Procedural Network Representations of Sequential Data

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Abstract
Sequential data collected for usability testing, knowledge engineering, or cognitive task analysis are rich with information-so rich that interpretation can often be overwhelming. This dilemma can be viewed as a data reduction problem. PRO-NET (PROcedural NETworks), a method for reducing sequential data in terms of procedural networks, is introduced and then applied and evaluated in two case studies-one involving human-computer interaction (HCI) in a simulated mission control operation at the National Aeronautics and Space Administration and the other involving avionics troubleshooting behavior for an intelligent tutor application. The method involves five steps-collecting data, encoding data, generating transition matrices, conducting Pathfinder analysis, and interpreting procedural networks. The method employs the Pathfinder network scaling algorithm, which is particularly suited for asymmetric data. Evidence is presented to support the descriptive and predictive utility of this form of data reduction. In addition, lessons learned in applying PRONET to the two cases are discussed, applications of PRONET to HCI are described, and guidelines are offered for using PRONET in exploratory sequential data analysis.

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