The Role of Free Will in HCI

I have been wondering about the role of free will in HCI research lately. It’s a statement of the obvious to say that there are many different theories that inform HCI research, and those theories make different assumptions about knowledge and truth. And sometimes when I read or listen to conversations about those theories, and the methods associated with them, I hear talk about choice. Most specifically that we can choose the most appropriate theory for the research that we want to conduct.

But can we? Can we really choose among them, is it that simple? I am not so sure. Perhaps it’s just me but I find myself drawn to theories and methods that are commensurate with values I hold. I tend to choose things that produce results (even surprising ones) that I find compelling.

I should say that I am not opposed to others using methods that do not align with my values. In fact, I find the resulting scholarship quite interesting. But I also think I tend to be drawn to those papers in ways that take the results and use them to ask questions that are answerable using methods and theories that align with my values.

As HCI reflects on its methodological and theoretical plurality, I would like the field to reflect on how it talks about those methods and theories and whether we are in fact free to choose, and how free we are?

Sharing Instruments: SMS Logging

This is a second post sharing instruments to help others with their empirical research.

One of my most cited papers is “y do tngrs luv 2 txt msg?” which was a study that I did with Marge Eldridge when we both worked at EuroPARC in Cambridge, UK. What interested us both was how rapidly text messaging had been adopted by teens. What were they using it for? Why?

In the spirit of making more of my materials available I wanted to share the diaries that we asked the teens to keep. There are short excerpts in the paper, but here they are in full. I should say that we were trying to balance portability and privacy against collecting the type of data that would allow us to gain insight into how the technology was being used. This is why the diaries look the way they do.

We asked them to log all the messages that they sent and received, and provided instructions for how to use both the sent and received forms.

Writing about Methods

I’ve been meaning to read Kathy Charmaz’s book on Grounded Theory for a while and now I have I want to blog about something she drew my attention to: a paper by Howard Becker.In this paper he describes some of the discussions he had with Erving Goffman. One was about elaborating on research methods. Becker writes

I don’t remember, though I haven’t made an exhaustive search through his works to verify this, Goffman ever writing about any of the standard questions that inevitably arise in doing field research, such questions as access of research sites, relations with the people studied, ways of recording or analysing data, problems of reliability. All of thee were much discussed at the time, and many of us (I was among them) write about them, in an effort to clarify for ourselves what we were doing. Goffman never did.

This was a principled refusal, which he and I discussed a number of times. He felt very strongly that you could not elaborate any useful rules of procedure for doing field research and taht if you attempted to do that, people would misinterpret what you had written, do it (whatever it was) wrong, and then blame you for the resulting mess. He refused to accept responsibility for such unfortunate possibilities.

I find this really interesting. The rest of the paper is also a fascinating read, but I want to pause here. I’ve written before that I think that one of the reasons that Grounded Theory is so popular in HCI is because it has well specified methods. It tells someone what to do and how. In so doing it provides a justification. And as Charmaz argues that was quite intentional for at the time when Grounded Theory was being developed Qualitative Sociology was in decline, and not taken seriously.

Writing about our methods is common in HCI. A common genre of reporting empirical HCI research is to have a section on Methods and Participants. And I’ve heard people discuss whether a paper is clear enough about methods in committee meetings. Once a long time ago, I tried something somewhat different, I wrote a methods section that had a section on the Methods I had followed and then a section called Practice, on how they actually worked out. I would have done this again, but I never got any feedback, either positive or negative, from anyone about whether this was valuable.

One major argument for writing about methods in HCI is so that we the reviewers/audience can assess the results based on the methods. But, I am now reminded of the arguments about inter-rater reliability, for some types of analysis will knowing the methods actually lead to understanding of whether the analysis is correct. For now, I’ll continue to write about methods when I write about HCI. But I think its worth asking, does what you read about the methods actually explain the analysis?

Academic Organisation

A colleague of mine, Mark Guzdial has written a thoughtful and thought provoking post on his Computer Science Education blog.

And I was drafting a reply, and I decided that I’d like to write it here.

The gist, as I read it, is that he asks why academic disciplines are organised by outcome rather than by methods. By asking this question you can explore other connections.  In the case of Computer Science Education it turns the focus away from outcomes (measures of learning success, and towards the experiences that will create these desired outcomes, what is the experience of good education).

This got me thinking about what I see as an interesting difference between some of the sciences and others, which has some origins in methods, and theories.

I’ve spent most of my research career as a practicing Computer Scientist. My education is reasonably traditional, and my career has been entirely within institutions focused on the advancement of Computer Science. But, that said, I’ve spent my research career as a user of methods/theories that do not hail from Computer Science, but from Sociology/Anthropology.  And to do that I’ve done my best to learn about the disciplines. And in that journey, I’ve been continuously struck by the volume of debate within those disciplines.

Specifically, I’m struck by how much discussion and difference there is in methodology and theory within both disciplines. My analogy, what would it mean if we had multiple and competing approaches to Computer Science. And I suppose we do. I understand that there are significant philosophical differences within AI. But, I don’t think we teach Computer Science in ways that amplify and centralise those philosophical differences.  I am aware that these differences exist, but I’ve never had a class or seen a book that talks about these philosophical differences and why they exist, and what their origins are.

Are we poorer for that? I increasingly think so…

Another example. I used to be a Software Engineer (which explains why I still review papers for ICSE I suppose, and why I can’t stop subscribing to Software Engineering Notes). So there are a variety of different methods to organizing the work of Software development. Some of the new Agile or Pair-Programming techniques contrast with the Chief-Surgeon model. And while I have read arguments about the differences, and the outcomes and experiences that they make possible (in pair programming people share a machine, so we say that it’s a good way to learn and a good way for the person watching to catch mistakes of the person typing, we argue that that comes with a certain productivity hit because there are half the number of machines in operation, and so we continue…)…

So we have those debates, based on outcomes, and elements of the experience (which we conveniently blur into the debate), but we never really systematically unpack and discuss the many different ways that work can be divided. (My first advisor Rob Kling told me never to use the word organization as a noun—it was a convenient gloss over the vast array of organizational types—I think he’s right). Organization is a verb, and it is the division of labor and the assumptions that frame that particular set of institutional arrangements.

And I think in disciplines where there is lots of debate about the philosophical nature of the world, there’s far more explicit discussion of the theories and methods and their explanatory power as it relates to that particular set of philosophical commitments. I think Computer Science could benefit from the same approach. Why do we disagree? What does the nature of the disagreement tell us about the nature of the world?

Perhaps we don’t because we focus on the machine (for example, explaining differences as technical tradeoffs, or as a science of the innards of the machine itself). But, I think that those machines do not exist in a world in any way without the presence of humans. The computer was a human creation. It is imagined and built for humans, with human-centered goals (such as faster machines capable of solving more complex problems, relying on novel algorithms, protocols, and architectures). Our philosophy turns in significant part on a belief that what is done in the machine is justifiable because it makes advances possible but those advances are human.