International Journal of Man-Machine Studies
International Journal of Man-Machine Studies
International Journal of Man-Machine Studies
Unified theories of cognition
Pathfinder associative networks: studies in knowledge organization
Pathfinder associative networks: studies in knowledge organization
Pathfinder associative networks
Assessing structural similarity of graphs
Pathfinder associative networks
Using pathfinder to extract semantic information from text
Pathfinder associative networks
Information retrieval using pathfinder networks
Pathfinder associative networks
Using pathfinder to evaluate user and system models
Pathfinder associative networks
Hypertext perspectives: using pathfinder to build hypertext systems
Pathfinder associative networks
International Journal of Man-Machine Studies
Experience with an adaptive indexing scheme
CHI '85 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Architecture of Cognition
Structuring and visualising the WWW by generalised similarity analysis
HYPERTEXT '97 Proceedings of the eighth ACM conference on Hypertext
The New Review of Hypermedia and Multimedia - Hypermedia and the world wide web
Exploratory sequential data analysis: foundations
Human-Computer Interaction
Sequences of actions for individual and teams of air traffic controllers
Human-Computer Interaction
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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. PRONET (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.