineffectiveness of the advocated system in transmitting the required basic foundational information to people entering the field. The write-up itself reflects naive assumptions about the group and individual dynamics that are required to make the approach succeed. I would support some of the proposed activities as part of an undergraduate education, but not as the primary approach.

The fact that so many university educators and graduates believe our current system exists in its current form because it is more efficient and effective than the alternatives -- and that it was designed intentionally for these reasons -- is a substantial cultural obstacle to any reform. Such is the challenge. We owe this reviewer our gratitude for laying out the issues so well.

In closing, I can't resist quoting one last passage from this review, for my friends in the other sciences:

The problem with putting students with no mastery of the basics into an apprenticeship position is that, at least in computer science, they are largely useless. (This is less true in sciences such as biology and chemistry, which involve shallower ideas and more menial activities. But even in these sciences, it is more efficient to teach students the basics outside of an apprenticeship situation.)

The serious truth behind this comment is the one that explains why building an effective computer science research program around undergraduates can be so difficult. The jocular truth behind it is that, well, CS is just plain deeper and harder! (I'll duck now.)

Make no effort to simplify language.

Also: Treat all students as equals, with expectation that all participate and learn. Teach where each student lacks.

I think I have a more detailed essay on this topic in me, in terms of how we teach programming and software development. But it will have to wait for another day. In any case, my agedness seems to be growing asymptotically faster than my wisdom.

Talking about patterns as grammar creates the potential for the sort of misunderstanding that Tauber discusses in his entry. Many people, including many linguists, think of grammar rules as, well, rules. I was taught to "follow the rules" in school and came to think of the rules as beyond human control. Linguists know that the rules of grammar are man-made, yet some still seem to view them as prescriptive:

It is as if these people are viewing rules of grammar like they would road rules--human inventions that one may disagree with, but which are still, in some sense, what is "correct"...

Software patterns are rarely prescriptive in this sense. They describe a construct that programmers use in a particular context to balance the forces at play in the problem. Over time, they have been found useful and so recur in similar contexts. But if a programmer decides not to use a pattern in a situation where it seems to apply, the programmer isn't "wrong" in any absolute sense. But he'll have to resolve the competing forces in some other way.

While the programmer isn't wrong, other programmers might look at him (or, more accurately, his program) funny. They will probably ask "why did you do it that way?", hoping to learn something knew, or at least confirm that the programmer has done something oddly.

This is similar to how human grammar works. If I say, "Me wrote this blog", you would be justified in looking at me funny. You'd probably think that what I speaking incorrectly.

Tauber points out that, while I might be violating the accepted rules of grammar, I'm not wrong in any absolute sense:

... most linguists focus on modeling the tacit intuitions native speakers have about their language, which are very often at odds with the "rules of grammar" learnt at school.

He gives a couple of examples of rules that we hear broken all of the time. For example, native speakers of English almost always say "It's me", not "It's I", though that violates the rules of nominative and accusative case. Are we all wrong? In Sr. Jeanne's 7th-grade English class, perhaps. But English grammar didn't fall from the heavens as incontrovertible rules; it was created by humans as a description of accepted forms of speech.

When a programmer chooses not to use a pattern, other programmers are justified in taking a second look at the program and asking "why?", but they can't really say he's guilty of anything more than doing things differently.

Like grammar rules, some patterns are more "right" than others, in the sense that it's less acceptable to break some than others. I can get away with "It's me", even in more formal settings, but I cannot get away with "Me wrote this blog", even in the most informal settings. An OO programmer might be able get away with not using the Chain of Responsibility pattern in a context where it applies, but not using Strategy or Composite in appropriate contexts just makes him look uninformed, or uneducated.

A few more thoughts:

So, patterns are not like a grammar for programming language, which is prescriptive. To speak Java at all, you have to follow the rules. They are like the grammar of a human language, which model observations about how people speak in the wild.

As a tool for teaching and learning, patterns are so useful precisely because they give us a way to learn accepted usages that go beyond the surface syntactic rules of a language. Even better, the pattern form emphasizes documenting when a construct works and why. Patterns are better than English grammar in this regard, at least better than the way English grammar is typically taught to us as schoolchildren.

There are certainly programmers, software engineers, and programming language theorists who want to tell us how to program, to define prescriptive rules. There can be value in this approach. We can often learn something from a model that has been designed based on theory and experience. But to me prescriptive models for programming are most useful when we don't feel like we have to follow them to the letter! I want to be able to learn something new and then figure out how I can use it to become a better programmer, not a programmer of the model's kind.

But there is also a huge, untapped resource in writing the descriptive grammar of how software is built in practice. It is awfully useful to know what real people do -- smart, creative people; programmers solving real problems under real constraints. We don't understand programming or software development well enough yet not to seek out the lessons learned by folks working in the trenches.

This brings to mind a colorful image, of software linguists venturing into the thick rain forest of a programming ecosystem, uncovering heretofore unexplored grammars and cultures. This may not seem as exotic as studying the Pirahã, but we never know when some remote programming tribe might upend our understanding of programming...

Brian's mention of New Math elicited some interesting comments. Kevin Lawrence hit on a point that has been on my mind in two contexts lately:

A big decision point in education is whether you are optimizing for people who will go on to be very good at a subject or for people who find it difficult.

In the context of university CS curricula, I often field complaints from colleagues here and everywhere about how the use of (graphics | games | anything post-1980 | non-scientific applications) in CS courses is dumbing down of our curriculum. These folks claim that we are spending too much time catering to folks who won't succeed in the discipline, or at least excel, and that at the same time we drive away the folks who would be good at CS but dislike the "softness" of the new approach.

In the context of reaching out to pre-university students, to show folks cool and glitzy things that they might do in computer science, I sometimes hear the same sort of thing. Be careful, folks say, not to popularize the science too much. We might mislead students into thinking that CS is not serious, or that it is easy.

I fully agree that we don't want to mislead middle schoolers or CS majors about the content or rigor of our discipline, or to give the impression that we cannot do serious and important work. But physics students and math geeks are not the only folks who can or should use computing. They are most definitely not the only folks who can make vital contributions to the discipline. (We can even learn from people who quote "King Lear".)

By not reaching out to students with different views and interests, we do computer science a disservice. Once they are attracted to the discipline and excited to learn, we can teach them all about rigor and science and math. Some of those folks won't succeed in CS, but then again neither do some of the folks who come in with the more traditional "geeky" interests.

If this topic interests you, follow the trail from Brian's blog to two blog entries by Kevin Lawrence, one old and one new. Both are worth a read. (I always knew there was a really good reason to enable comments on my blog -- Alan Kay might drop by!)