Archive for February, 2010

update: model for integrating technology into the literacy classroom

I’ve upgraded.

As part of an ongoing assignment for a course I’m taking called Computational Technologies in Educational Ecosystems, I’ve been designing and modifying a model for the role of technologies in the classroom. A previous version, a cellphone picture of a drawing on a sheet of notebook paper, looked like this:

Well. This is for a class on computational technologies, so a hand-drawn model would never do. Besides, one of the more useful affordances of new design technologies is the ease with which designs can be modified–not the case with hand-drawn designs.

So I upgraded. The upgrade looks like this:

(You can click the image to enlarge it; if it’s still too small, you can open a powerpoint version here.)

As I mentioned in my previous post, I’m focusing in on the English / Language Arts classroom–what I’ve begun to call the “literacy sciences” classroom. I’m calling it this to reflect my vision for the kind of learning that can happen in the ideal ELA classroom. It’s a community where class activities reflect the real-world practices of people engaging in authentic, valuable and valued reading and writing practices. In the real world, reading and writing practices cross multiple media and platforms; and they’re all bound up in the context for which they’re necessary and useful.

Which is why this version includes one tiny but important addition: The open door leading to other content areas. This addition was inspired by reading I’ve done this week on participatory simulations and wearable computing. Vanessa Colella’s 2000 piece, “Participatory Simulations: Building Collaborative Understanding through Immersive Dynamic Modeling,” describes one aspect of these types of simulations: That they treat the classroom as what she labels a “cognitive system.” Colella describes the cognitive system as one comprised of all people, tools, data, and discourse that are both part of and a product of class activities.

What Colella doesn’t point out is that the simulations she describes call for a cognitive system not bound by any specific content domain. Her simulation is of a fast-spreading virus similar to HIV or influenza, and though students’ primary goal is to solve the problem of how the virus spread and to whom, related social and cultural implications are hinted at and have educational potential.

Indeed, the real-world literacy practices of literacy science are not bound to any domain. It’s hard to imagine what “pure” literacy science would look like: A solitary reader, engaging in literary analysis in a room by herself, without any tools other than her eyes and her mind and her memory? Though the cognitive systems that surround literacy performances are not always clear and not always stable, one thing we can say is that they extend far beyond the domain of English / Language Arts.

We must, therefore, prepare learners for this reality by opening up the doors and letting content bleed across boundaries, and letting readers move between contexts. The problems learners must be prepared to address–the deep, thorny problems of our time–call for a breaking down of content silos.

One other addition here is the citations around the borders. These are linked to varying extent to course readings; I’ve added a few other names where relevant. Upon completion of this project, I’ll post a list of all relevant resources, in case you’re interested in perusing them.

NRA types should maybe just be quiet for a while: some thoughts on the University of Alabama shooting

I find it painfully appalling that some people are using the recent shooting on the campus of the University of Alabama-Huntsville to make arguments for looser gun control policies.

Details are still somewhat sketchy, but it appears that the perpetrator was a faculty member who was denied tenure. Biology professor Amy Bishop apparently brought a gun to a faculty meeting and, after learning she had been denied tenure for the second time in her career at Alabama, opened fire on her colleagues. Three people were killed and three others were wounded.

It beggars belief to hear some people arguing that the solution to incidents like this is actually more guns. According to msnbc, one student at the university said that she had requested that students with gun permits be allowed to carry their guns on campus and was turned down.

“I’m scared to go back to school,” (the student) said. “However, if they were to allow me to carry my pistol on campus, I would not be as scared…. I’m sorry that nobody in that room had a pistol to save at least one person’s life.”

To sum up, here’s the argument that the above student and others like her are making: that we need to allow more people to carry more weapons in more places. I reject outright such a monstrously irresponsible stance. Giving more people access to more guns is what makes America the gold-medal winner in First-World Gun Deaths.

And I don’t want to hear the argument that stricter gun control laws won’t stop gun violence since criminals and emotionally disturbed people like the woman who allegedly carried out yesterday’s campus shooting will always find ways to get their hands on weapons. That may very well be true, but looser gun control laws only make it more likely that those people will get their hands on weapons, while increasing the likelihood of more deaths resulting from their attacks.

Are you going to tell me that if anybody at that faculty meeting had been carrying a gun, they would have had the presence of mind to pull it out, aim it, and take a shot before Bishop opened fire?

Are you going to tell me that putting guns in the hands of young adults who are passing through some of the most emotionally tumultuous times in their lives is by any stretch of the imagination a smart idea? Drunk kids at house parties? Young romantics who have been spurned by the targets of their affections? Academically ambitious students for whom the C they just received in a class may end their dreams of becoming a lawyer or doctor?

Using shooting rampages to argue for looser gun control laws not only makes for a really bad argument, but it’s also socially irresponsible to an appalling degree.

One year and 235 posts later…

Today is the one-year anniversary of the establishment of this blog. I count my decision to start this blog, and after that decision the decisions to cultivate it, populate it, and spread the word about it as the most significant aspect of my developing identify as an academic.

And I don’t mean “academic” in the stuffy, yes-quite kind of way, either. I mean that the decision to start this blog–a decision that came suddenly, without much by way of any warning–was a decision to speak. It was a decision to move from “Yes, that’s something I care about, and I wish there was something I could do about it” to “Yes, I care about that, and here’s what I think about it and here’s what I’m doing to change things.”

I love blogging. It has opened doors for me. It has allowed me to say things I wouldn’t have otherwise had the space to say, to people I want to hear those things. And if I sometimes go a little overboard on extolling the virtues of blogging, it’s only because I hope for everyone to experience a similar falling away of the weights and chains that for so long kept me close to the earth.

I have a dim memory of the person I was before–a much smaller, much timider person who was horrified at the prospect of taking up too much space or too much of your time. I know that version of me is killed for good, and I’m glad for it. I hope that all of you have the chance, at least once, to experience this kind of total transformation. I hope you get the chance to experience the power of some tool, some network, some community, some practice, online or off, to change your life and trajectory and goals and plans for good.

a model for designing the ELA classroom in support of “literacy science”

You guys, I think I have a model to show you.

This makes me extremely happy, because as I’ve explained (more than once), I’ve struggled mightily with the very concept of modeling. I’ve also struggled with representation. The purpose of designing this model is to show my take on the role of new technologies in educational environments. But articulating a theory, even a working theory, about the role of technologies has been such an insurmountable challenge for me–which technologies? for which students? and for what purpose?

But the elements for building this rudimentary model have been around me for some time. It just took time and reflection for me to be able to put the elements together.

(image description: this is a pen-and-ink drawing of a classroom. In the center of the room, the class is seated, facing each other, around a square of tables; on the table in front of them are combinations of books, notebooks, and electronic equipment. Around the edges of the room are, clockwise from the upper lefthand corner: an easel labeled “representational literacy;” a table with extra pens and extra notebooks; a chalkboard with a variety of marks on it, labeled “design thinking”; book shelves; a workbench labeled “computational literacy”; open space lining most of one wall; a laptop labeled “new media literacy”; a safe filled with bundles of cash; and a laptop cart. Below the picture is the phrase, “If you can’t build it, then you don’t understand it.”)

Inspiration for this model
Design of the periphery: Multiple intelligences schools. A few years ago, I read the 25-anniversary edition of Howard Gardner’s Multiple Intelligences. Throughout the book, Gardner describes a variety of approaches to integrating his theory of multiple intelligences into learning environments, and one description–of the Key Learning Community in Indianapolis–has stuck with me. In this school, students work in “pods” that represent each type of intelligence outlined by Gardner; a founding principle of this school, he explains, “is the conviction that each child should have his or her multiple intelligences stimulated each day. Thus, every student at the school participates regularly in the activities of computing, music, and bodily-kinesthetics, in addition to mastering theme-centered curricula that embody standard literacies and subject matter.”

You don’t have to agree with this approach to appreciate its effort at offering a range of avenues for learning to happen. From time to time I think about those multiple intelligences schools and wonder what aspects might be applied to my current area of focus, the English / Language Arts classroom. Clearly, more avenues toward literacy is better than fewer avenues; and since we know that traditional literacy practices taught through traditional means are insufficient preparation for the types of literacy practices people are called upon to demonstrate in real life, we might think of “pods” for different groupings or categories of literacy learning.

Design of the center and periphery: A real life ELA classroom. I’ve had the unBELIEVABLE good luck to sit in on Becky Rupert’s ELA classroom at Aurora Alternative High School here in Bloomington, IN. Much of the design of this model is based on how she has arranged her class. To begin with, the main focus of the room is a square of tables where students meet at the beginning of each class. My model does not identify the teacher’s location; that’s because in Becky’s classroom, she sits at the table right alongside her students. She does this on purpose, and it works in service of developing a learning community.

Becky’s classroom is absolutely stuffed with books–you have to move books in order to get to other books. A new addition this year is a laptop cart, which sits against the far wall of the room.


Inclusion of design thinking: my work with SociaLens. For the last several months, I’ve been working with a new organization called SociaLens. The purpose of this organization is to consult with businesses and offer strategies for integrating new types of communications tools and ways of thinking into their organizational plans, with a particular eye toward social media technologies. Two key categories that we think make for highly adaptive, potentially highly successful organizations are new media literacies and design thinking.

Until I started working with SociaLens, I had not thought to consider the connection between these categories. I also hadn’t thought about what educational researchers can learn from corporate innovators and vice versa. But what has been seen cannot now be unseen. I’ve come to see design thinking as an essential element of literacy learning, and especially if you believe (as I do) that computational flexibility (which I’ll describe briefly below) is key to preparation for success in a new media age.


Inclusion of new media literacy, representational literacy, design thinking, & computational literacy “pods”: Some stuff I’ve read. I’ve been immersed in new media literacy research for a good chunk of years, and I drank that kool-aid long ago. If you believe in the value of teaching new media literacy practices in schools, then computational literacy kind of comes with the territory. These categories of literacy are similar in lots of respects: Both are better described as a set of proficiencies and attitudes–what Lankshear and Knobel call a combination of “technical stuff” and “ethos stuff”–than as concrete, teachable skills. Both require a kind of openness–a flexibility–to meet the quickly changing demands with emerging technologies. But new media literacies are the required skills to engage in collaborative knowledge-building or collective meaning-making or problem-solving activities, while computational literacy is, in my mind, linked to a kind of “hacker’s mentality.” It’s the act of simultaneously making use of and resisting the affordances of any technology; of knowing when and how to say “no” if a technology doesn’t meet your purposes; and of finding (or developing) a new technology that better meets your needs and interests.

Design thinking, as I mention above, comes out of my work with SociaLens and the (admittedly very surface-level) reading I’ve done about this approach to problem-solving. This type of thinking has also made an appearance in the recent work I’ve been reading about research in science and math instruction. Many researchers whose work focuses on supporting an inquiry-based focus in science instruction, in particular, emphasize the value of embracing the epistemological basis of science-as-inquiry. As William Sandoval and Brian Reiser explain in their 2004 piece, “Explanation-Driven Inquiry: Integrating Conceptual and Epistemic Scaffolds for Scientific Inquiry,” the epistemic elements of this approach include

knowledge of the kinds of questions that can be answered through inquiry, the kinds of methods that are accepted within disciplines for generating data, and standards for what count as legitimate interpretations of data, including explanations, models, and theories. Placing these epistemic aspects of scientific practice in the foreground of inquiry may help students to understand and better conduct inquiry, as well as provide a context to o
vertly examine the epistemological commitments underlying it.

Wilensky & Reisman, in their work with computer-based modeling, argue in support of what they call “the engineer’s dictum”: “If you can’t build it, then you don’t understand it.” They work with a modeling language called NetLogo, which is a loose descendant of Seymour Papert’s Logo program. The program requires students to solve problems by developing models of real-world processes like population fluctuation within predator-prey (wolf-sheep) communities and the phenomenon of fireflies synchronizing their flashes. The authors make a strong case that model-based thinking–or what we might also call “design thinking”–is key to students’ ability to engage in deep learning about a specific phenomenon and about scientific inquiry more broadly.

I included a pod for “representational literacy” in this model because of my own recent experience grappling with model-building. The ability to design, critique, and modify representational models is a set of skills with relevance across content areas, and we don’t typically think of it as extremely valuable in the literacy classroom. But it should be news to nobody that “literacy” is becoming an increasingly visual category of proficiencies, and that representational literacy is quickly becoming even more tightly bound up with traditional literacies than it ever was before.

What I haven’t yet noted is that these categories of literacy practices make up what we might call “literacy science.” I mean this term to hold the same place in the literacy classroom as “mathematician” or “scientist” or “historian” or “musician” hold in their respective classroom-based environments. As a culture, we haven’t spent enough time yet thinking about the purpose we hope the new literacy classroom to serve. Science class is supposed, ideally, to get students thinking like scientists; in math class you (ideally) learn to think like a mathematician; in history class you think like a historian; but in general English class has been designed as a sort of catch-all, a place where students can learn the basic reading and writing skills that enable them to think like historians, mathematicians, and so on.

What if we shifted the focus of the ELA classroom to more explicitly broach the notion of “literacy science”: A way of being in the (literate) world characterized by an ethos, a set of skills, and a set of norms and behaviors? What would it mean to turn the ELA classroom into a place where we support the growth of literacy scientists?


Inclusion of open space: a nod to the future work of literacy science. Howard Gardner’s list of multiple intelligences has grown over the years, and my model is designed to accommodate new categories of literacy practices. Filling up the entire classroom does nobody any good, especially since we know–we absolutely know–that new valued practices are emerging along with the breakneck speed of emergent technologies.

I should mention, too, that my model includes a safe filled with bundles of cash. This is a nod not only to the future work of literacy science but also to the current conditions of the typical public school. On top of the training required, every one of the pods in my model costs money, and it’s money that schools simply don’t have.

So that’s it: That’s my current model for the role of technologies in the literacy classroom. I would love to know your thoughts. Comments, questions, and suggestions are most welcome and will be read with great joy, thoughtfulness, and enthusiasm.

References: In case you’re interested in reading the work I identified above, here are the citations.

Wilensky, U. & Reisman, K. (2006). Thinking like a Wolf, a Sheep or a Firefly: Learning Biology through Constructing and Testing Computational Theories — an Embodied Modeling Approach. Cognition & Instruction, 24(2), pp. 171-209. http://ccl.northwestern.edu/papers/wolfsheep.pdf.

Sandoval, W., A., & Reiser, B.J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientific inquiry. Science Education, 88:3, 345-372.

on conceptual models, native competence, and (not) learning to play rugby

I had the deeply unsettling experience recently of feeling like the stupidest person in the room. This type of experience is (both fortunately and unfortunately) fairly rare for the typical educational researcher, though it’s far more common for members of the learning communities researchers study. For this reason, I believe it’s incredibly important for researchers to examine the contexts that make them feel stupid, if only so they can better understand the groups they’re studying.

The context was a graduate-level class. I’m one of just under a dozen students; the class, “Computational Technologies in Educational Ecosystems,” draws students from my university’s school of education and from the Informatics Department. A key assignment in the course is design, reflection on, and revision of a model that represents our take on the role of technologies in learning environments.

I have previously noted my despair over my apparent inability to complete this assignment in a meaningful way. The most progress I’ve been able to make was in presenting an unfinished model that draws the vaguest possible connection between humans and technology:

Then in class this week we spent a large chunk of time working with a representation developed by the instructor, the fanTASTIC Joshua Danish. His representation, which is also available on his website, is intended to point to key features of the week’s readings on cognitive tutors, Teachable Agents, and computer-aided instruction. Here’s the representation:

This representation literally carries no meaning for me. I mean, I get the basic idea behind it, but only because I did the assigned reading and get the basic themes and goals of computer-aided instruction. I get that research in this area focuses on domain-oriented issues, learning theories, and the role of these tools in classroom environments; but I do not understand how the above representation articulates this focus.

Yet I sat there in class and listened to my classmates interpreting the representation. They understood it; they could ‘read’ it; they could point to areas of weakness and suggest corrections to improve it.

The experience reminded me of the time I tried to learn rugby by joining an intramural team. After 20 minutes of basic instruction, we all got thrown into a game and the first time I got the ball, I apparently did something wrong and the team captain tackled me hard, hollering at me as she pulled me down. I never did find out what I’d done wrong. And actually, I didn’t much care. That was the last time I tried rugby.

Of course, Joshua’s never tackled anybody. He’s a fantastic teacher–one of the best I’ve ever had–who’s deeply invested in fostering an authentic learning community and supporting his students in their growth. But I sat there, watching my classmates speak a language I didn’t understand, getting more and more frustrated, and I absolutely felt like walking right off the field and never coming back.

At least two important lessons are nested in this experience, and one is linked to the other.


1. There are kids who feel this way all the time, every day. It’s easy for educational researchers to forget this point, mainly because most (though certainly not all) of us have experienced raging success in our own educational experiences. We got A’s in everything. Or we found a niche within a certain content area and pursued it with a fair amount of success. Or we figured out how to game the system, so that even if we didn’t get A’s in everything, we still felt somehow smarter than everyone else. Or if we had bad experiences with school early on, we still came to think of ourselves as smart, or at least smart enough to deserve advanced study in education.

So maybe we know in theory that schools are stacked against some kids, that the entire education system is designed on the premise that some kids will always be labeled the failures, the losers, the learning disabled, the stupid. (If it weren’t for the stupid kids, after all, how would we know what an A student is worth?) We know in theory that some kids feel frustrated and lost in school, and that some kids end up feeling like it’s hopeless to even bother trying.

But the fact is that we don’t know how it feels in practice. We can’t know how it feels. And we should never be allowed to forget this.

Even as I was feeling like the stupidest person in the room, I also felt an absolute certainty that this wasn’t my fault. Here, too, my experience diverges from that of many learners in the classrooms we study. I knew that my experience was neither right, nor fair, nor my fault; because of this, I knew to curb my strong initial impulse, which was to throw things, to disrupt the class, to walk out and never return. Instead of following my gut, I saved up all that frustration and spent it on a short burst of research. Which is how I got to my second point:


2. Modeling ability is a disposition, one that is (or is not) cultivated through sustained educational focus. Andrea diSessa calls this disposition “metarepresentational competence”; by this, he means a learner’s ability to:

  • Invent or design new representations.
  • Critique and compare the adequacy of representations and judge their suitability for various tasks.
  • Understand the purposes of representations generally and in particular contexts and understand how representations do the work they do for us.
  • Explain representations (i.e., the ability to articulate their competence with the preceding items).
  • Learn new representations quickly and with minimal instruction.

As Richard Lehrer and Leona Schauble point out, model-based reasoning is not only essential to the established practices within many varied domains, but it’s also a set of proficiencies that can and must be cultivated through focused instruction. In offering their own discussion of metarepresentational competence, they write:

Modeling is much more likely to take root and flourish in students who are building on a history of pressing toward meta-representational competence (diSessa, 2004). Developing, revising, and manipulating representations and inscriptions to figure things out, explain, or persuade others are key to modeling but are not typically nurtured in schooling. Instead, students are often taught conventional representational devices as stand-alone topics at a prescribed point in the curriculum, and may be given little or no sense of the kind of problems that these conventions were invented to address. For example, students might be taught in a formulaic manner how to construct pie graphs, but with no problem or question at hand to motivate the utility of that design over any other, students are unlikely to consider the communicational or persuasive trade-offs of that or any alternative representational form.

Though modeling has its application in most, if not all, content areas, it’s typically emphasized in science and math classes and de-emphasized or ignored in the social sciences and read
ing and writing instruction. At best, students are told to make a timeline to represent the events of the Civil War (without being shown the affordances and constraints of this sort of representation); or they’re required to make a diorama (or, now, a digital version of a diorama) to prove they understand a key scene in a literary text.

Representations don’t always take the shape of graphs or pictures; in fact, we might say that a musical score or a piece of descriptive writing is a representation in its own right. But as Lehrer and Shauble point out, a thing is only a model insofar as it is treated as such. “One might suggest,” they write, “that a pendulum is a model system for periodic motion. Yet, for most, the pendulum simply swings back and forth and does not stand in for anything other than itself.”

Some disciplines, in fact, actively resist the notion of representation, of language as representational. In a previous iteration, I was a poet and even spent several years’ worth of sustained study in an undergraduate, then a graduate, creative writing program. In the MFA program especially, I was immersed in a sustained discipline-wide effort to divorce language from its representative nature. There was an effort to fight against narrative, against what many writer-types believed was “easy” poetry. This is, as poets are wont to remind us, the basis of Postmodernism.

Though I’m in a Learning Sciences graduate program, I am by no means a scientist, at least in the more general sense of the term. This is even more true if we think of modeling as a key element of scientific practice. For multiple reasons, I do not have what diSessa calls “native competence,” which he explains is a proficiency that develops over time both in and out of school. I could point, for example, to the shame I felt in 6th grade when I was required to build a model of the solar system using styrofoam and coat hangers; my final product, the absolute best work I could have done, was pitiful and humiliating. I remember thinking: everyone else can do this; what’s wrong with me?

Now I know it’s not a problem with me but with a system of schooling, which helps me direct my rage outward but still doesn’t really solve the problem of how I’ll ever build a goddam model that makes any sort of sense to anybody at all.

In case you’re interested in reading the work I reference above, here are the citations:

diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22, 293-331.
Lehrer, R., & Schauble, L. (2006). Cultivating Model-Based Reasoning in Science Education. In R. Keith Sawyer (ed.), The Cambridge Handbook of the Learning Sciences. Cambridge: Cambridge University Press.

Lawrence Lessig on getting our democracy back

I have stated that I believe campaign finance reform to be the most significant political issue of our era. The issue was made even more pressing by the recent Supreme Court decision overturning a century’s worth of effort toward pushing lobbyists back out of politics.

Lawrence Lessig, who is perhaps the best legal thinker we have going today, makes his unbelievably compelling case for campaign finance reform in the Feb 22 issue of the Nation. He rails against

[t]he choice (made by Democrats and Republicans alike) to leave unchecked a huge and crucially vulnerable segment of our economy, which threw the economy over a cliff when it tanked (as independent analysts again and again predicted it would). Or the choice to leave unchecked the spread of greenhouse gases. Or to leave unregulated the exploding use of antibiotics in our food supply–producing deadly strains of E. coli. Or the inability of the twenty years of “small government” Republican presidents in the past twenty-nine to reduce the size of government at all. Or… you fill in the blank. From the perspective of what the People want, or even the perspective of what the political parties say they want, the Fundraising Congress is misfiring in every dimension. That is either because Congress is filled with idiots or because Congress has a dependency on something other than principle or public policy sense. In my view, Congress is not filled with idiots.

This article is called “How to Get Our Democracy Back,” but the title’s misleading: Lessig appears near to throwing his hands up in despair. There’s a petition being passed around (and a link to sign the petition closes the article); there are passing references to what Lessig appears to see as our last best hope at reform–especially since, as Lessig argues, the promises of President Obama’s campaign have fallen far short of the results he has delivered. He explains that the Obama administration

has stepped down from the high ground the president occupied on January 20, 2009, and played a political game no different from the one George W. Bush played, or Bill Clinton before him. Obama has accepted the power of the “defenders of the status quo” and simply negotiated with them. “Audacity” fits nothing on the list of last year’s activity, save the suggestion that this is the administration the candidate had promised.

I have no words of hope to finish this post off. When I think about these things, I start to feel like I did in the days immediately following the 2004 election, when more than 50 percent of the American electorate told Bush to stay right where he was. I couldn’t believe it. I couldn’t believe it.

I still have faith in President Obama, renewed some by the roar we’ve been seeing from him in the days following his recent State of the Union address. But for right now at least, I don’t want to think too hard about how his performance so far measures up to his promise. I’m worried the same yawning chasm of despair will open up and swallow me. I don’t think I could continue to stand under the weight of that disappointment.

the sleeping alone review of films: And Then Came Lola

summary: I have a big problem with this movie.

I’ve been sitting on a review of And Then Came Lola (2010), described in press materials as a “time-bending, comedic and sexy lesbian romp-loosely inspired by the art house classic Run Lola Run,” since it showed at Bloomington’s Pride Film Festival last weekend. On the one hand, yay! This film presents a welcome antivenin to the cultural poison of heterosexual action-romances, romantic comedies, action-comedic romances, thriller-romances, romantic melodramas…you get the idea. On the other hand…well, I’ll get to that in a minute.

The story is much more than loosely inspired by Run Lola Run, the 1998 German film that has a fire-haired Lola desperate to get 10,000 Deutsche Mark in 20 minutes in order to save her boyfriend’s life. The conceit of this film is that when Lola fails, she gets to try again: shot by a police officer and dying on the sidewalk, she yells “stop” and starts over, armed with an awareness of what went wrong the first time. As the story resets itself again and again, the audience is offered backstory: Lola’s relationship with Manni, her boyfriend, is not fully secure; there are doubts about whether each feels a genuine love for the other. There is a question, then, over why Lola would risk her life, again and again.

And Then Came Lola works with several of the plot points of its inspiration, not least of which is the main character’s ability to go back in time and try again. As in Run Lola Run, there is a punk with a dog; there is a homeless man; there is a beautiful woman named Lola and a camera that cannot look away from her as she runs through the streets of her city. This time, though, Lola is a photographer running through the streets of San Francisco to deliver prints to her girlfriend, Casey, who needs them right away in order to secure a Big Client. Beneath this is a backstory: Lola has issues with commitment, has issues with being dependable and on time, but thinks that Casey might be The One and wants to prove that she can change. As in Run Lola Run, this Lola needs multiple tries to secure the happy ending.

And Then Came Lola is basically a lesbian retelling of Run Lola Run, which isn’t in itself a bad thing. In this version, every character is gay (or gay-curious, as in the mixed-sex tourist couple who invite Lola to share their taxi and then put the moves on her), and the film starts from an assumption that same-sex romances are neither perfect nor fundamentally much different from heterosexual romances. And thank god for that–it’s about time we started moving beyond the startpoint of needing to justify same-sex attraction and romance.

On the other hand, for a lesbian action-romance, And Then Came Lola feels pretty heteronormative. First of all, the main characters are beautiful in a way that most straight men could probably get behind. Here are Lola and Casey, played by Ashleigh Sumner and Jill Bennett:

I don’t challenge the notion that some lesbians look like Lola and Casey (and, in fact, the actors made an appearance at the showing I attended, and they look about the same in real life as they do in the film*). But I do have a problem with a film that aligns femininity with heroism and turns anything else into comedy. In this relationship, it’s Lola who’s the problem–she’s emotionally distant and because of this, as one character explains, sex with her is “like sex with a man.” In order to get the girl, Lola has to learn to access her feelings; her big breakthrough comes when she can no longer have sex with Casey without knowing if Casey loves her.

This film is pretty overtly about sex, and its plot is pushed forward through presentation of sexual fantasy. In their fantasy, Lola and Casey get romance, with candles, caresses, and glasses of wine. They are therefore the heroes of the story.

Here are the villains: The punk with a dog is a little butch lesbian who trips Lola up again and again and, it’s revealed, has a disturbingly close relationship with her dog. The most evil villain of the movie is a lesbian parking officer, who’s presented as a fat, disheveled Latina. She’s ugly, we’re told, and also mouthy; and her fantasies are therefore presented as hilarious. They’re offered up as a joke, as comic relief.

It’s not enough, not anymore, to make films with tons of gay characters. What we need is films with tons of gay characters that also strive to complicate our understanding of sexuality, attraction, romance, and what it means to be human. And Then Came Lola would have us believe that the stereotypes are correct, that the more traditionally beautiful you are, the more right you have to your sexuality. That’s not only blatantly wrong, it’s deeply problematic, especially for a film making the rounds at LGBTQ film festivals.

*Note: I’m making a fairly big leap in assuming that Sumner and / or Bennett are gay, when it’s entirely possible that both are straight. If they are, that doesn’t negate the fact that there are plenty of lesbians who are approximately as heteronormatively beautiful as Sumner and Bennett are.

devising a model for technology in education: my version of writer’s block



I believe the following principles to hold true:

  • Human goals are mediated by, and thenceforth only achieved through, the widespread adoption and use of new technologies.*
  • Human purposes for adopting and making use of new technologies are often highly individualized (though nearly always aligned with an affinity group, even if that group is not explicitly named and even if that group is not comprised of other members of the learning community).
  • While no educational researcher is qualified to articulate achievable goals for another human, the researcher is ethically obligated to support learners in articulating, and achieving, ethical educational goals.
  • The efficacy and success of new technologies can be measured through multiple lenses, among which only one is the achievement of mainstream educational goals as articulated and assessed through traditional, often standardized, measurement tools.

If you (a) know me, (b) follow me on Twitter or a similar social network, or (c) read my blog, you know that being at a loss for something to say just doesn’t happen to me. (On the one hand, this makes me perfectly suited to social media, blogging, and academia; on the other hand, it means I’ll mouth off about the social revolution in nearly any social situation.)

But for weeks now, I’ve been trying to devise a model to represent the role of computational technologies in education. And for weeks, I’ve been failing miserably. Here’s the closest I’ve come:

As you can see, this model is incomplete. I was in the middle of drawing an arrow from that word “technology” to something else when I realized that this model would never, ever do. So I tried to approach modelling from other perspectives. I tried backing my way in, by thinking of technologies metaphorically; I’ve tried presenting technology integration in the form of a decision tree. Which is fine, except that these don’t really work as models.

And I have to come up with a model. I do. Though I don’t often mention this, I’m not actually only a blogger. In real life, I’m a graduate student in Indiana University’s Learning Sciences Program. Because I believe in the value of public intellectual discourse, I’ve chosen to present as much of my coursework as possible on my blog or through other public, persistent and searchable communications platforms.

I will, at some future point, discuss the challenges and benefits of living up to this decision. For now, you guys, I just need to come up with a goddam model that I can live with.

I tried thinking of technologies as sleeping policemen; or, in other words, as objects that mediate our thoughts and actions and that have both intended and unintended consequences. This was a reaction to a set of readings including a chunk of Bonnie Nardi’s and Vicki O’Day’s 1999 book, Information Ecology: Using Technology with Heart; a Burbules & Callister piece from the same year, “The Risky Promises and Promising Risks of New Information Technologies for Education”; and Stahl & Hesse’s 2009 piece, “Practice perspectives in CSCL.” The theme of these writings was: We need to problematize dominant narratives about the role of technologies in education. Burbules & Callister categorize these narratives as follows:

  • computer as panacea (“New technologies will solve everything!”)
  • computer as [neutral] tool (“Technologies have no purpose built into them, and can be used for good or evil!”)
  • computer as [nonneutral] tool (the authors call this “(a) slightly more sophisticated variant” on the “computer as tool perspective”)
  • balanced approach to computer technologies (neither panacea nor tool, but resources with intended and unintended social consequences)

Nardi & O’Day, who basically agree with the categories identified above, argue for the more nuanced approach that they believe emerges when we think of technologies as ecologies, a term which they explain is

intended to evoke an image of biological ecologies with their complex dynamics and diverse species and opportunistic niches for growth. Our purpose in using the ecology metaphor is to foster thought and discussion, to stimulate conversations for action…. [T]he ecology metaphor provides a distinctive, powerful set of organizing properties around which to have conversations. The ecological metaphor suggests several key properties of many environments
in which technology is used.

Which is all fine and dandy, except the argument that precedes and follows the above quote is so tainted by mistrust and despair over the effects of new technologies that it’s hard to imagine that even Nardi and O’Day themselves can believe they’ve presented a balanced analysis. Reading their description of techno-ecologies is kind of like reading a book about prairie dog ecologies prefaced by a sentence like “Jesus Christ I hate those freaking prairie dogs.”

So the description of technologies as sleeping policemen was an effort to step back and describe, with as much detachment as possible for an admitted technorevolutionary like me, the role of technologies in mediating human activity.

But the metaphor doesn’t really have much by way of practical use. What am I going to do, take that model into the classroom and say, well, here’s why your kids aren’t using blogs–as you can see (::points to picture of speed bump::), kids are just driving around the speed bump instead of slowing down….?

This became clear as I jumped into a consideration of so-called “intelligent tutors,” which I described briefly in a previous post. Or, well, the speed bump metaphor might work, but only if we can come up with some agreed-upon end point and also set agreed-upon rules like speed limits and driving routes. But the problem is that even though we might think we all agree on the goals of education, there’s actually tons of discord, both spoken and unspoken. We can’t even all agree that what’s sitting in the middle of that road is actually a speedbump and not, for example, a stop sign. Or a launch ramp.

The Cognitive Tutors described by Kenneth Koedinger and Albert Corbett are a nice example of this. Researchers who embrace these types of learning too
ls see them as gateways to content mastery. But if you believe, as I do, that the content students are required to master is too often slanted in favor of members of dominant groups and against the typically underprivileged, underserved, and underheard members of our society, then Cognitive Tutors start to look less like gateways and more like gatekeepers. Even the tutoring tools that lead to demonstrable gains on standard assessments, well…ya gotta believe in the tests in order to believe in the gains, right?

So I’m back to this:

A “model,” explains Wikipedia,

is a simplified abstract view of the complex reality. A scientific model represents empirical objects, phenomena, and physical processes in a logical way. Attempts to formalize the principles of the empirical sciences, use an interpretation to model reality, in the same way logicians axiomatize the principles of logic. The aim of these attempts is to construct a formal system for which reality is the only interpretation. The world is an interpretation (or model) of these sciences, only insofar as these sciences are true….

Modelling refers to the process of generating a model as a conceptual representation of some phenomenon. Typically a model will refer only to some aspects of the phenomenon in question, and two models of the same phenomenon may be essentially different, that is in which the difference is more than just a simple renaming. This may be due to differing requirements of the model’s end users or to conceptual or aesthetic differences by the modellers and decisions made during the modelling process. Aesthetic considerations that may influence the structure of a model might be the modeller’s preference for a reduced ontology, preferences regarding probabilistic models vis-a-vis deterministic ones, discrete vs continuous time etc. For this reason users of a model need to understand the model’s original purpose and the assumptions of its validity.

I’m back at the original, simple, incomplete model because I’m not ready to stand in defense of any truth claims that a more complete model might make. Even this incomplete version, though, helps me to start articulating the characteristics of any model representing the role of computational technologies in education. I believe the following principles to hold true:

  • Human goals are mediated by, and thenceforth only achieved through, the widespread adoption and use of new technologies.
  • Human purposes for adopting and making use of new technologies are often highly individualized (though nearly always aligned with an affinity group, even if that group is not explicitly named and even if that group is not comprised of other members of the learning community).
  • While no educational researcher is qualified to articulate achievable goals for another human, the researcher is ethically obligated to support learners in articulating, and achieving, ethical educational goals.
  • The efficacy and success of new technologies can be measured through multiple lenses, among which only one is the achievement of mainstream educational goals as articulated and assessed through traditional, often standardized, measurement tools.

Ok, so what do you think?

*Note: I’m kinda rethinking this one. It reads a little too deterministic to me now, a mere hour or so after I wrote it.