Step 36: Define “Department”, “Program”, and “Major”

Oxy just had their winter faculty retreat, in which the two main topics were discussion were the content and structure of the shared freshmen sequence, and the creation of a Black Studies program. Both conversations made me think about the task of starting a computer science department and creating computer science majors – but more importantly, what it really means to be a “department” or a “program”, or even what it means for students to “major” in something, which is what I want to explore today.

These thoughts were actually a long time coming. One of my earlier posts mentioned in passing that I’m not sure what it means to major in something, an uncertainty I still hold. From a faculty perspective, defining a major is perhaps not difficult: pull together twelve classes which all relate to the discipline, make sure it generally fits what other colleges require, and that’s really it. (I’m speaking in jest, but less jest than it might seem.) For a student, though, I can’t help but think that a major is just a convenient label for what they know, but where a lot of detail is hidden.

Take, for example, my own undergraduate career. It would not be inaccurate to describe me as a computer science major – that is after all the degree I received. But that description would leave out my engineering design work, as well my the not insignificant number of courses I took in psychology, cognitive science, and education (seven courses total). I thought about minoring in psychology, but decided instead to just take the courses instead, as I didn’t want to fulfill the requirement of junior seminars and so on.

I kept some of this in mind when I rethought the computer science curriculum – this is partially why the introductory courses try to make students independently capable of writing programs, because I know many of them would not become computer science majors (or minors). Although I do not yet have advisees, I know that some of my colleagues advise students to do exactly that – take courses that interest them, and don’t worry too much about the academic institution of majors and minors. Even though it would skew enrollment statistics, I certainly agree with this approach to college courses.

At the larger scale, I think this also applies to college departments/programs. As the speaker for the faculty retreat suggested, whether it’s a black studies “program” or a black studies “department” is really five conversations in one. One of those conversations is about college resources (ie. money), and whether there is an allocated amount to spend on speakers, events, etc. A related, but slightly separate, conversation is about tenured faculty lines. At Oxy, for example, it turns out that a program becomes a department when the first tenure-track faculty is hired in that discipline; this was apparently done on accident for cognitive science, which is why we are now a department. A third conversation may be about requirements for a major, or whether it’s a major at all or simply a concentration; and a fourth conversation may be about the cohesion of the faculty. Finally, there’s also the question of how new faculty should be evaluated, especially if there are not already exiting faculty who understand the new department/program.

To be honest, I’m still not entirely sure what rights and responsibilities a department is entitled to. Computer science at Oxy is just starting to think about these issues, and at least for me it’s useful to know what can be accomplished separately from whether we’re a department, and which requires a department first. I do know that establishing a major does not require a department, even though the faculty are then in the awkward position of explaining their appointment (as I often do when introducing myself). I am still fuzzy on whether I should ask for funds from math (which technically houses the computer science program) or from cognitive science (which I am technically part of), and more personally, the composition of my tenure review committee is something of a puzzle. Some of this I will eventually figure out, but I suspect others (like the funding situation) will require collaboration between departments as well as from the administration.

The last thing I will say is that, because establishing a major and establishing a department does not occur simultaneously, one of the issues we are facing right now is deciding which should be the priority, and how we should go about achieving that goal. The major obstacle at this point is the faculty: since there are insufficiently many computer scientists at Oxy, it would be difficult to evaluation tenure cases of new computer science faculty. At the same time, trying to hire faculty who can teach, and is dedicated to, computer science, while feeling comfortable in another department, may significantly limit the applicant pool. There is of course the possibility of making a senior hire to more directly establish a department, but there are other (administrative) reasons that this may not be desirable.

If there is any moral to this post, it’s that even academia is sometimes bogged down by issues of naming. I suppose the only takeaway is that students should simply pursue whatever interests them, whether it could turn into a major/minor or not, and whether it’s offered by a department/program or not. While these decisions may loom over the faculty (at least, they loom over me), they only have an indirect effect on the lives of students – if they have any effect at all.

Step 36: Define “Department”, “Program”, and “Major”

Step 35: Respond to Christopher J. Phillips’ The New Math: A Political History

The New Math on Goodreads

[I apologize for skipping last week – I just came back from vacation and wasn’t quite back in the game.]

This book was suggested to me by a colleague when we were talking about different pedagogical philosophies/emphases in computer science. An op-ed version was published by the NYTimes, but I found the whole book engrossing. As the title suggests, the book is about the rise and fall of the New Math reforms in the late 1950s and early 1960s. Motivated by Russia’s successful launch of Sputnik, the NSF funded a reform of the K-12 mathematics curriculum, with the idea that Americans need to move away from rote memorization of calculations, and focus on what mathematicians consider core to mathematics: the structure of objects and the relations between them. For example, grade school textbooks were written that first introduced groups of objects (ie. sets), then cardinality, then unions, before finally getting to addition. The idea is that schools should teach students what mathematicians actually do, as opposed to the memorization and symbol manipulation that are normally presented as mathematics.

There are a lot of details that I won’t repeat here, but the punchline is that the fate of the reform rested entirely on political favor. It garnered attention because of the Cold War, and the belief that communist Russian children can only follow rules – much like how they merely memorize the multiplication table or rules for symbol manipulation – while the free American children would be better served by learning how to think. That is, the teaching and learning of mathematics was politicized such that it was no longer about the subject, but about “intellectual rigor and discipline” and the ability to determine the Truth. The nationalistic fervor was how the reform was funded by the NSF, but also how it was defunded later, when the Cold War lost its urgency in the late 1960s, and also when parents were aghast that their children could not rapidly provide the answer to (for example) 6*7. The radical textbooks thus fell out of favor, although the reform was not entirely erased: the presentation of connections between geometry and algebra in high school mathematics trace back to the New Math.

(Side note: I have had much of an interest in political history, but I would read a thousand pages about how governments influenced science and education.)

My own personal interest in the book is simply the substitution of computer science for mathematics in the major theme. What caught my attention first was the emphasis of the reformers that students should learning the mathematical way of thinking, and not merely mathematical “facts”. Implicitly, this is also a statement that mathematics is about the thinking and not about the calculation. Third, designing the curriculum that follows the standard mathematical approach (ie. by first providing definitions and axioms before proving some result) was a pedagogy that I had not considered before. Finally, I was hoping that I apply what I learned to the debates about K-12 computer science education. I will address these interests in reverse order, and talk about both what happened in the New Math and my thoughts on its relevant to computer science education.

Let me first talk about the New Math as a political reform. I must admit that I was shocked by the degree to which education reform was political. The author, Phillips, took great pains to omit any discussion of whether the reformers were correct in their view that students should be trained to think. This was frustrating for me at times (see below), but it highlighted how it wasn’t truly the education of children that was under discussion. The obvious parallel to computer science is the rhetoric around why earlier instruction should be required. Yes, computers are increasingly important, but just as frequently cited is the fact that other countries – especially China and India – are producing more STEM majors (proportionally). In some abstract way, this parallels the Cold War story in how the US is reacting to a diminished nationalistic pride, measured either by Sputnik or by standardized math tests.

If we take the New Math reform as a parable, this says that computer science education may fall out of favor for the same reason: that the government could either accept that other countries rank higher in STEM education, or decide that the ranks are not meaningful, and simply decide that Americans should be more (say) “well-rounded” than other students and defund computer science. The extreme version of this is unlikely, since mathematics education was already in place before the New Math; it is possible, however, for politicians to consider the use of email or other such “information technology” as computer science and only provide funding for that purpose. Regardless, it is likely trivially true that computer science education is at the mercy of political rhetoric of why it is necessary, and the history of the New Math (and of the space program) hints at what could happen.

As I mentioned above, Phillips’ book is more about the political history of the New Math than its pedagogical merits. Nonetheless, the book describes the New Math as a curriculum, which includes not only the placement of set theory as central to mathematics, but also the need to move between different perspectives (say, geometric and algebraic) of the same objects. The latter has remained in high school mathematics, so I want to say more about introducing set theory before addition. Even as I was reading, the idea struck me as either utterly fantastic or utterly idiotic.

Phillips does not offer a definitive opinion either way, remaining neutral and only reporting what contemporary mathematicians, politicians, teachers, and parents said. Although some arguments were provided for why the historical progression of mathematics should not be followed – namely, that modern mathematical development provides much better grounded – I found the explanation a little shallow. Set theory was developed until the late 1800s because not directly connected to everyday experiences, and this abstractness made it difficult to teach even to college students, never mind grad school children. The equivalent in computer science would to be first talk about Turing machines and computability, before developing programming languages as symbol systems that encapsulate some of those ideas. Students would not even know what the “computability” means without prior experience with programming, never mind the abstract idea of a universal Turing machine (which to me a while to understand it was only a mathematical model, not a physical object). It’s possible that this curriculum could work – I have no data either way – but I was astonished that the reformers, even with the help of grade school teachers, had not planned for this obstacle.

The third aspect of the New Math (for me) is the discussion of what is the nature of mathematics. Phillips writes that the mathematicians have “neatly divided the mathematical world into two camps: one that envisioned the practice of mathematics as fundamentally divorced from the subject’s usefulness, and another that believed physical applications determined the course of mathematics.” Implicit here is that mathematicians will push whatever agenda they believe in onto their students. This particularly affected me, since I am still thinking about computer science curricula and, in a broader sense, what computer science is about. I think the divides in computer science – being both a science and an engineering discipline – are not quite the same as those in math. If I had to draw the lines, I would say that there are three things:

  • Computational thinking, the ability to consider the structure and processing of information. The mathematical equivalent of this is what the New Math reformers focused on.
  • Computational skill, the ability to write correct and efficient programs. The mathematical equivalent of this, at least at the grade school level, is to be able to do mental math, although I think it would extend to the ability to prove known theorems as well.
  • Computational knowledge, having facts about how computers and specialized computer systems work. A mathematical equivalent might be the ability to explain what (say) Fermat’s last theorem is, without necessarily proving it or relating it to other contexts.

It’s uncontroversial that computer science is really all three, but if resources are limited to focus on only one of these, which one should be chosen? I am personally predisposed towards computational thinking as key, which is why I am sympathetic to the New Math reform. The issue – as occurred in the reform – is that people disagree on which aspects of computer science is most important. Their ordering in a computer science curriculum aside, it’s important to keep in mind that (contrary to the quote) that this is not a debate about the “core” of computer science, but about what should be taught to students.

The last perspective I want to take on the New Math is its philosophy of thinking as the goal of education, which is obviously related to what each instructor thinks is the core of computer science. Biased as I am towards computational thinking, the story of the New Math reform made me wonder if such a goal is attainable. It should noted that the New Math never promised to raise test scores (which made me wonder if a similar reform would succeed in today’s data-driven political culture), but that a new curriculum was necessary to develop students’ “intellectual rigor and discipline” – which is, of course, hard to test for and measure.

The question I don’t have an answer to is whether computational and mathematical thinking should be an educational goal at all, or if it develops naturally with increase in skill, regardless of what instruction is used in the classroom. My fear – despite explaining away the political causes of the New Math’s failure – is that there is no way to cultivate cross-domain transfer. I don’t have a good idea what this looks like – much as the New Math reformers can’t say what intellectual rigor looks like – and it’s possible that interdisciplinary computational thinking is not a thing at all. Still, I think there is a place for computer science in the liberal arts (that is, in the development of someone who might be considered a well-rounded human being) beyond learning about the objects capable of computation (ie. computers), and I think that is something worth setting as the goal of introductory computer science education. What the political history of the New Math reform cannot tell us is whether there is a way to confer these abstract benefits to students.

I will end with a short paragraph from the book about the theoretical and applied side of mathematics, and its ties to education and (through that) to society. I find it presents many of the concerns around the debate of mathematics and compter science education:

The issue was not reducible to a simplistic dichotomy between the backers of rote learning and those of conceptual learning. Mathematicians, teachers, and citizens have never agreed completely on the proper balance between those two poles, but few, if any, have thought that it was a matter of only one or the other. That sort of easy distinction is a red herring, drawing attention away from the more important assumption that learning mathematics counts as learning to think. One’s view of the new math was understood to be a view about the desirability of elite, technical, structural knowledge in the shaping of American minds.

Step 35: Respond to Christopher J. Phillips’ The New Math: A Political History

Step 34: Review the R1 to SLAC Transition

When I started earnestly thinking about life after grad school, I talked to as many people as possible who went to a small liberal arts college (SLAC) and asked them what it was like. I knew about halfway through grad school that I wanted to end up at a SLAC, but I slowly realized that my “standard” college experience may not generalize to liberal arts students. Both my undergraduate and graduate institutions were research universities (Research-1 or R1, after the Carnegie classification), so outside of reading about SLACs, I had no personal experience of what it meant for students and professors. It was only through informally interviewing my friends that I got some ideas of what to expect. All of them were grad students, and I thought that would provide a common experience to contrast against. Nonetheless, some things are hard to get across, and there were other things that I thought I understood but I actually didn’t. So for this post, I want to talk about what I learned from my friends and whether I found now agree with them after one semester. Maybe this will help others thinking of going through a similar transition from R1 to SLAC.

The first thing anyone will say about liberal arts colleges is that classes are smaller. At Oxy, classes with more than fifty people were rare (there have only been thirty or so such classes since the 2010 Spring semester), with classes of five students not uncommon. While class size is an obvious difference, I now think that it’s actually a shorthand for referring to other differences. I have taught discussions at Michigan with ten students before, but it was not at all like the classes here; the atmosphere is different. Part of it is that SLAC students are more willing to speak up and to answer questions, and are less likely to just sit and listen – Michigan students would only reluctantly participate in classroom activities. It’s not a matter of whether the students of one institution are “better” than the other, but I think there is a cultural difference between the two types of schools, and I’m not just referring to students. I actually find myself teaching differently to involve students more, because students have the expectation that I will, and this expectation is passed on through generations of students. (I have also grown as a teacher since I taught the ten person discussion.) I was initially afraid that I won’t have enough students in my class, but even fifteen students is a good amount of interest, and required more managing than I thought due to less lecturing in class.

Related to this, something that surprised me is that students are more willing to critique my teaching, and in fact take that role seriously. This was something I even noticed at my interview, when I had lunch with students and they had a list of questions prepared. Maybe it’s just me, but when I had lunch with faculty candidates as a graduate student, I just glanced over the candidate’s publications. Similarly, in my teaching evaluations a student actually discussed their view of the balance between lecture and active learning and which direction they would like me to shift. This is not a type of feedback I have received before, and I can’t quite tell how it’s affecting my teaching yet. I don’t think students necessarily know how to best teach a subject, but instructors can be wrong on that count as well, and I find this new pressure from students invigorating.

On to something different. Most of the people I talked to were in computer science grad school, and a number of them expressed the sentiment that they felt their undergraduate education did not adequately prepare them for graduate work. This was not a universal statement, but there was enough of a trend that it stuck in my mind. For computer science at Oxy, this is confounded by the fact that no major exists yet, so I’m still undecided about this trend. From the students I have met, however, the distribution of ability doesn’t seem that different from the students at Northwestern. I suspect that had I also sampled students who went to a research university, they would also have said that grad school was difficult – that this is a universal phenomenon. It’s also possible that it’s a difference in perceived aptitude; I will have a better answer when I teach a computer science course next semester.

One difference I did find is in the attitudes of the students regarding computer science: the students at Oxy are more open to discussing the social or other non-technical aspects of computer science. This may be partially because I am situated in the cognitive science department, but it’s also possible that this a result of the structure of liberal arts colleges. A friend suggested that the smaller student population meant that most students in computer science classes are not computer science majors (or even minors). Instead, the students are studying other subjects and involved with other activities. It’s not that they care less about computer science on an absolute level, but that they care equally or more about other things. The roster for my course next semester is more diverse than other introductory courses I have taught, but I suspect the difference is rooted more in attitude than in actual interest – that is, students at larger schools are also interested in these issues, but may just be less willing to talk to professors about them. That students enjoy programming assignments that are situated in real-world contexts, and want to learn more about them, may be evidence of this. For a cognitive science AI class, this has worked out well, although I’m slightly apprehensive of whether I can remain sufficiently broad in an actual computer science class

There is one last aspect of liberal arts colleges that I want to talk about, which is the much touted student-teacher relationship. All of my friends told me that that was one of the defining parts of their college experiences, and so I was surprised that I don’t find my interactions with students outside of class to be that different from before. I would like to think that the anomaly here is not in Oxy’s students or my friends’ descriptions, but that even at Michigan I took an interest in my student’s lives outside of the classroom. To be fair, I do think Oxy tries harder to foster these relationships, but I think I was already predisposed to them. Because I had built up this expectation, I thought that students would be more proactive in coming to office hours, although in retrospect I understand that my friends were talking more about non-academic conversations. Still, I suppose if students are interested the broader impact of topics, they might also be more willing to talk to you about them.

Despite all the differences that people suggested, I actually thought the transition into a liberal arts college from a research university went fairly smoothly. I sought out these expectations six months before I even submitted my applications, and I have no doubt they helped focus my application material. I hope that this post will help someone with theirs as well.

Step 34: Review the R1 to SLAC Transition