M.E.I.

This holiday, I'm staying at the fancy-pants hotel my brother recently starting working at in Manhattan. Atop their stationary are three words: "Modern | Eclectic | Intimate." This got me thinking about the letters M, E, and I, and variations they might consider if they ever re-design their stationary. I think my revisions tell a better story.

Moderate Eccentric Inmate
Modal Electric Interstate
Module Enchanting Invertebrate
Metal Ectoplasm Inversion
Meticulous Elephant Instrument
Mucus Exposing Invalid
Mules Existing Illicitly
Metronome Elderberry Inkwell
Moss Euphonic Idlywild
Musky Encephalitic Indigestion

The Challenge of New Research

Since last spring quarter, I’ve found myself in an awkward place as I try to work on my Qualifying Paper (QP). The ideal case, as one of my Advisors recently stated, is for students to decide on a QP topic during the spring of the 1st year, to read and conceptualize over the summer, to write a proposal in the fall, to collect data in the winter, and to write up the research in the form of a QP in the spring of the 2nd year. Very few students follow this idealized path, but my process is especially odd because there are a number of fundamental assumptions about the research process built not only into the pathway as a whole, but in the particular (and particularly ordered) milestones along the way.

Over the last year, I’ve increasingly come to identify myself as a Learning Analytics (LA) researcher. LA is a field which is in its infancy, and as such has very few established norms, seminal papers, and codified methods. My belief is that, like in other areas that have undergone data revolutions, education and education research will be totally upended by LA as it matures. In a world of easily available big data, in short, it’s increasingly difficult to justify research that ignores big data, especially if the primary reason for ignoring it is discomfort or a lack of expertise, and not its irrelevance (since it can be relevant to almost any research agenda).

The implications of a coming data revolution in education for an individual researcher who is still very much “in training” are many. I find myself torn in multiple directions, needing not only to develop a broad and deep understanding of existing literature in educational research – particularly on topics that interest me – but also needing urgently to develop computational and statistical competencies that go well beyond what is traditionally taught in schools of education. Regression models don’t cut it anymore.

This raises an essential point about the research process – particularly for an apprentice researcher – at this stage in the development of LA as a field. In traditional education research, tremendous emphasis is put on the development of good research questions. This is equally important in LA research, but with a key difference: in LA the most important and interesting questions are liable to arise well after data has been collected and analysis has already begun. In traditional research, collecting data before articulating, motivating, and conceptualizing key research questions and developing or appropriating instruments would be insanity. In LA – at least for a beginner who is still developing technical competencies, in addition to conceptual ones – it’s sometimes impossible to ask the question without first knowing what the data looks like, how it can be manipulated, and what kinds of questions are liable to yield interesting results.

Now, I’ve heard in my first year and a half as a PhD student the admonition that research should never be driven by methods. But research is always driven by methods. It’s a fine ideal to say that we should ask the most important and interesting questions, regardless of the methods necessary to answer that question, but in practice that’s an impossible way to do research. Now, what I’m not talking about here is the narrow refusal of so-called “quantitative” and “qualitative” researchers to use each other’s methods to answer questions. It is patently absurd for a researcher to refuse to ask a certain type of research question, generating the answer to which would require conducting interviews, simply because his own professional expertise is in regression models. That’s what collaboration is for.

No, what I mean is that in the very definition of what makes a good research question in Education there are implicit methodological biases that go well beyond the quantitative / qualitative divide. There are a set of methods which, though vastly different from each other, generally constrain the entirety of modern educational research. These are effective methods, no doubt, in many cases. They are tried and true, tested and approved, beloved and practiced. They are what any first year PhD student in a school of Education will be trained in, or at least exposed to, as a part of the core curriculum. They are what Advisors teach their advisees. I speak, of course, of regression modeling, of survey design, of psychological 2-by-2s, of “think alouds” and semi-structured interviews, of observation protocols, of video capture, of discourse analysis, of… you get the idea: the bread and butter of research in education. This set of methods for data collection and analysis constrains research questions, not because researchers cannot ask questions that these methods cannot answer, but rather because researchers do not call such questions research questions.

If I want to know how students use language in a Massive Open Online Course, broadly, I cannot use any of those existing methodologies without significantly altering my question. Such was my first pass at developing a research question, in which I planned to do discourse analysis on a sub-set of threads in the forums, sampled based on certain key elements (namely, debates around a particular problem from one of the problem sets). To do that research, however, would have been arbitrarily limiting, driven by the methods of traditional educational research. But the data set is so much richer than that. The problem is that there is no existing literature from which to borrow research questions, and no set of accepted methods for this kind of data that can help scope a research agenda. In falling back on traditional educational research, my first efforts at crafting a study were arbitrarily limiting, driven by a lack of technical expertise.

So what is the best question to pose? The answer is: the first question that an LA researcher needs to ask is exactly, “what is the best question to pose?” The first pass at data analysis ought to be driven by theory, of course, and a sense of what questions might be interesting, but the wonder of computation and massive data sets is that it is relatively cheap to get data, relatively cheap to test hypotheses with it, and relatively easy to totally change one’s research questions. In contrast to traditional educational research, where a change in research question may totally invalidate all of the collected data, in LA research there is so much data that ignoring significant chunks of it is essential to making progress in the first place.

In my case, in particular, the question generation process is even more emergent because my computational and statistical abilities are a work in progress. I know I am interested in language use, in one form or another, but I am still a novice in the area of Natural Language Processing (NLP), and thus do not know what questions are answerable and what are not. Every time I learn to do something new computationally, the possible research questions I might be able to ask change. As research questions change with technical skill, so to do the needs and demands for conceptual literature reviews. I’ve been hesitant to commit to a single conceptual framing, therefore, and to do an in-depth literature review because almost every week the interesting and important questions look so different that the literature I need to review changes.

What’s more, I can think of only one researcher / research team in LA so far that has done much NLP (Simon Buckingham Shum’s group at the Open University in the UK). Of course I should learn from Simon, but his research alone hardly constitutes a field from which I can draw on accepted norms, best practices, and a technical or conceptual training regimen. NLP itself is a fairly well-developed field, but as I begin to learn its methods and apply them to my big educational data sets I am faced with a conundrum: spend more time building technical expertise, or use my current expertise – limited though it is – to begin to do educational research. This is an important point: LA is not, in the end, a computer science field. It uses computer science, it uses NLP, it uses machine learning, it uses database management… but at the end of the day it’s a branch of educational research, with its roots in the Learning Sciences. I suspect many LA researchers – and particularly Educational Data Mining (EDM) researchers – would disagree with that statement, valuing the higher prestige ties to computer sciences and mathematics over the less sexy relationship with education, but I think it is vitally important that we don’t let computational power totally overwhelm the theoretical and scientific knowledge generated by the last century of research in education, lest we make the classic mistake of assuming that learning is simpler than it is.

So the question is unresolved: draw on expertise from NLP and other computational fields (data visualization, for example, and data mining for the purposes of clustering before performing NLP analyses) and thereby invest my time and energy into expanding my technical base, which in turn opens up richer and more interesting avenues for research, or begin to develop a stronger conceptual base for the kinds of research questions I feel I can ask now, and make progress on completing the QP. The answer, of course, is both. The problem, of course, is that each step in the former direction heretofore has forced a (totally unanticipated) redefinition of the problem in the latter. Add to this that the process of LA research is almost necessarily collaborative (in my case it is), and the problem becomes even more intractable. I cannot arbitrarily stop my collaborators from developing new and exciting questions simply because I have now invested time and energy into better conceptualizing a prior question. Ultimately, as I build my technical expertise, and as the field adopts norms and expectations, and as training in LA becomes more formal and less ad-hoc, this problem begins to go away, both for me and those who will come after me. But for now there is a tension.

Such is the challenge of doing new research.

Poem

In memory,
  imagined language
    signals desires,
      pleading silently
        with constrained,
          subtle innuendo.
        Such secrets
      tease edges
    of devious
  minds, seeking
their owners.

Why I'm Voting for Jill Stein in 10 Arguments

As an aspiring researcher in a School of Education, it seems almost a foregone conclusion that I should be voting for Barack Obama. The Academy is famously lefty in rhetoric, but center-lefty in its actual electoral practice, which pretty much sums up the President. As a student who is actively being socialized to the academy, I have certainly felt significant pressure (not direct pressure, which is ineffective anyway, but the indirect pressure of assumed common perspective) to support President Obama in this election.

It's not just a matter of social, economic, or foreign policy, you see. It's a matter of our very livelihood in the Academy: research tends to do better under Democrats than Republicans, and especially President Obama, who is bullish on educational technology and the transformative potential of innovations like Learning Analytics (my emerging research area).

So it seems like I should vote for the President. But I'm not going to. I'm voting for Green Party nominee Jill Stein.

The following are a set of perspectives and arguments for why I would do such a crazy thing. You'll note that "I live in California, which is 'safe' for Obama" is not one of my reasons. I might think longer about my vote if I still lived in my native Colorado. But in the end I would still vote for Dr. Stein.

1) I reject the idea that politics can easily be placed on a linear spectrum.

The left-right dichotomy in political discourse serves to marginalize minority perspectives, and to ossify the debate. Because we apply broad categories called "left" and "right" to the Democrats and Republicans respectively, we ignore the complexity of actual policy-based problem solving. The Green Party, if it can be characterized as a fringe left-wing party, is a satellite of the Democrats. In reality, there is no line that can be drawn which contains all three points: Republican, Democrat, and Green. The Green Party does not offer policies "left" of the Democrats. They offer policies fundamentally and categorically different from the Democratic Party in complex ways.

2) Jill Stein is a Doctor, and she would make decisions as scientifically as possible.

Lawyers and businessmen often make good public speakers, and sometimes make good leaders. Barack Obama is both a good speaker and, I think, a good leader. But Dr. Stein is a scientist, who believes in making policy decisions based upon scientific knowledge, and not political expediency. That may be 'impractical,' but even getting the idea of data-driven policy on the table seems worthwhile to me. Too often our political discourse is dictated by sophistical pandering, so much so that we now inhabit a culture in which "you have your facts, I have mine," has become an acceptable rebuttal. It may be a futile fight, but it is worth fighting for a political discourse based on data and scientific reasoning. I don't believe either major party wants to fight that battle.

3) The Green Party has been right about climate change for decades.

Whereas the Democratic Party has certainly used rhetoric about climate change to energize its base, it has shown little to no leadership in actually passing legislation that would curb the potentially catastrophic impact of global warming. The Green Party was formed by and large because of the recognition that combating climate change and other environmental issues caused by overpopulation and international industrialization would require a paradigmatic shift in the way we organize our governments and societies. Though this has been an unpopular insight, it is increasingly proving to be true. Eventually America - and the rest of the planet - will need leaders with the foresight of people like Dr. Stein.

4) The Green Party is international.

Unlike other major political parties, the Green Party is an international organization with local branches. Their international focus is unique in global politics, and is entirely necessary at a time when the world is increasingly interconnected, and what happens in Europe or Asia has as much or more impact on what happens in the United States as what happens in Ohio. Parties that are merely national, who see "foreign policy" as a separate category of political discourse from "domestic policy," are woefully obsolete.

5) The Green Party and Dr. Stein have not been bought and sold.

I believe that Barack Obama is more idealistic than a President can afford to be. And afford is exactly the right word. In order to be elected President in the United States, you need to raise lots and lots of money. In order to raise money, you have to raise money from major corporations, who then have expectations around what kind of policies you will or will not enact. That's vastly oversimplified, of course - it's more about shaping the conversation and determining who gets to come to the table than dictating policy decisions. For example, there were no significant advocates for single payer health care allowed at the table in the Affordable Care Act conversations. That put boundaries on the debate.

Electoral necessity means Democrats and Republicans have to constrain the debate. But innovation does not come from putting narrow boundaries on conversations and limiting acceptable solutions before the conversation even begins. The biggest benefit to politicians who are not awash in corporate money is that they are free to engage in real dialogue and real problem solving.

6) We must change our democratic processes.

Related to the above point, the way we elect Presidents (and other officials) simply does not make sense, and simply is not democratic. There are several problems:

- Wealth has a disproportionate impact on an individual's political clout.
- Corporations count as "individuals," and are wealthier than any real person, and therefore more powerful.
- Politicians are by-and-large more focused on reelection than governance.
- Plurality is a poor way of choosing a winner in an election, and run-off is too expensive. We need Instant Run-off Voting.
- Along with plurality, federal representation by geography alone is outdated, as it ensures that only a limited set of ideologies get represented in the government. Most other first world countries apportion seats by party (also flawed, but better).
- Our current system creates far too much incentive to cynically manipulate voter turnout.

Raising these issues is important, because neither major party stands to gain by doing so. Supporting third parties is - in a wonderful catch-22 - the only way to raise these problems to prominence so they become a part of the discourse.

7) This is not the most important election of my lifetime.

Every single election of my lifetime has been described as the most important election of my lifetime. The truth is, none of them are (I suppose one will be, but it's hard to know now which). While the Democratic and Republican parties do offer stark differences on many issues, it's worth noting that on many fundamental issues of process (see point 6, above) there is no difference between the major parties. Because there is no difference, there is no discourse. Because there is no discourse, there is no opportunity for change. As long as we buy the hype around how important the differences between Democrats and Republicans are (and how vitally close the election is), we continue to push aside the opportunity to raise more fundamental issues. Indeed, it is particularly in states where elections are close where third parties - Green, Libertarian, Constitution, whatever - stand to have the greatest impact on the debate. That is, as long as we don't silence them.

8) Revolutionaries spoil corrupt systems.

I was going to call this point two different things. First, I was going to say: "third parties are often responsible for positive change." Then I was going to say: "third parties are not actually spoilers" (except inasmuch as they open up uncomfortable, but important conversations). I combined them into one.

To the first sub-point: it took a sustained and politically meaningful push by the Socialist Party to get FDR to actually adopt his New Deal reforms. Recall that he was President for a significant amount of time before he even began to implement reforms. Consider, more recently, how President Clinton's economic policies were at least in part shaped by the rise of Ross Perot. It is saddening that the Democratic Party, instead of adopting the pieces of the Green Party platform that made Ralph Nader so (relatively) successful in the late 90s and early 2000s, they actively worked to shut him out of the debates. Again, the lesson from history is: significant third party support demonstrates an avenue for opening up a new conversation. The Democrats of 1996-2012 have chosen instead to work to actively suppress 'marginal left-wing' Greens (see point 1).

To the second sub-point: Nader did not, in fact, cost Gore the election in 2000. For one thing, the logic of apportioning Nader votes to Gore and saying, "see the difference between Bush and Gore was smaller than the number of Nader votes" is deeply flawed. By that very logic, Pat Buchanan cost Bush more electoral votes than Nader cost Gore. But more importantly, exit polls suggest that Nader voters, if they had not voted for Nader, by and large would not have voted, period. It is naive to think that Nader was just a "more left-wing" version of Gore (again, see point 1).

In short, Nader was a "spoiler" because we re-wrote the narrative to describe it that way. In reality, though, what Nader spoiled was an already corrupt system. It was laid bare by virtue of his presence in the race.

9) The Presidency isn't as important as you think.

I am continually amazed at how important Americans think the Presidency is. Simply because the President is the most visible elected office in the country does not mean it is an all-powerful office. In reality, while President Obama may be the most powerful man in America, it would be quite easy to find two people combined (or 538 people...) who wield far more power than the President. Which is to say: even if the President gets to set an agenda in a broad sense, whether that agenda gets enacted or not has a lot to do with the congress, with the weather, with whether or not Greece stays in the Eurozone, with Chinese environmental regulations, with Mexican immigration patterns, with Iranian protesters, and so on. The President has direct control over precious little, so let's not over-emphasize his importance. Our nation was designed to ensure that no one man or woman was so important that his or her decisions would make or break our society.

10) The student debt bubble is as risky as the sub-prime mortgage bubble was.

Perhaps the most compelling argument for Barack Obama, to me, is his forward-thinking approach to education. He has instituted an office of educational technology, and the common core movement has blossomed under his administration. That said, there are still huge swaths of the educational conversation that I feel as though Jill Stein and the Green Party are willing to have that the Democrats and Republicans are not. Chief among these is the student debt bubble, and who ought to pay for education. I am perhaps naive in believing that education is one of those services for which the costs ought to be socialized, simply because an educated populace is a necessity for democracy. While the Democratic Party still uses that rhetoric, it is worth noting that modern public universities are as expensive now as private universities once were. The cost of higher education is unsustainable, as is the issuing of massive (and undischargeable in bankruptcy) student debt.

Two World Clouds

The following word clouds were inspired by the conversation this week in Education's Digital Future, a class I'm currently taking. The question: what is a University? In particular, we talked some about Stanford. While these word clouds are far from a total picture of what Stanford is, they are two interesting perspectives.

The data for the first world cloud comes from Stanford's Wikipedia page. 

What Wikipedia thinks about Stanford.

The data for the second world cloud comes from the mission statements from Stanford's six schools:

What Stanford's six schools think about Stanford.

In my opinion, the most interesting word that is in the Wikipedia page, but not in the missions, is "campus." The most interesting word that is in the mission statements but not Wikipedia is, I think, "resources," though "interdisciplinary," "collaboration," and "ideas" are notable as well.

We discussed seven metaphors for the University in class: temple, sieve, hub, incubator, mangle, quasi-sovereign, and fluid. All are fitting in certain ways, but none is a perfect metaphor. I think the mission statement word cloud emphasizes the "incubator" aspects of Stanford, in that they reflect both the commitment to training students and to producing research (and ideas). Wikipedia, on the other hand, reflects Stanford's role as a "hub" (and possibly "temple"), emphasizing its physical location and its place in history.

(Both word clouds made at Wordle.net)

MOOCtionary

Come check out the MOOCtionary. A little silly side project I'm doing as respite from my real work.

Summer School Creative Writing

I'm running my creative writing class on Tumblr this summer. Come on over and check it out.

Twenty Seven Worlds

1

Eden was never paradise. The scorched eluvium was proof that Mephistopheles was as much its master as anyone, if one would but dig. The virgin topsoil lied.

2

I sometimes wonder why so many places are named after saints. As if holiness could be inhabited without its deathly menace destroying even the most libidinous heart.

3

Dante, in his descent, came across a mausoleum of dead souls which had ceased to exist, as if this were a more horrible punishment than eternal torture.

4

Wherever I have gone, I have never ceased to carry with me homes I have never seen, let alone inhabited. Somewhere in Inverness Lady MacBeth still washes.

5

The past is no foreign country, nor is it fiction. It is a world much like our own, in which the men and women lived despite history.

6

John was also a poet. The origin of man was reason. The foundation was logic. Principally, there were stories. Translation, it seems, is the apotheosis of deception.

7

In Hawaii there is an island called Maui. Before it was covered in fake grass and tourists, it was fished from the ocean by an unsuspecting demigod.

8

Ought they to have called Venus by Aphrodite instead? The longer name makes up in grace what it loses in pith. Love is better graceful than pithy.

9

A symphony can be a love story, battling nations, or the birth of the universe. Even were it all of these, it might put someone to sleep.

10

The same infinite spaces that terrify Blaise Pascal inspire George Lucas to invent aliens with funny heads. But for each man, those infinite spaces are the same.

11

Perhaps so many movies are bland because no one bothered to come up with a better name than "movie." Or perhaps the causality is hard to decipher.

12

Virtual worlds are as real to those who live in them as fictional ones are to those who invent them. Middle Earth and Facebook matter in nonbeing.

13

I do not say that the soul exists, but I equally do not say that it does not exist. Not saying allows me to inhabit either world.

14

Supposing the Earth were a baseball, it would take a very large being indeed to grab and throw it. And what would such a being stand on?

15

A world can be as small as an automobile, if that automobile is sufficiently unusual. In particular, if it orbits a star and is actually a planet.

16

Euphemia is a dangerous place. Everything sounds more innocent than it is. Watching one's tongue can lead to a harsher world because soft words convey less meaning.

17

What would you say if I told you that Eden was not a place, but the psuedonym of a heavily tattoed adult actress of a kinky persuasion?

18

Jack Kerouac was right about women, beauty, and how you have to swing (and swing and so on). But he was wrong about the handkerchief and Buddhism.

19

Two of the wisest people I have known taught me two wise things. Good teachers know how to lie, and good scholars know how to choose failure.

20

Philosophers are good teachers and good scholars. In Artistotle's world nobody ever bothered to drop a boulder and a marble from a window. Maybe he actually knew.

21

I have driven through New Mexico on I-25, noticing that we make reservations for dinner and for the survivors of genocide. We prefer the former whenever possible.

22

If the objective of being is to achieve nonbeing, as our wantonly Westernized readings of the Buddha suggest, then Wagner may have been as wrong as Kerouac.

23

'On the other hand' is a phrase that proves that language has nothing to do with words, and everything to do with gesture, context, and Hegelian dialectic.

24

The best of the great poets have always been those who concealed beneath their flattery of the world equal measure of contempt and lust for its inhabitants.

25

In my dreams I have loved a woman in whom I saw a better me. Awake I love a woman in whom I see a better humanity.

26

Have you caught my meaning? I pray you, let me know if you have, for I fear I have once again tricked myself into believing I meant.

27

The difference between the living and the dead is that the living fabricate endless beautiful, fantastical, fictional worlds, while the dead compose all of our real ones.

Creative Writing, Draft Two

This summer I'll be teaching a Creative Writing class at Punahou for the second year in a row. Last year's class was a lot of fun, both because it gave me an opportunity to help my students tap into their creativity and because it compelled me to be creative as well. I'm looking forward to that opportunity this summer, especially as a kind of counter-balance to the kind of writing I have to do for my courses here. With a few notable exceptions, I'm rarely pushed to produce synthetically creative work as a PhD student, as analysis is the touchstone of academic discourse. That is not to condemn the academy by any means, but rather to point out that there are forms of writing it does not necessarily value, but which I still enjoy.

Last year I wrote a post about my curriculum writing process. I outlined the early stages of my backwards design process, the effort to determine an "enduring understanding" for my students, and the pitfalls of biting off too small or too big a goal. The results of this process I captured, at least in part, at the beginning of the course. I don't think I ever got around to writing a retrospective, but suffice to say the course was a blast, the students had a great time, and apparently I was well-behaved enough to be asked back this summer.

Which means that it's time to rewrite and revise. Writing a Creative Writing curriculum is a particularly recursive kind of activity, because the very principles you're trying to teach are - at least in part - the kinds of things you have to employ while constructing the course. Among these is the conviction that revision is necessary. For all of the successes in last year's course, there's so much that I want to do better, so many activities that I want to restructure, so many learning experiences that could be improved.

Perhaps the biggest weakness of my curriculum last summer was its overly workshop-y feel. That is, I treated the course more like a summer camp than a class. On some level this was appropriate, especially because I did select individual activities such that they all revolved around a small number of central themes. However, I believe that the summer school affords the opportunity for a more sustained engagement with a particular text or project than I attempted last year. With that in mind, I'm planning on expanding on a small set of activities I used last year concerning Italo Calvino's Invisible Cities.

In addition to being one of my favorite books, Invisible Cities is a wonderful jumping-off point for creative writing. It stands at the intersection of poetry and prose, traditional storytelling and vignette, abstraction and minute, concrete detail. My students struggled to understand the bits of it we tried to read in class last year. Until I sent them out to campus and had them write their own vignettes about Punahou. Invisible Punahou has been percolating in my mind ever since, and I think this year's class will create it.

So we'll spend more than a day with Calvino. We'll read him carefully, try to understand what he's doing and how he's doing it, and, most importantly, create our own version of the story. The purpose of doing so is to collaboratively create, through sustained engagement with a particular text and with a series of writing tasks and activities, a story that describes the campus that these students have spent the better part of their academic lives wandering.

There will be other revisions to the course as well, though none so significant as this sustained project. I have revamped and reworded my central purposes, making them more forceful and more meaningful. I have cut activities that didn't work last year, expanded those that needed it, and substituted in some new ideas. Above all, I have made space to better walk the tightrope that all teachers must walk between careful design and student freedom. I plan to enforce sustained engagement with Calvino, but the project will proceed very much on my students' terms. They will be the authors of whatever it is we produce. They will wield the creative power.

A Unifying Thread

Note: Long time no-see here on the blog. Such is PhD student life.


I had a conversation with my advisor yesterday in which I asked him what the unifying idea that runs through his research is. Roy has studied math, writing, science, and a host of other topics in his research, and has been at times a philosopher, at times a psychologist, and at times something resembling an ethnographer. He's a guru of video data analysis, but that's far from his only analytical methodology. In short, he's as multi-faceted as they come, and among his current advisees alone there is a huge range of interests and projects.

It should come as no surprise, however, that he could condense his work into a single sentence. He studies the way that emerging symbols and representations of knowledge can change student learning and understanding (that's not exactly what he said, but it's close). This got me thinking. One of my biggest struggles as an early PhD student has been defining my research interests. In short, I have too many. Almost no interesting question is unappealing to me, in a range of areas.

There are, however, some ideas that have started to rise to the top of my focus. In particular, I'm becoming very interested in the possibilities of two branches of what gets called "computational social science." The first is natural language processing (NLP), and the second is agent-based modeling (ABM). I won't go into detail about what those entail here, as a cursory web search will give at least a decent idea. Rather, I want to talk a little about how I've started to use these techniques, and to observe something about them that I think is salient to defining what my unifying thread is and will be.

Both NLP and ABM are ways of reconceptualizing what counts as data and how to analyze it. NLP takes text - a form of data which we usually look at according to some theory of reading (like hermeneutics) - and redefines it as a "big data" set. That is, instead of looking at specific meaning in specific places in a text, NLP can help uncover aggregate trends in the use of language. For example, a recent paper I wrote for a doctoral proseminar included an analysis of how Salman Khan uses language in his first four lessons about fractions. Watching those videos certainly tells you a lot, but even something as simple as word counts uncovers surprising information, like Khan's almost non-existent use of interrogative words like "what" or "why." That alone says nothing about his quality as a teacher, but it does say something about how he teaches, and what the technology he uses affords him from a linguistic standpoint. Needless to say, this is a project I'm hoping to expand upon.

ABM, on the other hand, is less about analyzing data and more about performing complex though-experiments that you would not otherwise be able to undertake. ABM is known in the social sciences and hard sciences for overthrowing misconceived paradigms of both human and animal activity. The classic example is bird flocking behavior. For a long time, it was believed that birds followed a "leader" bird while flocking, and that they traded off being the leader. Essentially, the hypothesis was that each individual bird just did as he was told by the leader via some arcane communication system. Enter ABM. A new hypothesis suggested that, instead of a leader, each bird followed a very small set of simple rules (namely, get closer to other birds, but not too close; move away from another bird if too close; turn to try to face the same direction as nearby other birds). Creating hundreds of randomized agents in a computer program and giving them these rules to follow produces, in a short amount of time, an almost perfect analogue to the flocking behavior we see in nature.

There has been little work using ABM in education, but another student and I are adopting and adapting some of the work of Paulo Blikstein as a part of our research assistantship. We've been working on a model that represents the process of sharing a technological artifact in a classroom task. One of Paulo's papers using ABM alongside empirical data suggests that collaborative classroom tasks often lead to the assigning of roles based upon ability, which means that more advanced students end up doing most of the cognition unless specific role sharing is assigned and enforced. Similarly, our model viewed alongside video data suggests that it is vitally important that students share representations of data while doing scientific inquiry, lest the student holding the representation do all of the cognitive work.

These two projects - running Khan's teaching through some NLP paces and analyzing inquiry data through ABM - on the surface have little in common. Except that both are ways of reconceptualizing data, as I said above, and both are computational. This, then, forms an important part of the thread that I see running through both the work that I'm doing now and the work that I've done in the past. It's not data, per se, that I'm oriented towards, but rather re-imagining the bounds of what we can do with a given idea.

The thread that unifies my thinking is this: I hope to find and use new ways of thinking about and representing ideas and questions such that seemingly oppositional knowledge structures can be synthesized so that they can become more meaningful. Essentially, I'm describing a dialectical process. However, I don't believe that its an entirely phenomenological one. That is, I don't follow Hegel the whole way: there's more than interpretation here, there's also creativity.

Necessarily, the synthesis of oppositional knowledge structures requires pulling away from the depths of either. Hyper-specialization in the pejorative sense of knowing more and more about less and less has always been counterproductive, but here is is particularly problematic. Too often, increasingly detailed knowledge about a given area does not move the area forward, except by a very narrow definition of "forward" that applies only within the field. Rather, the most important innovations in the history of almost any science come from moving backward so far that previously separate knowledge structures collide, get blown apart, and reintegrate facing a new and altogether more productive direction.

That's all oversimplified, on the one hand, and hopelessly unclear on the other. Nevertheless, I think it's a good starting point for defining an academic identity for myself that incorporates both my wide range of passions and the need to have a clear self-definition.

On a somewhat different note, I also hope that I won't be away from the blog for two full months this time. I've been struggling with where this blog fits in my academic life, whether to continue it at all, whether to start a new one, or what. For now, anyway, I've decided that this one can stay. In fact, "Nicht Diese Tone," while not the most marketable slogan, ultimately does capture very well the core of my intellectual project.

On Choosing to Learn

In my Learning Sciences and Technology Design seminar we recently read a draft of an upcoming book by Dan Schwartz, a Professor here at Stanford. He argues that we need to reconsider our assumptions about assessment in order to better capture the things we actually care about as educators. Instead of evaluating student knowledge, understanding, or even skills, he argues, we should try to assess student choices. That is, what a information or skills a student knows, and even her ability to learn further information or skills is not as important as the decision to learn.

That choices are central to how we move through the world is perhaps obvious, but it is striking how infrequently choice is a core part of any assessment framework (multiple-choice tests certainly do not count as "choice" in the sense that Dr. Schwartz means it). Even forward-thinking educators tend to focus more on acquisition of skills or overly-ambiguous notions of "metacognition" or "critical thinking" than on the specifics of decision making.  And specifics are the keys.  "Making good choices" is as bad a curricular goal as "being able to organize the following events chronologically."  A better option is a synthesis of the two, perhaps something to the effect of: "Given the task of explaining a historical event, choosing to organize parts of that event chronologically."

It seems to me that little education - indeed, even little of my own education, excellent though it has been - is organized around this kind of formulation. I believe that the most successful students end up adopting good strategies and making good choices, but Denise Pope's Doing School (about which I've written previously) suggests that a great many of our most successful students (if grades are our standard for success) in fact make terrible choices, ethically, academically, and even physically.

For my own part, I am struck by how often the schools I've attended - even Stanford - have not only tacitly accepted but in fact actively encouraged poor choices and unhealthy habits.  And I don't just mean ambiguous moral choices, but concrete habits of mind and academic practice. For example, in spite of Stanford's ostensible lack of concern over grades, their various administrative policy practically forces students to waste hours of time engaged in a gamey calculus over which courses to take credit/no-credit, and which to take for a grade. If I choose not to engage in this practice, I put myself at a disadvantage in terms of various small-scale political interactions that are inevitable in a graduate school.   On the other hand, the process itself, besides consuming intellectual energy better spend elsewhere, has undesirable effects on the way students engage with the particular courses they are not taking for a grade and the way professors engage with those students.

In other words, by separating this seemingly administrative cultural artifact from the curriculum, we've set up a situation where knowledge and skills are within the purview of the course, but where the most important actual decision making is made at an institutional level.  In graduate school - which is at least theoretically preparation for a life in the academy - this is perhaps not overly troubling, but as we shift our gaze to undergraduate or k-12 education, it's easy to see that students are often well-taught to make bad learning choices by the broader curricula and institutions in which they find themselves.  My rule of thumb for this kind of supposition is simple: if Stanford's graduate programs have a fundamental pedagogical or curricular problem, odds are that same problem can be found - and, indeed, is probably worse - at earlier stages of the education system.

So what is there to do? Unfortunately, it seems to me that this problem is virtually intractable in our current education system because it is endemic not merely to our curricula and pedagogy, but to our very culture, our policies, our institutions, and even our economics.  That is, there is a good reason we teach bad choices in schools: because our political, economic, and social systems are built upon the majority of people making bad choices.

Stop SOPA and PIPA

In support of the ongoing blackouts and various web-based protests to ongoing legislation in the US House and Senate, I encourage my readers to go to contact their representatives.  This site will tell you who to call.  Thank you.

College Education, College Sports

The United States is the only country in the world that weds higher education and amateur athletics inextricably together.  The University of Michigan, to most of us, is not just an elite post-secondary research institution, but a football team, the home of Denard Robinson, famous rival of Ohio State.  If I type "michi" into Google, auto-complete suggests "Michigan Football," "Michigan State Football," and "Michigan Football Schedule."  Somehow, this doesn't strike us as odd.

A narrow view of the phenomenon suggests that football is primarily a marketing mechanism.  Schools like Michigan attract both perspective applicants and alumni donors by virtue of having a competitive and successful football program.  Defeating rivals, landing in major bowls, and fielding exciting players are all a boon to the University as a whole because, like any good marketing, successful football leads to more visibility and more positive emotional associations with the school on the part of donors and prospectives.  All of which is true.  Football, in particular, is a wonderful way for American colleges and universities to promote their brand.

Branding alone, however, does not account for the phenomenon as a whole.  Any given university may want a great football program - or a great athletics program broadly - for branding and marketing reasons, but does the system on the whole really benefit higher education all that much?  While the University of Michigan may be better off with a great football team than with a bad one, is the entire state university system better off by virtue of their football programs?  I don't have an answer for that question, but it's worth posing.

Now, it's well-nigh impossible to imagine American higher education without competitive athletics.  College football won't be going away, even if we could somehow prove that college football is bad for universities and colleges.  That said, a better understanding of where sports fits into the ecology of higher education in the United States might help us think about hot-button issues in college athletics, including the debate over whether or not athletes should be paid.

The argument over paying college athletes - and especially big-time football and basketball players - has been hashed and rehashed in popular media.  In short, the argument for paying players is, essentially, that they are making tons of money for their schools (and for media companies) with almost no compensation.  The argument, at its heart, is capitalistic: college athletes do not get to participate in the free market for their services and skills, and that is unfair because there is a substantial market that others are participating in (coaches, ADs, ESPN, etc) built upon those very skills.  The argument against paying college athletes is, broadly, that college sports are supposed to be an amateur endeavor, and that colleges and universities are primarily geared towards educational missions.  Enrolling in a university - even on a football scholarship - means that you are a student first and foremost.  Paying athletes for playing college sports, then, would further degrade the educational mission of the university.  At its heart, what this argument really says is, "colleges and universities are not a capitalistic free market; they're a socialized public service."

Now, I'm not going to weigh in on either of those arguments, because the debate seems to me to be taking place in the wrong arena.  There's no question that college athletes ought - in a capitalist system - to be compensated at market value for the benefits they bring to their institutions.  There's also no question that colleges and universities do not by-and-large operate in anything resembling a true capitalist system.  The reality is, both sides are right because neither is willing to admit the systematic and philosophical assumptions of the other.

The debate, however, is academic, because the route we're traveling down is unequivocally the capitalistic one.  So what is that likely to look like?

In a purely capitalistic, free market higher education system institutions would have to pay their own players according to their market value.  Instead of offering a scholarship to a desired recruit, a football program would have to offer a scholarship plus a salary or contract.  In essence, college athletics would become professional, and colleges and universities would create professional athletics teams that operate alongside the University.  Realistically, a great many of the current D1 college athletics programs would not be able to afford the competition this would create, and so we'd be left with a small number of elite athletics programs at wealthy schools that can afford to fund not only athletic scholarships, but also player salaries.  As a result, the little money that travels from athletic departments to other parts of the university - in the form of both media contracts and donations - would almost certainly disappear (hence athletics would truly be "alongside" and not really "part" of the university).

From the athlete's perspective, there's nothing particularly wrong with this.  He is likely to be better off in this system than in the current one.  Unless, of course, he is a she.  Or plays a sport other than basketball and football.  Or isn't quite good enough to warrant anything beyond a scholarship.  In our current system, very few athletic departments are profitable, thanks in large part to the many sports that college athletes play but no one watches.  In a system with increased payouts to athletes in high-profile sports, it seems unlikely that those unprofitable sports programs would continue.  And in a free market, that's as it should be.  If women's volleyball can't turn a profit - or at least break even - it shouldn't exist.

Now, all of my analysis here is built upon the assumption that colleges, and not media outlets or other companies, will be paying student-athlete salaries.  I think that's a safe assumption, but someone might argue that an elimination of the amateur status of college athletes would have little effect on institutions themselves, as very very few college athletes would warrant exorbitant salaries that schools could not afford.  Rather, they might say, companies like ESPN or Nike or whoever would be the real payees in the form of sponsorships and commercial spots and so on.  That's a fair assumption, but free agency in other American - and international - sports leagues suggests to me that institutions overpaying for marginal players is a more likely outcome.

As for the educational missions of institutions of higher learning in the truly free market system, it seems to me that they will be little changed.  The biggest change is happening already anyway: less and less public money would go towards colleges and universities, which in turn would search for new revenue streams, including, most importantly, increased tuition and decreased financial aid.  Professionalization of college athletics might infuse new revenues into the higher education system, but those revenues would almost certainly be dedicated primarily (or exclusively) towards funding the professional athletics programs.  More and more universities would have to adopt the D3 sports model, where every player is a walk-on and no athletic scholarships are awarded because of how expensive it would be to field a competitive (and therefore economically viable) athletics program.

There is a good argument for this kind of system: it would allow the great majority of institutions the chance to essentially do away with one of their biggest expenses (both in terms of money and human capital).  In a free market, if the University of Hawaii cannot really afford a D1 football program (let alone D1 basketball, baseball, volleyball, etc), it simply won't have one.  In the end, that might actually be better for the university.

It's not hard to see, of course, that this is the direction we're going.  The super-conference phenomenon is a consolidation of the most economically robust football programs into a bloq that effectively excludes the University of Hawaiis of the world from the most meaningful and, therefore, lucrative events like big-time bowl games and high-profile rivalries.  It is probably only a matter of time before athlete compensation becomes a reality, and college sports is professionalized, as the institutions that can afford to pay their football players will already have divided themselves from those that cannot.  The end of the whole process is, ultimately, that college sports will be an extremely visible part of higher education, with very little actual bearing on the educational missions of those schools.

While the professionalization of college athletics may not have too direct a bearing on the educational missions of colleges and universities, the underlying trend towards increased capitalization will.  That's it's own post, but some of the results are already apparent: increased tuition, more and more course content moving online (though frequently with only dubious credentials, if any at all, available to graduates), an increasingly large access divide among socio-economic classes (that is, rich white kids who can afford tuition go to elite states schools or liberal arts colleges, while poor minority students go to vocational schools or online for-profits), and an ever-growing student debt bubble.  Which all raises a significant question that the sports debate largely ignores, but shouldn't: what is the purpose of college education, both for the student and for society at large?