Self-disclosure versus automation

A third design issue with regards to awareness is certainly the trade-off between automation and human agency (i.e. the capability of an individual to decide whether or not to act). In the CatchBob! experiment we saw the difference between self- positioning and having a MLA interface. In the first CatchBob! study, we found that automatically giving the location-awareness information to participants was not always fruitful in terms of collaborative interactions. It was better with regards to the modeling of the partners’ intentions to let users express what they estimated to be relevant through a broader channel of communication: the map annotations. The players with the awareness tool were able to annotate as well but did not use this opportunity. Letting people build their own representation of the spatial information appears to be more efficient than broadcasting mere location information. To some extent, not giving location-awareness information was a way to support collaboration more effectively; since players communicated more and better explained their activity and intents, which led interestingly to the reshaping of their strategy.

This fact is of particular importance because it shows the difference between automatic positioning in which location is just information versus self-declared positioning, which is both information and an act of communication act, intentional by definition. Both are coordination devices in Clark’s sense but the self-disclosure seems to better facilitate the construction of the common ground. The reason for this is due to the important difference between self-disclosure, which convey intentionality, and automatic awareness that is purely informational. This distinction corresponds to the one made by Malle (2003) between “observability” and “intentionality” as two dimensions that frame people’s understanding of their partners’ behavior. We can then complement Clark’s definition of coordination devices with this distinction between purely observable events that occurred in the environments and events that express the intentionality of the person who produced them. The awareness tools makes things observable but since they are automated, the receiver cannot detect any intentionality (unlike self-disclosure of one’s location). As a conclusion we can thus state that our second study showed that the intentional coordination devices are more important to collaboration than those that are automated. This also has interesting consequences for Computer Supported Collaborative Learning. These results are close to what socio-constructivist theories (Brown et al., 1989) value in an educational context (i.e. elaborated explanations, self- regulation, strategies explicitations). In particular, there seems to be two advantages in not providing collaborative mobile users with a location-awareness tool:

- To facilitate knowledge elicitation: Without the automatic location-awareness, subjects were more articulate about their strategy. It seems that the tool created a certain inertia within the group, with regards to communication. Participants who relied on the automatic positioning wrote fewer messages, which led them to be less explicit about the situation and how they could deal with it.

- To ease conflict solving though a better explanation of what players wanted to do or achieve in order to progress in the task completion. Being more verbose raised more conflicts, which is good for learning as stated by Doise and Mugny (1984). Another advantage for self-disclosing one’s location is that it allows people to employ the location names that make sense for the participants. This is related to the distinction between ‘space’ and ‘place’ (Harrison and Dourish, 1996). The difficulty of location- based applications in conveying a meaningful semantic of places makes it more efficient to let users express their location by using their own description, a topic already discussed by Persson and Fagerberg (2002).

This finding, that it is better to let people express their own location, is confirmed by what Benford et al. (2005) revealed: self-reported positioning could be reliable low-tech alternative to automated systems like GPS. However, our findings go further by proving that letting users declare their position themselves is better with regards to various processes like communication or the construction of a mental model about the partners. However, there are two disadvantages to self disclosure of location. A potential critique is that putting too much emphasis on self-location makes it more vulnerable to connectivity and lag problems. As a matter of fact, packet loss containing automatic location information with a timestamp (automatic location-awareness) is less prejudicial than losing free-hand annotations or voice interactions because an automated broadcast of information is expected and we know there is something wrong if it does not come. Another drawback is the additional workload that is created by such an approach since users would have to send explicit information. Further, scholars have reported how people are very poor at remembering to update system representations of their own state (Bellotti and Edwards, 2001). Nevertheless, given that in our case the participants do not only update a system but also intentionally give information to other people one can imagine that this drawback will be diminished.

A second major consideration is of course the context of the activity. Let us transfer it from the Catchbob! game to a real-world situation. If we had two groups of airplanes in flight, one with radar and one without, the planes without radar would certainly spend a lot more time communicating with one another to check on their mutual locations. The new fangled airplanes that had adopted radar would lack the mutual awareness that the non-radar group had. The issue, in other words is that it matters a great deal what Catchbob's equivalents are in actual distributed workplaces and what sorts of work mutual awareness tools are supporting or disrupting. The number of collaborators and the level of decentralization is certainly of importance. For some workplaces letting the user declare where they are is fine but for something like air traffic control or the navigation of shipping lanes it is not a practical possibility.

Take-away #3: Given these elements, the future of location-awareness applications might go beyond the opposition between self-disclosure of one’s location or automating MLA and lie in the combination of both. Figure 69 depicts two possible ways of combining them in CatchBob! depending on which interface we want to improve. If we take the current synchronous MLA interface of CatchBob! (Figure 69a), that displays

the partners’ location as dots; it would be interesting to add a circle around these dots when a player explicitly give his or her location. This way, the intentional part of the message about position is conveyed. If we use an interface without MLA (Figure 69b), it is possible to add the location of a player to the map only when the player sends a message. This might then help clarify the annotation written by the player: supplying this location can potentially narrow the shared context of the users.

Figure 69. Two possible ways of improving a MLA interface: (a) starting from the current MLA interface that automatically displays the location of others and by putting a circle around the position of a person when he/she communicates it (b) starting from the control condition without MLA and displaying the partner’s position when he/she write a message on the display.