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Full Description
Macrocognition Metrics and Scenarios: Design and Evaluation for Real-World Teams translates advances by scientific leaders in the relatively new area of macrocognition into a format that will support immediate use by members of the software testing and evaluation community for large-scale systems as well as trainers of real-world teams. Macrocognition is defined as how activity in real-world teams is adapted to the complex demands of a setting with high consequences for failure. The primary distinction between macrocognition and prior research is that the primary unit for measurement is a real-world team coordinating their activity, rather than individuals processing information, the predominant model for cognition for decades. This book provides an overview of the theoretical foundations of macrocognition, describes a set of exciting new macrocognitive metrics, and provides guidance on using the metrics in the context of different approaches to evaluation and measurement of real-world teams.
Contents
Contents: Preface, Emily S. Patterson, Janet E. Miller, Emilie M. Roth, and David D. Woods; Part I Theoretical Foundations: Theory -> concepts -> measures but policies -> metrics, Robert R. Hoffman; Some challenges for macrocognitive measurement, Robert R, Hoffman; Measuring macrocognition in teams: some insights for navigating the complexities, C. Shawn Burke, Eduardo Salas, Kimberly Smith-Jentsch, Valerie Sims and Michael A. Rosen. Part II Macrocognition Measures for Real-World Teams: Macrocognitive measures for evaluating cognitive work, Gary Klein; Measuring attributes of rigor in information analysis, Daniel J. Zelik, Emily S. Patterson, and David D. Woods; Assessing expertise when performance exceeds perfection, James Shanteau, Brian Friel, Rick P. Thomas, John Raacke and David J. Weiss; Demand calibration in multitask environments: interactions of micro and macrocognition, John D. Lee; Assessment of intent in macrocognitive systems, Lawrence G. Shattuck; Survey of healthcare teamwork rating tools: reliability, validity, ease of use, and diagnostic efficiency, Barbara Künzle, Yan Xiao, Anne M. Miller and Colin Mackenzie; Measurement approaches for transfers of work during handoffs, Emily S. Patterson and Robert L. Wears; The pragmatics of communication-based methods for measuring macrocognition, Nancy J. Cooke and Jamie C. Gorman; From data, to information, to knowledge: measuring knowledge building in the context of collaborative cognition, Stephen M. Fiore, John Elias, Eduardo Salas, Norman W. Warner and Michael P. Letsky. Part III Scenario-Based Evaluation Forging new evaluation paradigms: beyond statistical generalization, Emilie M. Roth and Robert G. Eggleston; Facets of complexity in situated work, Emily S. Patterson, Emilie M. Roth and David D. Woods; Evaluating the resilience of a human-computer decision-making team: a methodology for decision-centered testing, Scott S. Potter and Robert Rousseau; Synthetic task environments: measuring macrocognition, John M. Flach, Daniel Schwartz, April M. Courtice, Kyle Behymer and Wayne Shebilske; System evaluation using the cognitive performance indicators, Sterling L. Wiggins and Donald A. Cox; Index.
