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Predicting user performance and errors : automated usability evaluation through computational introspection of model-based user interfaces /
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-...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham, Switzerland :
Springer,
[2018]
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| Colección: | T-labs series in telecommunication services.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.