Tag Archives: ux

Ignite Toronto: Designing for Social Selvess

For those of you who caught my Ignite TO presentation, here are the slides. For those of you who missed it, below is a text summary that goes with the slides.

I’d like to give thanks to my teacher and friend, Dr. Karen Anderson, whose scholarly work underpins many of the ideas in this presentation.

Slide 1:

This presentation about is the self, that it is a social phenomenon not a biological one. Most theories of the self don’t give us a social angle but only a biological one. This has an impact for technology design.

Slide 2:The self is an uniquely human phenomenon. It is the internal private reality of the consciousness. It is not anatomical or physiological. It is not a body.It is only meaningful in social situations.

Slide 3: So we have this internal, private reality, this consciousness. Biological paradigms to explain it are inadequate. Bodies are the containers of selves, not the actual self. Containers matter. But they are not the only thing that matters.

Slide 4: Victor, was a “feral child” found in France. He would not wear clothes. Or Use a bed. He farted. He did not have a social self, but a biological one.His body functioned; his self did not.

Slide 5: HAL 9000 has a self. He is socially competent. Aware of his inner reality. He imagined that Dave and Frank were plotting against him. Victor had no inner reality but HAL did.  HAL understood the social.

Slide 6: All too often we think of the self as a piece of hardware, or an emotion chip. Unfortunately, most of our ideas about the self are really about our hardware.

Slide 7: For example, Sigmund Freud. Freud thought biological experiences created the self. In the form of ego and the superego. We learn about our anus and develop a self, but this doesn’t explain Victor or HAL’s development.

Slide 8: Even psychologist Piaget put biology first. Piaget’s theory of child development relies on sensory experiences. Not social experiences. For Piaget, learning starts with a bodily interaction, not social interaction.

Slide 9: Yet socially successful human beings must master the meaning of symbols. Symbols have fine nuances, depending on the context. Hand gestures are anatomically similar but mean different things at different times, in different places.

Slide 10: Social interaction is built upon symbols, not biological impulses. We are aware of our internal realities by interpreting social symbols. The degree of force in a gesture matters. Who gives it matters.

Slide 11: We interpret symbols, not react to them. We are not Pavlovian dogs who salivate at the sound of a bell. We are not somatically driven beings, but socially driven beings. Our bodies have influence over us but they are not the self.

Slide 12: George Herbert Mead offers us a theory of a social self. The “I” is what Victor has: a purely instinctual consciousness. The “me” is created through social interaction. “I should sit on a chair; it’s more socially appropriate.”

Slide 13: The “generalized other” is when we realize there is a whole world out there. That we then internalize into our own private reality. We begin to imagine what “others” might say about our actions. Our self imagines what other selves think of it.

Slide 14: Often we design technology to be USABLE, not to be SOCIAL. We don’t enable social selves to use technology without an awkwardness, or embarrassment.

Slide 15:  Google Street View. This technology has created a few embarrassing moments. Google’s face blurring does not solve our embarrassment of interpreting this image. Street View is functional, not social.

Slide 16: Facebook continually fails to sense what selves need. This self posted a picture of himself smoking. Unfortunately, his mom recognized the room. This is embarrassing.

Slide 17: If we design for selves, not bodies, we think of everyone’s internal private realities. Bodies need ergonomics, usability, accessibility. Selves need to be shielded from embarrassment, awkward situations, and social breaches.

Slide 18: Technology designed for bodies is like an awkward dinner party. The technology we design should provide a consistent, social lubricant. We must design technology like we design great parties. Where the right people sit in the right seats.

Slide 19: Socially meaningful symbols must be present. This can be discovered through contextual inquiry, Selves also require the ability to control their presentation to others. And finally, the social “place” of technology must be clearly demarcated.

Slide 20: In the end, we design our world for selves. Technology designed for bodies just gets in the way. If technology is designed for bodies, selves change to meet the needs of technology.

I would prefer that have technology adapt to selves.

Thank you

Designing for conversations: the critical importance of turn taking

Hugh Dubberly and Paul Pangaro had a great post on Interactions magazine about designing for conversations. They propose to use how a conversation actually works to make interactions better. They rely heavily on Claude Shannon’s conversation model to help guide the conceptual model of interaction designs.

In Shannon’s model an information source selects a message from a known set of possible messages, for example, a dot or a dash, a letter of the alphabet, or a word or phrase from a list. Human communication often relies on context to limit the expected set of messages.

I applaud Dubberly and Pangaro’s attempts to use rigourous theory to support interaction design. But I’d have to agree with Peter Jones as he wrote in the comment section, that other philosophically informed communication theories are more robust when it comes to designing for conversation. Peter specifically mentions Winograd and Flores’s “conversation for action model” which relies on Habermas’s contention that you are acting when you communicate.

I’ll add to Peter’s critique. Garfinkel’s ethnomethodological approach gave way to “conversation analysis,” which posits that speakers use “indexical expressions” (or phrases that are fraught with meaning but are meaningful to the participants through unspoken means). Where in Dubberly and Pangaro’s article is the discussion of such expressions?

Where also is the notion of turn taking? Turn taking is a very significant component of a conversation. Try to have a trans-atlantic mobile phone conversation and you’ll see how important smooth turn taking is to meaningful conversation.

I would exhort interaction designers to continue to read and integrate theory into their mental models. But I would also discourage them from taking the short route; theories are debated for a reason. Interaction design ought to be a robust digital representation of those debates, and include all aspects.

Why Web analytics won’t help interaction design

The data provided through Web analytics offer promise to interaction designers by pointing to potential user experience problems. But interaction designers who think they should base critical design decisions on Web data are misguided at best and downright irresponsible at worst.

Web user experience practitioners recently embraced web-traffic measurement as a user experience research method. This is not necessarily a bad thing — gathering design insight from a variety of sources is always advisable. The problem comes when interaction designers (and the data analysts that advise them) base critical user experience decisions on these often incomplete and even misleading data.

My beef with Web analytics boils down to two points:

  1. Web analytics are notoriously unreliable. Web traffic researchers continue to struggle with accurate visitor counts and missing data points, compromising both the validity and reliability of the method (Chatham, 2005).Practitioners are dogged by day-to-day limitations in both the techniques and the underlying technology, which limit their ability to reliably produce analyses that are universally accepted as legitimate (Wiggins, 2007).
  2. More troubling, is the claims that Web analytics capture meaningful data about the user experience. Certainly Web analytics capture important information about server loads, form completion, navigation patterns, and browser types.The unspoken belief, however, is that user keystrokes and mouse clicks represent the sum total of what there is to know about a Web site visitor’s experience. If you base user experience decisions on Web traffic measurement, you assume that an individual person intentionally initiates these keystrokes and mouse clicks for meaningful reasons.

Ask yourself, have you ever initiated a mouseclick unintentionally? Have you inadvertently typed in a URL? These mistakes of intention are not registered by Web analytics tools.

In his book Observing The User Experience, Kuniavsky argues that Web analytics have the same amount of insight as a “jewelry store clerk” who has a “much better understanding of customers” because they watch everything the customer does.

Web analytics are not equivalent to a jewelry store clerk gathering subtle, nuanced information about a person visiting their store. They are the equivalent of a blindfolded, deaf jewelry store clerk who uses a complex system of tapping to communicate with store visitors, who may or may not know the unique tapping language of that particular clerk.

Interaction designers must base critical user experience decisions on the results of qualitative research, rich with “thick description,” and subtle cues. Basing such decisions on Web analytics would remove the all insight into users’ actual intentions.

Web analytics tools have their place in interaction design. They should be limited to:

  • Measuring appreciable increases in specific, observable goals, such as form completion
  • As a final test after a battery of in-person, qualitative usability tests
  • As an infrastructure monitoring tool
  • As a lead generation or campaign effectiveness tool

Any other uses of Web analytics reduces interaction design to nothing more than blind counting of meaningless signals.

This is an abridged version of Ladner, S. (forthcoming). “Watching the Web: Suggestions for Improving Web-based Measurement.” In Jansen, J., Spink, A. and Taksa, I. (eds.). Handbook of Log File Analysis. Idea Group: Hersey Pennsylvannia.

Further Reading:

Chatham, B. (2005). What’s On Web Analytics Users’ Minds? : Forrester Research.

Gassman, B. (2005). How to Choose An Advanced Solution for Web Analytics. Stamford: Gartner Research.

Kuniavsky, M. (2003). Observing the User Experience: A Practitioner’s Guide to User Research. San Francisco: Morgan Kaufman.

Lecompte, M., & Shenshul, J. (1999). Designing and Conducting Ethnographic Research. Walnut Creek: Altamira Press.

Wiggins, A. (2007). Data Driven Design: Using Web Analytics To Improve Information Architecture. Paper presented at the Conference Name|. Retrieved Access Date|. from URL|.