Monday, May 25, 2015

How Does rhizoANT Work?

In my previous post, I summarized Farzana Dudhwala's article What is Actor-Network Theory?, but I didn't really explore what it might mean for the rhizo14 collaborative autoethnography (CAE). I want to do that here.

I start with Dudhwala's first observation that for ANT, the social is a network of relations and "does not exist as an objective reality prior to the research having even begun" (3). This is a particularly tricky issue for rhizo14 participants because we are all educators engaging an online class. Thus, we can easily bring to the class all of the social and educational structures that we have learned and learned very well, given that most of us are successful students, teachers, and administrators. For instance, we can easily assume that Dave Cormier is the teacher and that we are the students, bringing to our research all of the power and social relationships implied by those roles, which can blind us to the structures that actually emerged in rhizo14. If we expect Dave to be a traditional teacher, then we will interpret his behavior, for better or for worse, based on that expectation. An ANT approach to rhizo14 tries to drop expectations of Dave as the teacher and rhizo14 as a MOOC.

This is one of the issues Simon explores so well in his Hybrid Pedagogy article "Insoumis" when he responds to Mackness and Bell's published analysis of rhizo14. If I'm reading Simon correctly, then he suggests that Mackness and Bell bring to their analysis certain assumptions about the roles and responsibilities, especially of the facilitator, that do not apply to rhizo14, given that what emerged was not a traditional on-line class but something else.

Let me say that I do not believe researchers can bring no expectations to a given situation. We are always informed by our theories and models, and the best we can do is recognize and work with our biases, models, and theories. This takes great discipline and rigor. It also helps to have a swarm of researchers who can look at a given social event such as rhizo14 from many more angles.

ANT certainly begins with its own models of reality, which Latour has complained about. ANT assumes that rhizo14, for instance, is best approached as actors interacting in a network of relations, and the structure of rhizo14 is not given beforehand (say by the facilitator, Dave Cormier), but emerges from all those interactions. The global follows the local, unlike traditional classes in which the local interactions among students, teachers, tests, and texts follow from the carefully laid out global course plan.

Keep in mind, however, that ANT is still a model of reality, and while it's the model that I prefer, we have to recognize that it is a model. Therefore, it is wrong in the sense that like all models it is limited, it leaves out too much. I think we use ANT because we find it useful, but we must remember that the old models were useful in their day and may still be useful in some contexts for some tasks. Someday, ANT will not be so useful. Like all models, it will always be wrong.

This model means that we approach rhizo14 "not as attributors of a hidden social force or context, but simply as tracing the associations between heterogeneous entities and following their lead" (3). We don't attribute to rhizo14 characteristics of connectivist or constructivist educational theories; rather, we identify as many actors as we can and follow them, scribbling notes madly, to see where they go, how they get there, and what else they connect to. The hope is that if we look closely enough, long enough, then a shape will begin to emerge. We will identify that emerging shape as rhizo14. Perhaps a MOOC, but perhaps not. We will wait to see what emerges. It may have some patterns that resonate with other patterns we know about (MOOCs, connectivism, etc.), but it likely will also have patterns that are peculiar to itself. ANT wants to capture both.

Several distinctive characteristics of ANT emerge here. First, what are actors? We think of people, of course—all of us who engaged in rhizo14—but ANT takes a global view of actors: people, organizations, ideas, things, processes. Thus, when we explore rhizo14, we have to consider Google Docs along with Maha, Sarah, Simon, AK, and others. For ANT, actors are heterogeneous entities. AK writes in his post "Swarm the Google Doc, or so says the ANT" and Len in his post "Actor-Network Theory and Google Docs" about the characteristics of Google Docs that both enabled and shaped the interactions among the humans writing about rhizo14. For ANT researchers, Google Docs is an actor in its own right, just as the humans are. ANT says that we cannot understand the interactions between Rebecca and Sandra if we don't include their interactions with Google Docs.

Of course, we can't stop with just Google Docs. Once we begin this line of thinking, we have to include our devices (PCs, laptops, tablets, smartphones, ISPs, electrical grids, the Internet, and all the rest). In short, there is no end to the amount of detail that we can collect, and this is a real problem for ANT researchers. The work-load is overwhelming, as the CAE cohort has already discovered. Every relevant detail is interconnected with 10 other relevant details, all clamoring for our attention. It can drive a researcher mad.

To my mind, this is where the novelists come to our rescue. Ever since Laurence Sterne wrote Tristram Shandy, novelists have recognized that telling any story connects writers to more details than anyone will publish or read. A novelist is successful as much for what she leaves out as for what she puts in. I suspect that ANT researchers are in the same situation. I will have to read more from them to see how they handle this situation. For a hundred years, we've tried to deal with too much data through statistical analysis: collecting fewer random data points and applying statistical algorithms to them to extrapolate to the whole system. One new approach, though, has to do with big data and computers, which allow researchers to collect more, sometimes almost all, data points from any given situation and process that data with computers in ways that reveal patterns previously obscured by the sheer amount of data (weather patterns are an obvious example). So far in our swarm, we have taken a mostly novelistic approach to studying rhizo14 with our collection of ethnographic stories, but we can apply computers even to those stories, as I started to do with my work on the prepositions in the CAE. I used a computer and text analysis software to identify all the prepositions in the CAE and then followed the connections made by one preposition, identifying the actors and the network of interactions revealed by the CAE. Of course, a more complete study would look not just at the CAE, but also at all the tweets, the Facebook discussion, the blog posts, and even the more remote and obscure hallway discussions as rhizo14 participants discussed rhizomatic learning with their local colleagues. There is no end to data, and we should explore how computers can help us collect and analyze more data in rhizo14.

Another characteristic of actors is their flat status. As Len notes in his post:
ANT does not support levels of importance or status for any set of actants. In other words everything in a system takes on a sort of equal level of importance. While this is difficult to accept at times, I believe the general premise that you do not assign or think about levels of importance (agency/ a flat ontology ?) of actants. In fact, ANT suggests, I believe, our understanding of a system of actants cannot be determined a priori – that things unfold (in situ?).
So we don't assume going into our study that Dave Cormier is the key figure in rhizo14. Indeed, if you look at rhizo14 across the past year, you will most likely identify several figures more prominent than Dave (I think he will happily agree with that assessment). Certainly, this current swarm of participants has been more prominent in my experience of rhizo14 than Dave has been. Jenny and Frances have been more prominent. This flat ontology (thanks for that term, Len) does not mean that ANT doesn't recognize macro, meso, and micro actors—it does—but it doesn't recognize them before it sees them. If we examine all the interactions of rhizo14, and Dave does not emerge as a key player in most of them, then we cannot grant him some special Big Honcho status with special obligations and responsibilities (back to Simon's observations). If some were not happy with rhizo14, then we are all implicated, and all includes all the human and non-human actors. Twitter gets just as much consideration, and possibly as much blame and credit, as Dave does or I do.

Finally for this post, I want to mention a last characteristic of actors: that they are all mediators of the messages they carry and the relationships they form. In other words, when I talk about actor-network theory as I am doing in this post, I stain the message. Google Blogger stains the message. The Internet stains the message. English stains the message. Because I am an American, the U.S. stains the message. I always leave my fingerprints on any message I channel in or out. When you get this message, this post, you will put your fingerprints all over it with your peculiar reading. There is no clear communication free of noise and static. (This, by the way, is probably the single biggest fault of traditional education: the assumption that communication of knowledge from teacher to student can be clear and thus reliably tested. It cannot.) ANT researchers, then, must look for and account for the stains. When we look at Google Docs in rhizo14, we must look for the ways that Google Docs shapes and translates the energy and information that flows through it. When we used Google Docs to write both the original CAE and The Untext, Google Docs was as much a shaping, translating, forming actor as we humans were. And we all shaped and translated and in-formed. ANT recognizes this network phenomenon and tries to account for it.

For me, then, ANT itself is not so difficult an idea; rather, its practice is difficult as it exposes the researcher/s to an overwhelming swelter of information. Try this thought experiment: consider 4 or 5 children playing in a sandbox for an hour. Start with as few preconceptions as possible about what they are doing and how they should do it. Observe as much as possible with the hopes of later explaining what emerges through their play. Now imagine all the technical apparatus you would need to capture all the relevant data (speech, action, toys, games, personalities) unfolding in even this small a space/time. You could write a book about this one hour. Laurence Stern did.

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