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120 Socially Intelligent AgentsFigure 14.2. Eye gaze behaviours of seven children who interacted with the interactive robotand a passive toy truck in a comparative study. Shown is the percentage of time during whichthe behaviour occurred in the particular time interval analysed (%), as well as the number oftimes the behaviour was observed (#). Note, that the length of the trial sections can vary.robot very frequently but briefly. Chris, Sean and Tim direct slightly more eyegaze behaviour towards the toy truck. The quantitative results nicely point outindividual differences in how the children interact with the robot, data that willhelp us in future developments. Future evaluations with the full list of criteriadiscussed above will allow us to characterise the interactions and individualdifferences in more detail.2.3 A Qualitative ApproachThis section considers the organisation of interaction in the social settingthat involves the child, the robot and adults who are present. The followinganalysis draws on the methods and findings of Conversation Analysis (CA) anapproach developed by Harvey Sacks and colleagues (e.g. [13]) to provide asystematic analysis of everyday and institutional talk-in-interaction. Briefly,CA analyses the fine details of naturalistic talk-in-interaction in order to identifythe practices and mechanisms through which sequential organisation, socialdesign and turn management are accomplished. For overviews and transcriptionconventions see [5], [11]. This requires an inductive analysis thatreaches beyond the scope of quantitative measures of simple event frequency.A basic principle of CA is that turns at talk are “context-shaped and contextrenewing”([4], p. 242). This has a number of ramifications, one of which isthat the action performed by an utterance can depend on not just what verbalor other elements it consists of, but also its sequential location. Consider forexample how a greeting term such as “hello” is unlikely to be heard as “doing