A multidimensional analysis method for think-aloud protocol data

As part of a larger project we analyze think-aloud data to produce descriptions of thinking. This analysis requires inferring thinking from observable participant behaviors, primarily what participants say. To produce rich and reasonably accurate descriptions of the thinking we focus on several different features in the data. We analyze the participants' speech for both their description of their thinking and the insight provided into their context dependent expectations. We also attend to two non-verbal features in the data, gestures and pauses. In this paper we focus on each analytic feature, first describing the relevant research base and then explaining how we operationalize it in our analyses. We tentatively claim that coordinating the analyses of the four features produces more accurate descriptions of reasoning than traditional think-aloud analysis methods, which focus primarily on analyzing speech.