Discovering the events expert practitioners extract from dynamic data streams: the modified unit marking technique

One of the cornerstones of expert performance in complex domains is the ability to perceive problem situations in terms of their task-relevant semantic properties. One such class of properties consists of phenomena that are defined in terms of patterns of change over time, i.e., events. A basic pre-requisite for working towards tools to support event recognition is a method for understanding the events that expert practitioners find meaningful in a given field of practice. In this article we present the modified unit marking procedure (mUMP), a technique adapted from work on social perception to facilitate identification of the meaningful phenomena which observers attend to in a dynamic data array. The mUMP and associated data analysis techniques are presented with examples from a first of a kind study where they were used to elicit and understand the events practitioners found meaningful in a scenario from an actual complex work domain.

David D. WoodsEmail: