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Identification of Change Patterns for the Generation of Models of Work-as-Done using Eye-tracking
(Schriftenreihe Personal- und Organisationsentwicklung ; ; v. 22)
| Material Type | E-Book |
|---|---|
| Publisher | Kassel : Kassel University Press GmbH |
| Year | 2017 |
| Language | English |
| Size | 1 online resource (400 pages) |
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| Media type | 機械可読データファイル |
|---|---|
| Contents | Front Cover Reihentitel Titelseite Impressum Table of Contents List of Figures List of Tables Abbreviations Abstract Acknowledgements 1 Introduction 2 Theoretical Background 2.1 Understanding Emergent Effects From Safety-I to Safety-II 2.1.1 Decomposition 2.1.2 Bimodality 2.1.3 Predictability 2.1.4 Causality and Root-Causes 2.2 Safety-II: Models of Work-as-Done 2.2.1 Approximate Adjustments Everyday Work in Intractable Work Systems 2.2.2 The equivalence of success and failure 2.2.3 Sacrificing Approximate Adjustments and Trade-offs2.2.4 Functional Resonance and Emergent Outcomes 2.3 Modeling Work-as-Done with FRAM 2.3.1 Functions Building Blocks of Models of Work-as-Done 2.3.2 The six Aspects of Functions 2.3.3 Data used for Function Description and Preliminary Research Question 2.4 Eye-tracking as a Window to the Mind 2.4.1 Approximate Adjustments, Bounded Rationality and Eye-tracking 2.4.2 Conclusions for the Identification of Approximate Adjustments using Eye-tracking 2.4.3 Research Question 2.4.4 Limitations3 Analysis Identifying Change Patterns in the Eye-tracking Data 3.1 Principle of Eye-tracking 3.1.1 Use of Areas of Interest for Capturing relevant Changes in Visual Attention Allocation 3.1.2 AoI Choice 3.1.3 Evaluation of AoI 3.2 Transforming Eye-tracking Data into a suitable Representation for the Identification of Change Patterns 3.2.1 Time and Approximate Adjustments in Eye-tracking 3.2.2 Detection of Change Patterns in the transformed Eye-tracking Data 3.3 Identification of Change Patterns by Maximizing linear Relationships in a Representation focusing on linear Dependence3.3.1 Hierarchical Partitioning of the Eye-tracking Data 3.3.2 Mean Weighted MAC and Hierarchical Partitioning 3.3.3 Identification of a suitable Combination of Part Window Sizes 3.3.4 Generation of relevant Part Size Combinations 3.3.5 Additional Constraints for the Generation of Indices for Splitting the Eye-tracking Data 3.3.6 Number of Resulting Combinations 3.3.7 Summary Calculating the weighted mean MAC for a given Level of Resolution3.4 Determining the best Level of Resolution for Partitioning 3.4.1 Avoiding global weighted mean MAC Maxima due to Resolution Artifacts 3.4.2 Identifying additional relevant Levels of Resolution 3.4.3 Summary of the Approach for Change Pattern Identification 4 Description of included Datasets 4.1 Rationale for the Inclusion of the Studies 4.2 Validation against expected Functions 4.3 Simulation Aviation: Engine Failure 4.3.1 Participants 4.3.2 The Scenario |
| Notes | Open Access Includes bibliographical references ""4.3.3 Areas of Interest"" Print version record |
| Authors | *Arenius, Marcus. |
| Subjects | BSH:Electronic books LCSH:Social systems -- Mathematical models All Subject Search BISACSH:SOCIAL SCIENCE -- Essays All Subject Search BISACSH:SOCIAL SCIENCE -- Reference All Subject Search FREE:Social systems -- Mathematical models All Subject Search |
| Classification | DC23:300.1513 |
| ID | ED00003770 |
| ISBN | 9783737603577 |