News and blog posts from our students and faculty

For this position diagram, I focused on the following three articles:

Edward de Bono. “Serious Creativity.” Journal for Quality and Participation Sept. 1995: 12-18. Print.

Karl E. Weick and Kathleen M. Sutcliffe. “Organizing and the Process of Sensemaking.” Organization Science July/August 2005: 409-421.

Donald A Schön. “Problems, frames and perspectives on designing.” Design Studies July 1984: 132-136.

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Through the lens of these three articles, I laid out a diagram of the process of designers changing their surroundings. The color overlays indicate important junctures in this process. Each is explained/laid out in quotes below the accompanying detail images of the diagram.

The full project with details and quotes is hosted on my website, a preview is below:

And here’s one detail image as a teaser just because it’s my favorite part of the diagram:

There’s a particular process hump that design students inevitably encounter: that of quality. This comes after they have learned particular methods, and they realize that they can make a thing. If the student has been taught critique and self-reflection, they’ll also soon realize that their thing isn’t very good, because it’s the first iteration of the idea. Iteration one is a thinking artifact, not a presentation artifact, and for a new designer, the gulf between thinking and presentation is enormous. In the dialogue between maker and material, iteration one establishes boundaries around a problem space but it doesn’t actually solve the problem. That’s because the level of craftsmanship and finish of the artifact is directly related to the quality of the solution, and for all novice designers, their level of craftsmanship and finish is poor.

This learning moment is inevitable, because it’s a place where method and tacit skill collide. Simply, it’s easy to learn a method, because a method is – by definition – procedural. Just follow the steps, and you’ve applied the method. But tacit skill is neither easy nor procedural; it comes through practice over time.

Once a student has produced iteration one, the best thing they can do next is to produce iteration two. And for most students, this is the hardest thing they’ve ever done. Because producing iteration one was so hard, and took so long, and the results are so obviously bad, the student sees only failure. And they give up.

I think, in my teaching experience, this is a critical fork in the road for learning design. Does the student persevere and practice? Does she “play her scales” or “wax the floor”? Or does she cut and run, internalizing various rationalizations for her poor work? I’ve heard “I’m just not meant to be that kind of designer” more often than I care to count, always at this phase in learning. And I’ve also seen students practice through it, giving up their social life, practically living in the studio, and establishing a sense of confidence both with skill and process.

The separation of method and execution is a learning practice, a pedagogical trick for students to learn both. But in practice, there’s no separation. Methodology integrates with craft-based execution over time to form expertise. Once a student has learned method and learned about execution, the rest relies on their passion to practice. There’s no real way to game this system; expertise comes from practice and experience. And that takes time, personal and professional sacrifice, and a disciplined maturity.

The simple truth is that, for a student that’s gotten to this magical moment in learning, they have only one path towards success: practice.

In our latest batch of readings for our theory class, I was particular interested in deconstructing a lecture from Charles Pierce. He is considered “the father of pragmatism.” Given that he is a philosopher and logician, his lecture, “The Three Cotary Positions,” is particularly thick, and I found difficult to parse: which made it a great candidate for using diagrams to make sense of it! This brought to mind another reading, “Organizing and the Process of Sensemaking,” by Karl E. Weick, Kathleen M. Sutcliffe, and David Obstfeld. So in this diagram, I’ve woven together both concepts to describe a process and relationship between abductive inference, synthesis, hypothesis testing, and sensemaking.

Click to download a PDF version

Over the last five weeks, in our Rapid Ideation and Creative Problem Solving class, we have been working on a making a web app that helps students sign up and schedule classes for college.

First we start with a general design & layout, then user-test it, then go back and integrate that user feedback into the next iteration. And repeat.

This is the third iteration, and definitely my favorite so far. But that makes sense, right?

You can view the annotated slideshow here.

You can see the entirety of the wireframe here.

For the entire 2nd Quarter of our Rapid Ideation and Creative Problem Solving class with Jon Kolko we are doing one thing.

Six times.

The assignment was to develop a new and helpful way for a fictional student to sign up for and schedule classes at a university or college. After some quick brainstorming, I set to mocking up a wireframe of what I created as an online application.

As you see in the PDFs below, the student can click through a simple enough website to see what classes are available, how they will apply to his major & minor studies and add them to his schedule.

What I learned: Get to digital wireframing ASAP! I spent too much time with pen&paper thinking of cool little features that I then didn’t have time to actually digitize.

Good thing the motto of this class, and AC4D for that matter, is “do it again!”

Here’s the first iteration!

In the past two weeks at the Austin Center for Design, we’ve been exploring the difficulties designers face when attempting to solve complex problems. My position on this topic is expressed below in a triptych of diagrams. They refer to the following readings: ”Judgment under Uncertainty: Heuristics and Biases” by Amos Tversky and Daniel Kahneman; “The Structure of Ill-Structured Problems” by Herbert A. Simon; “The Shape of Problems” by Philip N. Johnson-Laird; and “Evolution of the Mind: A Case for Design Literacy” by Chris Pacione. If you’re unfamiliar with these readings, they’re hosted on the Austin Center for Design’s curriculum page here.

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If you’re interested in learning more, I recommend diving in to these readings.

In the last three Design, Society and the Public Sector classes, we covered several perspectives on ethnographic techniques, the difference between designing for and designing with, as well as frameworks for conducting research and discussing research methods. One reading was a controversial article from Don Norman (whose Design of Everyday Things was my first introduction to design).  He argues that truly radical or revolutionary innovation only occurs via technologists, and ethnographics is not relevant to this type of innovation. I thought this stood in stark contrast to an article by William Gaver et al about using cultural probes as a research technique to provoke startling insights. In this second visual diagram, I summarize these arguments and draw a conclusion about how haphazard revolution innovation may or may not be.

(The image is a little too wide for the blog format, so if the resized text is too small, you can get a full-size image here.)

There’s a simple way to illustrate the value proposition of a new company, and I’ve found it to be extremely effective in communicating the worth of a hypothetical new product or service.

First, introduce an actual user that you’ve spoken with. If you are using presentation software, like Powerpoint, use a full-screen image of the person doing their job. This suggests that you’ve spent time with a potential user, and it immediately humanizes your intent: it indicates that you are presenting design-led innovation, as opposed to technological or business-led innovation.

Describe the person’s main want, need, or desire. This is sometimes called a “pain point”, but I feel that the word “pain” is too simplistic (the language I’ve used – want, need, desire – is probably too simplistic as well) because this is often subtly aspirational. Illustrate that you both understand and empathize with the person by emphasizing the emotional result of this need not being met.

Use their words. Quote the user, verbatim, in order to substantiate the need. If you are using slides, I’ve found it extremely effective to overlay the quote in REALLY BIG LETTERS on top of the user.

Repeat for two or three users. Show that you’ve spoken with several users and identified a running theme, a pattern.

Summarize your synthesis. Using a single slide, show the users again, and illustrate the high level summary of your interpretation of your research. This is where you show an inferential leap: where you combine empathetic research data, and build upon it, to produce insights. I’ve found it useful to show each user again, summarize their quote, and then show my interpretation directly below it.

Identify the implications of your synthesis. Using a slide per insight (no more than three), explain what the implications of your insight are on a potential new system or service. At this point, you are identifying new constraints: you are describing how you are artificially constraining a blank canvas of new ideas, in order to suggest a new and valuable service.

Introduce the product or service. Use the widely used formula: We help [your most promising prospects] that [need help with the pressing concern you address] succeed by [providing the material improvement you will deliver].

From here, alternative approaches work, depending on the audience. If you are presenting to investors, you might show how the service works to generate revenue, and then transition into a discussion of financials. If you are presenting to a technical audience, this might be an opportune time to introduce a “how it works” diagram, emphasizing the technological stack and architecture.

Using this style of presentation works because it gives your audience a point of reference, a place from which to judge your design and idea. It helps them see the world from a different perspective. And it offers a rationalization for your product or service, but in human terms. It doesn’t try to prove the giant potential of your market, which investors see through quickly, and it doesn’t claim a massive technical innovation, which technologists are implicitly skeptical of. It’s a designerly way of showing value.

I was speaking with a friend yesterday about the work he’s doing, and he mentioned that he felt like design was failing him; specifically, that the problems he was encountering were so new, and so large, that he had no methods to use, no clear action to take. He found himself inventing new approaches, lumping on different techniques based on what “felt right.” I think he felt like, because he wasn’t following a specific method – creating a customer journey map or building a set of use cases – he was somehow doing it wrong.

Methods, in design, have a strange history. In the sixties, they were linked to a systems approach that attempted to frame design as a science, as a rationale and intellectual endeavor. I suppose you could track a line of thought from practitioners like John Chris Jones and Chris Alexander, through hybrid practitioner/academics like Buckminster Fuller and Jay Dublin, and to scientists like Herb Simon. All of these great thinkers, at one time, viewed design in a logical manner, as a path that could ultimately be predicted and eventually, perhaps, automated. This was through method: through repeatable, discrete activities that could be taught and learned, and applied as appropriate in different situations. Fuller called for a “design science revolution”, offering that “you change something by making it obsolete through superior methods.” Herb Simon’s focus on artificial intelligence and machine learning – attempts to duplicate problem solving efforts in humans, by computers – began, in his late years, to touch on the nature of problem solving during creation, and The Sciences of the Artificial can be (and usually is) read as a guide for a systematic view of design as an applied science.

Design methods are a tremendous way to teach and learn design. They have a structure and form, and you can offer a student both a set of steps to follow and a place to start. There’s a sense to design method that, if you follow the steps, you’ll arrive at an ending, and so there’s implicit trust placed in the method: it will lead me to finality, to a solution. And that’s factually accurate, because it will lead you to a solution. An experience of action is critical in design for building a foundation of skill, for self-reflection, and most importantly, for critique. You have to design something, and then reflect on the process of design, in order to learn how to design. A method forces this to occur. I’ve written a few books that have methods in them, and I continually teach workshops that emphasize a modular, method-driven approach to design.

But a design method won’t lead you to a good solution, because a design method has no natural relationship to the content of the problem. There’s no presumption of quality in the method, as each method is simply a series of artificial constraints that are introduced into a particular design context in order to help frame it. Personas, flow diagrams, ecosystem diagrams, 2x2s: these are ways of structuring problems and solutions. They don’t speak of the particulars. There is nothing implicit in, say, a customer journey map that has anything to do with homelessness, or garden tools, or ecommerce websites, or any other content of a design problem. That’s the benefit of the method, and the drawback.

What seems to get lost, when people learn of and begin to implement design methods, is a historic precedence of rejection of methods. Both Jones and Alexander gave up on the methods movement after helping to create it. Alexander describes that “… I have been hailed as one of the leading exponents of these so-called design methods. I am very sorry that this has happened, and want to state, publicly, that I reject the whole idea of design methods as a subject of study, since I think it is absurd to separate the study of designing from the practice of design. In fact, people who study design methods without also practicing design are almost always frustrated designers who have no sap in them, who have lost, or never had, the urge to shape things.” I interpret that as a statement of passion for content and form: a designer needs to have a desire to see the world in a new way, based on details of the subject matter, based on specifics. The method – which is clearly not mindless – may be, by definition, soulless. But the designer isn’t, and can’t be.

I think this is what people mean when they call design an “art and science”; I prefer “discipline.” Design is a discipline that is neither objective nor subjective, but is both at once, and it relies on rigid methods and, simultaneously, abstract influences of beauty and poetry and texture and emotion.

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Additional readings:

Designerly Ways of Knowing: Design Discipline vs. Design Science, by Nigel Cross [pdf]

Design Methods for Everyone, by John Chris Jones.

Craftsmanship, by me

Notes on the Synthesis of Form, by Chris Alexander

There’s a great article on A/B testing in Wired today; if you haven’t yet read it, you might read it now and then come back. I feel like somehow, I keep finding myself in a contrarian position related to Things That Are Going To Change Business, and I don’t do it on purpose, honest. But I’m skeptical of A/B testing, just as I’m skeptical of most experiment-driven behavioral economics research, just as I’m skeptical of the use of surveys to prove anything. And in all three cases, the reasons are the same: behavior is complicated, the method is overly reductive, and the approach ignores the magic and the soul.

Behavior is complicated.
I consume every book on behavioral economics and decision making that I can get my hands on, and while I can’t claim to understand all of what I read, I can make a few generalizations.

First, we have two main systems of decision making, one that’s historically and impulsively driven by an urge to stay alive, and one that’s reflective and considered. They both operate, all of the time, and they often contradict each other. That means that, depending on the broader circumstances of use, the same person will respond differently to a stimulus, and so attempts to consider causality related to A/B testing need to correct for things like the ambient environment in which the user is using the system.

Additionally, discrete behavioral rules are compounded by the world around you.  For example, there’s something called the mere exposure effect, where, as Daniel Kahneman explains, “repetition induces cognitive ease and a comforting feeling of familiarity.” Seeing a word, face, shape, or other design pattern over and over increases the likelihood that a person will view that word, face, shape or design pattern as “good.” You have control over your own web property, but none over the rest of the internet, and that’s where this exposure is going to happen. In other words, it’s likely that visual precedent set by other sites will change the way a user feels about your site. That’s just one of hundreds of discrete psychological effects that exist – discrete in how it was tested and observed, but when played out in real life, there’s nothing discrete about it.

Additionally, the way people act on the internet is highly irrational, and anyone who has ever observed a usability test realizes that many people seem to be in a state of chaos when using technology, clicking, quite literally, everywhere. A/B testing almost implicitly assumes a rational agent, one who is taking actions based on a logical assessment of what they see in front of them. My experience tells me that simply isn’t a good assumption, and so the results of your test are likely to be inconclusive (even when the data tells you otherwise).

The method is overly reductive, and we never learn why.
A scientific approach attempts to isolate one thing in order to predict causality. That’s the basis of A/B testing. The problem is, one thing isn’t being “isolated”: the human using the system. Statistical models can start to make predictive assumptions about the likelihood of the human using the system, fitting into various profile types, but it’s going to take someone a lot smarter than your average bear to produce these models. A well respected startup in Austin, Vast, employs David Franke, a brilliant mathematician, as Chief Scientist. A big company like Google has hundreds of people to do this work. But I’ve found it rare that the small companies most likely to engage in A/B think about this at all, much less employ someone with a background in statistics who is qualified to model it correctly.

There’s a great anecdote that I heard from Ron Kurti, also at Vast, and repeated at Luke Wroblewski’s site: putting forms in a mad libs style increases conversion by 25-40%. It’s safe to say that, immediately following this observation, mad libs style forms started appearing all over the internet (if you haven’t heard this yet, you are probably thinking the same thing: how can I change my site to have mad libs forms?). But we don’t know why this works, and because behavior is complicated, we have no way of creating generalized rules for where it works best. And yes, we can A/B test it on our own sites to know if it works for us, but again, we won’t learn why. I don’t want my products, systems, or services to be black boxes; I want to understand how they work, why they work, and I want to have some degree of control over the things I’m introducing into the world.

The approach abdicates responsibility.
The same problem I have with “Lean UX” is evident here: we’re throwing things out in the world without really thinking about the implications these have on real people. As Wired describes, “But with A/B testing, WePay didn’t have to make a decision. After all, if you can test everything, then simply choose all of the above and let the customers sort it out.” Your customers aren’t there to sort it out. They’re real people, with real emotions, and your test is having real implications on their real lives. This may not matter, depending on what it is your company does. It’s hard to argue that, on a site where people rate restaurants, it’s ethically irresponsible to change the color of buttons to determine which has a higher transaction rate. But I would make a much more adamant case that, in a system used on a daily basis by an at-risk population, your customers can’t be your guinea pigs.

The approach ignores the magic and the soul.
I understand the value of data and a rational approach to things like engineering. I would like someone who is designing an airplane to use a rational, data-driven, scientific, rigorous approach to understand how much weight that plane can hold. But in the same example, we find an obvious illustration of what happens when we only use an analytical approach. Flying sucks, and it sucks because it’s been engineered to death. Using Google is starting to be a lot like flying, probably because it’s being engineered to death. An emotional approach has value, because it provides things that are unexpected, sensual, poetic, and things that feel magical.

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Good design crafts a story, and I can’t think of anything more powerful than a good story. Brian Christian wrote a great piece for Wired, and I’ll be damned if he A/B tested multiple versions of it to find the one with just the right level of engagement. I don’t want to live in a world where things are optimized, much less optimized for transactions and consumption. I want up and down, and high and low, and things that are absurd, and things that have personality, and things that react in unexpected ways.

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Related:

Designers and A/B Testing

Why A/B testing of web design fails