Towards a DJ Methodology of Learning

Zachary Kaiser

Towards a DJ Methodology of Learning

Towards a DJ Methodology of Learning Zachary Kaiser

Your tag goes here.

This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Fine Arts in Design and approved by the MFA Design Review Board of the Massachusetts College of Art and Design. 2013

Brian Lucid, Thesis Advisor Professor of Design Dynamic Media Institute Massachusetts College of Art and Design Alison Kotin, Thesis Advisor Visiting Professor Dynamic Media Institute Massachusetts College of Art and Design Joseph Quackenbush, Thesis Advisor Associate Professor of Design Dynamic Media Institute Massachusetts College of Art and Design Jan Kubasiewicz Coordinator of Graduate Program in Design Professor of Design Dynamic Media Institute Massachusetts College of Art and Design Gunta Kaza Professor of Design Dynamic Media Institute Massachusetts College of Art and Design

Table of Contents



Acknowledgements vii

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Abstract 01

02 The Dr. Sample: My Window to the World 03 The Inadequacy of “Remix”

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04 djing, Creative Learning Experiences, and the Conceptual Construction Kit 05 Case Study: Sampler

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06 The Missing Dr. Sample and the Embedded Ideologies of our Designs 07 Case Study: Synthesis

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08 Data Deluge, Always on, and a Giving-Over of Power 09 Case Study: Teaching

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Case Study: r&b r&d 117

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The Embedded Ideologies of Learning Experiences 141

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Machine-Readable vs. Human-Readable Meaning 153

13 The dj Methodology as Agenda of Possibility: What Next?



Addendum: The dj Methodology Toolkit for Educators

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Props, Respect, and Thanks.



Acknowledgements

To Lisa, my lovely wife. There is no way to enumerate here the incredible support you have given me throughout graduate school. You are my filter, my chef, my collaborator, my editor, my test subject, and my best friend. To Mom for listening to me complain, nag, whine, babble, and practice my presentations. Your feedback, ideas, insights, and writing skills are unparalleled. You are the smartest person I have ever met. To Dad and Hannah for encouraging me and listening to me always. To Brian for the feedback, criticism, encouragement, and everything in between. To Alison for reading everything and helping me maintain my sanity and passion. To Pol for challenging my ideas at every turn. To Joe for always reminding us to square ourselves with our technology. To Gunta for helping me overcome, or at least acknowledge, my resistances. To Jan for laying the groundwork. To Gabi, Daniel, Jeff, and John for instilling in me the confidence to take risks and helping me realize there are people there to catch me when I jump. To Jonah for a wonderful collaboration and friendship. To the whole DMI Family. To the Proximity Lab Fund for helping me realize my ideas.

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Towards a DJ Methodology of Learning

Abstract

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Abstract

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Towards a DJ Methodology of Learning

As a DJ, every time I play a gig or make a song, I engage in a creative learning process. By selecting which records to bring to a gig or deciding what songs to sample, DJs create databases and establish limits within which they play, illuminating connections between database entries for themselves and their audiences. I believe that the most powerful learning experiences are, indeed, creative, in that the learner engages in an act of creation. While playing a DJ set is one example of such a learning experience, writing an engaging and thoughtful research paper is another. As our access to information increases, no matter what we are studying, our ability to manage complexity must increase. Our rapidly changing experiences of the world, shaped like rocks

Abstract

by the floodwaters of data, require a new methodology for learning and meaningmaking. My thesis argues that this new methodology is specifically a DJ methodology. It extends the philosophies of bricolage and constructionism by understanding that anything—ideas, events, characters or places—can be sampled and mixed in an exploratory, creative act. The relationship between the variability and modularity of a DJ set and the database of a student’s creative inquiry forms the foundation of my studio practice. I design learning experiences, systems, and technologies in order to better understand the DJ methodology. This methodology advocates for the design of learning experiences that leverage computation to facilitate the creation of human-readable, as opposed to machinereadable, meaning. In doing so, the DJ

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methodology begins to serve as a platform for a critical discourse. My thesis can be read both as an explication of the different facets of the methodology as well as a guide to employing it, thus illustrating a pedagogy and a philosophy of learning.

The Dr. Sample: My Window to the World

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The Dr. Sample: My Window to the World

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These are samples from my life. The fi rst project I did in graduate school is a visualization of emotions throughout my life. It incorporates the visual metaphor of sampling and music-production software.

The Dr. Sample: My Window to the World

Sherry Turkle’s book, Evocative Objects, contains a series of beautifully crafted essays on the “things-to-think-with” that helped shape the careers and research of her colleagues and friends. Dr. Turkle underscores the importance of concrete thinking—this thinking that our interactions with objects facilitate— and situates it in opposition to the canonically-celebrated abstract thinking of the academy. Sitting in a class at MIT taught by Dr. Turkle and reading the essays in her book, I couldn’t help but feel somewhat distant, an outsider to the world of engineers and computer scientists celebrating their experiences with LEGOs or gears.

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We had to write an essay about our own “evocative object,” and, still feeling inadequate, uncomfortable and unsure of myself in this class, I wrote about something that I thought made me sound like the engineers with whom I was studying. I should have known better. I should have written about my old sampler, my boss Dr. Sample sp-202, which helped me create my first real “beats” as a young, aspiring, hip-hop dj and producer. That small, black, plastic box changed my life (fig. 1). Fig. 1: The BOSS Dr. Sample SP-202

Enthralled by hip-hop music since before I should have really been listening to it, I began to learn at an early age about the tools that hip-hop producers used to make “beats,” the music over which an emcee would rap. The most popular and well-known tools of the trade were, of course, turntables and a mixer. Prohibitively expensive for the meager income from my first summer job after my freshman year in high school, turntables and a mixer were temporarily out of the question for me. I researched other ways that I could begin building up a studio, and quickly found the world of samplers, the physical

The Dr. Sample: My Window to the World

devices that recorded, looped and edited pieces of other songs. These “samples” form the building blocks of hip-hop music, and have created some of the most controversial moments of the genre’s history, foregrounding hip-hop as the center and bleeding edge of debates on intellectual property and copyright. I bought one of the few samplers that I could afford: the boss Dr. Sample sp-202. The Dr. Sample is a small, humble black box with orange buttons, orange trim and orange typography (Eurostile, I believe). I still remember exactly how the buttons feel— it’s nearly impossible to describe in words. They are this sort-of rubbery, squishy, semi-transparent resin, lighting up with a small, red led when touched. Triggering samples in this way, with this interface, is a stunningly satisfying experience. More than a textural, visceral, physical experience, the Dr. Sample offered me a new way to think about the world. I could record short samples of any kind of audio, and then mix these samples together. I heard new things and encountered new possibilities every time I pressed these glowing, rubbery buttons. Songs from the Muppet Show, Japanese koto recordings, and Mobb Deep acapellas were all of the sudden deeply connected. Everything acquired the potential to be sampled and mixed. More specifically, my Dr. Sample gave me my very first experience creating and manipulating a database. Though more sophisticated samplers could hold many more samples, I thought it was amazing that this little box could hold two banks of eight samples each (a total of sixteen entries), and that I could trigger as many as four samples at a time. I quickly realized that with a few different audio cables from RadioShack, I could sample practically anything around me. I sampled tv shows, records, tapes, cds, conversations, and live instrumentation. I loved the challenge of sampling something new and then integrating it into a composition or project I was working on. I sampled different things for different reasons. Some of the decisions were formal, based on tempo, chord progression or rhythm, while others were conceptual, based on the content or context of the sample. These compositional sampling decisions became more intentional as my skill and understanding of music progressed. I learned about the different eras of different musical genres, learned which artists were featured on which record labels and which instruments those artists played. I could begin to control, if only slightly, my happenstance encounters with inspirational records. At the same time, I learned about formal qualities of sound: the frequency spectrum, compression, and time-stretching algorithms, just to name a few.

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My approach to any creative act was forever shaped by these early experiences with my Dr. Sample. I found myself searching for “source material” for every creative endeavor in which I engaged during the rest of high school and college. The vast majority of my essays in high school are built on attempts to extract from the given topic some “samples” or source material, and then connecting them to other “samples” much as I would in a beat. I would often begin with a quote from a philosopher or writer that seemed somewhat unrelated to the task at hand. These quotes often served as the inspiration for my writing, and I attempted to weave them into my papers through both concrete and abstract connections. After declaring myself an art major in college, my work quickly became informed by the practice of sampling (fig. 3). I drew on samples of imagery that were around me in my studio or at my parents’ house. I would often look through old books, much as I would look through records, trying to find the right “sample” for what I wanted to say with my work. Sometimes the sample would be a starting place, a conceptual and visual point of departure from which I would begin to iterate. Fig. 2: T-shirt prototype, 2005; images traced from enlarged newspaper photograph; collaboration with Vukadin Backonja. When we saw this image we were interested both in the idea of “bending” and the idea of working out in preparation to do anything, whether it be to sell fish or to compete in athletics. What happens when you “mix” this imagery with the vernacular of the typical athletic T-shirt, often emblazoned with slogans like, “Just do it” or other allusions to sport or competition?

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My last show in college, Identify Yours, was my first true experience with a conscious interplay between “database” and “interface,” though these seeds had been planted in my early interactions with the Dr. Sample. Through an online interaction, users unknowingly helped create a database that determined the visual properties of pieces that would end up being designed for a gallery show. For each user’s entry in the database, we created a unique piece of art based on samples of the parameters determined during the user’s interaction with our website (fig. 3 – fig. 5).

While I was exploring sampling in my own work in college, I also used it as a way to teach design. During my sophomore year, I co-founded a non-profit design and screen printing studio for high school students, called Mess Hall Press (fig. 6). One of the eventual tenets of our teaching philosophy was to avoid beginning with a truly “blank page.” Many of our students were uncomfortable with being called an “artist,” and always told us that they were bad at drawing, fearful of a blank sheet of paper and a marker.

Fig. 3: Some gallery visitors who arrived early looking at the installation of the Identify Yours exhibition. Nick Ihm, one of the artists with whom I collaborated on this project, stands in the center of the photograph. Photo: Jim Escalante

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Fig. 4: Photograph from the opening reception of Identify Yours. A print-out of our entire database sat next to our sketches and explanations of the way the system worked. People combed the database for their entries so that they could see how the selections they made on the site influenced the artworks we had created for them. Photo: Jim Escalante

Fig. 5: Photograph of one of the art objects from the opening reception of Identify Yours. The show explored the relationship between the designer and consumer, with users of a website unknowingly making choices that helped guide the creation of art objects that would be designed for them. These objects were made available for users to pick up at the opening reception and for the following two weeks that the show ran.

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Fig. 6: An enthusiastic gallerygoer at a Mess Hall Press opening reception.

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Our humble studio was charged with designing posters for events at the teen center that housed our studio (fig. 7), so we had to make work no matter what: fear of the blank page was not an option. We would often begin a poster project with visual brainstorms, during which our students would cut images out of magazines, take pictures of, or draw anything that they thought related to the themes or goals of the project. They were sampling, building a visual database from which we could pull. Based on our ideas about what we thought the poster should be (our design brief), we would then begin mixing these ideas—our database entries—together, with the students tracing images or redrawing things on vellum. Soon, a rough design would emerge. We even did smaller warm-up projects based on sampling to help students get ready to work. One of my favorites was a mustache wallpaper pattern that we created after repeatedly photocopying an image of a mustache and having students draw around it, adding their own interpretations. Fig. 7: Mess Hall Press students working on projects at the Lussier Teen Center in Madison, Wisconsin. Photo: Aaron Davis

Though my first real design job after college and Mess Hall Press took up most of my time, I remained devoted to my love of music-making. In the wee hours of the night, I would sit in my apartment or at my parents’ house, making music. By this time, I had nice turntables (purchased halfway through high school), a mixer and an old desktop computer in my studio. My Dr. Sample, however, was always in the

The Dr. Sample: My Window to the World

center of the table on which all my gear was arranged. The more time I spent spinning records and making sprawling, hour-plus mixes, the more I realized that djing in the more formal, record-playing sense, was just like sampling and making beats. I was sampling from my record collection rather than from a song, and was mixing those samples on my turntables instead of triggering them on the Dr. Sample. Most of the mixes I made had a specific aesthetic or conceptual arc to them. For example, I often had a rough idea of how I wanted a mix to “feel” at different points before beginning to play. The songs in an hour-long mix cannot remain at the same intensity or tempo; that would be boring. A dj must think about how to create a sense of pacing for a mix as a whole. It’s the same as creating rhythm in a visual composition or the pacing of a film. I would, therefore, pull records from my collection to create a database for what I thought needed to go into a mix, just like I used to wander around looking for the right things to sample with my Dr. Sample. I was assessing each piece of data, each record, for its properties (tempo, chord progressions, overall energy, genre, rhythm patterns, etc.), and deciding whether it would be a good addition to the database for that mix. By this time, my skills had progressed to the point where I realized I could start “gigging.” I began to ask for gigs at bars that I frequented. Soon, I was lucky enough to be spinning records as a “Resident dj” at my favorite bar, and what I consider the best bar in the entire city of Madison: Restaurant Nattspil. This transition, from playing and creating music for myself and my friends, to facilitating and designing an aural experience for friends and complete strangers alike, helped my ideas about sampling and design evolve. There is a peculiar similarity between djing at a bar and creating a piece of design work or writing an essay. It’s almost like working on an assignment in school—just a really fun one. There is a formal design brief: the goal of the project is to play appropriate music for the night in question, and the night in question is always different. What day of the week is it? What time of year is it? What time of night is it? How am I feeling? What else has been going on around town? Will it be only regulars? Or new people? What specials are on the menu and who is behind the bar serving drinks? What have I been listening to that I’d like to introduce to others? The answers to some of these questions I could predict in advance, and this is how I would begin to construct my “database.” At home, in front of stacks and stacks of vinyl, I would pull about four crates worth of records, knowing that I would only play about three. Again, I would select these records based on the properties of each record, and how I felt it aligned with my goals for the evening. If my record collection were a song, these records would be “samples” of it.

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BPM stands for Beats Per Minute, the measure by which the tempo of a song is calculated.

Towards a DJ Methodology of Learning

I learned quickly, though, that to plan out an entire dj set before actually showing up at the bar is not necessarily a good idea. djing, at least when you are only playing vinyl, is also about improvisation. It is not, however, a loose, free-form improvisation. Rather, it requires planning, skill, and a deep knowledge of one’s database and the properties of each database entry. This is part of the reason why the best djs are often also veritable encyclopedias of music. Through a careful curation and creation of my “database” of records to play on a given night, I hoped to facilitate the conditions under which I could perfectly answer the brief I had been given, both through planning and improvisation. At 10 pm, for example, as patrons are finishing their dinner, I would typically begin with downtempo instrumental hip-hop, or some slow funk and soul records. I might play some northern soul—a little O.V. Wright, for example—and then follow that up with some Pete Rock instrumentals as the dinner hour winds down. If for some strange reason, however, the crowd was already a few drinks deep and ready to party, I would have to improvise, and begin my set with some more “hype” songs, at a higher BPM or with an overall higher energy than what I might typically play at that hour. This would mean, though, that the end of the night would likely be slower at the bar, and I could play the slow, downtempo stuff at the end of the night. The brief or assignment, to play appropriate music for the given conditions on a given night, is liable to be dynamic and change. My mix, the way I would filter and connect the items in my database to one another, had to be able to change as well. Every night that I played at Nattspil, I created with my records a thesis, a designed experience that attempted to achieve a particular goal given a certain set of conditions. After a two-year tenure djing at Nattspil, I moved to Boston, without my records, and without much of a plan. I came here originally because my wife (my girlfriend at the time) had been accepted to graduate school. My background in design, education, and, in particular, my work with high school students helped me land a job at a summer program for aspiring high school artists, called bima, the Brandeis Institute for Music and the Arts. Upon arriving at bima, I realized that my role was not all that different than my role as a dj. I had effectively been given a design brief with certain constraints that could change at any time. Such is the nature of teaching and education. My overall goal was to create experiences that integrated art, design and Jewish education, while enriching the artistic practices of the participants. In order to design my curricula, I researched the role of social responsibility in contemporary art and the history of scholarship on social responsibility in Judaism. In doing this research, I was building a conceptual record collection, a database of ideas, authors,

The Dr. Sample: My Window to the World

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writings, artworks, and other pieces of information relevant to my curricula. I then developed projects for participants by sampling and mixing pieces of my research in much the same way as I would prepare and perform a set at Nattspil. My most successful projects with participants at bima were those that allowed learners to “sample,” to engage in the creation of a database of ideas or points of reference about both art and Judaism, and then connect and filter that database through a sort of “mixing.” For example, participants highlighted the importance of recycling on campus by using plastic bottle wrappers to create installations decorating the spaces for our Shabbat dinners on Friday evenings (fig. 8). They referenced the artwork of Tara Donovan side-by-side with Jewish ritual. By understanding their databases, Fig. 8: A BIMA participant showing off the beginning of a textile-inspired sculpture made out of plastic bottle wrappers.

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participants were able to exploit the relationships between data points, between ideas about art and Judaism, in order to create meaningful work or facilitate meaningful experiences. Of course, there were times in my teaching when I had to improvise. The least successful lessons or projects that I facilitated were those that left me the least room for improvisation. Like the gigs to which I did not bring enough records, I didn’t have the right pieces of content—the right samples—when I needed them. Understanding my work—whether as a dj, designer or educator—as a quest for samples to mix, reflects the world view that the Dr. Sample offered me. The learning process in which I engage is an extension of those formative hours spent with my Dr. Sample. My dynamic media design work is therefore an attempt to explore what, from the outside, may seem like a cavernous conceptual void between djing and learning. I yearn and reach for ways to facilitate experiences for others that traverse similar seemingly vast thematic gaps, experiences that are creative and inquisitive, that help others find just the right samples.

The Inadequacy of “Remix”

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The Inadequacy of “Remix”

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Everything is a remix... sort-of. In 2011, Jeff Bartell and I did a research project on the history of the digitized voice and the qualitatively new phenomena that the software Auto-Tune enables. We “remixed” our research by creating a DJ mix which we annotated in the SoundCloud interface.

The Inadequacy of “Remix”

Remix relies on appropriation, and this act of appropriation is connected to, or often manifests itself as, an act of sampling (Navas, “Regressive and Reflexive Mashups in Sampling Culture”). It is impossible to build a methodology for learning based on DJing and sampling without a discussion of the nature of “remix.” Moreover, because the act of remix relies on appropriation and sampling, it is connected to the processes around which I have developed all my design work.

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Though my work is about something much more than remix, it is also important to address that term because it is the most popular lens through which people view the results of the process of djing and sampling. In examining the term “remix,” I will underscore the problems that may arise when it is used without context or definition. “Everything is a Remix” is a rather catchy phrase and is also the title of a four-part video series that has been catapulted to internet stardom. This phrase is so catchy in fact, that it is easy to overlook a muddy definition of remix and merely accept the credo as fact. The word “remix” on its own carries a great deal of cultural currency and relevance in our current epoch. Books about remix and remixing are being produced at an astounding rate (e.g., Lessig and Navas, though a cursory search on amazon.com reveals many more). Even more telling of the currency of remix is the fact that you can Google “remix” followed by any word, and someone has probably created a business or written an article based on your query’s concatenated phrase. It’s no surprise, then, that Kirby Ferguson’s series of short films, “Everything is a Remix,” was an instant online sensation. The title of the series, in and of itself, is emblematic of our culture’s enthusiasm for, and acceptance of, an oversimplification of the concept of remix. I would like to focus on two problems with the word “remix” when it is explored in the context of a methodology of learning built on sampling and djing. First, remix is a term that has certain cultural implications tying it to Western musical consumption and capital. Second, though quickly accepted as a cultural and musical phenomenon, remix is difficult to pinpoint and define. Remix cannot be viewed alone as a learning methodology; it is the result of a djing and sampling process, and must be described as such in order to extract the process from its roots as a commercial and imperialist endeavor. The term “Remix” with a “capital ‘R’” (Brewster and Broughton, cited in Navas, “Remix Defined”) is difficult to extract from its relationships to capital and the Western music industry. Our understanding of remix is based on the model of music remixes “which were produced around the late 1960s and early 1970s” (Navas, “Remix Defined”). These early remixes, however, have their roots in the “versions” created by Jamaican producers in the 1960s. Nonetheless, the term “Remix” has a relationship to consumption that is uniquely Western and American.

The Inadequacy of “Remix”

The early form of remix from Jamaica, known as “version” is not a remix in the popular sense. Remix (with a capital “R”) is not only defined by material activities but the political contexts of those activities. The remix of nyc was developed in large part due to commercial interests to promote specific songs in a growing consumerist market thriving on the wings of Disco and Hip Hop subcultures. Yet historically, it is agreed that the basic concept of remixing that was defined in nyc was already at play in Jamaica. When considering this, one should keep in mind that the type of consumption that took place in Jamaica’s culture is very different from what took place in popular culture in the United States and other places of the world, and that this does affect the different names that acts of appropriation attain. In short, there are cultural and political reasons why Jamaican musicians call their remixes “versions” and not “remixes.” –Brewster and Broughton, as cited in Navas, “Remix Defined”

The ties between remix and capital are also evident in the musical imperialism in which western djs engage as they mine less-privileged, underrepresented, or nonwestern cultures for the “new” and “undiscovered.” Indeed, Western, and predominantly North American, djs engage in cultural appropriation, searching far and wide for the “next big thing.” They then remix it, or, more often, use “it” as the way to remix a more popular, familiar tune, in an effort to extend its commercial success. In an effort to maintain credibility and a fresh sound, some djs attempt to harvest ideas from the “underground,” and in doing so, reinforce the “other” status of the group being “discovered” (Tucker). Leveraging the new discovery in the service of western commercial success through a remix of a popular song, djs may then engage in repetition (Attali), allegory (Adorno, cited in Navas, “Regressive and Reflexive Mashups”), and a dialog with our own musical history that relies on the spectacular nature of the popular music being remixed (Debord, cited in Navas). Music is inextricably tied to political economy, with the connection between remix and western (specifically, American) patterns of consumption merely another iteration of that linkage. In his essay, “Global Genre Accumulation,” dj Chief Boima (aka Boima Tucker), describes the imbalance of wealth, influence, and representation in dj culture, which, it turns out, isn’t so dissimilar from the current wealth and representation imbalance in the us and the world as a whole. Tucker argues that the result of this imbalance, and simultaneously the system that keeps it in place, is this musical exploitation, this discovery and perpetuation of the “other,” of which the dj Diplo is an exemplary practitioner. “[N]o matter where you are in the world, if there’s an underground

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King Tubby, a Jamaican producer and selector, often credited with the creation of the version.

Famous contemporary DJ, Diplo.

Towards a DJ Methodology of Learning

The Inadequacy of “Remix”

dance scene or marginalized community, Diplo has probably ‘discovered,’ reframed, and sold it [to] audiences in another part of the world. If he hasn’t yet, he’s on his way” (Tucker). In order to look at a process of sampling and djing as a methodology for learning, therefore, the cultural implications of the word “remix,” the term’s relationship to capital, and the emergent contemporary musical exploitation in which popular djs engage, become problematic. In addition to its relationship to capital and Western cultural production, “remix” is more than tricky to define. In his book, Remix, Lawrence Lessig, academic, cultural critic, activist, and a founder of Creative Commons licensing, defines the term in a variety of ways, looking to a wide array of sources outside of music. At first, Lessig emphasizes the nature of remix as an act of synthesis, drawing a parallel between remix and the writing of a legal brief: “A great brief seems to say nothing on its own. Everything is drawn from cases that went before, presented as if the argument now presented is in fact nothing new. Here again, the words of others are used to make a point the others didn’t directly make. Old cases are remixed. The remix is meant to do something new” (52). Although Lessig does not explicitly reference synthesis here, the best briefs indeed are syntheses, not merely a quick “copy-paste” job (Professor Aviva Kaiser, University of Wisconsin-Madison School of Law, personal communication, March, 2011). Soon, though, Lessig’s definition begins to loosen, calling the “right to quote,” the same as the right to “remix” (56), removing the act of synthesis from the process entirely. Lessig also uses paint as a metaphor for remix when discussing the work of the dj Girl Talk, stating that, “[s]ounds are being used like paint on a palette. But all the paint has been scratched off of other paintings” (70). Soon, Lessig quotes Negativland’s Don Joyce, stating that, “in its essence, remix is... ‘just collage’” (70). In these few pages, Lessig presents a cogent, yet sprawling, definition of “remix.” The closest Lessig comes to a taxonomy or classification of remix is in the discussion of the benefits of remix for education. “There’s good and bad remix, as there’s good and bad writing. But just as bad writing is not an argument against writing, bad remix is not an argument against remix” (81). At first, we are left wondering, however, what exactly a “bad” remix is, as opposed to a “good” remix. Lessig explains, “Remixed media succeed when they show others something new; they fail when they are trite or derivative. Like a great essay or a funny joke, a remix draws upon the work of others in order to do new work” (82).

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Eduardo Navas’ research seems to echo this sentiment, while at the same time exploring every angle of it. He investigates what it means to generate new meaning as opposed to what it means to create something “derivative,” and what cultural significance different types of remixes have. Navas develops his own classification system for remix. His article, “Regressive and Reflexive Mashups in Sampling Culture,” begins to create a taxonomy of remix that can be valuable when looking at the dj process as a methodology for learning. In “Remix Defined,” Navas defines remix broadly as “the activity of taking samples from pre-existing materials to combine them into new forms according to personal taste,” and asserts that this act “has been extended to other areas of culture, including the visual arts; it plays a vital role in mass communication, especially on the Internet” (Navas). In “Regressive and Reflexive Mashups,” Navas looks at mashups as a subset of remix, developing a classification system, which is explained in his paper and shown visually in a chart (fig. 1). While Navas thoroughly defines, describes, and explains remix, both with respect to culture and music, he focuses on two particular types: the regressive mashup (or regressive remix) and the reflexive mashup, the latter of which he also refers to as regenerative remix. The differentiation Navas describes is linked to Lessig’s ideas of “bad” and “good” remix, as well as to learning and the exploration of content in a “copy-paste” (Navas) or “read-write” (Lessig) culture. Regressive remixes are, by their very nature, allegorical, relying on the spectacular nature (Debord, cited in Navas) of that which is being remixed. These are almost exclusively the musical remixes we hear all the time. We celebrate a remix of a Beyonce song, to a great degree, specifically because it is a remix of a Beyonce song. In this way, regressive remixes do not generate new value (beyond commercial success). They do not “do new work,” as much as they emphasize or enhance the value of the work being remixed (in terms of capital). Regressive remix is not merely a dialog with history, but relies on history for its success in the first place. It is allegorical. In opposition to the regressive remix, the regenerative remix is specifically intended to do new work. It does not “recognize,” but rather is intended “to be of practical use.” Navas gives the example of software mashups as regenerative remixes. These software mashups do not allegorize the pre-existing software. Rather, by “selectively sampling in dynamic fashion,” the software mashup does something that neither of the softwares being mashed up could do on its own. Additionally, in Navas’ definition

The Inadequacy of “Remix”

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Fig. 1: Eduardo Navas’ taxonomy of remix represented in a diagram he designed.

of regenerative remix, sampling becomes supplanted by a computational “constant updating.” Though valuable for web applications, a “constant updating,” a deluge of data, becomes a challenge for critical reflection (Navas). This challenge presents a unique opportunity for the design of new systems of limitation that relate directly to sampling. It is this opportunity that I explore in my work. Navas’ structure and taxonomy of remix is fundamental to an understanding of the word itself. Although Navas’ definition of regenerative remix is geared specifically towards computation and the development of web technologies, the creation of

See Chapters 5, 7, 9, and 10 for case studies.

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regenerative remixes is not unique to web mashups and emerging technologies. What I believe is important to glean from Navas’ theory of regenerative remix is something more general, something in the vein of Lessig’s “good” remix—the ability to do new work, to create value through synthesis. Through examining the relationship between Navas’ regenerative remix and my process of learning and djing, I have come to believe in a dj methodology of learning that endeavors to enable learners to create regenerative remixes of content. works cited Adorno, Theodor. The Culture Industry. London, New York: Routtledge, 1991, 50 – 52. (Cited in Navas, “Regressive and Reflexive Mashups”). Brewster, Bill, and Frank Broughton. Last Night a dj Saved My Life: The History of the Disc Jockey. New York: Grove, 2000. (Cited in Navas, “Remix Defined” and “Regressive and Reflexive Mashups”). Davis, Meredith. “Toto, I’ve Got a Feeling We’re Not in Kansas Anymore.” Massaging Media Conference. Boston. 4 Apr. 2008. Lecture. Debord, Guy. The Society of the Spectacle. New York: Zone Books, 1995, 110-117. (Cited in Navas, “Regressive and Reflexive Mashups”). Lessig, Lawrence. Remix: Making Art and Commerce Thrive in the Hybrid Economy. New York: Penguin, 2008. Print. Navas, Eduardo. “Notes on Everything Is a Remix Parts 1, 2 and 3.” Remix Theory. Web. . Navas, Eduardo. “Regressive and Reflexive Mashups in Sampling Culture, 2010 Revision.” Remix Theory. N.p., 13 Aug. 2010. Web. 21 Oct. 2012. . Navas, Eduardo. “Remix Defined.” Remix Theory. Web. . Tucker, Boima. “Global Genre Accumulation.” Africa Is a Country. N.p., 22 Nov. 2011. Web. 10 Sept. 2012. .

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Samples are Conceptual Legos.

DJing, Creative Learning Experiences, and the Conceptual Construction Kit

Although I didn’t know it at the time, when I first began making music with my sampler, and when I made mixes and performed at bars as a DJ, I was experiencing a creative learning process. Creative learning, while having the potential to be interpreted in a variety of ways, was defined for me by a class called “Technologies for Creative Learning,” at the MIT Media Lab. Dr. Mitchel Resnick and Dr. Sherry Turkle dedicate the class to understanding what this term means, though at its most basic, it means learning through the act of creation.

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In fact, the entire class is devoted to exploring the facets of creative learning, and asking the question, just what is it that makes a creative learning experience? Resnick defines creative learning as a “spiral”-like process that includes five steps that repeat indefinitely, while each pass through the process builds on the previous one. These steps are: imagine, create, play, share and reflect, with reflection informing future imagination (fig. 1). In addition to this process, the components of creative learning experiences fall into 11 general categories: creativity, objects, construction, identity, styles, engagement, participation, collaboration, community, play, and reflection. Though there is no prescribed process to design creative learning technologies and experiences, Resnick’s research and the Technologies for Creative Learning class itself provide a framework to use in the analysis of learning experience design. In order to explore a process of creative learning based on the process of djing, it is important to understand the process of djing and sampling as a creative learning experience in and of itself.

DJing, Creative Learning Experiences, and the Conceptual Construction Kit

DJing and the Creative Learning Spiral On the first day of Technologies for Creative Learning, I immediately recognized Resnick’s “Creative Learning Spiral” (fig. 1) as something I had experienced over and over again as a dj and producer. Imagine: as a teenager walking around my parents house, listening to records and the sounds around me, I used my imagination both in the act of sampling (deciding, for example, to sample a few words from an episode of The Muppet Show) and in the act of mixing samples (mixing this sample with a sample from a Japanese Koto album because they had the same cadence). Create: in recording my beats and dj mixes, as well as in performing, I engaged in an act of creation. Play: in the context of designing construction kits for kids, the difference between “create” and “play” is clear: learners create something with which they can play. In sampling and djing, however, there is not such a distinct difference. In the act of creation, djs and producers play. Yet there is another element of play that happens during the editing stage of music making that is clearly distinct from the initial “creation” stage. For example, I use software to help edit and shape improvisation that is intended as a sketch or draft for a pattern or song structure. This editing process is both creative and playful. There is, therefore, a fluidity between “create” and “play” in djing. Share: I share my music both through performance and through giving my recordings to others. Reflect: in order to improve, djs reflect on their work—both their experience of their own music, as well as their impressions of the ways others experience the music. Not unlike the process of design, the process of djing is iterative. I may reflect, for example, on the response of my audience to a particular part of a performance. I may also reflect on the order and technique in which I blended a series of records, considering alternate sequences and techniques that might have been more smooth or seamless. Reflection helps djs imagine new and more compelling ways to sample and mix music.

DJing and the Facets of Creative Learning Experiences Objects

The first facet of creative learning experiences that Resnick addresses in his curriculum is “objects.” Interacting with material things and not just abstract concepts is a central tenant of Resnick and Turkle’s ideas about creative learning. Resnick and Turkle value objects as “things-to-think-with,” celebrating a connected, concrete thinking that brings “together thought and feeling” (Turkle, Evocative Objects, 5). Objects are an essential quality of human learning experience because they play a

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Scratch is a free programming language and development environment for kids, as well as an online community where learners share and remix one another’s work. (Resnick)

Towards a DJ Methodology of Learning

crucial role in human development. D.W. Winnicott, a pediatrician and psychoanalyst, explores several facets of the importance of objects in the development of a child. His theory of the transitional object introduces an object as the means by which the child navigates the relationship between self and other. A child’s “blankey” is a good example of a transitional object—it helps the child in times of anxiety and sits in a space between shared, external reality, and the child him or herself. “The transitional object is not an internal object (which is a mental concept)—it is a possession. Yet it is not (for the infant) an external object, either” (13). He also discusses the crucial role of the object in a child understanding her lack of control over her surroundings and the process by which an object finally becomes part of “external” or “shared” reality. By finally understanding that the object is part of external reality, children become able to use objects (Winnicott, 119). Winnicott’s work illuminated the centrality of objects in psychological development and learning. Resnick and Turkle expand the definition of “object” to include computational objects, building blocks of code, for example, with which learners interact when playing with Scratch. Computational objects share a quality of between-ness that is similar to the way in which a transitional object exists in a psychological space between self and other for a child. This between-ness is the space between a “machine that thinks” and a “machine who thinks” (Turkle, Second Self, 24). What else can we consider an “object?” Records are pieces of vinyl, and as such, they are most certainly “objects,” part of the physical world. But what about a song? Or a sample? I can’t touch a sound. But I can manipulate it (fig. 2). So is it an object? Like Turkle’s evocative objects, samples became, for me, “things-to-think-with.” By making beats, I traveled around the world, back in time, learning both about the properties of music and sound, as well as about the world and culture. Music represents ideas—whether a musical idea (e.g., a certain frequency range) or a theoretical idea (e.g., Cumbia music representing Colombian heritage). Manipulating music, like manipulating objects, allows for an exploration of the ideas with which the object is connected.

Fig. 2: A sound wave as it commonly appears in music-production and editing software.

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Construction

In mixing and manipulating samples and records, I am engaging in an act of construction with objects. Construction is another key facet of creative learning experiences identified by Resnick. This facet is framed by the philosophy of Constructionism, pioneered by Resnick’s mentor, Seymour Papert, who famously said, “You can’t think about thinking without thinking about thinking about something” (Papert, “You Can’t Think...,” 366). Papert directly links the importance of objects to the importance of constructing. Sitting in Resnick’s class, it seemed perfectly logical: if objects are so essential to human development and learning, then making with objects must be important as well. Papert argued that “constructions in the world” support those “in the head” (The Children’s Machine, 142 – 143). Constructionism is therefore about constructing knowledge by constructing in the world. It is an evolution of Claude Levi-Strauss’ bricolage, which is a process of “making do with what you have” in order to solve a problem (Papert, The Children’s Machine, 144). Constructionism celebrates this process and connects it with the construction of knowledge, going far beyond the way Levi-Strauss imagined the bricolage method to be useful. Do constructions “in the world” have to be physical? A condition of construction, according to Papert, is that “the product can be shown, discussed, examined, probed, and admired” (The Children’s Machine, 142). Beats and mixes exist in this way. Though they are not physical products that can be held, they are, indeed, constructions. Wayne Marshall, celebrated Boston-based dj, blogger, and Harvard ethnomusicologist, has pioneered and advocated for “musically expressed ideas about music” as a Fig. 3: The Ableton Live interface. Live is the software within which I do most of my musical “constructing,” and is one of the tools Marshall uses to engage in his research.

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pedagogical practice (“Mashup Poetics as Pedagogical Practice,” 310). He coined the term musikeando, a play on Spanish gerunds, to describe this idea of making music about music. His mashups, dj sets, and remixes are reflective and critical explorations of ideas about music, expressed through the creation of music. In his “Moments in Lambada” mix, for example, he “traces the flexible but familiar contours of one of the most popular melodies of the last 30 years… A stretchy bit of ear candy, the song has been reworked like so much tropical taffy, twisted and folded into an impressive array of styles” (Marshall, “Lambada is a Feeling”). Deep within this mix, Marshall explores the ways in which different cultures have appropriated the Lambada melody, finding new connections and learning through the making of the mix itself. Identity

In order to design creative learning experiences, it is important to understand the ways in which learners view themselves and construct their identities while they construct knowledge. djs construct their identities through the music they play. The first question you are asked when you tell someone you are a dj is, “what kind of music do you play?” Like today’s learners, djs explore the construction of their identity in performance, in public. The music we play and the samples we use say something about us. We try out new sounds, new genres, and see how the crowd and our peers respond. Learners post different things on Facebook or Tumblr, trying out different tones, different frequencies of posting, revealing more or less information, and all along, gauging the reactions of their friends. It is a “collaborative self ” (Turkle, Alone Together, 175 – 179) that we perform in front of and with others. Styles

Gardner’s nine types of intelligences are: linguistic, logical-mathematical, musical, spatial, bodily-kinesthetic, interpersonal, intrapersonal, naturalistic, and existential (Gardner, 21).

In addition to possessing identities that are simultaneously curated, performed, and in-flux, learners approach learning experiences in different ways. They have different styles of learning, sometimes even depending on the type of content with which they are working. More than different learning styles, even, learners have different types of intelligences, which are described in the landmark research by Howard Gardner. His revered taxonomy of multiple intelligences is not something to be trifled with, and I am by no means proposing that he add a “dj” category to these types of intelligences. I am interested in the way in which djing interfaces with the idea of epistemological pluralism described by Turkle and Papert. They add another dimension to Gardner’s work, describing differences between “hard” and “soft” styles of learning (Turkle and Papert, 8). I believe that there could be a continuum of “hard” vs. “soft” approaches for every one of Gardner’s intelligences.

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In order to explore their ideas about “hard” vs. “soft” styles of learning, Turkle and Papert use children creating computer programs as their case study, but I believe that djing offers similar variation in the learner’s approach to creation. A “hard” style or approach to learning is characterized by planning and a preference for abstract thinking, along with an emotional distance from the subject-matter. The “soft” style is characterized by a “negotiational approach and concrete forms of reasoning,” as well as by a closeness to the objects of study (Turkle and Papert, 8). djs engage, at different times, in both styles. Some favor one style over the other. For some performances, my dj set might be characterized by the “hard” style, tightly choreographed beforehand, leaving as little as possible to chance, hoping that the varying energy of the set matches that of the crowd over the course of the evening. During the performance, I am then distant from the music. Other times, I might engage in a “soft” approach to creation. I might not prepare at all, and let things evolve organically, interacting much more closely with the music during the performance itself. This preference for a “hard” versus “soft” approach to djing, sampling, and mixing is not only personal. It is also driven by the technologies that djs use to sample and mix. Jane Fulton Suri, in her tiny, visually-driven volume, Thoughtless Acts, discusses the ways in which we “adapt, exploit, and react to things in our environment” (1). She sees these “thoughtless acts” as the inspiration and starting point for various design projects. The reverse of her work is also instructive—the different affordances that our technologies offer us, the ways in which our music-making technologies force us to interact with them (another thoughtless act on our part), shapes the way we think and the way we make. Are there certain affordances for music making (e.g., software versus samplers) that make using a certain style (hard or soft) easier? I think this lies at the heart of interface design considerations both for music-making and for learning. Indeed, in my research into learning through the design of technologies and learning experiences, I have created technologies that cater more to a “hard” approach (Synthesis) and others that cater more to a “soft” approach (Sampler). Engagement

While acknowledging various styles of learning, the most powerful creative learning experiences are universally characterized by a learner’s deep engagement. This type of experience is commonly described as “flow,” referring to the term pioneered by Mihaly Csikszentmihalyi. In his book Flow, Csikszentmihalyi lays out the conditions for such an experience, all of which resonate with my experiences as a dj, whether I am making a beat or performing a set. Flow experiences occur when the level of

See Chapter 6 for more on the embedded ideologies of our designed experiences See Chapter 7 for more about Synthesis and Chapter 5 for more about Sampler.

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challenge exceeds a learner’s skill level by just enough to allow the learner to attain the skills needed to meet that challenge. The challenge must then dynamically shift, again increasing in proportion to the increase in the learner’s skill level (Csikszentmihalyi , 74). djs constantly aspire to improve and the dynamic shift in challenge is internal—at first we work on simple beat matching, then move on to more complex things like scratching, pacing and sequencing of an entire set, and then onto the production of music, considering the properties of sound itself. “[B]ecause flow activities are freely chosen and more intimately related to the sources of what is ultimately meaningful, they are perhaps more precise indicators of who we really are,” Csikszentmihalyi says (77). We become most deeply engaged with activities that we are intrinsically motivated to do, not relying on extrinsic motivation, such as reward systems. Failure does not present a blockade to the constant improvement for which many djs strive. This sense of intrinsic motivation is key in fostering learning experiences that are creative and transformational. These meaningful learning experiences are not motivated by goals based on performance. Carol Dweck, in her book Self-Theories, discusses the difference between performance goals and learning goals. Performance goals, she states, “are about winning positive judgments of your competence and avoiding negative ones. In other words, when students pursue performance goals, they’re concerned with their level of intelligence. They want to look smart (to themselves or others) and avoid looking dumb.” Performance goals can include grades, praise, and other reward systems. Performance goals can often work at odds with meaningful learning experiences because “[t]he tasks that are the best for looking smart are often ones that students are good at and won’t really learn much from doing” (15 – 16). Learning goals, as opposed to performance goals, involve challenge, difficulty, and, sometimes, failure. My primary goal in becoming a dj was never to be popular or to be praised. I learned about djing, sampling, and music because I loved music and I was deeply motivated, intrinsically, to learn through this act of making. This intrinsic motivation has catalyzed flow experiences that have made me a better dj over time. Yet no dj or learner is completely free of extrinsic motivation. Learners often experience a mix of intrinsic and extrinsic motivation (Dweck, 15 – 16). Just as different learners can be motivated differently, djs are motivated in different ways and at different times throughout their careers. The motivation to put together and perform a great set is different than the motivation to keep djing, to make beats, or to practice for hours on end in your parents’ basement. While feedback from the crowd can be

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exciting and is an important part of achieving the goals that come along with performing a set, allowing this rush (a performance goal) to become the driving force of my dj practice would keep me from becoming the best dj I could be. Community

Without the support of a community, the struggles that learners experience on the path towards learning something valuable or meaningful can become overwhelming. Moreover, communities can offer learning opportunities by allowing learners with different knowledge and experience to teach one another. djs often form learning communities in which older or more experienced djs help emerging djs learn new skills or network in order to be able to play at more venues. Some of the first djs to help create a vision of a broader, national or global community of learners were the Invizibl Skratch Piklz. The Piklz were one of the earliest groups of djs to put together videos that clearly showed scratching techniques and the labels of the records from which they were getting the sounds they scratched (Scratch) (fig. 3). It catalyzed the learning experiences of a new generation of djs and created a culture of learning among expert and novice djs.

“The Piklz were the first group to take the secrecy out of DJing. The Piklz were the first people to just be like, ‘hey, here’s exactly how to do what we do. We want you to go out and do it better so that we can learn from you.’” – DJ Shadow, in Scratch Fig. 3: Stills from various videos of the Invizibl Skratch Piklz.

In forming a community of learners, dj communities are similar to samba schools, which are oft-cited examples of informal learning communities, referenced by theorists such as Henry Jenkins and Seymour Papert. In Mindstorms: Children, Computers, and Powerful Ideas, Papert considers the vision of a world without formal schooling, and looks to samba schools as a model of a “nonschool” activity or learning experience. The centerpieces of the famous carnival in Rio de Janeiro—elaborately choreographed, costumed, and dramatized original performances—are often created by Samba schools from Rio and the surrounding area. Samba schools are “social clubs

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with memberships that may range from a few hundred to many thousands… Members of a samba school go there most weekend evenings to dance, to drink, to meet their friends” (Papert, Mindstorms, 178). Every member of a particular samba school also has a common goal: they work towards the creation of the next year’s carnival performance. Teaching and learning are contextualized by a common purpose where “experts and novices are all learning,” and “learning is not separate from reality” (179). I could not more accurately describe my own learning experiences at friends’ houses, making beats and spinning records. Reflection

Improvement does not derive solely from engagement and the support of a community. Creative learning experiences must allow for learners to engage in reflection, and it is through reflection that new iterations of ideas and improvement in practice come about. Constructions, as Papert said, must be able to be shown, discussed, examined, and probed. We reflect on our work by looking at it after we have completed it. We also reflect on our work while we are engaged in its construction. Donald Schön, author of The Reflective Practitioner, describes these two different types of reflection as “reflection-in-action” (during the action) and “reflection-on-action” (afterwards) (Schön and Bennett, in Winograd, 171 – 184). I listen to recordings of my work in order to critique it and reflect upon it. While I am playing a set, I react to my reflections on how the set itself is going, whether or not one record is blending with another as I imagined it might, and how that brief insight might inform the next transition. In Jots: Cultivating Reflective Learning in Scratch, Eric Rosenbaum presents four facets of reflection: cognitive, emotional, social, and temporal. The cognitive approach to reflection looks at the way learners use metacognition in problem solving, predicting the results of problem-solving actions, monitoring progress, and assessing solutions (Rosenbaum, 14). As I became a better dj, I needed to focus less on the plan or the groundwork I laid for a set, and became able to focus more on monitoring the way things were going, both musically and for the crowd. This was due to an advance in my metacognitive abilities as a dj. The emotional approach to reflection, meanwhile, acknowledges that feelings are part of every experience, and the consideration of those feelings is central to reflection and shaping future action (Rosenbaum, 15). I was more likely to repeat a certain sequence of records at a gig if the last time I played that sequence of records I experienced positive feelings. The social approach to reflection is focused on the benefits that can be gained from reflection through interaction with others (Rosenbaum, 16). djs form communities of learners, often going to listen to

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each other play, and giving feedback and discussing each other’s work. The temporal approach to reflection is best described by Schön’s differentiation between “reflectionin-action” and “reflection-on-action” (Rosenbaum, 17). The wide body of research on which Rosenbaum draws, as well as his own research, underscores the importance of reflection in creative learning experiences. Extending the Creative Learning Experience of DJing

This analysis frames djing as a creative learning experience. It is an experience of creation, of making, of constructing. While sharing a deep connection with the creative learning spiral through its process of sampling, mixing, reflection and iteration, a learning methodology based on djing extends the philosophies of bricolage and constructionism to understand that anything, not just physical or computational objects, can be sampled and mixed in an exploratory, creative act. The results of this act are regenerative remixes of content.

The Conceptual Construction Kit Samples are evocative for the djs that work with them. Wayne Marshall, writing about his “Moments in Lambada” mix, states, “[a]s with similar projects, I’ve grown fascinated by the way such a spreadable song can draw attention to the inflections of individual interpreters as well as the very conventions that give genres their ability to uniquely address an audience” (“Lambada is a Feeling”). For Marshall, samples are the building blocks for musically-expressed learning experiences and thesis statements. His muskieando—making music about music—is a method for critical reflection both about music and the ideas embedded within the music. In first exploring this method, he found “the power of such technologically mediated manipulations of prerecorded music to engender a certain kind of critical reflection and an analytical mode of reception” (Marshall, “Mashup Poetics as Pedagogical Practice,” 310). These samples stand for other ideas: they stand for the cultures from which they come, the time periods during which they were created, and the geographical locations from which they originate. “Through direct juxtaposition, mashups seem to have the power to shape, with potent immediacy, one’s sense of how musical style articulates ideas about community, tradition, influence and interaction” (309). For Marshall, although he is expressing ideas about music through music, he is also expressing ideas about the things represented by the music.

See Chapter 3 for more about regenerative remix.

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Now, more than ever before, consumers of content are also creators of content (Ito, Marshall, Richtel). This is not just the case in music, but in video, image-making, and the written (blogged and re-blogged) word. We have the power to critically engage in the experience of content through the creation of more content. The democratization of sophisticated music-production technologies and the subsequent rise of mashup culture have blurred the line between creator and consumer. One look at YouTube reveals that this is no different for video content, and Instagram is just the most recent example of this phenomenon in image-making. Similarly, blogging reshaped the landscape of publishing, for cookbooks (e.g., Smitten Kitchen) and technology authors alike (Carr, 15). If this is the case, Marshall’s statements about the way making music about music has allowed him to learn more and to have a more effective pedagogical practice can extend to every type of media, including the written word. What else do we do when we synthesize information in a well-articulated argument besides create a mix? The samples Marshall uses become his construction kit, not merely for the creation of music, but for the expression of ideas. Sampling becomes a way to construct ideas and the samples within a sampler the construction kit. I would like to suggest that sampling and djing can be leveraged in the service of the creation of media-neutral conceptual construction kits that facilitate creative learning experiences leading to critical, creative inquiry, and eventually, regenerative remixes of content. works cited Carr, Nicholas G. The Shallows: What the Internet Is Doing to Our Brains. New York: W.W. Norton, 2010. Print. Csikszentmihalyi, Mihaly. Flow: The Psychology of Optimal Experience. New York: Harper & Row, 1990. Print. Gardner, Howard. “A multiplicity of intelligences.” Scientific American 9.4 (1998): 19-23. Itō, Mizuko, ed. Hanging Out, Messing Around, and Geeking Out: Kids Living and Learning with New Media. Cambridge, ma: mit, 2010. Print. Jenkins, Henry. “What Samba Schools Can Teach Us About Participatory Culture.” Confessions of an AcaFan. N.p., 14 Nov. 2011. Web. 24 Nov. 2011. .

DJing, Creative Learning Experiences, and the Conceptual Construction Kit

Marshall, Wayne. “Mashup Poetics as Pedagogical Practice.” Pop-culture Pedagogy in the Music Classroom: Teaching Tools from American Idol to YouTube. Ed. Nicole Biamonte. Lanham, md: Scarecrow, 2011. 307 – 15. Print. Marshall, Wayne. “Moments in Lambada.” Cluster Mag. N.p., 11 Apr. 2011. Web. 21 Oct. 2012. Marshall, Wayne. “Musically-Expressed Ideas About Music.” Wayne & Wax. N.p., 13 Apr. 2006. Web. 10 Feb. 2013. . Papert, Seymour. Mindstorms: Children, Computers, and Powerful Ideas. New York: Basic, 1980. Print. Papert, Seymour. The Children’s Machine: Rethinking School in the Age of the Computer. New York: Basic, 1993. Print. Papert, Seymour. “You Can’t Think About Thinking Without Thinking About Thinking About Something.” Contemporary Issues in Technology and Teacher Education 5 (2005): 366 – 367 Richtel, Matt. “Growing Up Digital, Wired for Distraction.” New York Times. N.p., 21 Nov. 2010. Web. 12 Dec. 2012. Rosenbaum, Eric. Jots: Cultivating Reflective Learning in Scratch. Thesis. Massachusetts Institute of Technology, 2009. Scratch. Dir. Doug Pray. Palm Pictures, 2001. dvd. Suri, Jane Fulton. Thoughtless Acts? San Francisco, ca: Chronicle, 2005. Print. Turkle, Sherry. Alone Together: Why We Expect More from Technology and Less from Each Other. New York: Basic, 2011. Print. Turkle, Sherry. Evocative Objects: Things We Think with. Cambridge, ma: mit, 2007. Print. Turkle, Sherry, and Seymour Papert. “Epistemological Pluralism and the Revaluation of the Concrete.” Journal of Mathematical Behavior 11.1 (1992): 3 – 33.

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Winnicott, D. W. Playing and Reality. New York: Basic, 1971. Print. Winograd, Terry, et al. Bringing Design to Software. Vol. 86. New York: acm Press, 1996. 171 – 184. Print.

Case Study: Sampler

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Case Study: Sampler

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Manifest the process.

Case Study: Sampler

Sampler is a conceptual prototype for a research tool and service—a platform to catalyze the process of selection, sampling and mixing, in the hopes of revealing previously hidden connections between pieces of information. It is based rather explicitly on the interface and experience of the Dr. Sample. Sampler is a platform for rapid, improvisational exploration of connections between content and is based on the process that DJs go through in preparing and performing a set or creating a song. Sampler aims to foster an environment where limitations and improvisation can help learners find new and meaningful connections between pieces of information and help prompt curiosity and a deeper inquiry.

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For more information about the Dr. Sample, please see Chapter 2.

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For some reason, my mother has a thing for beginning essays with quotes. In high school, whenever I asked for her help writing a paper, she would often begin by ever-so-gently tearing my introduction to pieces. She always asked me, based on the current state of my introductory paragraphs, if I thought she would want to read the rest of the essay—if the introduction was enticing enough. At least in high school, my rhetorical strategy benefitted from having a “grabber” as she and I came to fondly refer to it. Her default solution to my woes of boring introductory paragraphs was, of course, a quote. She and I would kick ideas back and forth of things that related, even tangentially, to the topic I was addressing and the argument I was making. This process often required that I go back and do even more research than I had already done. To my high school self, this was maddening and frustrating. Somehow, though, I always ended up knee-deep in books and other resources that I would never have otherwise encountered or learned about. I would stand in front of my mom’s bookshelves and pull books out based on our conversations, or sometimes at random, and ask her if she thought we might find something there. More than a way of thinking about introductory paragraphs, it became a way for me to bring new ideas and interpretations to my early attempts at writing.

See Chapter 2 for more information about my approach to musical content.

This strategy was valuable beyond just writing papers. I realized that there were distinct similarities between the way I learned to approach content in my writing and the way I was already approaching musical content. What my mother and I were doing was much like the way I might go about searching for samples in my parents’ record collection—samples that I was hoping would fit with whatever song I was working on when I mixed them together. In fact, searching for records (not just at my parents’ house), commonly called, “digging,” is one of my favorite things to do. I have always been fascinated by the relationship between the dj’s preparatory “digging” and the dj mix or song being produced. When djs talk about digging for records, we are referring to the ongoing quest for the most obscure yet beautiful and appropriate sounds we can find for the work in which we are engaged. You might return from a trip to the local antique mall or record shop covered in dust and with a couple of gems—maybe a rare, spokenword poetry record by a relatively unknown poet and a record of classical songs recomposed for and performed by a 1978 Moog synthesizer duo. The challenge and the fun of making beats and djing is figuring out how to incorporate those records into your music. The ability to do so is a point of pride for many of us (fig. 1).

Case Study: Sampler

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Fig. 1: Me digging for records in a used bookstore in Jerusalem

I think that the ability to incorporate a diverse array of records into a dj set is similar to, if not the same as, an ability to traverse large conceptual distances in writing. To draw connections between pieces of data or information, I believe, is more a uniquely human ability than a skill that can be mastered by a computer or artificial intelligence. We humans aren’t constrained by tagging, metadata, or even semantic search. We can seek connections even when the information is not cataloged properly or organized appropriately. In fact, sometimes that’s when our best work happens. Sampler is a design project that asks, how can we leverage computation in order to give us more opportunities for creative connection and interpretation? It asks how computation can help us do more work and more meaningful work, not how computation can make our work easier. More specifically, it is a platform for exploring the connections between content. It aims to catalyze a process of selection and mixing in which limitations and improvisation help learners find new and meaningful connections between pieces of information, prompting curiosity and deeper inquiry.

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Genesis and Design Process Phase 1: Failures and Articulation of the Methodology

At the beginning of the fall of 2012, I struggled to identify a project idea that could help me explore the theory about learning that I had begun to develop. I spent a great deal of time writing and diagramming different processes which were strictly about learning while trying to map these processes to djing. I had not, however, clearly articulated the process of djing, nor had I really explored my own djing experiences or process in writing. At some point during these early struggles, I decided that I needed to look at specific topics that learners might explore and then investigate the different ways in which elements of those topics could be sampled and mixed. Because I had not actually described in detail my own methodology of djing, I did not have a method with which I, as the designer, could engage the content. I had a notion of the process, but was so focused on the content that I only thought in terms of sampling and mixing as general principles. My process, both as a learner and dj, is so deeply internalized that, despite my best efforts, I had not fully explained it even to myself. In my search for content through which I would explore my methodology, I looked to my roots as a graphic designer. I tried to think of how I could explore the topic of typography using this amorphous “process.” I investigated the Gill Sans typeface through a historical lens by analyzing its influences and heritage, and in doing so, came up with a thesis statement of sorts about the typeface and its relationship to contemporary forms of empire. Without considering process, I dived so deeply into the content that, although I had a wonderful learning experience, I did not come up with a project, an interface or experience concept that could relate to the topic of typography in general, much less “learning” as a whole. The more I tried to explore specific disciplines or topics in order to extract a project idea, the more obscured my own experience as a dj became. During the early stages of my thesis research, I was very much taken with Nicholas Carr’s concept of the unbundling of media. Carr discusses the unbundling of content at first through the lens of brevity, asserting that our increased access to information and shortened attention spans create a market for snippets of media (94). More compelling to me, however, were the examples of unbundling he begins to cite—individual pages or paragraphs from books being displayed on Google Books, individual songs being sold instead of entire albums, and sections of songs being used as ringtones or in videogames.

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I began to extrapolate the meaning of these terms to refer to the language of music, using unbundling in place of sampling. I tried to imagine the opposite of unbundling, deciding that re-bundling made sense. I may have been resisting more clear, direct references to sampling and mixing because I was afraid the language of djing was neither academic nor sophisticated. In exploring the meaning of unbundling and re-bundling content, I began to map learning experiences to three basic steps: unbundling, re-bundling, and juxtaposition. Continuing to believe that I needed to begin with content as opposed to a system or more concrete process, I decided to design an interdisciplinary curriculum around the word, “roots” (fig. 2). By asking learners to unbundle and re-bundle the various meanings of this particular word, I thought I might be able to facilitate a process of creative inquiry. It became nearly impossible, however, for me to do anything but engage with the content at the level at which I would have wanted learners to engage with the content. Because I wasn’t thinking in terms of a broader system and because I was only thinking about one particular curriculum or topic of study, my ability to see how I was working through the content was limited. It was around this time that I began writing in detail about my experiences as a dj, very specifically explaining my process and methodology, as well as the way in which that process translated to my learning experiences as a kid.

See Chapter 2 for more information on this process and these experiences. Fig. 2: Early whiteboard ideas for the “roots” curriculum

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Phase 2: The “digital analog” See Chapter 2 for more about the Dr. Sample.

See Chapter 4 for more information about the tenets of designing construction kits for kids

As I wrote about my earliest encounters with sampling and djing, including my Dr. Sample, and explored my development as a dj and music producer, I began to wonder whether the experience I had with the Dr. Sample could translate to disciplines outside of music. In my writing I had already drawn connections between the way I researched, made art, wrote papers, and the way I made beats or prepared a dj set. By writing in vivid detail about my djing process, specifically through the lens of the Dr. Sample, I found an interactive object that could serve as a model for an exploration of content. If I used the Dr. Sample as a way to explore relationships between musical content, there seemed to be no reason that it wouldn’t be worthwhile to design something similar for exploring other content. Having spent a semester learning from Mitch Resnick and the Lifelong Kindergarten Group at the mit Media Lab about the principles of designing construction kits for kids, I also began to see the Dr. Sample as a construction kit which helped me learn about music. Samples were the modular building blocks contained within this kit. What I did not realize, however, was that the Dr. Sample taught me more than about the formal properties of music. It taught me about the history of music—from my dad’s big band jazz records to my mom’s folk records, to the funk and soul records I was buying in an attempt to emulate my favorite hip-hop producers. The Dr. Sample became a construction kit about history. These history lessons, of course, are not inherent to the technology of the Dr. Sample itself: it did not come pre-loaded with historical lesson plans. The platform of the technology, the way it mediated my experience of musical content, helped me pursue a self-identified learning goal. Songs are made within a context: they have a time and place. They make references that are often both musical and cultural. My knowledge of the music I have sampled over the years has given me an understanding of the places (some temporal, some physical, some musical) from which it comes and, often times, how those places are connected. Taken a step further, samples of music can stand for samples of ideas. These ideas can be represented directly, such as words sung by the artist being sampled. Ideas can also be represented in a more abstract way: a sample of music may reference a particular time and location, and thus may reference the culture that existed in that place and time. The use of such a sample may also make use of the juxtaposition of that culture with the cultural context of the artist doing the sampling.

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One of the other features (or non-features) of the Dr. Sample was its technological limitations. Each bank of samples could hold only eight short samples of sound. I had to make decisions about when to delete certain samples from a bank in order to accommodate others, discovering a counterintuitive creative freedom that was strangely invigorating. I could not save anything I sampled. I used samples to make a song, recorded it to 4-track tape, and then wiped my sampler, or deleted specific samples that I knew I was not likely to use on the next song I made. This limitation forced me to improvise and to be much more creative in the way that I worked with the few samples I could use. If I wanted to build a song around certain samples, then I would test the way in which other samples complemented, connected with, my primary samples. I would save some and delete others in an iterative process attempting to find the connections that sounded best. These limitations facilitated serendipitous music-making experiences. If, after working on one song with a set of samples, I found myself beginning to work on a different song, I was forced to delete some samples from the first. I would often find that samples from each set sounded perfect when mixed. I have often thought that something changed when I started to make music solely on the computer, that part of the magic of my music-making process had been lost in some way. I could sample anything, and as much of it as I want. Possibility became overwhelming, and I began to look for ways of selecting samples that were the easiest, my decisions often being driven by the design of the software I was using. In gaining an infinite number of possible samples, I lost the potential for serendipitous discovery. I do not believe there is a great deal of difference between the evolution of my music making practice and the changes wrought by technology when it comes to analyzing or thinking about data as a whole. Many authors have discussed our societal, corporate, and personal struggles with the management and analysis of the large, complex data sets with which we are confronted on a daily basis (Thorp, Carr, Richtel). Although we need ways to manage all this data, we often look in desperation for computation to help us, to make our lives easier, to give us a sense of calm as the floodwaters of data rush over us. In our desperation to find a manageable way to interact with this information, we often rely on computation to tell us what is meaningful about our data (machine-readable meaning), instead of trying to figure out ways for computation to facilitate our own searches for meaning (human-readable meaning). Yet the ability of our digital tools to extract meaning for themselves, machine-readable meaning, has become increasingly important as more and more interactions that affect humans become strictly algorithmic (e.g. stock trading) (Slavin).

See Chapter 6 for more on the embedded ideologies of our designed objects and experiences.

See Chapter 12 for more on machine-readable and humanreadable meaning.

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In considering the way my Dr. Sample acted as a construction kit that facilitated serendipitous music-making experiences for me, I saw the Dr. Sample as a model for a conceptual construction kit of sorts. I began to wonder, could learners see samples of ideas or events as things with which they could build an argument or a new idea? I imagined other media besides audio that would be able to be input into each of the eight buttons in the Dr. Sample. What would the interface look like if a user could sample text, video, images and audio? How would a “mix” manifest itself? Could the interface and the interaction create spontaneous, improvisational experiences of facilitated serendipity and help users see and explore connections between content that they might never have otherwise noticed? I wondered if the Sampler could be a platform that could leverage computation in the service of human-readable meaning. Phase 3: Prototyping and Testing

I began to investigate these questions by making a paper prototype, effectively reproducing the basic interface of my sampler—eight buttons numbered “1” through “8.” This wasn’t exactly a prototype of the experience I imagined; it was just my old sampler, made out of paper (fig. 3). Having replicated the most basic components of my sampler’s interface, I realized that with music, no visual display is needed beyond the music-making interface itself. However, with research, whether visual or written, some kind-of display is necessary. I also used paper to create a display which parallels the button interface, with 8 squares, each of which, when activated, would display the content of the corresponding sample (fig. 4). I imagined that the sample-triggering functionality would mimic that of the Dr. Sample: as a user presses one of the buttons on the sampler, the corresponding sample is shown in the same location on the display surface (a screen or projection). A sampled text, for example, might continuously scroll upwards and then repeat when it reaches its conclusion (fig. 4). Upon being triggered, a user would be able to see it as it scrolled. Video would be displayed in a similar fashion. Videos would loop and would be activated when triggered (fig. 4). After discussing the idea with Professor Brian Lucid and showing the Post-it note mock-up at the dmi Mid-Semester Reviews, I decided it was important to create a functional, interactive prototype. We also talked about designing the system in which this interaction would function. Brian asked me about the different ways in which users might encounter the content they sampled, and how a user would be able to explore or reflect on his or her mix.

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Fig. 3: The first iteration of the paper Sampler interface

Fig. 4: The first iteration of the paper Sampler display

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MaxMSP/Jitter is a visual programming environment for the development of media experiences. Its interface is based on the metaphor of synthesizer patches.

Towards a DJ Methodology of Learning

Through a series of small design gestures, I began to investigate the possibility of a functional prototype as well as the answers to the questions Brian had raised during our meetings. To create an interactive prototype, I looked back at past projects and found that I could likely create something like my paper prototype with MaxMSP/Jitter. In the Spring of 2012, I had worked with Jeff Bartell on a physical interface for a museum installation about the American Labor hero, Joe Hill. For that project, Jeff and I used MaxMSP to trigger video and audio based on physical input from sensors embedded in objects that were being bashed with a sledge hammer (fig. 5). I wondered if I could leverage some of the software we had written in Max to display a user’s “research” when he or she triggered the samples on the Sampler interface. These samples would, of course, be triggered by touch instead of by a sledge hammer, but the input was effectively the same.

Fig. 5: A video still of me hitting one of the buttons with a rubber mallet during the testing of the Joe Hill piece.

See Chapter 10 for more information on the R&B R&D project.

I had still not determined a specific input device for the Sampler interface. I recalled that some of my early explorations for the r&b r&d project included the use of an iPhone app called TouchOSC that allowed me to send osc data from my phone to my computer as I touched the customized interface elements on the iPhone screen. TouchOSC would allow me to create an interface where a user could actually push buttons on a mobile device and trigger samples of any media type in MaxMSP via wifi and my computer’s ip address.

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After a few days of tinkering around with MaxMSP/Jitter and my iPhone, I had created an interactive prototype. A small digital projector served as the “display,” and a user could trigger eight pre-determined samples—text, images, and video. Video clips were triggered and played when a user held down the corresponding button, and turned off when a user let go of the button. Text samples scrolled upward and were triggered on/ off in a similar fashion (fig. 6). Fig. 6: The first test for the interactive display, using a grid of squares in TouchOSC on the iPhone as input.

While I was exploring the design of this interactive prototype, I worked to create a service ecosystem in which the Sampler experience would exist (fig. 7). Sampler would not just be an object or an app, but the combination of a browser extension, web experience, mobile application, and a display surface. It would be a service designed to facilitate serendipitous encounters between a user and information. It would create a sense of exploration and construction, in an effort to give users a way of interacting with information that relied on a more human, intuitive process of connection-making, while at the same time leveraging the power of computation to store and serve up the content. A user could, for example, log onto the web application and create a new bank of samples—effectively beginning a new research project (fig. 8). She could then use a prompting system to help her dig for pieces of research that might yield interesting connections, ones that might help create conditions for serendipitous construction (fig. 9). She would use the browser extension to save different pieces of research (media or text) to particular samples within the bank of 8 (fig. 10). Using the mobile application and a display surface of some sort (e.g. a projector, smart board or screen), she would then “perform” her research, triggering different samples and attempting to identify connections (fig. 11). Upon identifying a connection between samples, she could dictate a voice memo or take a screen shot, recording for herself reminders of

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the connections which she can explore later in the web interface (fig. 12). The design and planning of this service helped clarify the interaction that I had prototyped as part of a broader system that could more effectively facilitate the experience that I originally intended in my early paper prototype sketches.

Sampler

The Sampler system is comprised of an iPhone app, a web app, a display (a computer with an IP address, an AppleTV, or digital projector), and a browser extension.

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Add new sample bank

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Fig. 7: A user journey diagram that was part of the service ecosystem design process

Find an Image about "assimilation" that relates in some way to Science.

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Fig. 8: A user begins a new research project using the Sampler web interface.

Fig. 9: The user asks for a prompt to help her get started with her research.

Fig. 10: The user adds research to her bank of samples using the browser extension.

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Fig. 11: A user then performs her research.

Fig. 12: The interface with which a user interacts to hear the voice memos she recorded during her “performance” of her research. The interface shows her which samples were playing while she was recording the memo.

Fig. 13: A user prints and takes notes on the screen shots that she captured during her interaction.

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As I started to plan the video abstract for the project, which I hoped would capture its essence and my aims in working on it, Brian advised me to, instead of working with stills and animation, attempt to make it as real as possible, and tie it tightly to the process of djing in a clear, overt way. One of the most surprising and valuable things that I learned by attempting to make a video abstract in this way was that the process of making the video could serve as a theatrical or performative prototypingwith-the-body. By creating convincing faux-interfaces and walking through various facets of the service with my wife, who played the main character in the video, I saw her as someone with whom I was doing user testing. She could help me better understand the prototypes (interactive and otherwise) that I had created. There were fake interfaces for the web and mobile applications (fig. 14), a faked browser extension interface and the interactive prototype of the mobile application paired with the display surface that I had built in MaxMSP/Jitter. As we ran through the sequencing of the various interactions described in the video, my wife asked me questions about why I had chosen to have aspects of the piece a certain way. One of the most important results of this collaborative, performative prototype was her feedback. She told me that, at first, her experience with the interactive prototype of the “mix” was overwhelming, but after spending some time playing with it, she began to see connections and identify ways in which the tool might be valuable. Fig. 14: The prototype mobile application interface.

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Another facet of the Sampler service that I prototyped was the way in which the software might prompt a user in order to facilitate serendipitous encounters with information that may yield interesting connections when mixed. I was interested in other prompting systems, particularly with relationship to music-making. As a dj, I engaged in a pre-curation or self-imposed prompting/limitation system when I was preparing a dj set or getting ready to make a beat, especially when records were the predominant sampling currency of the day. There were things that I did as a dj which would help me find disparate samples or songs that somehow fit together perfectly. Sometimes I would force myself to use as the centerpieces of a dj set a few records that I bought at the nearest garage sale. Other times, I was interested in exploring particular styles of mixing and manipulation, and would select music that would cater best to those explorations. Professor Brian Lucid asked how I might facilitate similar prompting experiences of information for users. Although limitations in and of themselves can often be valuable prompts for creative experiences (Nachmanovich, 78 – 89), I investigated mechanisms that were specifically intended to catalyze creativity, particularly in music-making. Influential musician and artist Brian Eno developed a card deck of Oblique Strategies, prompts that are intended as a reminder of creative and productive ways of thinking and working. I found that, while inspiring, my users would likely need something more concrete. I decided to base the prompting experience on the types of data that could be input into the Sampler system. A user would be prompted to find a type of information about her topic of inquiry that related in some way to a specific discipline. Embedded in this prompt are three things: a type of data, the topic of inquiry, and a discipline. For example, “Find a video (data type) about assimilation (topic) that relates in some way to science (discipline).” I created a web-based prototype of this prompting experience where users could input a url which would be saved to a database for my reference (fig. 15). Although it has been invaluable as a way to better understand and explain the dj methodology, Sampler currently exists only as a semi-functional prototype. If the project is to move forward, I will need a great deal of help in order to realize a fully-functional prototype.

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Fig. 15: The website I used to test different prompting mechanisms.

Next Steps To better understand the value of the experience I am proposing with Sampler, I would like to get a prototype web application integrating most of Sampler’s functionality in front of late high school or college students. To me, it is crucial to test Sampler and the dj methodology as a whole on these populations, although Sampler could be a valuable research tool for sophisticated scholars as well. In personal conversations with high school and college educators, as well as in my own personal experience as an educator, I have found that there is an opportunity to instill in students a stronger sense of confidence regarding synthesis and analysis of information. Similar to the way in which user testing with prototypes at various stages of fidelity and interactivity has been valuable for me in the past, documentation and observation of users interacting with a more functional, interactive version of this tool would be instructive. For example, I would be able to more accurately identify the types of prompting systems users would need. I would be able to find out to what degree different users found the “mix” and annotation systems helpful. Through a more sophisticated prototype, I could investigate whether the system itself could exist without necessitating so many pieces, such as separate interfaces for control and display of samples. If I were able to create a prototype in the browser and get it in front of students or researchers, I would also be able to learn about ways in which educators could, in a more practical sense, incorporate it into their work.

See Chapters 7 and 10 for other examples of user testing in various stages of fidelity/interactivity.

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Dusan Koljensic, head of Experience Design at SapientNitro, once asked me, at what point does the analog of the sampler become irrelevant? At what point can you take the concept behind this piece and design a more compelling interface for it, keeping all the principles of the interface and experience? This is a question that bears further investigation. The interface itself does not absolutely need to mimic a sampler. I will not venture into guess-work here as to what the next iteration of the interface might be. An exploration of the possibilities, however, would be an important effort for the continuation of this project. In addition to the development of a more functional prototype, the next steps of the Sampler project also may manifest themselves through the design of other learning experiences built on its principles. It is possible that I could continue to design works that create similar platforms for inquiry-based exploration, tools that have common behavioral and functional properties. I wonder if Sampler, in addition to a proposed technology, is a gesture at other experiences.

Conclusions The first time I showed the paper-prototype animation of this piece in public, Professor Heather Shaw said that she would love to use such a tool in the Design History class she teaches. She pointed out that it would help her students draw connections between what happens in design and what happens in culture at large. Other educators (including my wife) have mentioned to me that they are interested in Sampler as a way to help students understand obscured or hidden relationships between certain disciplines and wider cultural phenomena. This idea seems to have a great deal of potential, regardless of how exactly it manifests itself. Dusan, however, is right in asking if the interface itself needs to be such a direct analogy. I feel as though I am only able to conclude here, then, that I do not know what that means for the project itself. It might mean that I’ve identified an experience that is important, relevant, and valuable, but I have not yet identified the ways in which that experience should be expressed in a technology or interface. In the past, of course, a Sampler-like experience has existed without the use of sophisticated technology. Some of the most powerful cultural and intellectual work of our time may be the result of a process not dissimilar from the methodology on which this project (and my thesis as a whole) is constructed. Yet the work in which I am engaged is particular to our time, and the Sampler project reflects this. The Sampler project has helped me gain a better understanding of why I believe the dj methodology is important and relevant for the evolution of learning in contemporary

Case Study: Sampler

society. I do not have the tools or the technical skill to build a fully functional prototype of the experience. I do know, however, that my interactions with computation in building the prototypes that I have built, combined with the theoretical underpinnings of the rest of my research, have helped me understand the Sampler project as a way to express my ideas about the role of computation in our increasingly interdisciplinary problem space. By leveraging the ability to mix—something that I understand to be a uniquely human faculty—the Sampler project helps me see the potential for computation and digital technologies to create meaningful learning experiences that are in and of themselves, human, and not computationally replicable. By giving me a way to physically manifest my ideas about the nature of an approach to learning, I began to see the reasons that I value such an approach. The Sampler project for me was a self-reflective praxis. In making a project that is able to be experienced and that manifests explicitly some of the foundational ideas about my thesis research, my work was reflected back at me in a new way. Sampler is a statement about a compromise between computation and human input. It is about an engagement with computation and a set of limitations that forces the user to become creative—to look at problems and ideas in new ways in order to come up with an innovative take on a set of information. Sampler is part statement part research: it aims at a future that I fear might not exist. works cited Carr, Nicholas G. The Shallows: What the Internet Is Doing to Our Brains. New York: W.W. Norton, 2010. Print. Nachmanovitch, Stephen. Free Play: Improvisation in Life and Art. Los Angeles: J.P. Tarcher, 1990. Print. Richtel, Matt. “Growing Up Digital, Wired for Distraction.” New York Times. N.p., 21 Nov. 2010. Web. 12 Dec. 2012. Slavin, Kevin. “Those Algorithms That Govern Our Lives.” Speech. Lift Conference 2011. cicg, Geneva, Switzerland. 4 Feb. 2011. Lift Conference. Web. 1 Dec. 2012. . Thorp, Jer. “Big Data Is Not the New Oil.” Harvard Business Review. N.p., 30 Nov. 2012. Web. 01 Dec. 2012. .

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The Missing Dr. Sample and the Embedded Ideologies of our Designs

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The Missing Dr. Sample and the Embedded Ideologies of our Designs

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Driven by the apparatus. Me performing a DJ set using Ableton Live.

The Missing Dr. Sample and the Embedded Ideologies of our Designs

I don’t know where my Dr. Sample is anymore. I took it to a friend’s house years ago to do a recording session, but, strangely, I don’t remember what happened to it afterward. I remember being so enthralled with my newly purchased Ableton Live software and my new MacBook Pro that I must have figured the Dr. Sample didn’t matter anymore to my DJing practice. It took a long time for my excitement about Ableton Live to wear off.

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MIDI controllers are physical interfaces, typically with buttons, keys or knobs, that connect via USB to the computer with which you are making music. MIDI is the protocol that these interfaces use to control the sounds you are making

I bought MIDI controllers that resembled samplers and piano keyboards, and still, my Dr. Sample remained a distant memory, rarely making its way to the foreground of my consciousness. Something about my music changed, however, when so much more of it was made directly on my computer. The way in which technology mediated my experience had shifted. Everything was possible. Yet there was a “rawness,” as one of my friends best described it, that faded away. I could feel this shift in the way I was making my music as well. There was an active, almost chaotic, exciting memory in my mind of my previous working methodology. I ran around the basement looking for records, smacking buttons on my sampler, flipping the sides of a tape in the old 4-track recorder and fast-forwarding and rewinding until I found a blank section of tape. Grabbing my little sister’s toys, I would run back downstairs, find an old microphone and identify any and all relevant cables and plugs needed to run the mic or the toy itself into my sampler. It was a whirlwind. It was weird. It was magical.

See Chapter 2 for more about the Dr. Sample.

These memories flooded back as I wrote about the Dr. Sample in the fall of 2012, and I began working towards a music-making practice (in the little free time I had) that looked more like the way I worked as a fifteen-year-old. I started to create limitations for myself and attempted to implement a different process for working. Though my music-making process is not exactly the same as it once was, I feel like certain parts of that “rawness” have come back, helping me produce stronger, more diverse songs. It took time, and the prompting of my research, to understand that I needed to design systems or processes in my music-making in order to facilitate the same sorts of magical moments that I had when making beats with the Dr. Sample. I believe that advancements in technology caused both the loss of that “rawness,” while at the same time helped me design limitations that facilitated a more sophisticated, streamlined, yet similarly meaningful experience. Despite my love for my Dr. Sample and the difficulties I later encountered in developing a music-production practice that mimicked my studio in my parents’ basement, I am no luddite, musically or otherwise. The ways in which technology has changed the face of music-production are astounding. To a great extent, it has leveled the playing field: professional-sounding music-production does not require more than a computer and a few pieces of software. Nearly anything is possible with a laptop. There are, however, ways of making that become much easier when we rely so heavily on software. The design of the software, the varying processor power required by

The Missing Dr. Sample and the Embedded Ideologies of our Designs

different actions, the hierarchy of the system, and many other computational factors—intentionally or not—guide music-makers towards making certain actions and decisions without even knowing it. Taken to a logical extreme, our algorithms, slowly but surely, make the danger of a musical monoculture much more likely. This is not to say that all music sounds the same, or the creators of contemporary music are not creative. This is not a dismissal of the creative, innovative output of contemporary electronic musicians. Nonetheless, upon listening to contemporary, popular hip-hop and r&b music, one cannot help but hear the similarities across songs. In an interview with npr, Tajai, of the 90s rap group Souls of Mischief, states, “if I turn on the radio now... it sounds like one long song” (Meraji). One of the most recent iterations of these stylistic similarities is “Trap” music. Sitting somewhere at about 70 (or 140) beats per minute, Trap music is characterized by an almost exclusive use of 808 Drums, stock synth sounds, and sixteenth-note high hats. Soaring synth lines reminiscent of europop often accompany the poppy r&b iterations of contemporary hip-hop beats as well (think Rhianna). Yet contemporary, popular electronic music (hip-hop included), is driven as much by the apparatuses with which producers work as it is by stylistic fads. Our decisions are based on the ways in which our technologies, our apparatuses, are programmed in the first place. And as the sophistication of the apparatus we use to create our work increases, our need for the help of the apparatus increases (Flusser, 32). Our decisions, directed by the options displayed to us in our increasingly sophisticated and comprehensive digital, music-making apparatuses, can only take so many directions, no matter how diverse these directions may appear. I wonder what other disciplines are subtly being affected by the increasing use of computation, a use that should make the actions in which practitioners previously engaged easier, but rather facilitates a particular version of those actions. What technologies are now unintentionally dissuading users from pursuing solutions that in the past may have seemed equally as viable as the path toward which users are now ever-so-subtly guided? Experimental Jetset, a design firm based in the Netherlands, has said that it is interested in the idea of “the function of a design as an embodiment of ideology.” For several years now, I have referred others to this saying. While it may seem intuitive, if one thinks about this saying more deeply and then looks back out at the world, the saying foregrounds the embedded ideologies in the designed world around us. Whether the design is a piece of music-making software or a nation’s education sys-

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tem, understanding that designs embody ideologies can illuminate the relationships between the design, the powers that influenced the creation of the design, and the way the design influences those who interact with it. works cited Experimental Jetset. “Design & Art Reader.” Experimental Jetset. N.p., Jan. 2006. Web. 04 Mar. 2013. . Flusser, Vilém. Towards a Philosophy of Photography. London: Reaktion, 2000. Print. Meraji, Shereen Marisol. “The Many Sounds Of 1993 Bay Area Rap.” npr. npr, 28 Mar. 2013. Web. 19 Apr. 2013.

Case Study: Synthesis

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Construct through connection.

Case Study: Synthesis

Synthesis is a service designed to help learners create specific, arguable thesis statements for research projects. A proposed web-based research software and service, it is intended to catalyze a process of selection, connection, and categorization. The interface itself helps learners visually explore and annotate connections between information, while also prompting learners to articulate the relationships between these connections. By identifying the similarities between the ways in which pieces of information are connected, learners synthesize their research and propel inquiry forward.

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Introduction Listening to the Zunguzung meme as it reappears again and again, accruing new meanings in new contexts and recalling (or not recalling) the connotations of previous occurrences, we hear how music can draw and redraw the lines of community, compelling us to follow along, sometimes whether we’d like to or not. —Wayne Marshall, author, researcher, ethnomusicologist, and self-described “technomusicologist” Reading—or, rather, reading and listening to—Wayne Marshall’s academic research is a powerful experience. To quote him doesn’t do his research justice, because, often literally, his thesis is in the music he creates. To explore his research, to identify and tease out connections across geographies and epochs, Wayne selects and mixes samples of songs, referencing and elaborating on these musical elements through his written research. The way Wayne, better known to club-goers as dj Wayne & Wax, approaches his academic research is quite similar to the way in which he or I might prepare for a dj gig at a club.

Serato Scratch Live is a combination of a sophisticated software, a small piece of hardware, and specially time-coded vinyl records allowing DJs to play any song from their computer, and manipulate it physically on their turntables, as if it were on the vinyl they are spinning. Ableton Live is a versatile music production and performance software that is used both for recording and live performance. It is extremely flexible and allows for a diversity of hardware integrations. From club DJs to contemporary composers, Live is one of the industry standards in music-making experiences.

One of the best and most time-consuming things about preparing a dj set for a night on the town is picking which records you are going to bring with you. Though the previous statement may feel antiquated in our world of Serato Scratch Live, Ableton Live and other digital performance tools that allow us to play almost anything at any time, preparation and curation have always been crucial elements of a good dj set. This basic principle of selection has a long history: djs in Jamaica were originally called “selectors.” This principle of selection is equally as applicable when you are rifling through your iTunes library as when you are staring at a wall of 1,000 records and you need to grab about four crates worth to take with you to the bar (fig. 1). The difference between your wall of vinyl and your iTunes library, of course, is that your iTunes library comes with you. With Serato Scratch Live and Ableton Live, as well as other music-making technologies that blend performance and production, djs can now play any song on their computer (or in the cloud, for that matter) at any time. We often view this ability as an evolutionary advantage, which it is if these technologies are leveraged appropriately. There is, however, a drawback to the ability to play anything at any time, particularly when this ability becomes an excuse for less preparation.

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Fig. 1: Four crates of records and an extra turntable in the back of a friend’s car, ready to head to a gig.

I’ll never forget the first time I heard one of my favorite djs play a set using Serato Scratch Live instead of playing vinyl. He played four Big Punisher songs in a row. Now, I don’t mind Big Pun, and there’s an outside chance that my friend had a particular rationale behind this portion of his set. The impact of a newly expansive access to data, however, felt increasingly transparent as I stood there on the dance floor, realizing that these songs were probably in alphabetical order in his iTunes library. A few years later, as I read The Shallows, by Nicholas Carr, I couldn’t help but feel that Carr’s assertions reinforced the feeling I was having, not only about my friend’s dj set, but about things I was reading and experiencing online. As access to information and data becomes easier, and as the amount of data available to us skyrockets, it becomes increasingly difficult to weave together disparate content into cohesive experiences that are truly meaningful.

Finding a Collaborator, Making Connections Around the same time as my data-glut-phobia was being fueled by Carr, I was working on an assignment to develop an interface for visualizing and organizing a collection of objects, an assignment given to many first-year students in our program. I decided to procrastinate by having a few beers with a new acquaintance, Jonah, who was

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a history teacher at a local high school. Standing outside the Publick House, a popular local watering hole, we began to talk about our overlapping passions—education and music. Before the doors to the bar had even opened, Jonah had already launched into the struggles he faced teaching history to high school students, these digital natives who, although having grown up with the internet, seemed paralyzed by their access to information. They were often unable to synthesize research into specific and arguable theses for their papers or assignments. Their assertions fell flat, mere regurgitations of information they had read at some point along the way. They quoted passages at length instead of paraphrasing and synthesizing or contrasting. Part of the problem, Jonah posited, was that access to an expanse of endless information was panic-inducing. The typical high school student, a procrastinator extraordinaire, would succumb quickly to the overwhelming amount of information, happily grabbing quotes from the first few articles across which he or she stumbled. The experience of needing to be a “selector,” to cull and curate research (or vinyl records for that matter) was a foreign concept to Jonah’s students. When it comes to the implementation of technology in a social studies classroom, other educators seem to agree with Jonah. “Valid concerns have been raised with regard to students’ readiness and ability to conduct meaningful and self regulated indepth research when the tendency of many students is to collect only the most easily accessible information via simplistic searches.” (Doolittle and Hicks, 97–98). This is especially the case when students are not enthusiastic about the research inquiry in which they are engaged. In discussing the importance of constructivist pedagogy in the social studies classroom, Doolittle and Hicks underscore the educational benefits of personally-meaningful student inquiry (98). Reminiscing about our most successful high school essays, Jonah and I arrived at a similar conclusion after a few beers: these essays were all, to some degree, self-guided research inquiries. Not only were our essays successful because we wanted to write them, but they were successful because of the ways in which we synthesized our research, weaving meaningful connections between pieces of information. One of the other things that we quickly realized was that we were trained to research employing a methodology still grounded in analog databases. We were trained to find the metaphorical four crates of records to bring with us to a gig. We used a limited number of “samples.” We had to—the internet was still in its infancy, and the card catalog, 4" × 6" notecards, and a number two pencil were still the research currency of the

Case Study: Synthesis

day. Was it this analog-oriented process that made us successful? We are not technophobes or luddites, and we therefore dismissed the nature of the tools as a precursor to a productive research engagement. Jonah and I did, however, identify a process that we felt was crucial to our understanding of material. We determined that this process involves selection of key data points, drawing connections between data, and categorization of both data and connections. As we discussed this process, I was momentarily transported back to my parents’ basement, standing in front of my sampler and my records. The creation of a meaningful experience for the patrons of a bar at which I was djing seemed, to me, to hinge on my ability to select, connect and categorize my records—my data. Jonah and I felt as though we understood and had experienced a process that his students had somehow missed. Could a strategic use of dynamic media help facilitate an environment in which students could engage in a self-initiated inquiry? Could it help students productively utilize limitation and constraint in order to explore connections and categorizations, ultimately yielding a specific, arguable thesis statement? What type of experience could help students understand the value of being a “selector” while being able to adeptly “mix” information to reveal meaningful connections? The questions that Jonah and I posed were exciting to me: there seemed to be an opportunity to explore in a new setting some of the principles I was learning in school. Since, however, this was the first time Jonah and I had actually spent any time together, I had to explain a bit about our graduate program and what it was I had been working on. I told Jonah about some of my ideas, which, at that time, included applying principles of contemporary music-making interface design to things that weren’t musical. I showed him some examples of music-making applications, such as Shapemix, that rely on organic or metaphoric visualizations of samples. In Shapemix, samples float around in a space on the screen, depicted as small, circular shapes. The appearance of the shapes varies depending on the musical properties applied to that sample (fig. 2). Another visual reference point for us became the more traditional “mind-map,” which we, as high school students, were encouraged to use as a way to prompt the essaywriting process. We also looked at the work of Mark Lombardi, who spent his artistic career diagramming the complex networks involved in some of the most scandalous events of the twentieth century (fig. 3).

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Fig. 2: The interface for Shapemix, a music-making application for the iPad that uses circles as a metaphor for samples, and illustrates the effects applied to a sample through the ways in which the circle’s visual properties change.

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Fig. 3: A detail of one of Marc Lombardi’s studies for a diagram in the Global Networks series, entitled “George W. Bush, Harken Energy and Jackson Stephens c. 1979 – 90, 5th version,” 1999.

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A Project and a Process Soon, Jonah and I realized that we weren’t just talking about his students’ problems writing papers, nor were we talking about the assignment I had planned to work on. We had actually begun to create a project together. We decided to work on it collaboratively, both reaping the benefits of the research. It would function as the work for my design studio class and could help Jonah develop his already innovative pedagogical practice. We even thought it would be something that we could prototype and put in front of students before the end of the semester. Our goal was to give students the ability to connect, categorize, and annotate research in a database-driven, dynamic, visual environment. We hoped this experience would help students gain new insights and propel their inquiries forward. We wanted to prompt what we saw as a creative process—to facilitate experiences similar to those that I had preparing dj sets or that Wayne Marshall has while collecting and mixing the samples he uses to conduct his musical research inquiries. Jonah and I immediately began describing our project as a service design project for learning. It went beyond the design of an interface and became the design of a system, a digital platform for learners to collect research and explore it through this process of connection and categorization. We identified key elements of the experience that we knew would be important. The first was that the interface of the digital tool itself should be highly visual—connections between pieces of research should be immediately apparent to the user. Those pieces of research should be viewed together, but appear distinct from one another. Annotations are helpful for both research and djing. They help us remember how things are connected. Annotations that appeared on my records (small pieces of artist’s tape on which I might write notes to myself such as, “same sample as ‘My Block’”) became central to my ability to create cohesive, evolving portions of an evening. Annotations also enhanced my ability to improvise. Thus, the ability to connect, categorize and annotate became guiding principles of the project. At one of our many meetings over the course of the 2011 spring semester, we discussed some sketches I had brought, wishing that we had a developer to help us quickly prototype our ideas. Lamenting the absence of a developer was futile, so we resolved to build a paper prototype and test it on ourselves. Though a paper

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prototype might be an intuitive step in the design process, it was important for us to understand that we had begun to explore much more than a digital tool; we were proposing a process, a methodology, for creative inquiry. We asked ourselves, if we had to write a research paper about something, how would we use this interface and methodology that we were proposing? Combining our analog research tools of notecards and post-its with painter’s tape and string, we created a physical version of the interface we had started to design (fig. 4). Fig. 4: Our paper prototype of the connection and annotation system and process.

We began testing our paper prototype by assigning ourselves a topic about which neither of us had any expertise—The Han Dynasty. We gave ourselves two hours to come up with as much as research as we could, reconvening with several key pieces of data, the records with which we decided to fill our crates. We limited ourselves to ten key pieces of data that we thought were central to our inquiry: the abolition of private minting, Confucianism, Emperor Wu, Liu Ban, the Qin Dynasty, The Rebellion of the Seven States, the Silk Road, Urbanization, Xiang Yu, and the Xiongnu. We wrote about each piece of data on a notecard, and then connected data points with string and painter’s tape, annotating each connection with a yellow post it. Watching our web of connections and categorizations grow, and finding out which pieces of data were most central to our topic (the ones with the most painter’s tape on them) became a powerful visual representation of the evolution of our research inquiry. After a few hours of drawing connections between data and articulating relation-

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Fig. 5 – 6: Synthesis was designed with multiple users, devices, and synchronous/asynchronous interactions in mind.

ships between categories of data, Jonah and I settled on a thesis statement addressing the Han Dynasty’s maintenance of power structures. It felt like an improvisational performance of sorts. We had, in effect, engaged in a rapid research prototyping and created an initial iteration of a thesis statement. We decided to pursue a digital iteration of this experience through wireframes and a narrative user scenario. We began to consider more deeply how this digital tool would work, what devices would support it, and how it would integrate with the classroom environment as well as asynchronous, homework environments. We envisioned it as a web application that would be sensitive to the devices on which it was being used (fig. 5). Educators would have a different type of account than students, which would allow more options and administrative capabilities across a class or range of classes (fig. 6). In addition to designing this framework for the experience for both educators and students, we also focused on the design and choreography of students’ experience of the interface itself. We determined how students would add pieces of research, connect research, annotate connections, and add categories. Students could filter by category, by number of connections, and by type of connection (fig. 7 – 11). We designed a simple visual language to communicate to the user connections and categories, as well as how many connections and categories each piece of research had applied to it. The system needed to be flexible and account for how these experiences might change in different situations or on different devices. Clocking in at over nine minutes, the original narrative scenario video, which outlines the process and the technology we were proposing, is by no means exemplary. Instead of giving a brief overview of the system, which Jonah and I had begun to refer

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Fig. 7 – 11: Adding a piece of research, drawing a connection, annotating a connection, adding a category, filtering by category.

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to as “Synthesis,” the video goes through every way a learner and educator might interact with the service we designed. The making of the video, however, did help us identify and refine elements of the interface and experience. At the same time as I worked on the visual design of the interface, storyboards and illustrations for the narrative scenario, Jonah worked to get permission for us to do some user testing in class with his students. Two of his classes were scheduled to begin working on a paper about the Nazis’ rise to power. We decided to try to implement our process in the classroom, using a paper prototyping method similar to what we had done at Jonah’s house. The students had about three weeks left to write their final research papers for this unit, and fortunately, Jonah had left the assignment rather open. In preparation for our activity, Jonah told his students to bring in all their research in an easily accessible format, whether on laptops or printed out and in binders. Our day of paper-prototyping and user testing with students arrived in late April. We began both classes by asking students to look through their research and to identify ten key pieces of their inquiry—the main facts, people, places, or events. Students looked through their collection of data and selected what they thought would best help them address the assignment. Using materials similar to what we had used to test the process and interface on ourselves, Jonah and I asked the students to first connect their data points and annotate those connections. Then, after about 20 minutes, we asked students to begin categorizing their research as well as the connections between pieces of research. After another half-hour or so, we asked the students to spend some time looking at these maps of their inquiries, and then come up with a draft thesis statement by the end of the class period (fig. 12 – 18).

Turning into Something: Moving Forward with Synthesis “It’s turning into something!” An exclamation of excitement sprung from the back corner of the room. There were several students in the class who were identifying new relationships between pieces of their research, seeing new ideas begin to seep toward them through the large sheets of newsprint on which they worked. Through our observations in the classroom over the course of the day, as well as through discussions with students and a feedback survey, I learned that our paperprototype for Synthesis, and the process it facilitated, resonated with students.

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Fig. 12 – 15: Students in Jonah’s history classes engaged in an analog version of the process that the Synthesis software would facilitate, drawing connections between research and then classifying research and connections.

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Fig. 16 – 17: Students in Jonah’s history classes engaged in an analog version of the process that the Synthesis software would facilitate, drawing connections between research and then classifying research and connections.

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The success or feeling of deepened understanding was by no means uniform across the classes. The students who had done a lot of initial research but who were less set on an idea already, who were more open and flexible (ready to improvise), seemed to get more out of it. Typically, the more focused and specific a student was able to be about a piece of data/research, a connection, or a category, the more interesting arguments he or she would propose at the end of class. In addition to findings about the impact of our process and proposed technology on the work of students, Jonah and I also learned that there are few research tools that function in the same fashion. We met with other educators as well as user-testing experts at wgbh, and at each stage we were met with encouraging feedback about the project. While this was exciting and encouraging for us, the Synthesis project is equally as valuable to me as a way to prompt an engagement with a methodology. From a technical standpoint, moving forward with the Synthesis project would be challenging, but not impossible. After creating an improved narrative scenario video and refining the design of the system, I could solicit the help of developers and educators to create an open-source, Creative Commons-licensed version of it. The first step in this process would be to build a functional front-end prototype for a single type of device with pre-determined content. This design gesture would approximate the user experience and could give developers and educators both an idea of how the tool might work. Regardless of the technical hurdles or the degree to which it is possible for Synthesis to become a reality in the way we envisioned, it has already become a reality for me in many ways. I have used the process that Jonah and I identified as a way to prompt the creation of expressive, digital artworks in one of the classes I taught at Emmanuel College. Our work on Synthesis, particularly the way we began to value the connections between the connections, has informed the way I assess other learning tools and experiences. In conversations with other educators, the emphasis we place on learners going this extra step has been met with enthusiastic agreement and interest. While Synthesis relies heavily on computational systems of metadata and tagging, it avoids endeavoring to draw conclusions for the user. Instead, it catalyzes an exploration driven by data and aided by computation, but in the end, is inconclusive unto itself. Without the user, it is useless. It facilitates for learners (for humans) a process that I understand as valuable, particularly now, in a world of ever-increasing access to information. In fact, having such a tool to help me build dj sets would be nice, too.

See Chapter 9 for more about the Manipulative Media Project.

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works cited Doolittle, Peter E., and David Hicks. “Constructivism as a Theoretical Foundation for the Use of Technology in Social Studies.” Theory and Research in Social Education 31.1 (2003): 72-104. Web. Marshall, Wayne. “Follow Me Now: The Zigzagging Zunguzung Meme.” Wayne & Wax. N.p., 10 May 2007. Web. 12 Oct. 2012. .

Data Deluge, Always on, and a Giving-Over of Power

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My Facebook feed.

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Data Deluge, Always on, and a Giving-Over of Power

[S]omewhere in my enthusiasm for the latest generation of electronic tools, I had gotten the old saw about knowledge and power turned around in my head: I was thinking that information was power. I now regard this as one of the great seductive myths of our time and do not feel so silly about falling prey to it; I think it happens to people all the time. – David Shenk, Data Smog: Surviving the Information Glut, 1997(!)

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Computation opened up the possibilities of my djing practice, and the potential of doing anything with any sample or piece of music that I could imagine became reality. However, my music lost the “rawness” it once had—a rawness characterized by small mistakes and the limits of my materials. My hi-hats were imperfectly synched and the soft, squished frequencies of samples revealed repeated tape recordings. Instead of this rawness happening naturally, my more computationally-mediated practice necessitated that I create systems to engineer what was an inherent hallmark of early hip-hop productions. My music had lost something that had made it innately compelling to my friends (my listeners). It seemed as though I was overwhelmed with possibility, that I was overwhelmed with data. My entire iTunes library sat there, an infinite number of samples at my fingertips. I had more effects, more potential samples, more songs to sample, more tracks to record on, and more places to store samples than I ever had before. It was so exciting. And yet, something was amiss.

See Chapter 7 for more on the Synthesis project. Digital natives are students who have grown up with technology; they are “‘native speakers’” of the language of computers, videogames and the internet” (Prensky).

Something not so different was happening to my friend’s history students, the ones who we aimed to help by designing a prototype for a new research tool. His students, digital natives from primarily upper-middle class families, could find any piece of in-

formation they wanted about a particular topic. They had access to online journals, to countless resources in their school, to websites, and history books. Yet many students were missing the ability to synthesize sources and express this synthesis through specific, arguable thesis statements. In The Shallows, Nicholas Carr argues that our inundation with information has shifted the way in which we consume media. Not only has it affected our attention spans and our abilities to engage in nuanced arguments or long-form reading experiences, but it has begun to fundamentally alter our brains. We are stimulated by, and increasingly addicted to, the literal and figurative buzz of constant updates and incoming information. Based on personal reflection, interviews, historical and scientific research, Carr argues that we are bombarded by information on the internet, and that we endanger our ability to focus and exert cognitive control (Carr, 220). In addition to difficulties focusing on tasks that require longer durations of engagement (Richtel), this bombardment, and our inability to appropriately cope with it, has changed our abilities to analyze and synthesize information. This deluge of data is, of course, not limited to the screens that sit on our desks. We carry the deluge with us at all times. It vibrates our pockets, text messages us, and tells us about the cute outfits that our friends wore last night to the bar. As our lives are punctuated by the irresistible temptation to check devices and social networks

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with ever-increasing frequency, our understanding of the world around us is informed by the now. We are “always on,” always connected (Turkle, 151). We live in a world of constant computational updating—whether in our stocks or our social networks. It becomes difficult to extract oneself from this cycle of now-ness. We even look to one another as digital barometers of our very own, real-time experiences (Turkle, 175 – 179), looking to the comments on a Facebook post to understand how we should feel about the situation that was the subject of the post. We are constantly looking at our actions and these actions are constantly being reflected back at us, applying peripheral temporal blinders that keep us from having a broad, historical view of our lives and our world. Much like status updates, the now-ness of quantifiable metrics (grades, leveling, badges, and standardized testing) and practical skills that make students employable (checklists of skills and software) become attractive ways to assess and think about learning. We lose our ability to look holistically beyond the now, to see the benefit in what we can’t easily understand or analyze computationally at this moment. As we create more Facebook posts and more data for marketers to understand, our increasing ability to handle vast amounts of information computationally becomes an attractive way to handle any type of information. The information we produce, then, begins to cater to the way it is parsed. Overhearing someone say, “ hashtag,” preceding a word or phrase is not uncommon. During the short-lived fame of female rapper, Kitty Pryde, Luke O’Neil of Vice magazine, celebrating her hyper-referentiality, wrote:

A hashtag is a word or phrase preceded by the “#” sign. It is a form of metadata tagging and is commonly used on social media sites.

Of course it doesn’t hurt her impending online lovefest, already well underway, that her subject matter is all so internetty. It’s not hashtag rap, but reblog rap, ideas dragged and clicked over from one page to the next, assembled in incongruous collage—like an unfolding Tumblr of seizure-inducing gifs and the contradiction of a cute animal pic followed by a porn gif followed by a violent film still broadcast from within the frame of a teenager’s messy bedroom. “I don’t do it on purpose,” she says of that meme-style. “Maybe I just think in internet. Oh my god, that’s horrible.” – Luke O’Neil, in “Why Kitty Pryde is our new Favorite Tumblr-Wave Rapper”

O’Neil does not delve deeply into Ms. Pryde’s statement about her own process of cognition. Her meme-style is not, however, merely a “style.” It is an approach to contentcreation that is fundamentally informed by the hyperlink, the hashtag, and a computational-readability. It is informed by and reflective of the deluge of data through which we now navigate on our iPhones, our Tumblr accounts, and our email inboxes.

Memes refer to those things on the internet that, over time, become referenced, parodied, remixed, and linked so much as to become, momentarily, a part of the popular internet consciousness

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The data frenzy into which we are now thrown, to which we are addicted but unable to parse, analyze, or synthesize on our own, reflects the essence of the age of big data. Not only are we consuming more information than we ever have before, but we are able to capture more information than we ever have before. An exponential increase in the creation or capture of data has spawned industries devoted to analyzing it and understanding it. ibm is one of the forerunners in developing “solutions” for this new frontier. The “big data” section of their software site tells users: “Every day, we create 2.5 quintillion bytes of data—so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone gps signals to name a few. This data is big data.” It is daunting, they tell us. You, mere human, on your own, without powerful computational tools and software, are incapable of understanding, identifying or leveraging the implications of this unimaginably large quantity of information. Our last, it seems, and only resort is to rely on technology, to give over our powers of synthesis and analysis to algorithm. Some researchers, while understanding and believing in the transformative potential of all this data, are skeptical of our current abilities to analyze it. In an article for Harvard Business Review, titled, “Big Data is not the New Oil,” Jer Thorp, former data-artist-inresidence at the New York Times and data visualizer extraordinaire, argues that for us to truly harness the power of big data, we need a “re-framing of data into a human context.” For this reframing to happen, “people need to understand and experience data ownership,” engage in open discussion about data and ethics, and not view data in terms of other past resources (e.g., oil) (Thorp). The ways in which our over-reliance on computation has shaped the (currently not-sohuman) context of our data go far beyond what we might refer to as “big data” and pose a problem deeper than Thorp’s call-to-action will likely be able to affect. Nicholas Carr argues: “When we look at a product recommended to us by Amazon or Netflix, we’re following a script… These scripts can be ingenious and extraordinarily useful… but they also mechanize the messy processes of intellectual exploration and even social attachment” (218). Carr is describing a loss of the capacity to make information meaningful. We now allow computers to do it for us. Not merely at the scale of big data, but at every scale, even at a scale that might be considered, well, human.

Data Deluge, Always on, and a Giving-Over of Power

There is an embedded (and quite explicit) ideology in the design of the tools that ibm has developed for the analysis of enterprise-level big data. This ideology is clear, simple, and a fundamental part of ibm’s identity in software development, so much so that it seems silly to even write it here: computation will take care of it for you. Computation will make your life easier. ibm tells you that you will be a more efficient executive, making better, faster decisions because you will have tamed the wild stallion of big data, harnessing its complexity. This ideology, this faith in computation, and the ways in which it has caused us to give over some of our most creative powers, those of synthesis and analysis, pervades many more computational experiences than just those of ibm’s big data software. works cited Carr, Nicholas G. The Shallows: What the Internet Is Doing to Our Brains. New York: W.W. Norton, 2010. Print. O’Neil, Luke. “Kitty Pryde Is Our New Favorite Tumblr-Wave Rapper.” VICE. N.p., May 2012. Web. 1 Feb. 2013. . Prensky, Marc. “Digital natives, digital immigrants part 1.” On the Horizon 9.5 (2001): 1-6. Richtel, Matt. “Growing Up Digital, Wired for Distraction.” New York Times. N.p., 21 Nov. 2010. Web. 12 Dec. 2012. Shenk, David. “Data Smog: Surviving the Information Glut.” Editorial. New York Times Books. New York Times, July 97. Web. 1 Feb. 2013. . Thorp, Jer. “Big Data Is Not the New Oil.” Harvard Business Review. N.p., 30 Nov. 2012. Web. 01 Dec. 2012. . Turkle, Sherry. Alone Together: Why We Expect More from Technology and Less from Each Other. New York: Basic, 2011. Print. “What Is Big Data? - Bringing Big Data to the Enterprise.” ibm. N.p., n.d. Web. 1 Feb. 2013. .

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Process and Pedagogy.

A modular series of posters created by my students in Introduction to Digital Process at Emmanuel College.

Case Study: Teaching

Teaching is, quite possibly, one of the best ways to learn. Through the process of teaching, educators learn about themselves, their students, and their subject matter. More than a window into the learning process and a learning experience unto itself, teaching became a “project” for me over the last two years, evolving into something that was inseparable from the design work in which I was engaged. The variables and dynamic systems involved in shaping learning experiences extend from the design of interactive technologies to the design of assignments and curricula.

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Indeed, many of the formal design considerations are the same: sequence, hierarchy, user research, perceived versus observed needs, database, interface, mapping, and narrative. These concepts are familiar to designers and educators alike, even though they may have different names. Over the course of my time in graduate school, I have had the opportunity to work as a teacher in a variety of capacities, first as a teaching assistant, and then as a faculty member. As a teaching assistant for first- and second-semester seniors in the Design Research and Senior Degree Project classes at MassArt, I had the opportunity to learn from the faculty for whom I worked as well as from the students I was teaching. As a faculty member teaching a variety of graphic design classes at Emmanuel College, my responsibilities changed along with the nature of the learning experience. There, I taught both introductory and advanced graphic design classes, working with students from their freshman through senior years. Though much of my learning derived from my experiences with my students, I learned from my colleagues, as well as from selfreflection, mistakes, and iteration. Throughout my work as an educator over the past three years, I have had a unique opportunity to explore and experiment with the ideas on which my design work is based. From my purview as a designer, it has been a user testing process combined with observational research and designed interventions; it has allowed me to better understand the needs of learners.

Prompts/Stimuli and the Unconscious Regenerative Remix As the focus of my thesis began to emerge during my second year of school, I had the opportunity to work as a teaching assistant with Professor Gunta Kaza, first in her Design Research class and then in her Senior Degree Project class. The Senior Degree Project class is intended to help graduating seniors develop, refine, and complete the final requirement for their bfa degrees at MassArt. Each student chooses the direction of his or her degree project research. Many students select a topic about which they are passionate. While these topics often seem to have very little to do with graphic design, some students identify opportunities for designed interventions or experiences that address a problem within the subjects they are researching. Meanwhile, other students, having spent their time defining themselves exclusively by their design work, struggle to identify a subject for their degree projects.

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In addition to being a ta for Professor Kaza, I was also doing an independent study with her based on my experience as her teaching assistant. She told me that in the past, students had trouble reflecting on their experiences working on their degree projects, often missing solutions that seemed to be right in front of their noses. Students could become so wrapped up in their ideas, or fearful of failure, or anxiously ambivalent about decisions, that it became difficult for some to complete the work in the way they wanted. When she works with graduate students, Professor Kaza assigns weekly “stimuli” or “response projects,” which, in my experience, help students reflect on their work in a low-pressure, fast-paced engagement with materials that ostensibly have nothing to do with a particular student’s research. Students are given an object, which seems to be chosen at random (the serrated edge of a roll of saran-wrap, for example), and are asked to create a visual response to the object (fig. 1 and 2). In imagining and creating a response, a student’s unconscious ideas bubble up to the surface, many of which are related to his or her thesis research. Sharing the response with classmates and reflecting on others’ experience of it allows students to better understand their own inquiries. Fig. 1 and 2: My response to the serrated saran wrap cutting blade. I filmed myself using it as an instrument, then cut and layered the video to create a beat.

Initially, I saw a direct correlation between Professor Kaza’s response projects and Mitch Resnick’s Creative Learning Spiral. Professor Kaza and I agreed that giving these stimuli or response projects to our Senior Degree Project students would be a good way to help them pursue their inquiries more effectively. We thought, based on her experience with graduate students, that it could help our undergraduates be more reflective about their practice, helping them make decisions and move forward. It would be my job, as Professor Kaza’s ta, to decide what these stimuli would be, and to obtain and distribute them to the students. As Professor Kaza’s independent study student, I would document the responses that the seniors in our degree project class created and write short summaries of the class’ work, reflecting on these summaries through my own projects as well.

See Chapter 4 for more about the Creative Learning Spiral and the DJing process.

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The response projects I gave students included the following: a quote on a sheet of photocopied notebook paper (fig. 3), a rubber band inside an envelope (fig. 4), a small packet of seeds (fig. 5), and a series of instructions for an action that forced students to discuss their degree projects with complete strangers. The responses that students created were diverse and instructive about both their work and their feelings about their work. Fig. 3 – 5: The prompts that I gave to students as response projects in the Senior Degree Project class.

Sometimes, the response projects were instructive regarding the student’s resistance to the work at hand. For example, a student who was wrestling with whether or not he was passionate enough about the restaurant industry to propose, for his senior degree project, a concept for a new restaurant, used the rubber band to help him tattoo a lemon (fig. 6). He told us that he was learning about becoming a tattoo artist and said that he did not see how this connected to his project. To a certain extent, his unconscious resistance to his degree project idea bubbled to the surface, which is why his response had decidedly little to do with the idea of restaurants (though he did tattoo a piece of fruit). Indeed, a few weeks later, his degree project evolved into a system for understanding what people want or need to eat depending on their emotions. Even the resistance he displayed to his initial idea became a way to move forward. At other times, the response projects became a way for a student to better understand or connect with his or her degree project. A student who was thinking about immigration, assimilation, and his identity compared to that of his siblings, embraced the rubber band response project by making large balls of rubber bands, then lighting them and photographing them up close (fig. 7). The powerful series of photographs captures the rubber bands losing their “rubber band-ness” and together becoming something entirely different—a new form unto itself. The relationship between

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this visual response and immigration and assimilation is poignant. This student’s aesthetic choices made these photographs even more profound: the images were beautifully composed and asked us (and him) to consider what merits assimilation might have. My experience working with Professor Kaza helped me understand the ways in which prompts can be effective and valuable when paired with reflection. Our prompts helped students create regenerative remixes of the prompts themselves, allowing, through praxis, students to see their work in new ways, understand their projects more deeply, or make necessary decisions to move their work forward. By sampling either the physical properties or the conceptual content of the objects and prompts they were given, and mixing these samples with samples of their degree project research, the things going on in their lives, or ideas that emerged from the unconscious, students created reflective, conceptual regenerative remixes. I borrow this term loosely from Eduardo Navas, meaning a remix that generates new value and does not allegorize or rely on the content being remixed for its value. The value created, in some cases, was a deeper understanding of the work in which the students were engaged.

I take praxis to mean action on the world and reflection on that action (Freire, 65, 66, 79). Paulo Freire states that “Liberation is a praxis: the action and reflection of men and women upon their world in order to transform it” (79).

For more on Navas’ research and his taxonomy of remix, see Chapter 3.

Fig. 6: A student in the Senior Degree Project class used the rubber band prompt to help him practice his tatoo skills on a lemon.

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Fig. 7: Another student used the rubber band prompt as a way to explore how repetition can transcend material. His visual explorations related directly to his degree project topic of assimilation.

Towards a DJ Methodology of Learning

Case Study: Teaching

To facilitate these reflective, regenerative remixes, the prompts served as limitations that sometimes fostered serendipitous encounters with content, particularly content that may be deep within a student’s unconscious. This reminded me of the technological limitations of my first sampler, the Dr. Sample sp-202. It could only hold two banks of eight short audio samples, so I was constantly engaging in a process of deleting and deciding what to keep. Often, I would forget about the samples in the bank that I wasn’t using, and, there, I would encounter samples that fit perfectly with the song on which I was working. The response projects helped guide the students towards a better understanding of their degree project work. Since that semester, however, I have begun to reframe this experience relative to the dj methodology. I see our work as an illustration of certain elements of the methodology, and in this way, I have gained new tools and a deeper understanding of prompting and mixing, helping me better design learning experiences for the students I would soon begin teaching at Emmanuel College.

Curricula, Assignments, and Testing the Methodology [W]e define courses by the objects made (motion graphics), segments of practice served (web design), or technical processes employed (Photoshop), not by the students’ developing awareness of concepts that transcend these categories, by the critical or problem-solving frameworks, or by the intended mediation by design. – Meredith Davis, Massaging Media Keynote, 2008 Just before beginning my work with Professor Kaza’s Senior Degree Project class, I was hired as an adjunct instructor of Graphic Design at Emmanuel College. I spent the spring semester of 2012 not only working with Professor Kaza and the seniors at MassArt, but also teaching my own class at Emmanuel. In July of 2012, I was brought on as a half-time faculty member at Emmanuel, increasing my course load and giving me the opportunity to help shape the graphic design curriculum and the direction of the Emmanuel Art Department as a whole. Initially, most of my learning about Emmanuel’s Art Department and the graphic design students in particular was about the ways in which students worked, what they were interested in, and how Emmanuel underclassmen differed from MassArt seniors. This difference and the overall cultural differences between the institutions are instructive about the truly diverse nature of design education. The students at

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For more on the Dr. Sample, please see Chapter 2.

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Emmanuel did not necessarily need what the students at MassArt needed. While MassArt is specifically a design school, Emmanuel is not. Emmanuel students take fewer than half the number of design classes that MassArt design students take. Emmanuel values the liberal arts, and students take classes in every department. The Emmanuel faculty takes seriously the school’s commitment to the liberal arts, and students are frequently reminded of the connections between the diverse topics they study. Meanwhile, students at MassArt are given the opportunity to deeply explore the discipline of graphic design while honing their craft and technical skills. Even though both institutions offer important learning opportunities for students, the learning goals within the graphic design curriculum at each institution are quite different. Identifying this gap between design at Emmanuel and design at MassArt gave me a framework within which I could begin to question whether there was an overarching methodology that could help all design students, or, maybe more accurately, whether or not I could design one. I believed that I had something to offer the Emmanuel students, particularly to help them look at design relative to the school’s liberal arts emphasis, but I did not have the language to define what that offering was. I struggled with assignments that focused on baseline technical competencies; some felt like an “abridged” version of traditional graphic design and did not integrate the liberal arts. I often wondered where the value lies in a focus on specific technical competencies when the tools of the trade and the trade itself are constantly evolving and when there are relatively few classes devoted to the discipline in the first place. Though the students at Emmanuel do learn about web design, I noticed an underlying lack of engagement with computation as a whole. It wasn’t that I felt the students should be writing Python all the time, but I felt a sense that they were missing a large part of the world that wields enormous influence over their lives because they didn’t have to engage with it and design for it. These students do not need to become excellent programmers. They do not need to become web developers or computer scientists or app developers or generative artists. But, they do need to have a foundational understanding of the way computation is shaping the world around them and the embedded ideologies in the computational objects with which they interact. Indeed, many authors and contemporary media theorists argue that it has become increasingly important for all people to understand the influence which computation wields over their lives (Rushkoff).

Case Study: Teaching

This sense that the students were somehow missing a critical outlook on a rather significant part of contemporary society, yet were deeply engaged in critical thinking in their liberal arts classes, was troubling. I wondered if students often saw my graphic design classes as outside the purview of their liberal arts studies. Design, much like science, can be focused on the development of technical skills and competencies that will make students employable. Technical competency is by no means bad. Design, however, much like science, can also be focused on the development of critical thinking skills with a foundation in creative inquiry (Moran). Critical thinking skills are difficult to measure on a multiple choice, standardized test, and the prevailing philosophy of the contemporary American education system has therefore divested itself of them, both at the high school and college level.

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See Chapter 11 for more detail on the shifting values in the American education system.

Despite my reservations about the impression students and other faculty had of design education, I was at Emmanuel to teach graphic design, and could not let my ideals keep me from assigning work or writing my syllabi. Additionally, the department and institution as a whole had broad, curricular goals that still needed to be met, and I knew that the time within which I had to develop my syllabi was limited. I therefore applied a “remix” to the syllabi from past years, looking at the assignments that were given and trying to find ways, if I could, to aim them towards the design education that I was beginning to envision. This vision was shaped, in part, by my reading of some of the work of Dr. Meredith Davis, which seemed to dovetail nicely with the type of preparation that I felt my liberal arts students needed in their design studies. Davis writes, “design education, at the most fundamental level, views complexity as a problem to be overcome through reductivist artifacts, not as an inevitable and pervasive attribute of life in the post-industrial community” (3). Dr. Davis argues that design education should focus instead on making the complex meaningful. In her classes, Dr. Davis learned that, given assignments designed to help students make meaning from complexity, “beginning students could articulate sophisticated positions on the issues nested within complex systems and frame problem statements that drive their own work” (Davis, 5). Students engaged in a self-motivated, creative inquiry. While I am preparing Emmanuel students to be professional designers, I believe it is equally valuable to help them learn how a design education can inform any profession and make them more responsible, conscientious, future global citizens. Intellectual curiosity, self-motivation, an understanding of the increasing complexity and

Meaningful to whom? See Chapters 11 – 13.

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interconnectedness of today’s world, and critical thinking are as essential for today’s workplace as they are for our world’s future. As I wrote about my Dr. Sample and started to believe that my experiences as a dj had imparted to me a methodology for learning that might have value for my liberal arts students at Emmanuel, I wondered how these “remixed” assignments could evolve to reflect this methodology. I also began to see this as an opportunity to user test, to try out some of my ideas and the basic principles of the interactive projects that I have developed. I realized that I could test some of these ideas on my students by redesigning assignments to incorporate the processes described in my writing. One of the classes I taught was called, “Introduction to Digital Process.” Students are taught fundamental design principles as well as the basics of the Adobe Creative Suite. Many students who sign up for the class are under the impression that it will be fun, easy, and that they will quickly learn the software. Their focus is on learning the tools, not a way of thinking, and many want to learn these tools with the least amount of effort possible. Some of the assignments that are given in this class cater to that mindset. The assignments are designed around software and the progression in which we, the “experts,” believe students should learn the software. Our focus on technical capability only serves to reinforce many students’ initial impression of the class. One assignment in particular that frustrated me was a project we used to call, “the Impossible Photograph.” It is commonly and tellingly referred to by students as “the Photoshop project.” The assignment was, indeed, about Photoshop more than anything else. Students were asked to create a series of “impossible photographs,” without needing to consider the content of the photographs themselves. Final products included images of gigantic spiders climbing up the sides of buildings, toes with eyes, and other rather pedestrian, vernacular imagery (fig. 8). This assignment, I realized, offered me the perfect opportunity to test out components of the dj methodology, including creative limitation, facilitated serendipity, sampling, mixing, and reflection. However, I only had a week to retool the assignment before I was going to introduce it to the students. To learn more about the Sampler project, see Chapter 5. For more on the Synthesis Project, see Chapter 7.

Drawing on my early work with the Sampler project, my work with Professor Kaza, the Synthesis project, and some of my favorite quotes from designers and media theorists, I began to work on my “remix” of the Impossible Photograph project. I called it “Manipulative Media: Thesis and Remix.” In the Synthesis project, a high school history teacher and I designed a tool for high school students that, through a process

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Fig. 8: One student’s work for “the Impossible Photo” project.

of sampling and mixing, of selection, connection and categorization, would help students write specific, arguable thesis statements for their research papers. Thesis statements, however, extend beyond research papers for history class. In fact, when we design something, knowingly or not, we communicate a point-of-view, a thesis statement about the world around us. Design firm Experimental Jetset states that they are interested in “the function of a design as an embodiment of ideology.” Even within the design of a basic chair is embedded the ideology, the thesis statement, that humans should want to sit down. Behind every design is a thesis statement. In the Manipulative Media project, students must communicate a thesis statement visually, using Photoshop. The deliverables are a printed statement and a digital triptych. When I introduce the project, I emphasize that Photoshop is just one tool through which designers communicate thesis statements about the world in which we live. This project became not “the Photoshop project,” but a project that happens to incorporate Photoshop.

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In order to arrive at a thesis statement, one must first engage in research. Though my students at Emmanuel are often engaged in research outside of the design classroom, they are not accustomed to discussing research in the context of design. To communicate a thesis through design is valuable from a practical standpoint because students first encounter the notion that design is more than just making pretty things. I decided to curate a starting point for their research, much like the prompts which Professor Kaza and I would give to our degree project students. I created a set of limitations within which students could engage in this research, limitations which might facilitate serendipitous encounters with content and prompt regenerative remixes. I gave the students three quotes from which they could choose one to begin researching (creative limitation): Quote 1: The discovery of the alphabet will create forgetfulness in the learners’ souls, because they will not use their memories; they will trust to the external written characters and not remember of themselves… You give your disciples not truth but only the semblance of truth; they will be heroes of many things, and will have learned nothing; they will appear to be omniscient and will generally know nothing. – Socrates, “Phaedrus” Quote 2: We look at the present through a rear view mirror. We march backwards into the future. – Marshall McLuhan Quote 3: There is a wisdom in smallness, if only on account of the smallness and patchiness of human knowledge, which relies on experiment far more than on understanding. The greatest danger invariably arises from the ruthless application, on a vast scale, of partial knowledge such as we are currently witnessing in the application of nuclear energy, of the new chemistry in agriculture, of transportation technology, and countless other things. (37) – E.F. Schumacher, Small is Beautiful

I then encouraged them to bring a wide variety of research back with them to class, including video, audio, images, and texts. They were required to bring at least ten pieces of research to class with them the following week (collection and sampling). I encouraged them to interpret the research of the quote rather openly, to look later-

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Fig. 9: Research done by a student who selected the Socrates quote.

Fig. 10: The connections that student started to draw between her research.

ally and at tangential references and sources (fig. 9). I then asked students to connect (mix) their research, and to express the different ways that their research is connected (fig. 10). Soon, each student arrived at a thesis statement (fig. 11). They then decided, through a rough sketching and collage process, what the images in their triptych might look like, and began to create them in Photoshop. The process became inquiry-oriented. Photoshop was not an end in itself, but rather just one way that students were using to express a thesis statement, with the understanding that it is

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Fig. 11: The student’s thesis statement.

Fig. 12: The student’s final triptych, created using a variety of tools in Photoshop.

only one tool that might be used. Though I changed the assignment drastically, I did not alter the technical components of the project that are central to the overall graphic design curriculum (fig. 12). In sampling the Impossible Photograph project and mixing it with samples from other aspects of my work, I created an assignment that leverages the dj methodology in order to foster critical thinking skills and inquiry-oriented process. I learned that this assignment allows students who feel less at home making art and design to become more confident because their academic abilities, previously outside of the purview of “art” or “design” in their minds, were suddenly valuable again. By shifting what it means to design, some students found their voices as designers. I assumed that, for other students, it would be a wake-up call—that design wasn’t all about having “fun” and “expressing

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yourself,” or making things pretty. For the most part, however, my assumption was not confirmed. Nearly every student, regardless of age, traditional academic aptitude, artistic inclination, or any other potentially contributing factor, engaged in an enthusiastic manner with his or her research. Students made Power Point slides about their research. They emailed me links. They asked their friends from other disciplines and majors to help them with their research. When it came time to begin working in Photoshop, those who felt they needed it sought out tutorials and assistance outside of class. They asked the monitors in the Mac Lab to help them. Though it was a class assignment, and yes, there was a grade attached, I do believe that the inquiry began to propel itself, that students worked to achieve their vision, to communicate their theses, because they had done the research, they had engaged in the process. While this process of creative inquiry yielded beautiful triptychs and increased students’ technical prowess, it also gave them the opportunity to identify and create meaning for themselves based on their research. Students did not write algorithms to find out how many authors cited a specific quote, nor did they attempt to find number of instances of particular words or phrases in the work of those authors. Instead, students were encouraged to trust their own processes of synthesis and create meaning that may escape machine-readability. The meanings they created are not reproducible through algorithm.

Next Steps The dj methodology has proved valuable for me as an educator. I learned that it could be applied, or even be more effective, outside the design of learning technologies themselves. The methodology can inform the design of assignments and learning experiences. In designing the Manipulative Media assignment and facilitating the experience for my students, I was not conscious of database or interface, a service ecosystem or the specifics of interaction. I had thought about much of that already through my previous design work. In reflecting on the systems I designed, and their successes and failures, I was able to use that knowledge to create an experience that didn’t hinge on technology, but was a designed experience nonetheless. The future of this assignment lies in its ability to provide a concrete example of how the dj methodology might be incorporated into a classroom. At times, I have tried to imagine how a curriculum or series of curricula for different ages might be designed using this methodology or even looking to this assignment as a helpful resource. It is still difficult for me to imagine. In considering my work as a professional design

See Chapters 8, 11, and 12.

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See addendum for the toolkit.

educator a design project, a project worthy of considering next steps, I would argue that it is my task to explore the methodology further in the context of curricula. This extension of the project may manifest itself in different ways. I would like to consult with high school educators (not in the arts), and help them incorporate the methodology into their schools. I believe the dj methodology has the potential to address formal curricular requirements while at the same time fostering the critical thinking skills that are being endangered by the education system. I would also like to incorporate the methodology more deeply into my practice as a design educator, and if I work as a professor, develop a curriculum that is structured more intentionally around it. My work as an educator has taught me that my ideas have the potential to be, at the very most, transformative, and at the very least, helpful. I look forward to pursuing them further, and have attempted to begin to do so with the creation of a dj Methodology Toolkit for Educators. works cited Davis, Meredith. “Toto, I’ve Got a Feeling We’re Not in Kansas Anymore.” Massaging Media Conference. Boston. 4 Apr. 2008. Lecture. Experimental Jetset. “Design & Art Reader.” Experimental Jetset. N.p., Jan. 2006. Web. 04 Mar. 2013. Freire, Paulo. Pedagogy of the Oppressed. New York: Continuum, 1993. Print. Moran, Lawrence. “The Problem with stem.” Sandwalk. N.p., 11 Nov. 2011. Web. 2 Feb. 2013. . Rushkoff, Douglas, and Leland Purvis. Program or Be Programmed: Ten Commands for a Digital Age. Berkeley, ca: Soft Skull, 2011. Print.

Case Study: R&B R&D

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This is not a polemic against technology. A user interacting with the second prototype of R&B R&D.

Case Study: R&B R&D

R&B R&D is a prototype for a gestural music-making experience that facilitates embodied learning, helping users learn about properties of electronic music. By combining computation, computer vision technology, motor memory, tacit knowledge, and a “learning by doing” approach to musical experience, R&B R&D demonstrates the potential to become a powerful tool for understanding principles of electronic music. It is a platform for position- and gesturebased musical interactions; its iterations began with a collaborative dance party and evolved into a prototype for a computationally-mediated, kinesthetic, embodied learning experience.

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While the dj methodology of learning celebrates the importance of humanreadable meaning, it is by no means a polemic against technologically-mediated experience. Interactions mediated by computation or technology can be powerful catalysts for human-readable meaning, and every project on which I have worked during graduate school has investigated the role of this mediation in the creation of meaning that extends beyond machine-readability. r&b r&d, while different than my other work because of its explicitly musical focus, is no exception. Although the project is primarily focused on shaping musical experiences, it tackles several underlying philosophical questions that lie at the core of the dj methodology. The work explores the relationship between computationally-mediated experiences and meaning-making, emphasizing through embodied learning the making of meaning that is not solely machine-readable. r&b r&d is also an investigation of the learning process, both from a user standpoint and reflexively, becoming a study of my learning about the design of physical and gestural interfaces.

Genesis and Design Process R&B R&D 1.0: A Collaborative dance party experience

Computer vision is the science of enabling computers and other machines to see and interpret visual information.

Like many graduate students, my first semester came to a close without me knowing exactly what my thesis topic would be. I knew that many of my ideas related directly or tangentially to music, and in particular, to djing and hip-hop. At the time, I was being exposed to a variety of new and emerging technologies, of which computer vision seemed the most mysterious and enthralling. Though complex in its philosophical implications and technical applications, computer vision is, at its most basic, the science of enabling computers and other machines to see and interpret visual information (Learned-Miller, 1). The first thing that came to mind when I thought of potential uses for computer vision technology in music was that it presented the possibility of shifting the relationship between audience and dj in interesting ways. It could alter the relationship between club-goers and the music that drives the experience of their evening. I approached my thesis advisor and asked to do an independent study, proposing an overhead-camera-based interactive system that allowed users to determine properties of dance music based on their position in a physical space.

Case Study: R&B R&D

My fascination with computer vision began just before Kinect-hacking became common practice, so the project began with a study of blob-detection-based interactions, including touch-tables, which effectively functioned the same as an infrared camera hanging in the ceiling of a space (fig. 1). Finger tips, I imagined, would be replaced by the tops of participants’ heads.

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The Kinect is a sophisticated computer-vision sensor created by Microsoft.

Fig. 1: The CCV interface and accompanying Processing sketch during my study of multitouch table interactions.

Processing is a programming language built on top of the JAVA programming language.

Influenced by the writing of Douglas Thomas and John Seely Brown in the book, A New Culture of Learning, the initial iteration of the project attempted to create a “bounded environment for experimentation,” (Thomas and Seely Brown, 96) where users would work in concert to shape their own experience of sound or music. The way this collaboration worked, the system itself, could be learned by the users. At the same time, I wanted the system to be designed so that users did not need to know how the system worked in order to enjoy interacting with the music and with one another. During some early testing of the first prototype, users seemed happy to know that they were manipulating sound in some way (fig. 2), whether or not the details of their control were apparent to them. I saw my friends walk and dance around in a space, smiling as they realized they were impacting the sound in some way, but unsure as to how.

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Fig. 2: Users engaging with the collaborative dance party experience, trying to figure out how they are affecting the sounds they are hearing.

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FIg. 3: The view from the overhead camera and the DJ mix in Ableton Live.

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In attempting to shift the relationship between dj and party-goer through a computationally-mediated experience, I sought to limit and change the role of the dj and expand the role of the party-goer/participant. I attempted to alter the traditional notions of both parties in this relationship in the hopes of creating a sort of collaboration between the two. In the first iteration of the piece, users had a limited amount of control over the selection of music being played, but at the same time, the dj’s ability to mix was limited (fig. 3). I hoped that the platform could eventually be applied to a club setting where djs would be forced to improvise in new ways depending on the actions of the participants. I was also interested in how long the system and interaction were able to hold participants’ attention. In deciding which elements of the interaction to leave up to the users and which to tightly curate (e.g., the selection of music itself or the properties of the music that the users could impact), I tried to find out how the piece could last the duration of a typical night on the town. In my initial user testing, when my friends realized they were affecting the sounds they were hearing, they would stop, and their motions would become more methodical (fig. 4). A friend would slowly shimmy to one side of the room and then sprint back to the other to see what happened. At the same time as the desire to understand the system engaged them, the music they were hearing continued to hold their interest as well. This was in part because they were hearing popular r&b music being affected and mixed in some way, and they knew they were part of that mixing. Like many djs, I decided to create something new out of elements that would be familiar to the crowd. I thought it would be fun to mix unfamiliar instrumental samples with altered vocal samples from popular r&b songs. This decision also influenced the title of the piece. I decided to call it, r&b r&d. It is important to explain here the system as it existed in its earliest functional stage in order to continue the discussion of the project and its subsequent iterations. In the most simple terms, the system worked in the following way: a user’s position on the dance floor determined certain properties of sounds in the mix. The “dance floor,” was invisibly divided into a predefined number of zones. Each of these zones used the x- and y-coordinates of the people moving around inside it to change the properties of the sounds being heard.

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FIg. 4: A user methodically testing the system

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Mashups combine the vocal and backing tracks of different songs, often creating a cognitive or sonic dissonance. Mashups can be a combination of two or more songs, or sampled sections of songs (Marshall).

CCV, or Community Core Vision, is a computer vision application written for the design of multitouch surfaces. OSCulator is a software that translates OSC data to MIDI data. MIDI is short for Musical Instrument Digital Interface, and is a protocol that allows electronic instruments and computational devices to communicate with one another. Multitouch surfaces sense touch from users’ fingers. In small form factors, they are commonplace in our world today. OSC is short for Open Sound Control protocol. Ableton Live is a professional music-production and DJing software. MaxMSP is a visual programming environment built on the metaphor of synthesizer patches.

Towards a DJ Methodology of Learning

The dj (myself in this instance) had created and curated a mix, but the users actively participated in mixing the sounds and songs, together creating their own experience of the dj’s work. In the initial iteration of the piece, I created a multi-track mix where all tracks played simultaneously all the time: one could think of it as a very dense, extended mashup. The users controlled how the music was mixed, as well as the extent to which effects were applied to each track (e.g., delay and reverb). A user’s x-position in a zone, for example, affected the volume of a particular track, while the user’s y-position changed the audio effects applied to that track. Very early on, I realized that the way the system worked was only one of many ways in which it could work. The system itself is flexible because different software handles different tasks and because the project has been broken down into separate components that can be changed without significantly affecting the others. This flexibility allowed me to continue onto later iterations of the project. There are four main components of the first iteration (and every iteration thereafter), each of which can be changed to alter the experience for the user(s). 1.

The platform: computer vision, Processing, osc and Ableton Live. Each one of these tools/software is doing a different job. Computer vision, namely either a Kinect or an infrared camera paired with CCV, tracks users and outputs data about the users’ positions. Processing takes that data, parses it and sends along pertinent information and OSC messages to a software called OSCulator, which translates these messages to MIDI messages. These messages control certain parameters of sounds in Ableton Live. It’s possible that some of these technologies could be swapped out for others— MaxMSP for Ableton/Processing/osculator, for example—to create new experiences for users.

2. The content: audio or otherwise. Deciding on the content of the first iteration of the experience—what it is exactly that the users would be mixing/controlling—was difficult. I wavered between ambient sounds and a more dance-able dj set. Although for my initial prototyping, I worked with the idea of remixing r&b songs, the content could be anything, not just audio. This dovetails with changes in platform as well. For example, if I were to use Processing to create generative visual designs based on the movements of users in a space, the creation of some sort-of collective artwork would be the content. In fact, for my own wedding invitations, this is exactly what I did.

Case Study: R&B R&D

3. The rules of the system. The rules of the system are simple, but also simple to change because they are determined primarily by the code written in Processing. In the first iteration of the piece, a space (the “dance floor”) is separated into 8 zones, two rows of four. When a user enters one of these zones, the user’s x- and y-position within that zone are calculated. The x-position controls one property of the music, while the y-position controls another. Later iterations of the system track the x-, y-, and z-positions of a user’s body joints as he or she dances in front of a Kinect. 4. The context, location and type of interaction. In the first iteration of the project, the context of the project is spatial and physical in nature, and the type of interaction is movement- and location-based. I use the term “dance floor” frequently to describe the space in which users are interacting. During subsequent iterations of the piece, the context evolves. It becomes a gallery space or hallway for a singleuser interaction. Later, in a proposed scenario for r&b r&d to become a software for learning about music production, this context becomes a user’s own home. R&B R&D 2.0: Single-user experience

The more I worked on the first iteration of the piece using infrared cameras and ccv, the more frustrated I became with the inherent limitations in commerciallyavailable computer vision technology. As I was searching for solutions to this problem, I learned about the ways in which some programmers had “hacked” the Microsoft Kinect and used its relatively sophisticated computer vision capabilities in a variety of experiments. I soon purchased a Kinect and started to play around with it. Instead of fixing the Kinect to the ceiling of a space as I had done with the infrared cameras, I put it right in front of me. I realized that, just like finger tips on a touch table or people in a space, the Kinect could send data about the x- and y- (and z-) position of a user’s joints. I therefore began to adapt the system described above, swapping out the position of users on a dance floor with the position of a single user’s joints. Although the Kinect (and computer vision in general) is still rather inaccurate in terms of user input (compared to an established form of user input such as a mouse click), it was more simple to set up and easier to use than the infrared cameras that I had been using (fig. 5). Because early iterations of the project had a camera situated in the ceiling, the user’s actions—things that someone might do in a club, like dancing or jumping around— were reduced to a simple x- and y-position. The interaction seemed almost incongruous with the musical and physical content of the experience. As I danced in front of

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Fig. 5: Users dancing in front of the Kinect

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my Kinect, working to understand how to write code to alter sound based on my gestures, I felt like this experience was much more intuitive. A major change in the experience that results from this simple substitution of user-position with joint-position is that the piece lost its collaborative or multi-user nature. It became more of a single-user experience. A Kinect cannot capture the dancefloor of an entire club. Though the piece lost something that had been a part of its original goal, the Kinect opened up new opportunities for more nuanced and varied interactions and musical output. The Kinect sensor was more accurate and responsive than the pairing of the infrared camera and ccv. With a more sophisticated sensor, I began to wonder if a user could become a performer. I soon deviated further from the original intent of the project by removing the club atmosphere from the piece. No longer was I investigating the relationship between partygoers and djs, but, rather, I was creating ways for users to shape a musical score or experience through gestural interactions. I began to use only sounds instead of an extended, multi-track mashup. Y-position of a given joint in a particular zone might affect the volume of a sound (e.g., drums), while the x-position of that same joint in the same zone might change the intensity of the effects applied to that sound (e.g., the echo applied to those same drums). The switch from an interaction that allowed multiple users to mix and shape a precurated dj set to an interaction that was more related to composition, wherein users might affect pitch, tone and timbre of certain sounds, created new aural interaction design challenges. Much as I had to decide what songs and samples to include in the first iteration of the project, I had to make decisions about the degree to which users could affect the pitch, the harmonies, the tones, and the general shape of the sounds that they were hearing in their one-on-one interactions with the piece. If a user were able to control the pitch of a synthesizer sound with the y-position of her right hand in a given zone, would she be able to change the pitch from the lowest note on the keyboard to the highest? Would I allow her to only change it to certain notes in certain intervals? What if she knew nothing about music? What if she knew a great deal about music? What limitations would I put on the system so that users would have rewarding interactions that were not overly simple? And how do those limitations translate into code? I had to strike a balance between resonance and dissonance, and between creating a challenge and making it too easy to create something pleasing. I prototyped the project using a variety of sounds, scales, effects, and durations of looped sounds.

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I first installed the functional prototype of this iteration of the project at dmi’s Fresh Media 2012. I made the single-user nature of the piece very explicit for the show’s opening. The piece was situated in a small alcove adjacent to a short, ascending staircase. It was visible from the side where viewers on the ground floor could look up and see a user interacting with the piece, but was hidden by a curtain once a user arrived at the top of the staircase. I decided to keep visual feedback to a minimum, using an iMac screen to display the user’s silhouette and the delineation between zones, with small circles on top of the user’s joints that were being tracked (fig. 7). This visual interface did not show the user in any way which sounds or which properties of those sounds were being affected by which joints. I was not concerned about the lack of intelligible visual feedback. I believed that the musical variations users would hear based on their actions would be strong enough cues about which properties of the sounds users were affecting and how. I was wrong. The most common and most obvious critique I received from this installation was that users needed more feedback in order to understand their role in the system. Some users thought that the entire goal of the piece was to control the “balls” that “floated” above their joints on the screen in front of them: they didn’t even know that the piece itself was about sound, despite the giant speakers and loud audio in the small space. Regardless of the ineffective audio and visual feedback perceived by users, the piece itself was successful in a different way. During both the opening and closing receptions, someone was always interacting with the piece, dancing in front of it, playing around, whether or not he or she knew exactly what the system was actually doing. As often as I heard the confusion about the goal of the interaction, I was complimented on how much fun it was (fig. 8). Watching people interact with this prototype, as well as engaging them in conversation afterwards, was informative and valuable for me. I realized that the visual interface was evocative: there is something about seeing yourself, even if only as a silhouette, that is attractive and engaging at a very fundamental, human level. To a certain degree, the user’s knowledge of what he or she was doing mattered less than I imagined. As a musical interaction, however, I felt it was a failure. There was not enough feedback for users to understand and manipulate the system as they wished. It was disappointing that the way in which computation mediated the interaction obscured the relationship between gesture and sound instead of highlighting it.

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FIg. 7: The initial visual feedback for the single-user experience.

FIg. 8: A user enjoying dancing with the R&B R&D prototype at Fresh Media 2012.

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The installation of r&b r&d 2.0 at Fresh Media underscored my relative naiveté regarding the design of both visual and musical feedback for users in physical and gestural interactions. I realized that visual feedback was much more important than I imagined, because, if it is there—if a user is staring at a screen showing her silhouette—she will inherently pay a great deal of attention to it. Thus, if any visual feedback is to be part of the interaction, it cannot be an afterthought. It has to be a central part of the system. In addition to my underestimation of the importance of visual feedback, my understanding of a user’s ability to apprehend audio feedback was also inaccurate. As a dj and producer, I possess a high sensitivity to changes in pitch, tone, and other properties of sound. The majority of the users I observed interacting with the piece did not possess this same sensitivity. Between this relative lack of sensitivity to changes in audio feedback and the abstract nature of the visual feedback, users were, more often than not, confused (yet entertained) by their interactions with the piece. R&B R&D 3.0: Learning about the production of electronic music

One of the most interesting moments in my observations of users interacting with the project during the Fresh Media show was when a friend said, “I’m starting to learn it!” I thought to myself, “learn what?” In reflecting on the observations I had made about the failings of the system’s audio and visual feedback, I began to see an opportunity. The obscured relationship between gesture and sound precipitated by the mediation of the interface itself had created, for some users, a knowledge gap that they began to traverse. Some users began to identify relationships between what they were seeing on the screen, what they were physically doing, and what they were hearing. A discussion with Pol Pla of the mit Media Lab’s Fluid Interfaces Group led me to believe that my concerns about user feedback could help me create a system through which users could learn about music through gestural interaction. Users could, in fact, learn about all the parameters which I had merely attempted to help them control, if only the visual and audio feedback were designed properly. The singleuser experience I had installed at Fresh Media could become an experience explicitly designed to help users learn about the production of electronic music. Not only could the project become a stand-alone learning experience, but Pol helped me think of the piece as more than a one-time interaction in a gallery setting. Rather, we discussed the idea of creating and distributing a software, developing a system and community based around this learning tool.

Case Study: R&B R&D

Imagine a teenager downloading a free software to his computer, plugging in his Kinect and immediately being able to play around, making music with his body using stock sounds and effects that come with the software. An interface helps this learner understand which properties of which sounds he’s affecting with which joints. As his knowledge of music becomes more sophisticated, he learns through an online community that he can easily swap out the stock sounds for his own, and can map different types of effects and properties of sound to different joints. Soon he has programmed his own gestural music-making experience and performs for his friends. He contributes regularly to the online community, giving tutorials and comments (fig. 9). By allowing this user to “sample” different pieces of a musical vocabulary, he can begin to see connections between not only what he is doing and what he is hearing, but also between the things that he is hearing in this interaction and music to which he listens everyday. Although the process and interactions that the software would facilitate presents a challenge for a single graduate student to design, I was able to identify a scenario, service ecosystem and consider the end-to-end user experience. The proposed software would help a user gain a scaffolded understanding of the concepts and ideas being introduced, fostering an ever-increasing sophistication because each new concept understood and skill gained builds on the previous ones. At the outset, a user could only use the basic sounds and effects that come with the software, but slowly the user would learn how to remove those constraints and limitations, exposing more sophisticated elements of electronic music-making, until the interfaces with which the user interacted would mimic those of a professional production software, such as Ableton Live. The lens through which the users would experience this progression, and the lens through which they would learn about electronic music, is that of gestural interaction. It was impossible for me to design and program all the elements of this system, but it was possible to prototype moments, vignettes of the overall experience, to show what it might actually be like. The experience design concept that I was proposing through narrative and prototyping is an embodied learning experience—learning done through the body, through physical action (fig. 10). Two semesters before Pol and I arrived at this idea of a software designed specifically to help users learn about music, and in particular the properties of electronic music, I had worked on another project that involved embodied learning experiences and had done some research on the topic. That project was called “Illuminate,” and was designed during the semester I spent in the Technologies for Creative Learning class

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Fig. 9: Early sketches for the R&B R&D learning experience concept.

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Fig. 10: Stills captured while testing various prototypes for the R&B R&D learning tool.

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at the mit Media Lab. Two of my classmates and I investigated how we could help museum visitors better understand the basic principles of reflection and refraction in optics by allowing them to create music with light, mirrors, prisms, and the movement of their bodies (fig 11). We were fascinated by the ways in which contemporary computation can facilitate and mediate learning experiences that engage the whole body. “A growing body of evidence supports the theory that cognition is “embodied”-grounded in the sensorimotor system [2–5]. This research reveals that the way we think is a function of our body, its physical and temporal location, and our interactions with the world around us. In particular, the metaphors that shape our thinking arise from the body’s experiences in our world and are hence embodied [6].” (Birchfield, et. al., 2). Moreover, “gesturing has been shown to lighten cognitive load for both adults and children [22]; even congenitally blind children gesture [32]. A less obvious point is that systems that constrain gestural abilities (e.g., having your hands stuck on a keyboard) are likely to hinder the user’s thinking and communication.” (Klemmer, et. al, 2 ). In working with Pol, I realized that r&b r&d had become a mix unto itself— a combination of audio, visual feedback, and gestural interaction, which, when mixed together in the most effective ways, can create engaging, embodied musical learning experiences. Although the way in which computation can augment embodied cognitive experience is an emerging field of study (e.g. smallab), the notion that the body can help a person learn about music is nothing new. In the early 1900s, Dalcroze Eurythmics, an approach to music pedagogy that integrates ear-training, bodymovement, and improvisation, was designed by Émile Jacques-Dalcroze. “Dalcroze Eurhythmics primarily teaches habits of musical action or, more generally, ‘a bodily way of being in sound’, rather than a conceptual, or abstract knowledge of music… According to Jaques-Dalcroze, eurhythmics gets students to listen and to imbue the whole of their bodies and being with musical sounds; this, in turn, reinforces sensations, regulates habitual actions and awakens imaginative faculties (J-D 1935)” (Juntunen, 21). Jaques-Dalcroze knew how important the body was to learning, how we can use our bodies to create a new way of “knowing” something. Motor memory and tacit knowledge paired with a “learning by doing” approach to musical experience became a powerful tool for understanding musical concepts. A renewed interest in the way bodily action can help users learn or simply make the most of interfaces and affordances has emerged with sophisticated physical and gestural interface technologies (Klemmer, 141). Designers are working to create

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Fig. 11: Still from a video describing the system for the Illuminate project

immersive, physical environments that leverage physical computing or computer vision in order to allow learners to explore concepts with their bodies (Kelliher, et. al). I imagine the future of r&b r&d to be the further development of a software or platform that combines the experience which Jaques-Dalcroze sought to create with sophisticated computation in order to help learners understand the properties and production of electronic music.

Next Steps? I would like to be able to work with developers who have experience building and releasing software platforms and create a plan for the release of a beta version of r&b r&d v. 3.0. Alongside the release of the free software, I would like to build a website where a community of users could grow and interact, much as I imagine in the brief scenario describing the goal at which my research with Pol has aimed.

Additional Reflections After my first summer working intensively on this project (see “r&b r&d 1.0”), I wrote a great deal about my process. Some of that writing appears in the narrative about the genesis of the project. Much of the writing reflected on learning to program and my role in developing a collaborative musical experience. The initial value of the project seemed, to me, to stem from the learning I had done about programming and about designing for interaction in physical space. I wrote about the ways that learning about programming reminded me of the way I learned about music and music history. When I started making hip-hop music 15 years ago, I sampled what-

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ever records were at my parents’ house. Anything I could get my hands on—from old folk records to Yiddish song festival albums to Blood Sweat and Tears. At first, I just listened to these records to find stuff to sample, but as my appetite for great samples increased, I became a voracious record collector and student of many musical genres. I had to learn about these genres in order to find the best records to sample—especially the ones that no one else had. I couldn’t do that without learning as much as possible about a musical genre, sub-genre, era or geographic location. As a hip-hop producer, I started learning about the incredibly rich history of music through the lens of the genre that I loved the most. This describes a depth of knowledge and a sensitivity to the medium in which I worked most frequently. Similarly, as I worked on r&b r&d, I began to see programming and code through the lens of something I love: music. By filtering my work through this lens, learning the tools I needed became more accessible and more desirable to me. I became more interested in the nitty-gritty details of the project, the code, the medium in which I was working. As I continued to make other projects and do other research about learning and djing, I started to see the ways in which r&b r&d had taught me more than programming. Though initially focused exclusively on the design of new musical experiences, r&b r&d ended up reflecting my simultaneous concern and enthusiasm for the way computation can mediate the process of human meaning-making. I learned that I could leverage computation and emerging technologies in order to facilitate learning experiences that were embodied, leveraging tacit knowledge and motor-memory. I believe these musical learning experiences escape machine-readability in some way—that a “bodily way of being in sound” is not algorithmically reproducible. As emerging, immersive learning technologies begin to create powerful learning experiences for students across the country, a “bodily way of being” in other disciplines becomes possible. Yet some of these powerful, human-readable experiences get evaluated quantitatively, through the lens of machine-readability, assessing “number of utterances” of key phrases during user interactions, for example (Birchfield [2009], 403, 417). More important than the way in which immersive, embodied, “mixed-reality” learning experiences are being evaluated are the learning goals these technologies serve. While we may celebrate the increase in achievement and better retention (Birchfield [2009], 405) that such collective learning environments might foster, the underlying ideologies (educational goals) driving the design and assessment of these learning experiences often remains unquestioned.

Case Study: R&B R&D

Much of this work would not have been possible without the generous support of the Proximity Lab Fund. works cited Birchfield, David, and Colleen Megowan-Romanowicz. “Earth Science Learning in smallab: A Design Experiment for Mixed Reality.” Computer-Supported Collaborative Learning 4.1 (2009): 403-21. Web. Birchfield, David, Harvey Thornburg, M. Colleen Megowan-Romanowicz, Sarah Hatton, Brandon Mechtley, Igor Dolgov, and Winslow Burleson. “Embodiment,Multimodality, and Composition: Convergent Themes across hci and Education for Mixed-Reality Learning Environments.” Advances in Human-Computer Interaction (2008): n. pag. Web. Juntunen, Marja-Leena. Embodiment in Dalcroze Eurythmics. Diss. University of Oulu, 2004. Oulu, Finland: oulu up, 2004. Web. Kelliher, Aisling, David Birchfield, Ellen Campana, Sarah Hatton, Mina Johnson-Glenberg, Christopher Martinez, Loren Olson, Philippos Savvides, Lisa Tolentino, Kelly Phillips, and Sibel Uysal. “smallab: A Mixed-Reality Environment for Embodied and Mediated Learning.” Proc. of acm Multimedia 2009, Beijing Hotel, Beijing, China. N.p., n.d. Web. Klemmer, Scott R., Bjorn Hartmann, and Leila Takayama. “How Bodies Matter: Five Themes for Interaction Design.” Proceedings of the 6th Conference on Designing Interactive Systems. New York: acm, 2006. 140-49. Web. Learned-Miller, Erik G. Introduction to Computer Vision. 19 Jan. 2011. University of Massachusetts-Amherst, Department of Computer Science, Amherst, ma. Marshall, Wayne. “Mashup Poetics as Pedagogical Practice.” Pop-culture Pedagogy in the Music Classroom: Teaching Tools from American Idol to YouTube. Ed. Nicole Biamonte. Lanham, md: Scarecrow, 2011. 307 – 15. Print. Thomas, Douglas, and Brown, John Seely. A New Culture of Learning: Cultivating the Imagination for a World of Constant Change. Createspace, 2011. Print.

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Learning is being designed for machine-readability.

The web interface I use to grade student work seems well-suited for grading exams, but now is used for grading everything from essays to artwork. See Chapter 6 for more about the embedded ideologies of our designs.

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The Embedded Ideologies of Learning Experiences

[T]he central problems with American education are not pedagogical or organizational or social or cultural in nature but are fundamentally political. That is, the problem is… that we are fighting amongst ourselves about what goals schools should pursue. – David Labaree, “Public Goods, Private Goods,” 1997

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In his article, “Public Goods, Private Goods,” David Labaree argues that there are three competing ideologies about the goals of education that have, over the course of history, wielded varying degrees of influence over American education. These goals (or ideologies as we might call them) are the following: democratic equality, social efficiency, and social mobility. The democratic equality ideology argues that the goal of education should be to prepare conscientious citizens to participate equally in a democracy. “Democratic equality, then, is the perspective of the citizen, from which education is seen as a public good, designed to prepare people for political roles” (Labaree, 42). The democratic equality goal is a goal to improve society as a whole, to improve the experience of every citizen, through preparing a citizenry that is both considerate of fellow citizens and active participants in the democracy. The social efficiency ideology argues that the goal of education should be to train workers. While market-focused in one way—the nature of the available jobs for which students are being prepared is of course driven by capital—the social efficiency goal is also in part about improving the lives of the people as a whole. “The social efficiency approach to schooling argues that our economic well-being depends on our ability to prepare the young to carry out useful economic roles with competence. The idea is that we all benefit from a healthy economy and from the contribution to such an economy made by the productivity of our fellow worker” (Labaree, 42). The social mobility goal, however, is focused on the individual student as a consumer of education. Education itself becomes a market-based good. “The social mobility approach to schooling argues that education is a commodity, the only purpose of which is to provide individual students with a competitive advantage in the struggle for social positions… [E]ducation is seen as a private good designed to prepare individuals for successful social competition for more desirable market roles” (42). Labaree argues that, paired together, the social efficiency goal and the social mobility goal, “portray education as a mechanism for adapting students to the market.” Labaree argues, further, that over the course of the history of American education, the social mobility goal “has emerged as the most influential factor in American education. Increasingly, it provides us with the language we use to talk about schools, the ideas we use to justify their existence, and the practices we mandate in promoting their reform” (43).

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Markets are driven by quantification. We cannot explore a market without understanding, at a fundamental level, the supply and demand graphs involved. Markets are not driven by qualitative experiences. We don’t talk about how a stock makes us feel or how we interact with it. We talk about its numerical value. We evaluate education increasingly in the same fashion, merely as a market-driven commodity. We can measure grades, badges achieved, number of hires, number of students admitted, test scores, and the list goes on. As the power of computation to process large amounts of data increases, we continue to find, develop, and manufacture metrics by which we can “measure” the effectiveness of education. No Child Left Behind is likely the most oft-cited example of our focus on quantifying the success of education. A more recent example that seems to continue this trend towards the devaluation of the qualitative nature of human learning experience is the College Scorecard, an initiative of the Obama administration. In his February, 2013, State of the Union address, President Obama stated, “My administration will release a new ‘College Scorecard’ that parents and students can use to compare schools based on a simple criteria—where you can get the most bang for your educational buck.” An affordable education is not inherently bad. It is tellingly reductionist, however, to express the value of the education at a university with a “score” or series of numbers (tuition, graduation rate, loan default rate). Even more tellingly, is the overall user experience of the site on which the College Scorecard is housed—the header of the site states, “Education: Knowledge and Skills for the Jobs of the Future.” It is yet another example of the de-emphasis on education as a way to prepare an active, engaged citizenry (Benninga and Quinn, cited by Malin, 7) (which is difficult to quantify), and of the emergence of a market-based quantitative view of education that is both driven by the market and a market unto itself. Indeed, educational policies and school governance increasingly reflect the prevailing ideologies of academic excellence and economically-valuable work-preparedness (Mintrom, 615). Consequently, our students become dangerously performance-oriented learners (Dweck, 15). This shift in the embedded ideologies driving our education system and our assessment of it, is not merely a shift in formal education. I would suggest that these ideologies (democratic equality, social efficiency, and social mobility) can often play a role in the design of learning experiences that are not inherently a part of the American education system.

See Chapter 8 for more about the age of big data.

See Chapter 3 for more information on learner motivations.

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Massive open online courses (or moocs) sit at the intersection of online learning experience design and the formal American higher education system. moocs are online courses for which the materials, assignments, and exams are available for free to anyone. They are being developed by universities while platforms for their dissemination have become a rapid-growth part of the tech industry. moocs have been lauded not only as the future of education but as the thing that will save the world (Friedman). The way moocs might impact learning and education in the future is both terrifying and wonderful. In one sense, it is thrilling to imagine anyone—regardless of income, previous academic achievements or intellectual pedigree—being able to take a class at Stanford or mit, and find within him or herself a new passion or untapped genius. Yet the social mobility and social efficiency goals still seem to exert an undeniable force. Friedman ends his article in a telling fashion. Instead of celebrating the learning experiences that moocs might someday facilitate, the ways in which they might be able to help learners dig deeper into our most pressing problems and expose their root causes, helping to create a more just society and a more equal and active global citizenry, he focuses once again on the market. “…[M]any universities will offer online courses to students anywhere in the world, in which they will earn “credentials” – certificates that testify that they have done the work and passed all the exams… I can see a day soon where you’ll create your own college degree by taking the best online courses from the best professors around the world… paying only the nominal fee for the certificates of completion. It will change teaching, learning and the pathway to employment.” The market references here are overt, both to education as commodity and learner as future employee. Indeed, other authors have argued that moocs pose a danger to the study of any discipline that is not profitable. “While a strong supporter of free online education, we are therefore extremely wary of the consequences this potentially emancipatory project could have on knowledge as a whole if harnessed by market forces that enter it into competition with other forms of academic knowledge,” argue Aurélien Mondon and Gerhard Hoffstaedter in a piece for the Guardian. Though it is impossible to describe the history of moocs here, I would like to suggest that it seems as though moocs suffer the same problems that plague the American education system as a whole: the goals of these systems are often at odds, simultaneously celebrating a sort-of equality and openness, yet subject to the influence of the market and subject to becoming part of the education-as-commodity market itself. Quantifiable metrics subsume qualitative human learning experience.

The Embedded Ideologies of Learning Experiences

Online learning experiences not facilitated by organizations with inherent ties to the American education system are often still plagued by the combination of the social efficiency and social mobility goals of education, subject to market influence and problematically driven by quantification and easy-to-compute metrics. Consider the Badges for Lifelong Learning initiative, pioneered by Mozilla and the MacArthur Foundation in 2011. Mozilla and the MacArthur Foundation, in their working paper, “Open Badges for Lifelong Learning,” define a ‘badge’ as “a symbol or indicator of an accomplishment, skill, quality or interest” and a “digital badge” as “an online record of achievements, tracking the recipient’s communities of interaction that issued the badge and the work completed to get it” (3). The authors assume that badges enable humans to present a more nuanced picture of themselves. “Badges are simple, easy and, if done well, can present a more nuanced picture of what an individual knows and can do” (Cupaiuolo). Whether or not one possess a badge seems to be the opposite of nuanced. It is binary, a one or a zero, a yes or a no. Easy to compute. Though Mozilla claims that the goal of these badges is motivation to be a lifelong learner, by leveraging behaviorist approaches to motivation through the dissemination of a binary badge system, Mozilla opens the door for future employers to assess potential employees through a simple binary evaluation process. Whoever has the most badges (ones) gets the job. Who needs interviewers when all you need is a computer? Learning is a commodity to be consumed and this helps the market as a whole function more efficiently. Other researchers have focused on the learning experiences inherent in making, in particular, acts of making that involve computation and new media. Many researchers investigating the relationship between youth, computation, new media and participatory media engagement don’t assume that a particular type of learning is happening, but rather take an ethnographic approach. These researchers explore the different learning that is being done by youth through their interactions with emerging technologies and media experiences (Ito). The primary focus here is not the content of the learning experience, but rather the shifts occurring in the nature of learning experiences themselves. Mizuko Ito, in Hanging Out, Messing Around, and Geeking Out, suggests that “… participation in networked publics is a site of youthdriven peer-based learning that provides important models of learning and participation that are evolving in tandem with changes in technology” (339). The contributors to this book describe the varied places online that have become sites of learning outside the purview of parents or educators and how the meanings of “literacy, learning and authoritative knowledge” are shifting, in part because of emerging new media competencies in which youth lead the way.

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Meanwhile, some research on youth engagement with technology is focused more on creativity as a core competency for students. The underlying premise of creative learning technologies such as Scratch (fig. 1) is to facilitate a “kindergarten approach to learning… helping learners develop the creative-thinking skills that are critical to success and satisfaction in today’s society” (Resnick, 1). While this does not overtly declare an intent to prepare learners to become productive workers and participants in the market economy, some organizations connect creativity and critical thinking directly to future workplace readiness (http://www.p21.org). Meanwhile, some research on the development of new media competencies relates more directly to Labaree’s democratic equality goal, including research on empathy and civic engagement through participation in the creation of media (Daily, 2). Fig. 1: The interface for Scratch.

While youth engagement with technical skills and competencies can be a fruitful lens through which to foster creative learning experiences, the American education system’s emphasis on science, technology, engineering and math education (stem) reveals that goals such as empathy and civic engagement occupy the periphery of mainstream education and learning. The social efficiency and social mobility ideologies have shaped the way educators and policy-makers view science education, bundling it, says Larry Moran, a professor of biochemistry, with technology and engineering education, as if all three are the same and should serve the same purposes. Moran argues that “the goal of true science education should be no different than the educational goals in history, philosophy, or English literature. It’s to teach students

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how to think.” Science education is “supposed to teach you how to think critically” (Moran). Critical thinking skills, however, are more difficult to quantify while lab results and technical competencies are not. Moran ends his essay in clear defiance of the dangerous pairing of the social efficiency and social mobility goals: Physics is not about sending spaceships to Mars. Geology is not about finding oil. Chemistry is not about better plastics. Biology is not about drugs.

Despite the more open-ended, exploratory learning experiences being studied by some researchers, experiences which may even echo the democratic equality goal of education, the embedded ideologies in many of today’s learning experiences, both online and in classrooms, reflect the dangerous combination of the social mobility and social efficiency goals as defined by Labaree. Moreover, an emphasis on quantification is driven by the emergence of the market-based ideologies about the goal of learning and education. This emphasis on quantification is linked with, and is not coincidentally emerging at the same time as, increasingly powerful computational tools, to which we are more and more willing to give over our capabilities of synthesis and analysis in the faith that computers can do it better than we can. Dr. Meredith Davis, professor at North Carolina State University, has advocated an aim for learning and education that provides an alternative view to Labaree’s three goals. Though she speaks in the context of design education, her claim feels especially relevant in the context of our experiences with data today. She argues that the goal of design education should not be to celebrate simplicity through the creation of “reductivist artifacts,” but rather to “render the complex manageable and to make complicated things meaningful” (Davis, 2). This quest to make meaning seems to be particularly relevant to our contemporary society, where the maker and interpreter of meaning can be either human or machine.

See Chapter 8 for more about the emergence of this faith.

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works cited Benninga, Jacques, and Brandy Quinn. “Enhancing American identity and citizenship in schools.” Applied Developmental Science 15.2 (2011): 104-110. Cupaiuolo, Christine. “Digital Media & Learning Competition Aims to Recognize and Reward Learning Outside the Classroom.” Spotlight on Digital Media and Learning. MacArthur Foundation, 16 Sept. 2011. Web. 10 Nov. 2011. . Daily, Shaundra Bryant, and Karen Brennan. “Utilizing technology to support the development of empathy.” Proceedings of the 7th international conference on Interaction design and children. acm, 2008. Davis, Meredith. “Toto, I’ve Got a Feeling We’re Not in Kansas Anymore.” Massaging Media Conference. Boston. 4 Apr. 2008. Lecture. Dweck, Carol S. Self-theories: Their Role in Motivation, Personality, and Development. Philadelphia, pa: Psychology, 1999. Print. “Framework for 21st Century Learning.” Framework for 21st Century Learning - The Partnership for 21st Century Skills. Partnership for 21st Century Skills, n.d. Web. 28 Mar. 2013. . Friedman, Thomas L. “Revolution Hits the Universities.” Editorial. New York Times 27 Jan. 2013: n. pag. The New York Times. The New York Times, 27 Jan. 2013. Web. 1 Feb. 2013. Itō, Mizuko. Hanging Out, Messing Around, and Geeking Out: Kids Living and Learning with New Media. Cambridge, ma: mit, 2010. Print. Labaree, David F. “Public Goods, Private Goods: The American Struggle over Educational Goals.” American Educational Research Journal 34.1 (1997): 39-81. jstor. Web. 09 Jan. 2013. Malin, Heather. “American identity development and citizenship education: A summary of perspectives and call for new research.” Applied Developmental Science 15.2 (2011): 111-116.

The Embedded Ideologies of Learning Experiences

Mintrom, Michael. “Educational governance and democratic practice.”Educational Policy 15.5 (2001): 615-643. Mondon, Aurélien, and Gerhard Hoffstaedter. “Could Online Courses Be the Death of the Humanities?” Web log post. The Guardian Higher Education Network. The Guardian, 7 Dec. 2012. Web. 1 Feb. 2013. . Moran, Lawrence. “The Problem with stem.” Sandwalk. N.p., 11 Nov. 2011. Web. 2 Feb. 2013. Mozilla Foundation, and MacArthur Foundation. Open Badges for Lifelong Learning. Working paper. N.p.: n.p., 2011. Print. Obama, Barack. “President Barack Obama’s 2013 State of the Union Address.” Address. State of the Union Address. Washington, dc. State of the Union 2013. Web. 01 Feb. 2013. . Resnick, Mitchel. “All I Really Need to Know (About Creative Thinking) I Learned (By Studying How Children Learn) in Kindergarten.” Creativity & Cognition. N.p.: n.p., 2007. N. pag. Print.

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Machine-Readable vs. Human-Readable Meaning

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Are we designing ourselves out of the picture? This carrier hotel, or colocation center, which is one of the world’s largest internet hubs, sits at 60 Hudson Street in Manhattan.

Machine-Readable vs. Human-Readable Meaning

I doubt if the increasing influence on learning and education of the marketbased goals of social efficiency and social mobility is a coincidence. I believe that it is connected, overtly and implicitly to the increasing power of computation and our increasing faith that computation will help us make meaning from the overwhelming amount of data with which we are faced. Quantitative, however, may not be the most accurate word to describe the overarching property of the data we are creating, whether that data is grades or Facebook status updates.

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Computer vision is the science of enabling computers and other machines to see and interpret visual information. QR codes, or quick-response codes, are 2-D barcodes that hold more information than a typical barcode.

See Chapter 8 for more about Kitty Pryde and how we cater the creation of our content to the way in which it is parsed. SEO is short for Search Engine Optimization, and refers to the way in which a website is designed and written in order to increase its visibility within a search results list. An API, or Application Programming Interface, is a protocol that allows software components to communicate with each other. Flickr, Instagram, Twitter, and other online services have APIs that allow developers to build projects that utilize the content from these services. I also discuss our reliance on computational meaningmaking in Chapter 8.

Towards a DJ Methodology of Learning

A more appropriate way to describe the type of meaning in which we increasingly put our faith, the type of data in which we traffic, the metrics by which we measure our school systems’ successes and failures, and the algorithms that help us understand big data, might be to call them machine-readable. The concept of machine-readability is nothing new. It simply refers to that which a computer can process. In today’s culture it is sometimes referenced in discussions about computer-vision technologies, specifically visual codes that contain information legible only to computers. Examples of machine-readable meaning extend far beyond QR codes. Think about your Netflix queue, or your Amazon wish list. The relationships between those items and other items in the databases of those corporations will be used by recommendation algorithms to deliver suggestions of films to watch or books to read. These algorithms are communicating with one another, deriving meaning from the connections between the data and metadata of database entries. You, of course, would think that these recommendations are being served up to you. But you would be wrong. These recommendations are merely one algorithm suggesting database entries to another algorithm based on the properties of the entries in each database. Your sensibilities, your idiosyncrasies, your preferences, your identity, are represented only by algorithms and database entries. Your data serves as a proxy for you. Like Kitty Pryde, we are all trafficking in the manufacture of machine-readable meaning, whether we know it or not. Today, we increasingly design for machinereadability, whether we are designing our own online identities on Tumblr and Facebook, creating e-commerce sites (SEO is a prime example of design for machinereadability), or creating web applications that interface with several different APIs. The data deluge, our perceived inability to cope with it, and the steadily increasing power of computation to do more and more for us, coupled with our reliance on computational meaning-making, have shifted our ideas about for who, or for what, we are designing. In user-experience design, our users are no longer just human. We have started to design algorithms to interact with other algorithms, not with us. We are, slowly but surely, designing ourselves out of the picture. Kevin Slavin, in his keynote “Those Algorithms that Govern Our Lives,” at the 2011 Lift conference, discussed the way we write algorithms and then give over our power to those algorithms. These algorithms communicate with one another and leave humans as bystanders, merely there to watch the interaction and evolution of systems that cater more and more to things that aren’t human (Slavin).

Machine-Readable vs. Human-Readable Meaning

One of the most astounding examples cited by Slavin of the influence that machinereadable meaning wields over our lives, and an indicator of its future impact on the design of both our physical and digital realities, is the relationship between algorithmic trading in the stock market and the Manhattan cityscape. He states that the image of a city is now “a network topology.” He uses the example of real-estate prices skyrocketing in Manhattan near 60 Hudson Street, one of the world’s largest internet hubs. Because 70% of today’s trading is done algorithmically (Lavin), the closer a company’s data center is to 60 Hudson Street, the fewer milliseconds it takes for a trade to go through. These minuscule periods of time are infinitely valuable, costing or earning companies tens of millions of dollars. Because our trading is done by the communication of algorithms to one another, we have become bystanders to realestate prices. Effectively, Slavin says, New York City is being optimized to run like a motherboard. Unlike the design of systems that facilitate the communication of algorithms with one another, djing is a peculiarly human activity. It is a communication between people. Though mediated by technologies, it is a different sort of mediation, particularly when the dj is playing vinyl, as I used to. In an interview on the podcast The Conversation, Douglas Rushkoff, one of the most popular authors advocating a critical engagement with technology, asserts that there are certain aspects of humanity that are subtle and weird, things that can’t be assigned a value or coded. “Before we relegate humanity to the dustbin, we should accept that humans are interesting and strange, and no the dna codon doesn’t seem to explain everything… This obsessive need to nail it all down denies us access to all of these other great tools for observation which are subtler, weirder tools” (Anderson). These subtle, weird tools are not only tools for observation. dj sets that surprise and delight the audience with unexpected, beautiful, and conceptually compelling connections are the result of these subtle, weird tools. So are creative, well-crafted, and insightful research papers. These subtle, weird, human tools are tools for the making of human-readable meaning.

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works cited Anderson, Aengus, Micah Saul, and Neil Prendergast. “The Conversation: Episode 34 – Douglas Rushkoff.” Audio blog post. The Conversation. N.p., 19 Nov. 2012. Web. 30 Nov. 2012. . Lavin, Timothy. “Monsters in the Market.” The Atlantic July 2010: n. pag. The Atlantic. 8 June 2010. Web. 1 Feb. 2013. . Slavin, Kevin. “Those Algorithms That Govern Our Lives.” Speech. Lift Conference 2011. CICG, Geneva, Switzerland. 4 Feb. 2011. Lift Conference. Web. 1 Dec. 2012. .

The DJ Methodology as Agenda of Possibility: What Next?

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As a process, DJing is inevitable and necessary for our times. This bold statement was made by Jace Clayton (AKA DJ /rupture) in a 2008 article for N+1 magazine. Clayton is a DJ, author, cultural critic, and one of my musical idols.

The DJ Methodology as Agenda of Possibility: What Next?

I believe that DJing acknowledges the magic of being human. It is, indeed, uniquely human, and impossible to reproduce through algorithm. I believe that as performers, as musicians, as producers and artists, we engage in a process that is subtle, strange, and magical. DJing fights the homogeny and hegemony of the algorithms that are shaping our lives (Slavin). The DJ methodology of learning is about taking algorithms and applying a humanness, a subtle, weird tool, to create meaning for ourselves. It’s about using the things that make us human, the connections that we intuit, that we can’t quite articulate right away, to ask better questions, to engage in more deep and meaningful inquiry.

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Our perceived inability to cope with the deluge of data, our faith in computation to make meaning for us, and the influence of market-based goals of education and learning, which are inextricably connected with quantification and machine-readability, have helped us strip ourselves of the ability to think critically. I believe there is a gap, an absence of an advocacy for deep learning that requires synthesis. This gap is felt when law students write sloppy briefs. It is felt when history students submit poorly organized papers that lack specific, arguable thesis statements. It is also felt outside of schools, when employees are unknowingly dissuaded from pursuing a solution because their actions are being ever-so-subtly guided by the underlying computational architecture of the software they are using. The missing advocacy for learning that requires critical thinking and synthesis is indicative of a giving-over of our powers of analysis and synthesis to algorithms, to these things that are not trying to create meaning for humans, of course, because they are not human. The challenge we face is to find ways to utilize and leverage data to create meaning for humans, meaning that goes beyond semantics and metadata. Between the increasing influence of the market and machine-readability on the goals of contemporary education and an increasing reliance on the power of computation, a real danger to meaning that is truly humanreadable emerges. We are beginning to find ourselves in a world governed not by our interpretations of information, but rather the interpretation of information by algorithms that we developed, but of which we are neither the audience nor the users. We are blind to this shift because we increasingly, and unknowingly, embed this ideology into the learning experiences and educational systems that we design. The dj methodology is a step in combating this danger. While built on experiences that reference or engage with computation, its creative application of computation and algorithm refrains from investing technology with the power to make meaning. Rather, it endeavors to facilitate the human application of our subtle weird tools, helping humans illuminate new connections, synthesize information, and create humanreadable meaning. I hope that the dj methodology can lead to the design of learning experiences that address the increasing complexity and connectedness of our world in order to generate meaning that is truly human readable.

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Critical Pedagogy and the Agenda of Possibility Throughout my life both as a learner and educator, the dj methodology has, whether I knew it or not, helped me and my students illuminate previously unseen connections, giving us the opportunity to explore those connections more deeply. This methodology can be used, I believe, to help learners explore relationships that expose those embedded ideologies, the quiet, hidden, relationships that are shaping our world without our knowing, and sometimes, without our consent. My friend the high school teacher is now getting his doctorate in education at Stanford. He sent me a few readings on critical pedagogy after I told him that I had tried to make my way through Paulo Friere’s Pedagogy of the Oppressed. After reading what he sent me, I realized that my work is both at an end and a beginning. This thesis, containing a description and explanation of the dj methodology, is potentially a window into a way to serve experiences of critical pedagogy. I will conclude by discussing critical pedagogy in relationship to the dj methodology, and by suggesting that future research, design work, and teaching work will be necessary to fully explore the ways in which the dj methodology could be applied in the service of critical pedagogy. “Teaching and learning should be a process of inquiry, of critique; it should also be a process of constructing, of building a social imagination that works within the language of hope. If teaching is cast in the form of what Henry Giroux refers to as a ‘language of possibility,’ then a greater potential exists for making learning relevant, critical, and transformative” (McLaren, 80). Peter McLaren’s declaration here, viewed from a more general perspective that extends beyond classroom teaching, connects with the principles and experiences of the dj methodology of learning. We can look at Sampler, a conceptual construction kit that facilitates creative inquiry, as one example. McLaren states that “Teachers can do no better than to create agendas of possibility in their classrooms” (80). Future work on the dj methodology of learning should be focused on exploring how the methodology can create an agenda of possibility for learners to engage in a process of creative inquiry that illuminates the relationships producing the hegemony of the dominant ideology, facilitating a critical discourse. “Critical pedagogy is fundamentally concerned with understanding the relationship between power and knowledge,” (72) states McLaren in “Critical Pedagogy: A Look at the Major Concepts.” McLaren begins his discussion of the basic concepts

See Chapter 7 for more about our work on the Synthesis project.

See Chapter 5 for more about the Sampler project.

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of critical pedagogy by saying, “Critical theorists begin with the premise that men and women are essentially unfree and inhabit a world rife with contradictions and asymmetries of power and privilege” (61). This reminded me, strangely, of the way in which Kevin Kelly, in What Technology Wants, describes the onward, perpetual march of technology, with its own agenda and humans at its disposal. I wonder if the embedded ideologies in our school systems and designed objects are evidence of multiple levels of asymmetrical power relationships between humans, between humans and algorithms, and between algorithms themselves.

See Chapter 9 for more about the Manipulative Media project.

McLaren describes an example of critical theory that has very practical implications for educators: Henry Giroux’s distinction between macro and micro educational objectives. This difference recalls the difference between a school based on Labaree’s democratic equality goal and a school based on the market-focused social efficiency and social mobility goals. McLaren states that, “The micro objectives are concerned with the organization, classification, mastery, and manipulation of data. This is what Giroux calls productive knowledge. Macro objectives, on the other hand, center on the relationship between means and ends, between specific events and their wider social and political implications” (63). The dj methodology has the potential to leverage micro objectives to access macro objectives (e.g. the Manipulative Media project). The relationship between hegemony and ideology is essential to explore in order to understand power-knowledge relationships. McLaren states: “The challenge for teachers is to recognize and attempt to transform those undemocratic and oppressive features of hegemonic control that structure everyday classroom existence in ways not readily apparent,” and that hegemony “could not do its work without the support of ideology” (69). Read more generally, the project of learning could be seen as an attempt to transform undemocratic and oppressive features of hegemonic control that are the manifestations of the embedded ideologies in the designed world around us, ideologies that structure our everyday existence in ways not readily apparent. As a human-centered methodology for revealing relationships and reflection upon the action of this revelation, the praxis of the dj methodology of learning seems to be well-suited for this project. Another crucial component of critical pedagogy is the facilitation of a critical discourse. “A critical discourse focuses on the interests and assumptions that inform the generation of knowledge itself. A critical discourse is also self-critical and deconstructs dominant discourses the moment they are ready to achieve hegemony.

The DJ Methodology as Agenda of Possibility: What Next?

A critical discourse can, for instance, explain how high status knowledge… can be used to teach concepts that reinforce the status quo” (73). In my work on the dj methodology of learning, I have not designed tools or experiences specifically for the facilitation of a critical discourse. This facet of critical pedagogy relative to the methodology bears further investigation. I believe in the potential of the dj methodology to facilitate critical discourse. It can facilitate the creation of agendas of possibility for learners to engage in a process of creative inquiry that illuminates the relationships producing the hegemony of the dominant ideology. This belief in the potential of the dj methodology to serve critical pedagogy necessitates deeper inquiry, more research, and more design work. It is a project in which I hope to engage for the duration of my career as a designer and educator. works cited Clayton, Jace. “Confessions of a dj.” N+1 Magazine. N.p., 25 Nov. 2008. Web. 05 Mar. 2013. . Freire, Paulo. Pedagogy of the Oppressed. New York: Continuum, 1993. Print. Kelly, Kevin. What Technology Wants. New York: Viking, 2010. Print. McLaren, Peter. “Critical Pedagogy: A Look at the Major Concepts.” The Critical Pedagogy Reader. Ed. Antonia Darder, Marta Baltodano, and Rodolfo D. Torres. New York: RoutledgeFalmer, 2003. 69-96. Print.

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