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notes on being the chainsaw you wish to see in the world: Closing remarks for the AERA 2010 annual meeting

Posted by Jenna McWilliams on May 6, 2010

I just got back from my first trip to the annual meeting of AERA, the American Educational Research Association. AERA is apparently the biggest educational research conference in America. I had a fantastic time (highlight: I got to have dinner with Jim Gee!) and my presentation went well (highlight: I argued with the panel’s discussant over why thinking about gender inequity isn’t enough if you’re not also thinking about class inequity!), and I don’t think I made too much of a fool out of myself.

I really enjoyed my first trip to this conference, though when I got home I learned from others that there are significant challenges to be made about the structure, format, and ethos of AERA. I am coming around to that way of thinking and will post my thoughts on this soon.
For now, though, I want to share with you the paper I had to writereallyfast when I got back from the conference. It’s a final paper for a course on computational technologies, and because I was thinking about AERA, social justice, and why the conference’s biggest events mostly featured staid, mainstream thinkers, I decided to write the paper as closing remarks for the conference. I am sure that once the AERA organizers read my closing remarks, they will invite me to deliver next year’s closing remarks in person. I am also available to deliver opening remarks and keynote addresses.

Notes on being the chainsaw you wish to see in the world: On a critical computational literacy agenda for a time of great urgency
Closing Remarks for the AERA Annual Meeting
Jenna McWilliams, Indiana University
May 4, 2010

I want to thank you for giving me the opportunity to speak this evening, at the close of this year’s annual meeting of the American Educational Research Association.

I want to talk to you tonight about the nature of urgency.

Because urgency characterizes the work we do, doesn’t it? The education of our children—our efforts to prepare them to join in on this beautiful and necessary project of naming and claiming the world—it is certainly a matter of the deepest urgency. Even more so because of the war being waged over the bodies and minds of our children.

It’s a war whose contours are deeply familiar to many of us—more so the longer we have been a part of this struggle over education. Certainly the issues we’re fighting over have limned the edges of our educational imagination for generations: How do we know what kids know? How can we prepare them for success in their academic, vocational, and life pursuits? What should schools look like, and how can we fill our schools up with qualified teachers who can do their jobs well? No matter what else, then, at least we’re continuing to ask at least some of the right questions.

Yet a deeper than normal sense of urgency has characterized this year’s annual meeting. It was a “hark ye yet again” sort of urgency: We stood, once again, on a knife’s edge, waiting for word of legislative decisions to be passed down from the policymakers—among whom there are very few educational researchers—to the researchers—among whom there are very few policymakers.

And what sorts of decisions were we waiting to hear on? The same sorts we’ve been wringing our hands over for a decade or more: Decisions over the standardization of education. Development of a proposed set of Common Core Standards whose content seemed painfully anemic to many of us. We’re waiting to learn whether teacher pay will be linked to student performance on standardized tests. Massive budget cuts leading to termination of teachers and programs—these certainly feel familiar to us, though the scope of these cuts and the potential consequences of these decisions seem to loom larger than ever before. The decision by the Texas Department of Education to pervert and politicize its K-12 curriculum by removing references to historical events and even terminology that might offend members of the political Right-—the specifics are new, but the story feels familiar.

A call to action was paired with the clanging of the alarm bells. Ernest Morrell told us that he had counseled his kids to prepare presentations that not only described their work and achievements but that also included a call to action. “I told them, ’Don’t let them leave this room without marching orders’,” he said. “We need to do better. AERA needs to do better.”

He’s right, of course. And I plan to heed Ernest’s advice and not let you leave this room without your marching orders. But first I want to explore the edges of this new urgency, explain why critical computational literacy is part and parcel of the urgency of this moment, and explain exactly what I mean by the term.

There are at least two reasons for the acuteness of the urgency that has characterized this year’s AERA conference. The first is that many of us had hoped for something more, something better, something more honorable from the Obama administration. After eight years living in a political wasteland, many of us felt a glee all out of proportion with reality upon hearing Barack Obama’s position on educational issues. We felt hope. Even a warm half cup of water can feel like a long, tall drink when you’ve just walked out of a desert.

It’s a long revolution, you know. And if Obama authorizes something that looks very much like No Child Left Behind, and if he mandates merit pay based on student performance on standardized tests, and if the recent changes made by the religious right to the Texas state history curriculum stand, and if school board nationwide continue to make terrible, terrible decisions about how to cut costs, and if we see the largest teacher layoff in our history and class sizes creep up to 40 students per room and if computers get taken over by test prep programs and remedial tutoring systems, well, we’ll do our best to live to fight another day. The other day, I listened to Jim Gee talking about his deep anger at the people who run our education system. But he also said something we should all take to heart: “I’ll fight them until I’m dead,” he said. Let’s embrace this position. If they want to claim the hearts and minds of our children, let’s make it so they do it over our cold, dead bodies.

Let’s not let ourselves begin to believe that the stakes are any lower than they actually are. This is the second reason for the urgency this year: There is the very real prospect that the decisions we make within our educational system will get taken up by education departments across the globe. Around 30 of us attended an early-morning session called “Perspectives From the Margins: Globalization, Decolonization, and Liberation.” The discussants, Michael Apple and Dave Stovall, spoke with great eloquence about the nature of this urgency. You’ll forgive me for secretly recording and then transcribing a piece of each of their talks here.

Michael Apple, responding to a powerful presentation on rural science education by researcher Jeong-Hee Kim and teacher-researcher Deb Abernathy, spoke of the far-reaching implications of the local decisions we make:

As we sit here, I have people visiting me from China. They are here to study No Child Left Behind, and they are here to study performance pay. All of the decisions we make that that principal and Deb and you are struggling against are not just struggles in the United States, they are truly global—so that the decisions we make impact not just the kids in the rural areas of the United State, but the rural areas of the people who are invisible, the same people who deconstruct our computers.

Dave Stovall, from the University of Illinois in Chicago, underscored the need to think of the global implications of the policy decisions that intersect within the realm of education:

Arizona is Texas is Greece is Palestine is where we are. This day and time is serious. When a person in Texas cannot say the world capitalism in a public school, we live in serious times. When a person in Arizona can be taken out of a classroom at five years old, to never return, we live in serious times. When we can rationalize in the state of Illinois and city of Chicago that having 5 grams of heroin on a person accounts for attempted murder, we’re living in different times. When we can talk about in Palestine that young folks have now been deemed the most violent threat to the Israeli state, we’re living in different times. And now, how do we engage and interrupt those narratives based again on the work we do?

These times are different and serious, and talking about critical computational literacy may make me look a little like Nero with his fiddle. But critical computational literacy, or indeed its paucity in our education system, is the dry kindling that keeps Rome burning.

I’ll explain why. Let’s talk for a minute about another Apple, the electronics company Apple Corp. The year 2010 marked the release of Apple’s iPad, a tablet computer designed as a multipurpose information and communication tool. Despite mixed reviews of its usability and features, records show an estimated 500,000 units sold between pre-orders and purchases in the first week after the iPad’s release.

This has been accompanied by a push for consideration of the iPad’s utility for education, especially higher education, with schools working to develop technical support for iPad use on campus and at least one university, Seton Hall, promising to provide all incoming freshmen with iPads along with Macbooks. One question—-how might the iPad transform education?-—has been the topic of conversation for months.

“The iPad,” crowed Neil Offen in the Herald-Sune (2010), “could be more than just another way to check your e-mail or play video games. It has the potential to change the way teachers teach and students learn.”

Certainly, these conversations reflect a positive shift in attitudes about what comprises literacy in the 21st Century. If you attended the fantastic symposium on Sunday called “Leveraging What We Know: A Literacy Agenda for the 21st Century,” you heard from the panelists a powerfully persuasive argument that “literacy” is no longer simple facility with print media. Indeed, facility with print media may still be necessary, but it’s no longer sufficient. As the emergence of the iPad, the Kindle, and similar literacy tools make evident, the notion of “text” has become more aligned with Jay Lemke’s (2006) description of “multimedia constellations”—loose groupings of hypermediated, multimodal texts that exist “not just in the imagination of literary theorists, but in simple everyday fact” (pg. 4). Add to this the ongoing contestation of the tools we use to access and navigate those constellations of social information, and the urgency of a need to shift how we approach literacy becomes increasingly obvious.

As anyone who works in the literacy classroom knows, this is by no means a simple task. This task is complicated even further by the dark side of this new rhetoric about literacy: There’s a technological determinism hiding in there, an attitude that suggests an educational edition of Brave New Worldism. Offen’s celebration of the iPad aligns with the approach of Jeremy Roschelle and his colleagues (2000), who a decade ago trumpeted the transformative potential of a range of new technologies. In explaining that “certain computer-based applications can enhance learning for students at various achievement levels,” they offer descriptions of
promising applications for improving how and what children learn. The ‘how’ and the ‘what’ are separated because not only can technology help children learn things better, it also can help them learn better things” (pg. 78, emphasis mine).

More recently, the media scholar Henry Jenkins (2006) described the increasingly multimodal nature of narratives and texts as “convergence culture.” As corporate and private interests, beliefs, and values increasingly interact through cheaper, more powerful and more ubiquitous new technologies, Jenkins argues, our culture is increasingly defined by the collision of media platforms, political ideologies, and personal affinities. Jenkins celebrates the emergence of this media convergence, arguing that “(i)n the world of media convergence, every important story gets told, every brand gets sold, and every consumer gets courted across multiple media platforms” (pg. 3).

Brave new world, indeed. But there is reason to wear a raincoat to this pool party, as a cursory examination of the developing “Apple culture” of electronics confirms. The iPad, celebrated as a revolution in personal computing, communication, and productivity—and marketed as an essential educational tool—is a tool with an agenda. The agenda is evident in Apple’s decision to block the educational visual programming software Scratch: Though Apple executives have claimed that applications like Scratch may cause the iPad to crash, others argue that the true motivation behind this decision is to block a tool that supports media production. The Scratch application allows users to build new applications for the iPad, which Bruckman (2010) suggests goes far beyond Apple’s unstated interest in designing its products primarily for media consumption.

There is no closest competitor to the iPad, so users who want to leverage the convenience, coolness, and computing power of this product must resign themselves to the tool Apple provides. Similarly, as Apple develops its growing monopoly in entertainment (iPods), communications (iPhone), and portable computing (Macbook), Apple increasingly has the power to decide what stories to tell, and why, and how.

Now let’s go back to the other Apple, Michael Apple, who argues quite convincingly about the colonization of the space of the media by the political right wing (2006). We have, he argues, politicians deciding what we pay attention to, and we have corporations deciding how we pay attention to it. This makes the need for critical computational literacy even more important than ever before. Perhaps it’s more important than anything else, though I’ll leave that to the historians to decide.

What is this thing I’m calling “critical computational literacy”? Since I’m almost the only person using this term, I want to start by defining it. It has its roots in computational literacy, which in itself bears defining. Andy diSessa (2001) cautions us against confusing computational literacy with “computer literacy,” which he describes as being able to do things like turning on your computer and operating many of its programs. His definition of computational literacy, he explains, makes computer literacy look “microscopic” in comparison (p. 5). For him, computational literacy is a “material intelligence” that is “achieved cooperatively with external materials” (p. 6).

This is a good start in defining computational literacy but probably still not enough. And please do remember that I will not let you leave this room without marching orders; and if I want you to know what to do, I have to finish up the definition. Let’s add to diSessa’s definition a bit of the abstraction angle given to us by Jeanette Wing (2008), who shifts the focus slightly to what she labels “computational thinking.” She describes this as

a kind of analytical thinking. It shares with mathematical thinking in the general ways in which we might approach solving a problem. It shares with engineering thinking in the general ways in which we might approach designing and evaluating a large, complex system that operates within the constraints of the real world. It shares with scientific thinking in the general ways in which we might approach understanding computability, intelligence, the mind and human behaviour. (pg. 3716)

For Wing, the essential component of computational thinking is working with abstraction, and she argues that an education in computational thinking integrates the “mental tool” (capacity for working with multiple layers of abstraction) with the “metal tool” (the technologies that support engagement with complex, abstract systems).

So. We have diSessa’s “material intelligence” paired with Wing’s “computational thinking”—a fair enough definition for my purposes. But what does it look like? That is, how do we know computational literacy when we see it?

The answer is: it depends. Though we have some nice examples that can help make visible what this version of computational literacy might look like. Kylie Peppler and Yasmin Kafai (2007), who by the way have a new book out on their work with the Computer Clubhouse project (you can buy a copy up at the book fair), offer instructive examples of children working with Scratch. Jorge and Kaylee, their two case studies, are learners who make creative use of a range of tools to build projects that extend, as far as their energy and time will allow, the boundaries of what is possible to make through a simple visual programming language. Bruce Sherin, Andy diSessa, and David Hammer (1993) give an example of their work with Dynaturtle to advance a theory of “design as a learning activity”; in their example, learners work with the Boxer programming language to concretize abstract thought.

Certainly, these are excellent examples of computational literacy in action. But I would like to humbly suggest that we broaden our understanding of this term far beyond the edges of programming. Computational literacy might also be a form of textual or visual literacy, as learners develop facility with basic html code and web design. It might be the ability to tinker—to actually, physically tinker, with the hardware of their electronics equipment. This is something that’s typically frowned upon, you know. Open up your Macbook or your iPhone and your warranty is automatically null and void. This is not an accident; this is part of the black box approach of electronics design that I described earlier.

Which leads me to the “critical” component of computational literacy. This is no time for mindless tinkering; we are faced with a war whose terms have been defined for us by members of the political Right, and whose battles take place through tools and technologies whose uses have been defined for us by corporate interests. Resistance is essential. In the past, those who resisted the agendas of software designers and developers were considered geeks and freaks; they were labeled “hackers” and relegated to the cultural fringes (Kelty 2008). Since then, we have seen an explosion in access to and affordability of new technologies, and the migration to digitally mediated communication is near-absolute. The penetration of these technologies among young people is most striking: (include statistics). Suddenly, the principles that make up the “hacker ethos” (Levy, 1984) take on new significance for all. Suddenly, principles that drove a small subset of our culture seem more like universal principles that might guide cultural takeup of new technologies:

  • Access to computers—and anything which might teach you something about the way the world works—should be unlimited and total.
  • All information should be free.
  • Mistrust authority—promote decentralization.
  • Hackers should be judged by their hacking, not criteria such as degrees, age, race, sex, or position.
  • You can create art and beauty on a computer.
  • Computers can change your life for the better. (Levy 1984)

If these principles seem overtly ideological, overtly libertarian, that’s because they are. And I’m aware that in embracing these principles I run the risk of alienating a fairly significant swath of my audience. But there’s no time for gentleness. This is no time to hedge. I believe, as Michael Apple and Dave Stovall and Rich Ayers and others have argued persuasively and enthusiastically, that we are fighting to retrieve the rhetoric of education from the very brink. It’s impossible to fight a political agenda with an apolitical approach. We must fight now for our very future.

That’s the why. Now I’d like to tackle the how. If we want our kids to emerge from their schooling experience with the mindset of critical computational literacy, we need to first focus on supporting development of critical computational literacy in our teachers. They, too, are subject to all of the pressures I listed earlier, and add to the mix at least one more: They are subject to the kind of rhetoric that Larry Cuban (1986) reminds us has characterized talk of bringing new technologies into the classroom since at least the middle of the 20th century. As he researched the role of technologies like radio, film, and television in schools, he described the challenges of even parsing textual evidence of technologies’ role:

Television was hurled at teachers. The technology and its initial applications to the classroom were conceived, planned, and adopted by nonteachers, just as radio and film had captured the imaginations of an earlier generation of reformers interested in improving instructional productivity…. Reformers had an itch and they got teachers to scratch it for them. (p. 36)

This certainly hearkens, does it not, of the exhortation of Jeremy Roschelle and his colleagues? I repeat:

promising applications for improving how and what children learn. The ‘how’ and the ‘what’ are separated because not only can technology help children learn things better, it also can help them learn better things.

Teachers are also faced with administrators who say things like these quotes, taken from various online conversations about the possible role of the iPad in education.

I absolutely feel the iPad will revolutionize education. I am speaking as an educator here. All it needs are a few good apps to accomplish this feat.

Tablets will change education this year and in the future because they align neatly with the goals and purposes of education in a digital age.

And finally, the incredibly problematic:

As an educational administrator for the last eleven years, and principal of an elementary school for the past seven…after spending three clock hours on the iPad, it is clearly a game changer for education.

Three hours. Three hours, and this administrator is certain that this, more than any previous technology, will transform learning as we know it. Pity the teachers working at his school, and let’s hope that when the iPad gets hurled at them they know how to duck.

We must prepare teachers to resist. We must prepare them to make smart, sound decisions about how to use technologies in the classroom and stand tall in the face of outside pressures not only from political and corporate interests but from well-meaning administrators and policymakers as well. There is a growing body of evidence that familiarity with new tools is—just like print literacy—necessary but not sufficient for teachers in this respect.

There is evidence, however, that experience with new technologies when paired with work in pedagogical applications of those technologies can lead to better decision-making in the classroom. I recommend the following three-part battle plan:

First, we need to start building a background course in new media theory and computational thinking into our teacher education programs. My home institution, Indiana University, requires exactly one technology course, and you can see from the description that it does its best to train pre-service teachers in the use of PowerPoint in the classroom:

W 200 Using Computers in Education (1-3 cr.)Develops proficiency in computer applications and classroom software; teaches principles and specific ideas about appropriate, responsible, and ethical use to make teaching and learning more effective; promotes critical abilities, skills, and self-confidence for ongoing professional development.

Fortunately, we can easily swap this course out for one that focuses on critical computational literacy, since the course as designed has little practical use for new teachers.

Second, we need to construct pedagogy workshops that stretch from pre-service to early in-service teachers. These would be designed to support lesson development within a specific domain, so that all English teachers would work together, all Math teachers, all Science teachers, and so on. This could stretch into the early years of a teacher’s service and support the development of a robust working theory of learning and instruction.

Finally, we might consider instituting ongoing collaborative lesson study so that newer teachers can collaborate with veteran teachers across disciplines. I offer this suggestion based on my experience working in exactly this environment over the last year. In this project, teachers meet monthly to discuss their curricula and to share ideas and plan for future collaborative projects. They find it intensely powerful and incredibly useful as they work to integrate computational technologies into their classrooms.

I’m near the end of my talk and would like to finish with a final set of marching orders. If we want to see true transformation, we need first to tend our own gardens. Too often—far, far too far too often—we educational researchers treat teachers as incidental to our interventions. At the risk of seeming like an Apple fanboy, I return once again to the words of Michael Apple, who argued brilliantly this week that it’s time to rethink how we position teachers in our work. We say we want theory to filter down to the “level” of practice; the language of levels, Apple says, is both disingenuous and dangerous. Let’s tip that ladder sideways, he urges us, and he is absolutely correct. We live and work in the service of students first, and teachers second. We should not forget this. We should take care to speak accordingly.

These are your marching orders: To bring the message of critical computational literacy and collaboration during this time of great urgency back to your home institutions, to the sites where you work, to the place where you work shoulder to shoulder with other researchers, practitioners, and students. I urge you to stand and to speak, loudly, and with as much eloquence as you can muster, about the issues of greatest urgency to you. This is no time to speak softly. This is no time to avoid offense. In times of great urgency, it’s not enough to be the change we wish to see in the world; we need to be the chainsaws that we wish to see in the world. That is what I hope you will do when you leave this convention center. Thank you.

Apple, M.W. (2006). Educating the “right” way: Markets, standards, God, and inequality. New York: Routledge.
Bruckman, A (2010, April 15). iPhone application censorship (blog post). The next bison: Social computing and culture. Retrieved at
Carnoy, M. (2008, August 1). McCain and Obama’s educational policies: Nine things you need to know. The Huffington Post. Retrieved at
Carter, D. (2010, April 5). Developers seek to link iPad with education. eSchool News. Retrieved from
Cuban, L. (1986). Teachers and machines. New York: Teachers College Press.
diSessa, A. A. (2000). Changing minds : Computers, learning, and literacy. Cambridge, Mass.: MIT Press.
Jenkins, H. (2006). Convergence culture: Where old and new media collide. Cambridge, MA: MIT Press.
Kelty, C. (2008). Two bits: The cultural significance of free software. Durham, NC: Duke University Press.
Kolakowski, N. (2010). Apple iPad, iPhone Expected to Boost Quarterly Numbers. eWeek, April 18, 2010. Retrieved at
Korn, M. (2010). iPad Struggles at Some Colleges. Wall Street Journal, April 19, 2010. Retrieved at
Lemke, J. (2006). Toward Critical Multimedia Literacy: Technology, research, and politics. In M.C. McKenna et al. (Eds.), International handbook of literacy and technology: Volume II. Mahwah, NJ: Lawrence Erlbaum Associates Inc. (3-14).
Levy, S 1984. Hackers: Heroes of the computer revolution. New York: Anchor Press/Doubleday.
McCrae, B. (2010, Jan. 27). Measuring the iPad’s potential for education. T|H|E Journal. Retrieved from
New York Times (2010, March 17). Editorial: Mr. Obama and No Child Left Behind. New York Times Editorial Page. Retrieved from
Offen, N. (2010, Jan. 28). The iPad and education. The Herald-Sun. Retrieved from—education?instance=main_article.
PBS (2010, Jan. 7). How will the iPad change education? PBS TeacherLine Blog. Retrieved from
Peppler, K. A., & Kafai, Y. B. (2007). From SuperGoo to Scratch: exploring creative digital media production in informal learning. Learning, Media and Technology, 32(2), 149-166.
Roschelle, J. M., Pea, R. D., Hoadley, C. M., Gordin, D. N., & Means, B. M. (2000). Changing how and what children learn in school with computer-based technologies. The future of children, 10(2), 76–101.
Sherin, B., DiSessa, A. A., & Hammer, D. M. (1993). Dynaturtle revisited: Learning physics through collaborative design of a computer model. Interactive Learning Environments, 3(2), 91-118.
Smith, E. (2010, April 16). The Texas Curriculum Massacre. Newsweek. Retrieved at
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions A, 366(1881), 3717-3717.


**Update, 5/6/10, 1:09 p.m.: I have changed this post slightly to remove an unfair attack against a presenter at this year’s AERA Annual Meeting. He points out in the comments section below that my attack was unfair, and I agree and have adjusted the post accordingly.

Posted in academia, computational literacy, conferences, convergence culture, education, graduate school, Henry Jenkins, Jim Gee, Joshua Danish, President Obama, public schools, schools, teaching, Twitter | 7 Comments »

why I am not a constructionist

Posted by Jenna McWilliams on April 6, 2010

and why you should expect more from my model for integrating technologies into the classroom

I recently showed some colleagues my developing model for integrating computational technologies into the classroom. “This is,” one person said, “a really nice constructionist model for classroom instruction.”

Which is great, except that I’m not a constructionist.

Now, don’t be offended. I’ll tell you what I told my colleague when she asked, appalled, “What’s wrong with constructionists?”

Nothing’s wrong with constructionists. I just don’t happen to be one.

a brief history lesson
Let’s start with some history. Constructionism came into being because two of the greatest minds we’ve had so far converged when Jean Piaget, known far and wee as the father of constructivism, invited Seymour Papert to come work in his lab. Papert later took a faculty position at MIT, where he developed the Logo programming language, wrote Mindstorms, one of his canonical books, and laid the groundwork for the development of constructionism.

Here’s a key distinction to memorize: While constructivism is a theory of learning, constructionism is both a learning theory and an approach to instruction. Here’s how the kickass constructionist researcher Yasmin Kafai describes the relationship between these terms:

Constructionism is not constructivism, as Piaget never intended his theory of knowledge development to be a theory of learning and teaching…. Constructionism always has acknowledged its allegiance to Piagetian theory but it is not identical to it. Where constructivism places a primacy on the development of individual and isolated knowledge structures, constructionism focuses on the connected nature of knowledge with its personal and social dimensions.

Papert himself said this:

Constructionism–the N Word as opposed to the V word–shares constructivism’s connotation to learning as building knowledge structures irrespective of the circumstances of learning. It then adds the idea that this happens especially felicitously in a context where the learner is consciously engaged in constructing a public entity whether it’s a sand castle on the beach or a theory of the universe.

Examples of constructionist learning environments include the well known and widespread Computer Clubhouse program, One Laptop Per Child, and learning environments built around visual programming tools like Scratch and NetLogo.

why I am not a constructionist
Constructionism is really neat, and some of the academics I respect most–Kafai, Kylie Peppler, Mitch Resnick, Idit Harel, for example–conduct their work from a constructionist perspective. A couple of things I like about the constructionist approach is its emphasis on “objects to think with” and some theorists’ work differentiating between wonderful ideas and powerful ideas.

Constructionist instruction is a highly effective approach for lots of kids, most notably for kids who haven’t experienced success in traditional classroom settings. But as Melissa Gresalfi has said more than once, people gravitate to various learning theories when they decide that other theories can’t explain what they’re seeing. Constructionism focuses on how a learning community can support individual learners’ development, which places the community secondary to the individual. I tend to wonder more about how contexts support knowledge production and how contexts lead to judgments about what counts as knowledge and success. If it’s true, for example, that marginalized kids are more likely to find success with tools like Scratch, then what matters to me is not what Scratch offers those kids that traditional schooling doesn’t, but what types of knowledge production the constructionist context offers that aren’t offered by the other learning contexts that fill up those kids’ days. I don’t care so much about what kids know about programming; I’m far more interested in the sorts of participation structures made possible by Scratch and other constructionist tools.

If you were wondering, I’m into situativity theory and its creepy younger cousin, Actor-Network Theory. So what I’m thinking about now is what sorts of participation structures might be developed around a context that looks very much like the diagram below. Specifically, I’m wondering: What sorts of participation structures can support increasingly knowledgeable participation in a range of contexts that integrate computation as a key area of expertise?

why I’m mentioning this now
My thinking about this is informed of late by what I consider to be some highly problematic thinking about equity issues in technology in education. A 2001 literature review by Volman & vanEck focuses on how we might just rearrange the classroom some to make girls feel more comfortable with computers. For example, they write that

to date, research has not produced unequivocal recommendations for classroom practice. Some researchers found that girls do better in small groups of girls; some researchers argue in favor of such groups on theoretical grounds (Siann & MacLeod, 1986, Scotland; Kirkup, 1992, United Kingdom). Others show that girls perform better in mixed groups (Kutnick, 1997, United Kingdom) or that girls benefit more than boys do from working together (Littleton et al., 1992, United Kingdom). Other student characteristics such as competence and experience in performing the task seem in any case to be equally important, both in primary and secondary education. An explanation for girls’ achieving better results in mixed pairs is that they have more opportunity to spend time with the often-more-experienced boys. The question, however, is whether this solution has negative side effects. It may all too easily confirm the image that girls are less competent when it comes to computers. Another solution may be that working in segregated groups compensates for the differences in experience. Tolmie and Howe (1993, Scotland, secondary education) argue strongly for working in small mixed groups because of the differences they identified between the approaches taken by groups of girls and groups of boys in solving a problems.

For the love of pete, the issue is not whether girls feel more comfortable working in small groups or mixed groups or pairs or individually; the issue is why in the hell we have learning environments that allow for these permutations to matter to girls’ access to learning with technologies.

Also, just for the record, the gender-equity issue in video gaming cannot be resolved just by building “girl versions” of video games, no matter what Volman and vanEck believe. They write:

Littleton, Light, Joiner, Messer, and Barnes (1992, United Kingdom, primary education) found that gender differences in performance in a computer game disappeared when the masculine stereotyping in that game was reduced. In a follow-up study they investigated the performance of girls and boys in two variations of an adventure game (Joiner, Messer, Littleton, & Light, 1996). Two versions of the game were developed, a “male” version with pirates and a “female” version with princesses. The structure of both versions of the game was identical. Girls scored lower than boys in both versions of the game, even when computer experience was taken into account; but girls scored higher in the version they preferred, usually that with the princesses.

I don’t think that the researchers cited by Volman and vanEck intended their work to be interpreted this way, but this is exactly the trouble you get into when you start talking about computational technologies in education: People think the tool, or the slight modification of it, is the breakthrough, when the breakthrough is in how we shift instructional approaches through integration of the tool–along with a set of technical skills and practices–for classroom instruction.

Looking at my developing model, I can see that I’m in danger of leading people to the same interpretation: Just put this stuff in your classroom and everything else will work itself out. This is what happens when you frontload the tool when you really mean to frontload the practices surrounding that tool that matter to you.

This is the next step in the process for me: Thinking about which practices I hope to foster and support through my classroom model and deploying various technologies for that purpose. I’ll keep you posted on what develops.

One last note
I’ve included here a discussion about why I’m not a constructionist along with a discussion of gender equity issues in education, but I don’t at all want anybody to take this as a critique of constructionism. I declare again: Nothing’s wrong with constructionism. I just don’t happen to be a constructionist. Also, I think a lot of really good constructionist researchers have done some really, really good work on gender equity issues in computing, and I’m just thrilled up the wazoo about that and hope they can find ways to convince people to stop misinterpreting constructionism in problematic ways.

References, in case you’re a nerd

Joiner, R., Messer, D., Littleton, K., & Light, P. (1996). Gender, computer experience and computer-based problem solving. Computers and Education, 26(1/2), 179–187.
Kafai, Y. B. (2006). Constructivism. In K. Sawyer (Ed.), Handbook of the Learning Sciences (pp. 35-46). Cambridge, MA: Cambridge University Press.
Kirkup, G. (1992). The social construction of computers. In G. Kirkup and L. Keller (Eds.), Inventing women: Science, gender and technology (pp. 267–281). Oxford: Polity Press.
Kutnick, P. (1997). Computer-based problem-solving: The effects of group composition and social skills on a cognitive, joint action task. Educational Research, 39(2), 135–147.
Littleton, K., Light, P., Joiner, R., Messer, D., & Barnes, P. (1992). Pairing and gender effects in computer based learning. European Journal of Psychology of Education, 7(4), 1–14.
Papert, S., & Harel, I. (1991). Situating Constructionism. In Papert & Harel, Constructionism. Ablex Publishing Corporation. Available online at
Siann, G., & MacLeod, H. (1986). Computers and children of primary school age: Issues and questions. British Journal of Educational Technology, 2, 133–144.
Tolmie, A., & Howe, C. (1993). Gender and dialogue in secondary school physics. Gender and Education, 5(2), 191–210
Volman, M., & van Eck, E. (2001). Gender Equity and Information Technology in Education: The Second Decade. [10.3102/00346543071004613]. Review of Educational Research, 71(4), 613-634.

my model, in case you were wondering

Posted in computational literacy, education, feminism, gender politics, graduate school, Joshua Danish, schools, teaching, technologies | 15 Comments »

notes from the {computational} revolution

Posted by Jenna McWilliams on March 2, 2010

As part of an ongoing effort to design a model for integrating computational technologies into the formal classroom, I have turned my focus to computational literacy. My current model already has a space for considering computational literacy, so in this post I want to spend some time exploring my definition of computational literacy. This includes a discussion of the key features of computational literacy and how these features might be taught. The models I’ve created are included at the end of this post.

I started learning to play the flute at age 8. I kept it up for 10 years. At age 15, I took a typing class and surprised myself by how easily I mastered the QWERTY system. At my fastest (in my early 20’s, when I was a reporter), I could type more than 160 words per minute. I’m a fan of languages, studied French from high school all the way through a master’s-level class, picked up enough German during a 2-week visit to Austria to order my food, ask for directions, and hold a basic conversation with a native Austrian. I studied computer science for about a minute in college –I hated it, I was no good at it–but I’ve taken to html, CSS, and other simple programming languages that support my ongoing efforts at web-based social revolution. I don’t understand, though I wish I did, the inner workings of computer hardware. I don’t understand the difference between Newtonian and pre-Newtonian physics, though I know the pre-Newtonian stuff is naive and kinda wrong. I build web pages for fun, mainly relying on templates but recently branching off into my own web design. Fairly soon, in fact, I will be leaving Blogspot behind in order to build a brand new website to my exact specifications. I have an M.F.A. in Creative Writing, with an emphasis in poetry.

I don’t understand physics. I don’t like most programming languages. I play the flute and like to tinker with language. I’m a fast typist but a slow web designer. I am a computational thinker.

Computational literacy is like all true categories of literacy: a cluster of practices whose meaning emerges as the learner deploys those practices in increasingly knowledgeable, increasingly socially valuable ways.

And increasingly, computational literacy is both part of and separate from other clusters of literacy practices. Computational proficiencies are similar to but distinct from those proficiencies we label “new media literacies,” and they’re similar to but distinct from those proficiencies we label, for lack of a better phrase, “traditional literacies.” They’re often but not always, and not fully, aligned with the “hacker mentality”: an attitude that treats nearly everything as potentially bendable to the user’s will.

Like all other forms of literacy, computational literacy can be taught–though not if we treat it, as Jeanette Wing does in her 2008 treatise “Computational thinking and thinking about computing,” as a set of abstractions. Wing writes that “the nuts and bolts in computational thinking are defining abstractions, working with multiple layers of abstraction and understanding the relationships among the different layers. Abstractions are the ‘mental’ tools of computing.”

You don’t have to be much of a hacker to know that Wing misses something essential here. It may be that the language of a program is abstract, and that programming is dealing in abstractions, but only in the sense that letters, words, and sentences are abstractions leading to language. Even fairly young children develop an innate sense of grammar and know when a speech act violates the rules.

This is to say that the elements of language may very well be abstract communicative units, but native speakers develop a concrete mastery over their language nonetheless. (Though this mastery is often belied by our near absolute inability to articulate a single grammar rule.)

Teaching in support of computational literacy
My focus is on the English / Language Arts classroom, or what I’ve lately been calling the “literacy sciences” classroom. In describing the categories below, then, I’ve included a few ideas about how these aspects of computational literacy might be fostered in the secondary literacy sciences classroom.

I believe that computational literacy is comprised of the following sets of proficiencies:

Programming skill: This may include proficiency with one or more programming languages; or it may include creativity with language (the primary programming language of our culture); or it may include mathematical or scientific know-how.

What to teach: Basic web design can help to foster some foundational programming skills. Students might start a blog or, working within a closed social network like Ning, build personal profile pages complete with modified color templates and extra widgets. For many, the notion that what users see gets controlled by a kind of puppet master can be both surprising and empowering.

Technical expertise: Colin Lankshear and Michele Knobel might refer to this category as “the technical stuff.” One feature of new media, for example, is its modularity–the ease with which we can copy, remix, and move media elements. Technical ability includes facility with the tools that allow for this kind of work, as well as ease with unfamiliar interfaces and comfort with just-in-time learning.

What to teach: I’ll never forget hearing games and education expert Katie Salen talk about the approach her Quest2Learn school takes toward computer literacy. She wondered why we have computer classes where kids learn how to use word processing, spreadsheet, and similar programs instead of folding that instruction into authentic learning experiences. “Why not teach kids how to use Word in the context of having to write something for their English class?” she asked. And she’s right. Of course, this means that English teachers will need to start developing more technical know-how–we’re long past the days when facility with Microsoft Word was a sufficient condition for effective writing, even in the English classroom. Let’s start having students use email programs, work with social networks, do some basic image and video editing with the programs that come standard on most newer computer systems.

Hand-eye coordination: Another feature of new technologies is that they often stretch across the virtual and the physical. I busted laptop screens and frayed charging cables until I learned to work with the physical affordances of computing technologies; I’m graced with excellent typing skills; these make any task that requires text generation between 20 and 40 percent easier than they would be for the typist of a more average speed.

What to teach: Typing is of course an important skill, though many kids build up their dexterity through text messaging. I’m going to argue for the practice of building things in the English classroom. There is, for example, the brilliant piece of rhetoric embodied in this recent OkGo music video:

You can’t tell me that the building of that enormous mousetrap didn’t foster not only increased hand-eye coordination but a deeper sense of space and rhetoric, as well. We may not have the tools for building a better mousetrap in the typical classroom, but the building of small sets for video productions, the designing of costumes and backdrops and other visuals, can help support increased motor confidence in learners.

Visual literacy: Lev Manovich explains the visual basis for all digital media, and even goes so far as to explain that even the very letters and numbers we see on our computer screens have been converted into binary code, then converted back into visual representations so that we can easily make sense of the information. This brings a new imperative to visual literacy. Previously, visual literacy was treated as the ability to think critically about advertising, television, and films; today, we add a near-limitless number of visual media formats in addition to our new roles as producers of visual media in addition to our role as consumers.

What to teach: Visual rhetoric is a growing field. Many teachers are already incorporating video projects, website design, and other forms of visual rhetoric into their classrooms, and we can look to them for advice on how to proceed in this area.

Tolerance for tinkering: Pastimes like crocheting, woodworking, and gardening took up time but didn’t necessarily take up all of our attention. When we weren’t counting or focusing on a particularly difficult maneuver, we could talk or watch TV or sing a song. Coding doesn’t allow for this split of attention. Neither does building a digital scrapbook or designing a webpage or building a virtual model. At best we can devote all of our attention for a time to the code, then shift our full attention away, then shift our full attention back again. Mimi Ito and her colleagues talk about “geeking out,” and part of geeking out is hours passed immersed in one activity or another, sometimes to the exclusion of all else. As a culture, we haven’t really had much tolerance for geeking out, though that’s starting to change. What we need now is to build up a tolerance for geeking out in our learners. There are those who argue that we lost something when young people stopped reading books–that those children lost the ability to immerse themselves in an entire world. It’s possible that what’s been lost in the decline of books can be compensated for through the emergence of computational thinking–of geeking out.

What to teach: Immersive, lengthy projects. We might consider trying to turn the classroom into a structured workshop space, much as fine arts programs balance studio time with critique. We’re already halfway there with peer review and collaborative activities; if we can just shift the focus away from critique and toward construction of powerful projects, we can easily build a tinkering-tolerant learning community.

I’m not saying it’s easy to support computational literacy in the formal classroom. What I am saying is that it’s necessary.

Posted in education, Joshua Danish, literacy, reading, social revolution, teaching, writing | 5 Comments »

update: model for integrating technology into the literacy classroom

Posted by Jenna McWilliams on February 14, 2010

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.

Posted in academia, education, graduate school, Henry Jenkins, Joshua Danish, literacy, patent pending, reading, schools, teaching, writing | 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.

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.

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