sleeping alone and starting out early

an occasional blog on culture, education, new media, and the social revolution. soon to be moved from http://jennamcwilliams.blogspot.com.

Archive for the ‘learning sciences’ Category

how Jim Gee and I soothe our guilty consciences

Posted by Jenna McWilliams on June 8, 2010

In the video below of a presentation to the Education Writers Association 2010 Annual Conference, Jim Gee says this about how to introduce innovative ideas into education:

There’s a choice of strategies here…. One strategy is: Let’s take our innovations to the center of the school system and spread them as fast and quickly as we can. People believe that this current school system as it is will just co-opt those innovations and make them … just better ways to do the old thing. Another strategy is: Let’s make these innovative learning and assessment tools and put them at the margins, in places that will tolerate innovation, and then show it works. Now if you think about it, in technology outside of schools, going to the margins first and then to the center–that’s always been the way innovation happens. The only place we’ve ever tried to keep putting the new thing right in the center at once is in schooling, and it’s never worked. What i would love to see is that we hive of some of the (Race to the Top) money for a national center that would trial these new assessments, show they work in places that tolerate innovation, and then spread them there, just the way you would want if we have to keep coal and oil–let’s at least have something trying out new forms of energy, so that we’re ready for these markets but also we can prove they work. if we don’t do that, we’re just gonna get a better mousetrap.

I absolutely agree with the sentiments in the quote above, except for the BP oil spill. Let’s say there’s some innovative energy research going on in the margins, ready to prove it works and to take over where coal and oil left off. That’s fantastic, and it doesn’t do a single goddamned thing to help the birds, the fish, the sea mammals, the tourist industry, the ecosystem, the fisheries, and the human residents of the Gulf Coast. Those are simply casualties, not a single thing we can do to help them now no matter what awesome innovative fuel source we finally embrace, no matter how much more quickly we may embrace a cleaner fuel source as a result. Even if tomorrow’s birds are safe from Big Oil, today’s birds are drowning right in front of us.

Working at the margins of education is a fantastic way to innovate and offer useful evidence that innovations work. I fully support this approach–but not at the expense of the kids who exist at the center of our education system today. Yes, the school system can and does and maybe always will co-opt any innovation we try to introduce. But that doesn’t excuse us from trying anyway. That doesn’t give us license to give up on today’s children, even if it keeps tomorrow’s children safe.

And of course this isn’t what Jim Gee wants to do, anyway. But the Jim Gees of the world who urge us to work at the margin live in symbiosis with the Jenna McWilliamses of the world who believe we must also work from the center, where–ironically–the most marginalized kids in education commonly reside. I can’t innovate as much as I’d like from the center, maybe I can’t help tomorrow’s marginalized kids as much as I’d like either.  And Jim Gee can’t help today’s marginalized kids as much as he’d probably like from the edges. So we need each other, if for nothing else than to assuage our guilty consciences for being unable to do more of what we know must be done.

I should probably also note that Jim Gee is one of my absolute all-time heroes, so I hope he’s not mad at me for this post.

This video also stars Daniel Schwartz, who I believe is one of the smartest guys thinking about assessment and learning these days. I had the great luck to attend an assessment working group with him and a big crew of assessment-focused researchers, and I was amazed and blown away by just about everything he said.

In a recent publication, Choice-Based Assessments in a Digital Age (.pdf), Schwartz and his co-author Dylan Arena make this argument:

Educational assessment is a normative endeavor: The ideal assessment both reflects and reinforces educational goals that society deems valuable. A fundamental goal of education is to prepare students to act independently in the world—which is to say, to make good choices. It follows that an ideal assessment would measure how well we are preparing students to do so.

I can’t remember when I’ve agreed more emphatically with the introductory sentence of a scholarly article about education.

Here’s the video, which is well worth a watch.

Posted in academia, assessment, education, Jim Gee, journalism, learning sciences, public schools, schools, teaching, technologies, video games | Leave a Comment »

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

Posted by Jenna McWilliams on February 7, 2010

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 overtly 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.

Posted in creativity, education, Joshua Danish, learning sciences, literacy, new media, participatory culture, pedagogy, schools, teaching | Leave a Comment »

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

Posted by Jenna McWilliams on February 5, 2010

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 reading 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.

Posted in academia, conspiracy theories, graduate school, Joshua Danish, learning sciences, Ph.D. | Leave a Comment »

"Math class is tough!" a few thoughts on a problematic metaphor for learning

Posted by Jenna McWilliams on January 21, 2010

Academics, and especially academics who think about culture (which is to say, more or less, all academics), seem to really like metaphors and similes. Here’s one that made me mad this week.

Jim Greeno: Learning how to participate is like being in a kitchen.
Situativity theorist Jim Greeno, in “Number Sense as Situated Knowing in a Conceptual Domain,” considers how people develop conceptual models for participating in disciplinary communities (what he calls “conceptual environments”). He explains that

knowing how to construct models in a domain is like knowing how to work in an environment that has resources for a kind of constructive activity, such as a woodworking shop or a kitchen.

A shop or a kitchen has objects, materials, and tools that can be used to make things. Knowing how to work in such an environment includes knowing what objects and materials are needed for various constructive activities, knowing where to find those objects and materials in the environment, knowing what implements and processes are useful for constructing various things, knowing how to find the implements, and knowing how to use the implements and operate the processes in making the things that can be made.

In constructing conceptual models, the ingredients are representations of specific examples of concepts…. We can think of the conceptual domain as an environment that has representations of concept-examples stored in various places. Knowing where to find these, knowing how to combine them into patterns that form models, and knowing how to operate on the patterns constitute knowledge of the conceptual domain. The representations of concept-examples have to be understood in a special way. They are not only objects that are drawn on paper or represented in the mind. They are objects in the stronger sense that their properties and relations interact in ways that are consistent with the constraints of the domain.

This example would be fine if everybody agreed on a.) where everything belongs in a kitchen; b.) what everything in the kitchen should be used for; c.) what activities afforded by the kitchen are most appropriate; and d.) whether the kitchen is appropriately and effectively designed.

Let’s say, just for kicks, that the cabinets are made for glass and have been installed at just the right height for someone who is, say, at least 5 feet 9.2 inches tall. I’m 5’3″. If I want to get to the materials I need to, I’m going to need to find something to stand on.

If there’s nothing to stand on (and if most people who use the kitchen stand around 5 feet 9 inches, there would be no reason to keep stepstools or the like around), I might try to climb up onto the counters. I might try to find some sort of utensil–a spatula, maybe, or a wooden spoon–to help me access the ingredients I need. If I’m really desperate, I might try to throw things in an effort to shatter the glass cabinet.

To an outside observor, none of the above activities would appear appropriate in the kitchen setting. The spatula is made for cooking, not for prying open cabinets. And shattering glass cabinets–that’s just destructive.

You see my point, I hope.

Then there’s the unavoidable issue of choice of metaphor. Greeno offers a kitchen or a woodworking shop, which we might say is a nice way to offer one example for each gender! But though it’s true that Greeno doesn’t take it a step farther to prescribe who gets to enter which type of space, the gendered nature of the examples is undeniable. These examples are not neutral, just as the practices that occur in the examples are not benign, at least not always, and not for everybody.

Metaphors do lots of good work for us; indeed, it may be that our entire culture rests on a bed of shared metaphors. As Bonnie Nardi and Vicki O’Day write in their 2000 book Information ecologies: using technology with heart,

Metaphors are a useful form of shorthand…. But it is important to recognize that all metaphors channel and limit our thinking, as well as bring in useful associations from other contexts. That is the purpose of a metaphor, after all–to steer us to think about the topic this way rather than some other way.

What are you doing? Stop–stop throwing soup cans at the cabinets! You’re liable to break something!

To which you respond: I never liked tomato soup much anyway. And I sure as hell hate glass cabinets. Good riddance, you say, even as you’re being hustled out of the kitchen. Good–

And that’s when you realize they’ve shut the door behind you. Maybe even locked it. See what kind of trouble metaphors get us into?

Posted in academia, education, feminism, gender politics, language, learning sciences, social justice, teaching | Leave a Comment »

technologies as sleeping policemen: or, how I learned to stop worrying and…

Posted by Jenna McWilliams on January 18, 2010

Nicholas Burbules and Thomas Callister worry for us. Or, at least, they were worried, over 10 years ago when they offered up their take on new technologies in a paper called The Risky Promises and Promising Risks of New Information Technologies for Education. Among their concerns: that too many people adopt a “computer as panacea approach” to new technologies. This is uniquely problematic in education, they argue, where

(r)ather than acknowledge the inherent difficulty and imperfectability of the teaching-learning endeavor, rather than accept a sloppy pluralism that admits that different approaches work in different situations—and that no approach works perfectly all the time—educational theorists and policy makers seize upon one fashion after another and then try to find new arguments, or new mandates, that will promote widespread acceptance and conformity under the latest revolution.

As problematic as the “computer as panacea” approach is, it pales in comparison to the relativistic “computer as neutral tool” approach, the one that has people saying that any technology can be used for good or for evil. Burbules and Callister explain that:

this technocratic dream simply errs in the opposite direction from the first. Where the panacea perspective places too much faith in the technology itself, the tool perspective places too much faith in people’s abilities to exercise foresight and restraint in how new technologies are put to use; it ignores the possibilities of unintended consequences or the ways in which technologies bring with them inherent limits to how and for what purposes they can be used. A computer is not just an electronic typewriter; the World Wide Web is not just an on-line encyclopedia. Any tool changes the user, especially, in this instance, in the way in which tools shape the conception of the purposes to which they can be put. As the old joke goes, if you give a kid a hammer they’ll see everything as needing hammering.

They prefer a middle approach, which assumes that a simple cost-benefit analysis fails to account for the possibility that benefits and costs are highly dependent on perspective. They offer as proof the history of antibiotics, which through widespread use greatly decreased humanity’s likelihood of dying from bacterial infection but in the process led to the emergence of drug-resistant forms of bacteria. (“That is a very bad thing,” they write.)

Though it’s fairly simplistic to compare new information technologies to antibiotics, I’ll go with the analogy for now, mainly because I agree with the authors’ effort to problematize attitudes toward new technologies. It’s perhaps more accurate to consider the social effects of antibiotics: they have led to a general increase in life expectancy, but in the process have enabled imperialistic societies (cf. the United States) to effectively colonize cultures, communities, and countries worldwide. In the same way, new technologies offer unprecedented access to information, communities, and tools for mobilization, but they simultaneously support new forms of colonization, both across and regardless of national borders.

Which brings me to the metaphor of technologies as sleeping policemen.

The sleeping policeman: In America, we call it a “speedbump.” It looks like this:

The speedbump’s intended effect is to get drivers to slow the hell down, and it’s commonly used in neighborhoods and suburban areas with lots of kids. And it does get people to slow the hell down, primarily because they have no choice. There are also tons of unintended effects: Parents feel more comfortable letting their kids play outside. And, as this post points out, kids playing outside tend to get to know each other better. They–and, by extension, their parents–connect with other neighborhood residents, and everybody feels more connected: “Parents come to know the nearby children. And, inevitably, they come to know those childrens’ parents. They begin trading favors like driving children around. They become neighborly.”

There are potential negative effects, too. Using sleeping policemen to slow drivers down changes driving practices in unintended ways. When a driver hits the last speedbump, she hits the gas and jets on down the road. This might increase the risk of an accident just beyond the range of the speedbumps. Drivers may choose to avoid areas with speedbumps, thereby increasing traffic through other areas–even, potentially, nearby neighborhoods whose streets lack speedbumps. And when a driver is not forced to monitor her own driving practices, the decision to simply drive more slowly in neighborhoods is taken away from her, thereby increasing the possibility that she will not adopt slower driving as a general practice.

Still, I think we can all agree that the benefits outweigh the costs. Nobody sees the speedbump as a panacea, and I don’t imagine many people see the speedbump as a neutral technology.

So why do we worry so much more about the emergence and increasing ubiquity of new media technologies than we do about sleeping policemen or antibiotics?

One reason is that it’s easier to see new media technologies as actors that shape our practices than it is to see how speed bumps and antibiotics have shaped us.

Actors: Any person or tool that exerts force upon any other person or tool, thereby shaping its use or practice. In Actor-Network Theory, everything is a potential actor, everything a potential actant.

Speed bumps act upon cars, drivers, kids, parents, neighborhood dynamics. Antibiotics have acted upon people, policies, government spending, and attitudes. We live longer now. We therefore reshape our lives, our goals, and our relationships to others. It’s all very chaotic and complicated, because our reshaped attitudes in turn act upon our use of antibiotics. Everything mediates everything.

Because new media technologies have emerged and been adopted so quickly, their role in reshaping thought and action–and even, it’s becoming clear, physiology–is clear, even if the outline of how this reshaping is shaking out remains quite fuzzy. New technologies as sleeping policemen: They shape not only how we drive, but how we think about driving. We move them, we reshape them, we add more or take a few away, we develop cars with better suspension…and it goes on down the rabbit hole.

Posted in academia, education, learning sciences, new media, participatory culture, pedagogy, philosophy, public schools, schools, social media, social revolution | 3 Comments »

you have to watch this vlogpost

Posted by Jenna McWilliams on October 27, 2009

file under: world’s most awesome 16-hour vlog project

This link to pure awesomeness comes to you courtesy of my buddies, Jeffrey Kaplan and David Phelps. If you care about literacy or the learning sciences, you will die of joy.

Posted in awesome, creativity, graduate school, joy, learning sciences, literacy, poetry | 1 Comment »