Human-Computer Interaction - The Design of User-Friendly, Transparent Interfaces; Research and Evaluation

Survey Research in HCI
Short Description of the Method
History, Intellectual Tradition, Evolution
What Questions the Method Can Answer
When Surveys Are Appropriate
When to Avoid Using a Survey
Using Surveys with Other Methods
How to Do It: What Constitutes Good Work
Research Goals and Constructs
Population and Sampling
Probability Versus Non-probability Sampling
Determining the Appropriate Sample Size
Mode and Methods of Survey Invitation
Questionnaire Design and Biases
Types of Survey Questions
Types of Closed-Ended Survey Questions
Questionnaire Biases
Satisficing
Acquiescence Bias
Social Desirability
Response Order Bias
Question Order Bias
Other Types of Questions to Avoid
Leveraging Established Questionnaires
Visual Survey Design Considerations
Review and Survey Pretesting
Cognitive Pretesting
Field Testing
Implementation and Launch
Piping Behavioral Data into Surveys
Monitoring Survey Paradata
Maximizing Response Rates
Data Analysis and Reporting
Data Preparation and Cleaning
Analysis of Closed-Ended Responses
Analysis of Open-Ended Comments
Assessing Representativeness
Reporting Survey Findings
Exercises
References
Overview Books
Sampling Methods
Questionnaire Design
Visual Survey Design
Established Questionnaire Instruments
Questionnaire Evaluation
Survey Response Rates and Non-response
Survey Analysis
Other References
Observations on concept generation and sketching in engineering design
Abstract
Introduction
Related work
Measures of creativity
Types of sketches
Methods
Test bed
Design data
Brainstorming and morphology charts
Sketching and logbooks
Design outcome evaluation
Results and discussion
Type of sketching
Concept quantity and design outcome
Sketch quantity and design outcome
Sketching over time and design outcome
Conclusions
Concept generation and sketching
Design outcome
Sketching and prototyping
Limitations
Future work
Acknowledgments
References
Abstract
1 Introduction
2 Related work
2.1 Collaboration in MR
2.2 MR and the physical environment
2.3 Summary, positioning, and goals
3 Scenario
3.1 Scenario apparatus
3.2 Scenario context: A small game studio
3.3 Synchronous part
3.4 Asynchronous part
3.5 Scenario: Summary
4 Asynchronous Reality Concept
4.1 Reality constraints
4.2 Causality graph components
5 Architecture and prototype
5.1 Components and algorithm
5.2 System tests
5.3 Object-based versus region-based detection
6 Discussion and future work
6.1 Prototype versus vision
6.2 Human factors
7 Conclusion
Acknowledgments
References
Abstract
1 Introduction
2 Related Work
2.1 Content Design for Educational Videos
2.2 Design Guidelines for Mobile Learning
3 STUDY1: LEARNER PERSPECTIVES
3.1 Survey Study
3.2 Interview Study
3.3 Survey and Interview Results
4 STUDY2: CONTENT ANALYSIS
4.1 Data Set
4.2 Evaluated Design Guidelines
4.3 Results of Guideline Compliance Analysis
5 STUDY3: ENGINEER PERSPECTIVE
5.1 Participants and Recruitment
5.2 Interview Protocol
5.3 Interview Analysis
5.4 Interview Results
6 Design Guidelines
6.1 Design Guidelines on Mobile-Friendly Lecture Types
6.2 Expert Evaluation of Design Guidelines
7 Discussion
7.1 Gap between Learners and Engineers
7.2 Extended Content Analysis
7.3 Design Opportunities for Design Tools
7.4 Design Opportunities for Mobile Design Guidelines
8 Conclusion
Acknowledgments
References
Abstract
1 Introduction
2 Related work: algorithmically curated bittersweet memories
2.1 Technology-Mediated Reflection
2.2 Curation in practice
2.3 Bittersweet emotions and nostalgia
3 Background: Facebook Memories
4 Methods
4.1 Recruitment and interviews
4.2 Research ethics
4.3 Analysis
5 Findings
5.1 What makes content bittersweet
5.2 Factors that influence whether an encounter with bittersweet content is (un)wanted
5.3 Actions taken depending on whether an encounter with bittersweet content was (un)wanted
6 Two Challenges for the Recommendation of Bittersweet Content
6.1 Challenge 1: Detection of Bittersweet Content
6.2 Challenge 2: Understanding Feedback on Bittersweet Content
7 Provocations for design practice and implications for design
7.1 Designing for expectedness: drawing inspiration from non-technological artifacts
7.2 Designing for contextual factors: examining the relationship between context and sensitive content
7.3 Designing for how humans understand problems: affective sense-making and computational tractability
8 Limitations
9 Conclusion
Acknowledgments
References
Abstract
1 Introduction
2 Related Work
2.1 Engaging stakeholders in algorithm design
2.2 Disagreement, datasets, and machine learning
2.3 Interactive Machine Learning
3 Jury Learning
3.1 Design goals
3.2 Approach and interaction
3.3 Example scenario
4 Technical approach
4.1 Implementation for toxicity detection
5 Extensions
5.1 Conditional juries
5.2 Counterfactual juries
6 Model evaluation
6.1 Individual juror performance
6.2 Jury-level performance
7 User Evaluation
7.1 Study design
7.2 Participant recruitment
7.3 Analysis approach
7.4 Results: Jury composition diversity (Q1)
7.5 Results: Jury prediction outcomes (Q2)
8 Discussion
8.1 Implications for design
8.2 Ethical considerations
8.3 Limitations and future work
8.4 Positionality statement
9 Conclusion
Acknowledgments
References
Abstract
1 Introduction
2 Related Work
2.1 The Privacy Paradox
2.2 Privacy Control and Data Literacy
2.3 Affect
3 Method
3.1 Scenarios
3.2 Questionnaire Components
3.3 Data Collection and Sample Characteristics
4 Findings
4.1 Confirmatory Factor Analysis of the AIPC Scale
4.2 Principal Component Analysis of Affective Perceptions
4.3 Differences Across Scenario Variations
4.4 Violation of Expectations (VE)
4.5 Breach of Personal Boundaries (PB)
4.6 Ambiguity of Threat (AT)
4.7 Summary of Findings
5 Discussion
6 Implications
7 Limitations
8 Conclusion
Acknowledgments
References
A Scenario Text
A.1 Control (Core Scenario)
A.2 Violation of Expectations (VE)
A.3 Breach of Personal Boundaries (PB)
A.4 Ambiguity of Threat (AT)
B Questionnaire
B.1 Commitment Question
B.2 Scenario
B.3 Scenario Reading Check
B.4 Affective Perceptions
B.5 AIPC Scale
B.6 General Technical Expertise Subscale of the Digital Difficulties Scale
B.7 Technology Use
B.8 Demographics
Abstract
1 Introduction
2 Related Work
2.1 Explainable AI techniques
2.2 Human-Centered Explainable AI
2.3 Speech Emotion Recognition
2.4 Model Explanations of Audio Predictions
3 Intuition and Background
3.1 Perceptual Processing
3.2 Desiderata for Relatable Explanations
3.3 Vocal Emotion Prosody
4 Technical Approach
4.1 Base Prediction Model
4.2 RexNet: Relatable Explanation Network
4.3 Relatable Explanation User Interface
5 Evaluations
5.1 Modeling Study
5.2 Think-Aloud User Study
5.3 Controlled User Study
5.4 Summary of Results
6 Discussion
6.1 Usefulness of Relatable Explanations
6.2 User Evaluation of Relatable Explanations
6.3 More relatable vocal emotion explanations
6.4 Relatability for human-centric XAI
6.5 Generalization of Relatable XAI
7 Conclusion
References
A Appendix
A.1 Vocal cues for different emotions
A.2 User Study Survey
A.3 User Study Analysis: Statistical Model
Abstract
1 Introduction
2 Related Work
2.1 Video-Mediated Communication, and Media Spaces in HCI Research
2.2 Issues and Challenges of Contemporary Video-Conferencing
2.3 Critical Art and Design for Video-Conferencing
3 Research Context: Zoom Obscura
4 Analysing Zoom Obscura Through Counterfunctional Design
4.1 Analysing Counterfunctionality
5 Counterfunctionality in the Zoom Obscura Projects
5.1 Andrea Zavala Folache: Erotics of Discontinuity / Touching through the Screen
5.2 B Wijshijer: ZOOM_mod-Pack
5.3 Foxdog Studios: itsnotreally.me
5.4 Ilse Pouwels: Masquerade Call
5.5 Martin Disley: How They Met Themselves
5.6 Michael Baldwin: Group Dialogues
5.7 Paul O'Neill: For Ruth and Violette
6 Discussion
6.1 Strategies for Counterfunctional Video-Conferencing
6.2 Design Considerations for Living with Zoom
7 Conclusions
Acknowledgments
References
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Article Contents
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Part II Approaches to Early Engagement
Chapter 4: Value Sensitive Design and Information Systems
4.1 Introduction
4.2 What Is Value Sensitive Design?
4.2.1 What Is a Value?
4.2.2 Related Approaches to Values and System Design
4.3 The Tripartite Methodology: Conceptual, Empirical, and Technical Investigations
4.3.1 Conceptual Investigations
4.3.2 Empirical Investigations
4.3.3 Technical Investigations
4.4 Value Sensitive Design in Practice: Three Case Studies
4.4.1 Cookies and Informed Consent in Web Browsers
4.4.1.1 Conceptualizing the Value
4.4.1.2 Using a Conceptual Investigation to Analyze Existing Technical Mechanisms
4.4.1.3 The Iteration and Integration of Conceptual, Technical, and Empirical Investigations
4.4.2 Room with a View: Using Plasma Displays in Interior Offices
4.4.2.1 Multiple Empirical Methods
4.4.2.2 Direct and Indirect Stakeholders
4.4.2.3 Coordinated Empirical Investigations
4.4.2.4 Multiplicity of and Potential Conflicts Among Human Values
4.4.2.5 Technical Investigations
4.4.3 UrbanSim: Integrated Land Use, Transportation, and Environmental Simulation
4.4.3.1 Distinguishing Explicitly Supported Values from Stakeholder Values
4.4.3.2 Handling Widely Divergent and Potentially Conflicting Stakeholder Values
4.4.3.3 Legitimation
4.4.3.4 Technical Choices Driven by Initial and Emergent Value Considerations
4.4.3.5 Designing for Credibility, Openness, and Accountability
4.5 Value Sensitive Design’s Constellation of Features
4.6 Practical Suggestions for Using Value Sensitive Design
4.6.1 Start with a Value, Technology, or Context of Use
4.6.2 Identify Direct and Indirect Stakeholders
4.6.3 Identify Benefits and Harms for Each Stakeholder Group
4.6.4 Map Benefits and Harms onto Corresponding Values
4.6.5 Conduct a Conceptual Investigation of Key Values
4.6.6 Identify Potential Value Conflicts
4.6.7 Integrate Value Considerations into One’s Organizational Structure
4.6.8 Human Values (with Ethical Import) Often Implicated in System Design
4.6.9 Heuristics for Interviewing Stakeholders
4.6.10 Heuristics for Technical Investigations
4.7 Conclusion
4.8 Addendum: Practical Considerations of Value Sensitive Design
4.8.1 Practical Value Sensitive Design Challenges
4.8.1.1 Nature of Values
4.8.1.2 Role of Stakeholders
4.8.1.3 Concrete Methods
4.8.1.4 Summary of Practical Questions
4.8.2 VSD Case: Safety for Homeless Young People
4.8.2.1 Socio-technical Context
4.8.2.2 Stakeholder Analysis
4.8.2.3 Value Analysis & Value Tensions
4.8.2.4 Value Sketches
4.8.2.5 Stakeholder Generated Value Scenarios
4.8.2.6 Envisioning Cards
4.8.2.7 Reflection on the Use of VSD
4.8.3 Discussion
4.8.4 Conclusions and Future Work
References
References
Introduction
The Prototype Home
Our background
Technology-Centered Research Agenda
Context Awareness and Ubiquitous Sensing
Individual Interaction with the Home
The Smart Floor
Finding Lost Objects
Human-Centered Research Agenda
Specific application: Support for the elderly
Evaluation and Social Issues
Future Challenges
Qualitative understanding of everyday home life
Acknowledgements
References
Abstract
Introduction
Related Work
Data and Methods
Results
Discussion, Conclusion, and Future Work
References
Abstract
1 Introduction
2 Related Work
Smart Homes
Domesticity and Public Policy
Aging in Place
3 Context and Method
Participatory Design Workshops
4 Workshop Analysis and Outcomes
Tracking and Monitoring
The Boundaries of Personal and Public Privacy
Shifting Baselines
5 Discussion
Accountabilities of Tracking
Self-determination in Data and Use
From Endpoint to Infrastructure
6 Conclusion
Acknowledgments
References
ABSTRACT
Author Keywords
ACM Classification Keywords
INTRODUCTION
CONCLUSIONS
FUTURE WORK
REFERENCES
Introduction
User-Centred Design (UCD)
Integrating UCD and Agile Development
Similarities and Differences Between UCD and Agile Development
Fieldwork
Method
The Project Teams
Results
User Involvement
Collaboration and Culture
Prototyping
Project Lifecycle
Discussion
Five Principles for Integrating UCD and Agile Development
Conclusion
References
Abstract
1 Introduction
2 Background
Human Trafficking Defined
Law Enforcement in the US
3 Related Works
ICT and Law Enforcement
Big Data and Law Enforcement
4 Methods
Participant Backgrounds
Limitations
5 Findings
Overview of Investigation Process
Tools Used During an Investigation
Collaboration During an Investigation
Sociotechnological Needs
6 Discussion
Design Challenges for Building Solutions for Law Enforcement
Information Visualization Implications
Policy Implications
Privacy Implications
7 Conclusion
Acknowledgments
References
Abstract
1 Introduction
2 Contributions
3 Related Work
4 Designing the Serpentine Prototype
Triboelectric Nanogenerators
5 Sensor Structure and Fabrication
Sensor Operation
Sensor Design Parameters
6 Recognizing Human Interaction
Designing the Interactions
Data Processing Pipeline
7 Applications
8 Evaluation
Participants and Setup
Study
9 Results
Quantitative Analysis
Qualitative Analysis
10 Discussion
Does it matter that Serpentine is self-powered?
Interaction Design Parameters
Stiffness versus electrical output
Limitation of Sensor and Study
Future work
11 Conclusion
12 Acknowledgments
References
Abstract
1 Introduction
2 Related Work
2.1 Nondominant Families and Technology
2.2 Parental Engagement as an Actor-Network
3 Methodology
4 The Parenting Network
4.1 The Familial Unit
4.2 The Schooling Environment
4.3 The Larger Community
4.4 The Technology
5 Discussion
5.1 Design Challenges: Clashing Interests
5.2 Design Opportunities: Promising Alliances
6 Conclusion
References
5 The CHI of Teaching Online: Blurring the Lines Between User Interfaces and Learner Interfaces
5.1 Introduction
5.2 Background
5.2.1 Program Background
5.2.2 Course Background
5.3 Flexibility
5.3.1 Geographic Flexibility
5.3.2 Temporal Flexibility
5.3.3 Preference Flexibility
5.4 Equity
5.4.1 Equity Through Flexibility
5.4.2 Equity Through Admissions
5.4.3 Equity Through Anonymity
5.5 Consistency
5.5.1 Assignment Cadence
5.5.2 Announcement Cadence
5.5.3 Administrative Decisions
5.6 Distributed Cognition
5.6.1 Offloading Through Announcements
5.6.2 Offloading Through Documentation
5.6.3 Offloading Through Assessment Design
5.7 Additional Principles
5.7.1 Structure
5.7.2 Perceptibility
5.7.3 Tolerance
5.7.4 Feedback
5.8 Course Evaluation
5.8.1 Institutional Surveys
5.8.2 Course Surveys
5.9 Conclusion
References