Predicting user performance and errors : automated usability evaluation through computational introspection of model-based user interfaces /

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Bibliographic Details
Author / Creator:Halbrügge, Marc, author.
Imprint:Cham, Switzerland : Springer, [2018]
©2018
Description:1 online resource
Language:English
Series:T-Labs series in telecommunication services
T-labs series in telecommunication services.
Subject:User interfaces (Computer systems)
Human-computer interaction.
COMPUTERS -- User Interfaces.
Imaging systems & technology.
User interface design & usability.
Human-computer interaction.
User interfaces (Computer systems)
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11541368
Hidden Bibliographic Details
ISBN:9783319603698
3319603698
9783319603681
331960368X
Digital file characteristics:text file
PDF
Notes:Includes bibliographical references and index.
Print version record.
Summary:This book proposes a combination of cognitive modeling with model-based user interface development to tackle the problem of maintaining the usability of applications that target several device types at once (e.g., desktop PC, smart phone, smart TV). Model-based applications provide interesting meta-information about the elements of the user interface (UI) that are accessible through computational introspection. Cognitive user models can capitalize on this meta-information to provide improved predictions of the interaction behavior of future human users of applications under development. In order to achieve this, cognitive processes that link UI properties to usability aspects like effectiveness (user error) and efficiency (task completion time) are established empirically, are explained through cognitive modeling, and are validated in the course of this treatise. In the case of user error, the book develops an extended model of sequential action control based on the Memory for Goals theory and it is confirmed in different behavioral domains and experimental paradigms. This new model of user cognition and behavior is implemented using the MeMo workbench and integrated with the model-based application framework MASP in order to provide automated usability predictions from early software development stages on. Finally, the validity of the resulting integrated system is confirmed by empirical data from a new application, eliciting unexpected behavioral patterns.
Other form:Print version: Halbrügge, Marc. Predicting user performance and errors. Cham, Switzerland : Springer, [2018] 9783319603681 331960368X
Standard no.:10.1007/978-3-319-60369-8
Table of Contents:
  • Acronyms; List of Figures; List of Tables; 1 Introduction; 1.1 Usability; 1.2 Multi-Target Applications; 1.3 Automated Usability Evaluation of Model-Based Applications; 1.4 Research Direction; 1.5 Conclusion; Part I Theoretical Background and Related Work; 2 Interactive Behavior and Human Error; 2.1 Action Regulation and Human Error; 2.1.1 Human Error in General; 2.1.2 Procedural Error, Intrusions and Omissions; 2.2 Error Classification and Human Reliability; 2.2.1 Slips and Mistakes
  • The Work of Donald A. Norman; 2.2.2 Human Reliability Analysis.
  • 2.3 Theoretical Explanations of Human Error2.3.1 Contention Scheduling and the Supervisory System; 2.3.2 Modeling Human Error with ACT-R; 2.3.3 Memory for Goals Model of Sequential Action; 2.4 Conclusion; 3 Model-Based UI Development (MBUID); 3.1 A Development Process for Multi-target Applications; 3.2 A Runtime Framework for Model-Based Applications: The Multi-access Service Platform and the Kitchen Assistant; 3.3 Conclusion; 4 Automated Usability Evaluation (AUE); 4.1 Theoretical Background: The Model-Human Processor; 4.1.1 Goals, Operators, Methods, and Selection Rules (GOMS).
  • 4.1.2 The Keystroke-Level Model (KLM)4.2 Theoretical Background: ACT-R; 4.3 Tools for Predicting Interactive Behavior; 4.3.1 CogTool and CogTool Explorer; 4.3.2 GOMS Language Evaluation and Analysis (GLEAN); 4.3.3 Generic Model of Cognitively Plausible User Behavior (GUM); 4.3.4 The MeMo Workbench; 4.4 Using UI Development Models for Automated Evaluation; 4.4.1 Inspecting the MBUID Task Model; 4.4.2 Using Task Models for Error Prediction; 4.4.3 Integrating MASP and MeMo; 4.5 Conclusion; Part II Empirical Results and Model Development; 5 Introspection-Based Predictions of Human Performance.
  • 5.1 Theoretical Background: Display-Based Difference-Reduction5.2 Statistical Primer: Goodness-of-Fit Measures; 5.3 Pretest (Experiment 0); 5.3.1 Method; 5.3.2 Results; 5.3.3 Discussion; 5.4 Extended KLM Heuristics; 5.4.1 Units of Mental Processing; 5.4.2 System Response Times; 5.4.3 UI Monitoring; 5.5 MBUID Meta-Information and the Extended KLM Rules; 5.6 Empirical Validation (Experiment 1); 5.6.1 Method; 5.6.2 Results; 5.6.3 Discussion; 5.7 Further Validation (Experiments 2
  • 4); 5.8 Discussion; 5.9 Conclusion; 6 Explaining and Predicting Sequential Error in HCI with Cognitive User Models.
  • 6.1 Theoretical Background: Goal Relevance as Predictor of Procedural Error6.2 Statistical Primer: Odds Ratios (OR); 6.3 TCT Effect of Goal Relevance: Reanalysis of Experiment 1; 6.3.1 Method; 6.3.2 Results; 6.3.3 Discussion; 6.4 A Cognitive Model of Sequential Action and Goal Relevance; 6.4.1 Model Fit; 6.4.2 Sensitivity and Necessity Analysis; 6.4.3 Discussion; 6.5 Errors as a Function of Goal Relevance and Task Necessity (Experiment 2); 6.5.1 Method; 6.5.2 Results; 6.5.3 Discussion; 6.6 Are Obligatory Tasks Remembered More Easily? An Extended Cognitive Model with Cue-Seeking.