The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.02 - March-April (2012 vol.27)
pp: 61-66
Wayne Zachary , CMZ Health Techologies
Robert Hoffman , Institute for Human and Machine Cognition
Beth Crandall , Applied Research Associates
Tom Miller , Applied Research Associates
Christopher Nemeth , Applied Research Associates
ABSTRACT
Cognitive task analysis is the study of macrocognitive work and the modeling of the knowledge and reasoning of domain experts, which inform the creation of intelligent systems. Over the past quarter-century, most research on decision-making and other aspects of macrocognition has been conducted using some form of cognitive task analysis. In this essay, we focus on the cost of cognitive task analysis. Cognitive task analysis can be resource-intensive and requires skilled practitioners of the craft to make the investment of time and resources worthwhile. Providing valid, useful, actionable findings from cognitive task analysis requires thoughtful analysis and representation in ways that communicate meaning. Yet, many system development efforts have not relied enough on cognitive task analysis, and have instead fallen into the trap of designer-centered design. Not only does this preclude the development of a human-centered system, it obviously avoids the actual challenge. But does cognitive task analysis have to take as long as it seems to? We explore how we might "rapidize" cognitive task analysis, by looking in detail at factors that affect the time and effort involved.
INDEX TERMS
cognitive task analysis, knowledge elicitation, knowledge acquisition bottleneck, designer-centered design, expertise, program management
CITATION
Wayne Zachary, Robert Hoffman, Beth Crandall, Tom Miller, Christopher Nemeth, ""Rapidized" Cognitive Task Analysis", IEEE Intelligent Systems, vol.27, no. 2, pp. 61-66, March-April 2012, doi:10.1109/MIS.2012.29
REFERENCES
1. R.R. Hoffman, “The Problem of Extracting the Knowledge of Experts from the Perspective of Experimental Psychology,” AI Magazine, vol. 8, no. 2, 1987, pp. 53–67.
2. K.M. Ford, and J.M. Bradshaw eds., Knowledge Acquisition as Modeling, John Wiley & Sons, 1993.
3. R.R. Hoffman et al., “Knowledge Management Revisited,” IEEE Intelligent Systems, vol. 23, no. 3, 2008, pp. 84–88.
4. R.R. Hoffman et al., “Franchise Experts,” IEEE Intelligent Systems, vol. 26, no. 5, 2011, pp. 72–77.
5. R.R. Hoffman, and W.C. Elm, “HCC Implications for the Procurement Process,” IEEE Intelligent Systems, vol. 21, no. 1, 2006, pp. 74–81.
6. B. Crandall, G. Klein, and R.R. Hoffman, Working Minds: A Practitioner's Guide to Cognitive Task Analysis, MIT Press, 2006.
7. R.R. Hoffman and L.G. Militello, Perspectives on Cognitive Task Analysis: Historical Origins and Modern Communities of Practice, CRC Press/Taylor and Francis, 2008.
8. J.-M. Schraaagen, S. Chipman, and V. Shalin, Cognitive Task Analysis, Lawrence Erlbaum Assoc., 2000.
9. R.R. Hoffman, B. Crandall, and N. Shadbolt, “A Case Study in Cognitive Task Analysis Methodology: The Critical Decision Method for the Elicitation of Expert Knowledge,” Human Factors, vol. 40, no. 2, 1998, pp. 254–276.
10. R.R. Hoffman and S.M. Fiore, “Perceptual (Re)learning: A Leverage Point for Human-Centered Computing,” IEEE Intelligent Systems, vol. 22, no. 3, 2007, pp. 79–83.
11. J.M. Bradshaw et al., “Beyond the Repertory Grid: New Approaches to Constructivist Knowledge Acquisition Tool Development,” K.M. Ford, and J.M. Bradshaw eds., Knowledge Acquisition as Modeling, John Wiley & Sons, 1993, pp. 287–333.
12. J.W. Coffey, R.R. Hoffman, and A.J. Cañas, “Concept Map-Based Knowledge Modeling: Perspectives from Information and Knowledge Visualization,” Information Visualization, vol. 5, no. 3, 2006, pp. 192–201.
13. R.R. Hoffman and L.G. Shattuck, “Should We Rethink How We Do OPORDs?” Military Rev., vol. 86, no. 2, 2006, pp. 100–107.
14. M.R. Endsley, “Measurement of Situation Awareness in Dynamic Systems,” Human Factors, vol. 37, no. 1, 1995, pp. 65–84.
15. J. Rasmussen, “The Role of Hierarchical Knowledge Representation in Decision Making and System Management,” IEEE Trans. Systems, Man, and Cybernetics, vol. 15, no. 2, 1985, pp. 234–243.
16. R.R. Hoffman, “Influencing versus Informing Design, Part 2: Macrocognitive Modeling,” IEEE Intelligent Systems, vol. 23, no. 6, 2008, pp. 86–89.
17. R.R. Hoffman and S.V. Deal, “Influencing versus Informing Design, Part 1: A Gap Analysis,” IEEE Intelligent Systems, vol. 23, no. 5, 2008, pp. 72–75.
18. S.B. Regoczei and G. Hirst, “Knowledge and Knowledge Acquisition in the Computational Context,” The Psychology of Expertise: Cognitive Research and Empirical AI, R.R. Hoffman ed., Lawrence Erlbaum Assoc., 1992, pp. 12–28.
19. J.H. Boose, and J.M. Bradshaw, “Aquinas: A Knowledge Acquisition Workbench,” AI Tools and Techniques, M. Yazdani, and M. Richer eds., Ablex, 1989, pp. 151–182.
20. T.C. Swanson, Capturing Intellectual Knowledge with E-mail Systems, T.C. Swanson Books, 2006; www.thephishpond.com.
21. A.M. Bisantz et al., “Integrating Cognitive Analyses in a Large-Scale System Design Process,” Int'l J. Human-Computer Studies, vol. 58, no. 2, 2002, pp. 117–206.
22. E.M. Roth, and D.D. Woods, “Cognitive Task Analysis: An Approach to Knowledge Acquisition for Intelligent System Design, Topics in Expert System Design, G. Guida, and C. Tasso, eds., North-Holland, 1989, pp. 233–264.
23. K. Neville et al., “The Procurement Woes Revisited,” IEEE Intelligent Systems, vol. 23, no. 1, pp. 72–75.
34 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool