hci-notes

Human-Computer Interaction

• Definition:
  • The study of how humans interact with computer technology.
  • How computer technology influences human work and activities.
  • Goal: Design, evaluate, and implement interactive computing systems that are:
    • Safe: Preventing human error and enabling error recovery.
    • Effective: Enabling users to do the right things.
    • Efficient: Requiring minimal effort.
    • Satisfying: Providing a positive user experience.

ABCS Framework

• Anthropometry: Focuses on posture, load, reach, and view.
• Behavioural Psychology:
  • Sensation: Sight, hearing, touch, smell, taste.
  • Perception:
    • Visual: Interpreting surroundings through light.
    • Auditory: Receiving and interpreting sound information.
    • Tactile: Perceiving objects through touch.
    • Proporioception: Body’s ability to sense movement, action, location
• Cognition: Involves memory, attention, learning, mental representation, problem-solving, decision making, language, reading, and information seeking.
• Social Factors: Considers teams, teamwork, team performance, and team decision-making.

Cognitive Engineering (Norman, 1986)

• Goals:
  • Understand Issues:
    • Identify the user, the problem, and the context of use.
    • Understand why the problem is a problem for the user.
  • Improve Choices: Show how to make better choices when they exist.
  • Trade-offs: Illustrate trade-offs when improvements in one area lead to deficits in another.

User-Centered Design

• Focus: Understanding how users think and reason during problem-solving.
• Cognitive Engineering ensures that the semantics of the process are visible to the user, so that the significant relationships within the system that allow a user to control it and the performance targets to achieve are obvious and presented in the context of system goals, invariants, and constraints.

Task Complexity and Interaction

• Wizard of Oz Technique:
  • A method where a human simulates computer responses during an interaction experiment.
• Psychological vs. Physical Variables:
  • Psychological Goals:
    • Exist in the mind of the person and relate to the needs and concerns of the person.
  • Physical Variables:
    • Relate to the physical controls and variables of the task.
    • Task is performed on a physical system, with physical mechanisms to be manipulated, leading to changes in the physical variables and system state
  • Interpretation:
    • The person must interpret the physical variables into terms relevant to the psychological goals and must translate the psychological intentions into physical actions upon the mechanisms

Mental Model

• Definition:
  • A description or explanation of how something works (process model) or how different parts of a system are related (structural model).
• Types:
  • Process vs. structural.
  • Designers’ model vs. users’ model.

Norman's Action Cycle Stages

1. Establish a goal.
2. Execute the action.
3. Evaluate the action.

Execution Stages

1. Formulate a plan.
   • A sequence of system operations to
   • be performed on system entities
2. Translate the plan into action.
3. Output the action.

Evaluation Stages

1. Observe the system response.
2. Interpret the output.
3. Output action.

Norman’s 7 Design Principles

1. Discoverability: Understand available options and where to perform them.
2. Feedback: Communicate responses to actions or system status.
3. Conceptual Models: Use simple explanations or visual metaphors.
4. Affordance: Indicate possible actions.
5. Signifiers: Indicate where to act.
6. Mapping: Show the relationship between controls and effects.
7. Constraints: Restrict types of interactions.

Attributes of Measurable Performance

Attributes of Usability

• Ease of Use: Efficiency in performing tasks.
• Ease of Learning: How quickly a user can learn to use the system.
• Ease of Remembering: How well a user can remember how to use the system.
• Ease of Recovery from Errors: Ability to recover from mistakes.
• User Satisfaction: Overall user contentment with the system.

Theories in Human-Computer Interaction (HCI)

• Explanatory Theories:
  • Help in observing behaviour, describing activities, conceiving designs, and comparing high-level concepts of different designs.
• Predictive Theories:
  • Enable designers to compare proposed designs for execution times or error rates through analytical evaluations.
  • Focus on perceptual, cognitive, and motor-task performance such as visual search performance, keystroke, or pointing times.
• Taxonomy
  • Creates order among complex sets of phenomena, organizing them systematically.

Task-Artefact Cycle

• Tasks lead to the creation of Artefacts, which then influence new Requirements and Design ideas.
• The cycle continues with the Adoption, Appropriation, Use, and Possibilities of these artefacts.

Human Error

• Low-level: Slips of execution, misperceptions.
• Mid-level: Problems in translating to machine I/O
  • Cannot produce input that the machine understands or produce input that it ‘misinterprets
  • Cannot understand or misinterpret the output from the machine
• High-level: Inability to conceive or recognize goal satisfaction, making wrong choices, uncertainty about outcomes.

Skills, Rules, and Knowledge (SRK) Framework (Rasmussen, 1983)

• Signals: Sensory data processed as continuous variables.
• Signs: Indicate states in the environment and activate stored patterns of behavior.
• Symbols: Represent other information, variables, relations, and properties.

Error Detection and Correction

• Influenced by factors like fatigue, situation awareness, workload, memory, familiarity, training, interruptions, and distractions.
• Error detection and correction rates differ based on skill-based, rules-based, and knowledge-based behaviors.

Foley and van Dam (1982) 4-Level Approach

• Conceptual Level
  • User’s mental model of the system’s goals and purposes.
  • Conceptual level relates to goal of the program (what is its purpose)
  • Conceptual model of text editing eg line editors, screen editors
• Semantic Level
  • Meanings conveyed by user commands and system output.
  • Action or meaning associated with a statement is its semantics, semantic level.
• Syntactic Level
  • How units/words conveying semantics are assembled into complete instructions.
• Lexical Level
  • Device dependencies and precise mechanisms for specifying syntax.
  • Keywords, symbols, numeric constants and identifiers are lexemes, lexical level.

Object-Action Interface Paradigm (Shneiderman, 1998)

Object-Action Interface (OAI)

• OAI paradigm is a model for understanding how a windows GUI works or how one interacts with it.
  • The OAI model tells us that we select an object in our screens and then choose an action to perform on it.
  • An AOI model (e.g. a command line interface) we specify an action and then specify the object e.g. copy
• Users select an object and then choose an action to perform on it, mirroring interactions with the physical world (e.g., GUI).
• This distinction became important when we changed from command line interfaces to direct manipulation interfaces; and significantly affected usability.
• One of the reasons for use of terms like “more intuitive, more natural” interfaces
• When in Action-Object Interfaces, eg command line interfaces
  • syntactical knowledge required – awareness of the correct sequences and how they might be combined
• When in Object-Action Interfaces, GUI, permissible actions / commands are associated with the object
  • visibility is important
  • How actions / commands are organized and made available to the user, e.g. are they harmonized with the user task?
  • One can imagine a very unusable OAI based GUI if the relevant commands are hidden and placed in unexpected places.

Analytic Methods Predictive Models

• Include various methods for predicting user performance and error rates analytically.

Keystroke-Level Model (KLM) (Card, Moran, & Newell, 1980)

• Physical Motor Operators
  • K: Keystroke or button press.
  • P: Pointing to a target with a mouse.
  • H: Homing hands on the keyboard or other devices.
  • D: Drawing line segments with a mouse.
• Mental Operators
  • M: Mental preparation for physical actions.
• System Response Operators
  • R: System response time that the user waits for.

GOMS Model (Card, Moran, & Newell, 1983)

• Goals
  • Tasks users aim to achieve.
• Operators
  • Elementary perceptual, motor, or cognitive acts needed to change the user’s state or task environment.
• Methods
  • Procedures used to achieve goals.
• Selection Rules
  • Control structures for choosing among methods to accomplish goals.

Fitts’ Law (Fitts, 1954)

• States that the time to acquire a target is a function of the distance and the size of the target.
• Movement time (MT) increases with the distance to the target (A) and decreases with the size of the target (W).
• MT is constant for a given ratio of A to W.
• MT = a + b binom{log_2( binom{2A}{W})}

Miller’s Law (Miller, 1956)

Information Processing Capacity

• A person can keep 7 ± 2 items in working memory.
• Chunking: Grouping information to improve memorability.

Human-Centered Design

• The design is based on an explicit understanding of users, tasks and environments
• Users are involved throughout design and development
• The design is driven and refined by user-centered evaluation
• The process is iterative
• The design addresses the whole user-experience
• The design team includes multidisciplinary skills and perspective

Four Fundamental Principles of Human-Centered Design According to Don Norman:

1. Understand and Address the Core Problems:
   • Focus on solving the right problems, not just creating attractive interfaces.
2. Be People-Centered:
   • Prioritize human needs and behaviors over technological capabilities.
3. Use an Activity-Centered Systems Approach:
   • Look at the entire activity system involving users and their tasks.
4. Use Rapid Iterations of Prototyping and Testing:
   • Develop prototypes quickly and test them frequently with users to refine and improve the design.

Design Thinking Concept and Origin

• Design Thinking is a methodology that integrates the designer's approach to problem-solving to match people's needs with technological feasibility and business viability.
• It was popularized by David Kelley and Tim Brown of IDEO in the 1990s.

Why Design Thinking?

• Design Thinking is valuable because it:
  1. Starts with User Data:
     • Uses real user data to create solutions that address genuine needs.
  2. Leverages Collective Expertise:
     • Establishes a shared language and collective understanding within the team.
  3. Encourages Innovation:
     • Promotes exploring multiple solutions to find the most effective one.
  4. Focuses on Solving the Right Problems:
     • As emphasized by Jakob Nielsen, a great interface for the wrong problem will still fail.

Design Thinking Process

• Design Thinking is a non-linear, iterative process comprising five key phases:
  1. Empathize:
     • Understand the users, their needs, and their challenges.
  2. Define:
     • Clearly articulate the problem that needs to be solved.
  3. Ideate:
     • Generate a wide range of ideas and potential solutions.
  4. Prototype:
     • Build tangible representations of the ideas.
  5. Test:
     • Test the prototypes with real users and refine the solutions based on their feedback.

Practical Applications and Techniques

Design Thinking Phases in Practice

1. Inspiration:
   • Identify the business problem, opportunity, constraints, and changes. Involve multidisciplinary teams and consider insights from extreme users.
2. Ideation:
   • Brainstorm, sketch, and prototype. Focus on user journeys and communicate ideas effectively.
3. Implementation:
   • Refine prototypes, test rigorously, and assist marketing with communication strategies.

Storytelling in Design

• What is storytelling? a way of describing or explaining a concept or situation or problem that weaves a lot of information into a story- like narrative that creates new worlds and experiences in a reader or listener's imagination.
• When to use storytelling? in the design process based on the Double Diamond and IDEO Design Thinking process.
• Why use storytelling? When used in combination with other techniques eg whiteboarding – it enables users and designers to create a common understanding of what is intended

Problem Analysis Techniques

1. Symptoms vs. Problems:
   • Symptoms of a problem are the observable effects or indicators that point towards an existing problem; they are not the problem themselves. These are the signs that something is wrong, but they often don't reveal the underlying cause.
   • A problem is generally considered to be a task, a situation, or person which is difficult to deal with or control due to complexity and intransparency, and is the cause of difficulties or poor performance.
2. Fishbone Diagram (Ishikawa Diagram):
   • A tool for identifying the cause and effect of specific problems.
3. 5Ws Analysis:
   • What is the problem?
   • Who is impacted by it?
   • Where does it occur?
   • When does it happen?
   • Why does it happen?

Whiteboarding

• Definition: A collaborative brainstorming session, conducted in person or virtually (e.g., using Figma).
• Purpose: To generate rough designs and possible solutions.
• Stage in Double Diamond: Discover
  • In this stage, whiteboarding helps to explore and outline potential ideas and solutions broadly.

Storyboarding

• Definition: Using creative and design techniques to visualize ideas about a problem statement and potential solutions.
• Purpose: To refine and develop selected ideas from the rough designs.
• Stage in Double Diamond: Develop
  • Here, storyboarding allows the team to select and expand on a few promising ideas for further development into more detailed concepts.

Storytelling

• Definition: Presenting ideas through a narrative that integrates words and images from whiteboarding and storyboarding, stimulating the listener's imagination.
• Purpose: To convey the final developed ideas and facilitate decision-making.
• Stage in Double Diamond: Deliver
  • At this stage, storytelling is used to showcase the fully developed designs, aiding in the selection of the best final design.

Semantic Mapping

• Definition: The process of breaking down a word or concept into different factors, symptoms, and categories to understand its relationships and overall structure.
• Purpose: To dissect the main problem into its symptoms and categorize them, showing how they interrelate.
• Benefits:
  • Overall Picture: Provides a comprehensive view of the problem.
  • Key Problems Identification: Helps to identify underlying key problems.
  • Problem Solving: Facilitates the development of targeted solutions by understanding the interconnections.

Double Diamond Process

1. Discover:
   • Utilize whiteboarding for initial brainstorming and exploration of possible solutions.
2. Develop:
   • Employ storyboarding to develop and refine selected ideas into more detailed concepts.
3. Deliver:
   • Use storytelling to present the refined ideas, helping in the final decision-making process to select the best design.

UI Design Steps

1. User Problem Analysis
   • Example: Dang Pham needs real-time flight updates.
2. Data Grouping Exercise:
   • Data available includes airline, aeroplane model, weather, flight number, status, and more.
   • Group data into logical categories to aid in information architecture.
3. Content, Containers, Controls Framework:
   • Implementing the framework to organize data and make it accessible and usable.
4. Sketching:
   • Initial concept sketches to visualize how the UI will work.
5. Storyboarding:
   • Create storyboards to map out the user journey and interaction flow.
6. Wireframes:
   • Determine the placement and organization of UI elements.
   • Ensure consistency and purposeful layout.
7. Mock-ups and Hi-fidelity Prototypes:
   • Progress from wireframes to detailed, interactive prototypes.

Design Principles and Guidelines

1. Golden Ratio and Fibonacci Sequence:

   • Applying these principles for aesthetically pleasing and proportionate designs.

   • The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding numbers.

   • The simplest Fibonacci sequence 0, 1, 1, 2, 3, 5, 8, 13, 21 …

   • Golden ratio: Two quantities are in the golden ratio if their ratio is the same as the ratio of their sum to the larger of the two quantities.

• Grid Structures:
  • Using grids for visual order and predictability.

Visual Search Performance

• Ensuring information is easy to find through good visual design practices.
• Gestalt Principles of Visual Grouping:
  • Proximity, similarity, common fate, continuity, closure, symmetry, and figure-ground principles to create coherent and intuitive interfaces.

Key Theories and Studies

1. Gestalt Principles:
   • Proximity, similarity, common fate, continuity, closure, symmetry, figure-ground.
2. Typography and Readability:
   • Factors affecting legibility and readability, such as font type, size, color, contrast, spacing, and layout.
   • Automated Readability Index
3. Visual Predictability:
   • Consistent placement of elements to aid in user navigation and understanding.
   • The F-Pattern is a common layout for web content that reflects how users typically read and scan text-heavy pages. It is based on eye-tracking studies and suggests that users often read the first few lines of text fully and then scan down the left side of the page, occasionally reading across the page in an F-shaped pattern.
   • The Z-Pattern is suitable for pages that are not heavily centered on text. It is often used for designs where visual hierarchy and simplicity are critical, such as landing pages, advertisements, and other layouts with minimal text.

Evaluating a Prototype

• To determine if your prototype will work well, you must evaluate it through various methods. These methods fall into three main categories: usability testing, heuristic evaluation, and cognitive walkthroughs. Each method offers unique insights into the usability and functionality of your design.

The HCI Analysis-Design-Evaluate Process

Analysis (User Research)

• Understanding the user and their context is crucial. This involves:
  • User Problem: Identifying what problems users face.
  • Context of Work: Understanding the environment in which users operate.
  • User Behavior: Observing what users do and how they think.
  • Methods:
    • Interviews: Task Analysis (TA), Critical Task Analysis (CTA), Wizard of Oz (WOZ).
    • Observation: Watching users in their natural environment.
    • Surveys and Questionnaires: Collecting data from a broad audience.
    • User Journey Map: Visualizing the user’s interaction with the system over time.

Design

• Based on your analysis, you start designing:
  • Empathize: Understand the user’s needs.
  • Define the User Problem: Create a design specification.
  • Ideate: Generate several design alternatives.
  • Prototyping: Create initial versions of the design.
    • Low-Fidelity: Sketches, storyboards, and paper prototypes.
    • High-Fidelity: Detailed mock-ups and working prototypes.
    • Types: Throw-away, incremental, and evolutionary prototypes.

Evaluate

• Evaluation involves testing and assessing the prototype:
  • Usability Testing:
    • Controlled Settings: Experiments, usability labs, set tasks, A-B testing.
      • user activities controlled to test hypotheses and measure or observe behaviours
    • Natural Settings: Living labs, field studies (observing actual users in situ).
      • Little or no control over users’ activities to determine how the product will be used in real world
    • Analytic Methods: Heuristic evaluation and cognitive walkthroughs. Predictive models: GOMS / KLM, PUMA, Fitts Law.
      • Consultants, experts, critique, predict, and model aspects of the interface to identify the most obvious usability problem

Heuristic Evaluation

• A heuristic evaluation is a method where experts critique a system using a set of guidelines (heuristics). These heuristics are general principles for good design.
• Nielsen and Molich's heuristics (1990) include:
  1. Simple and Natural Dialog: Use clear and straightforward language.
  2. Speak the User’s Language: Use terms familiar to the user.
  3. Minimize User Memory Load: Reduce the need for the user to remember information.
  4. Be Consistent: Maintain uniformity in design elements.
  5. Provide Feedback: Ensure the system responds to the user's actions.
  6. Provide Clearly Marked Exits: Allow users to easily leave unwanted states.
  7. Provide Shortcuts: Enable experienced users to perform tasks quickly.
  8. Good Error Messages: Offer helpful error messages.
  9. Prevent Errors: Design to minimize the chance of user errors.

“Eight Golden Rules” Shneiderman et al 2016

1. Strive for consistency
2. Seek universal usability
3. Offer informative feedback
4. Design dialogs to yield closure
5. Prevent errors
6. Permit easy reversal of actions
7. Keep users in control
8. Reduce short term memory load

Cognitive Walkthrough

• A cognitive walkthrough is a detailed review of a sequence of actions required to perform a task. It evaluates how easy it is for new users to learn and perform a task.
• This method involves:
  • Describing the prototype.
  • Defining a representative task for the user.
  • Listing the actions needed to complete the task.
  • Identifying the users and their background knowledge.
  • Stepping through each action to identify potential usability issues.

Key Questions for Cognitive Walkthrough

1. Will users try to achieve the right result?
2. Will users notice the correct action is available?
3. Will users associate the correct action with the result they want?
4. Will users see that progress is made towards their goal after the action?

Usability Lab Components

1. Controlled Setting:
   • Typically two rooms: Control room (experimenter observes) and Evaluation room (participant performs tasks).
   • Minimizes distractions and confounding variables to ensure valid performance data.
2. Equipment:
   • Video Cameras: At least HD resolution, strategically placed, and often remotely controlled for facial expressions, keyboard/mouse actions, and screen activity.
   • Microphones: Ambient or lapel mics, sometimes requiring a mixer for multiple feeds.
   • Data Analysis: Identifying, coding, and measuring physical actions and user behavior using Video and Verbal Protocol Analysis.

Eye Tracking

1. Equipment and Use:
   • Eye tracking equipment like Tobii Pro Glasses.
   • Used in usability studies to understand where and how users are looking at the screen.

Usability Testing

1. Goals:
   • To test whether a product is usable by the intended users to achieve the tasks for which it was designed
   • Measure user satisfaction.
   • Collect performance data: video recordings, facial expressions, keystrokes, mouse movements.
   • Use techniques like "Think Aloud" and user satisfaction surveys.
2. Metrics:
   • Task completion rates
   • Time to complete tasks
   • Errors per task/unit time
   • Navigations to help/manuals
   • Consistency in user errors.

Experimental Methods

1. Hypothesis Testing:
   • Determine relationships between variables.
   • Independent Variable: Manipulated by the researcher.
   • Dependent Variable: Measured by the researcher.
   • Formulate null and alternative hypotheses (one-tailed or two-tailed).
2. Experimental Design:
   • Between Subjects: Different participants for each condition.
   • Within Subjects: Same participants across conditions.
   • Pair-wise Design: Reduces learning effects and order effects.
3. Reliability and Validity:
   • Ensure consistent results across different instances.
   • Measure intended variables accurately.
   • Consider ecological validity (real-world relevance).
   • Minimize biases and ensure generalizable results.

Tactile Reasoning

• Does the direct physical manipulation of data by moving individual pieces of information to create temporary groups or sequences, or eliminating pieces of information from a group enhance sense-making and analytical reasoning ability and help generate insight?
• Epistemic actions - Physical actions that make mental computation easier, faster, or more reliable -are external actions that an agent performs to change his or her own computational state.

Definition and Importance

• User Research: Collecting data to understand how users interact with a product.
• User Experience (UX): The total effect felt by the user before, during, and after interaction with a product or system in an environment.

UX Researcher Job Description (Meta)

• Understand user behaviors.
• Use qualitative and quantitative methods: surveys, focus groups, field studies, usability tests, and 1:1 interviews.

The Wheel: A Model of the UX Lifecycle

1. Planning and Preparation:
   • Determine design requirements based on understanding user needs and behavior.
2. Observing and Interviewing:
   • In context of work/play practices to gather accurate data.
3. Data Collection:
   • Seek to understand user behaviors and needs through observation and interviews.

User Research Methods

1. Qualitative Research:
   • Subjective, focuses on understanding user experiences.
2. Quantitative Research:
   • Objective, focuses on measuring user behaviors and preferences.

Types of Questions

• Open Questions: “How would you describe …?”
• Leading Questions: “What are the top 3/best features of …?”
• Closed/Direct Questions: “Which is the best option: A, B, or C?”

User Research Approaches

1. Behavioral Research:
   • Observations: Provide tasks, observe users, take notes, and ask questions.
2. Attitudinal Research:
   • Focus Groups: Ask questions in a group setting; beware of dominant individuals.
   • Interviews: One-on-one conversations to gain insights.
   • Surveys/Questionnaires: Collect structured data; ensure anonymity.
   • Card Sorting: Organize content/features based on user intuition.

Recent Trends

• Online/Google opinions, AI tools like ChatGPT.

Observational Studies

Planning for Activity

• Prepare task lists and anticipate focus points.

During the Visit

• Collect data, resist helping users, and allow them to think independently.
• Record video/voice notes for detailed analysis.

Interviews and Surveys

Interviews

• Gain insights into user experiences, frustrations, and preferences.
• Pros: Follow-up questions, observe non-verbal cues.
• Cons: Response bias, difficulty in arranging interviews.

Surveys

• Collect large-scale data.
• Pros: Honest responses, convenience, low-cost.
• Cons: No follow-up questions, potential for bias.

Card Sorting

Pros:

• Helps start organizing content, discover user priorities, and identify patterns.

Cons:

• Tedious to set up and analyze, depends on quality of content.

Computing Devices Considerations

1. Pointing Device:
   • Finger, mouse, stylus, etc.
2. Start Small:
   • Focus on the core functionality and content.
3. Device Capabilities:
   • Portability, battery life, etc.
4. Operating System Variations:
   • Differences between Mac, Windows, Linux.
5. Responsive Design:
   • Adapt to different screen sizes and resolutions.
6. Multi-Device Interaction:
   • Synchronize content across devices.

AI as a UX Research Assistant

Applications:

• Competitor analysis, best practices for focus groups, UX research plans, crafting interview questions, and building user personas.

Limitations:

• Not reliable for quantitative research or data analysis.

User Journey Mapping in VR Environment

Components:

• Tasks, processes, workflows, systems, tools, data usage, decision-making, and information needs.

Critical Decision Method (CDM)

Knowledge Elicitation Techniques:

• Cognitive Task Analysis (CTA)
• Concept Mapping
• Knowledge Analysis of Tasks

Introduction to Cognitive Task Analysis (CTA)

• Definition: CTA is a methodological approach used to understand how individuals perform tasks, make decisions, and solve problems.
• Importance: Provides insights into cognitive processes, mental models, decision-making strategies, and expertise development.
• Methods in CTA: Includes Concept Mapping, Conceptual Graph Analysis, Knowledge Analysis of Tasks, and Function-based CTA (F-CTA).

The Critical Decision Method (CDM)

• Definition: CDM is a specific approach within CTA that focuses on eliciting knowledge about critical decisions made by individuals in real-world scenarios.
• Purpose: To uncover decision-making strategies, mental models, and factors influencing decisions.
• Origins: Developed based on the Critical Incident Technique (Flanagan, 1954) and Naturalistic Decision Making (NDM) research.
• Applications: Used across various domains such as military command, emergency services, and complex industrial operations.

Key Characteristics of CDM

• Interview Approach: In-depth, semi-structured interviews with open-ended questions.
• Focus: Retrospective analysis of memorable incidents rather than hypothetical scenarios.
• Data Collection: Emphasizes the importance of capturing real-world decision contexts and the rationale behind choices made.

Conducting a CDM Interview

• Preparation: Obtain management authorization, develop interview guides, and familiarize with the domain and terminology.
• Interview Process:
  • Select Incident: Identify a significant, challenging incident personally experienced by the interviewee.
  • Obtain Account: Record events on sticky notes, encouraging interviewee participation in organizing events chronologically.
  • Timeline Construction: Develop a timeline of events and decision points.
  • Decision Point Identification: Identify key decision moments within the incident.
  • Decision Point Probing: Use progressive deepening to explore details of decision-making (e.g., cues observed, knowledge used, options considered, basis for decision).

Analyzing CDM Data

• Challenges with Data: Described as "messy" due to its subjective, context-dependent, and voluminous nature.
• Data Analysis Techniques:
  • Decision Charts: Visual representations of decision sequences.
  • Decomposition Tables: Systematic breakdown of decision factors.
  • Pattern Identification: Extracting significant patterns from qualitative data.

Advantages and Limitations of CDM

• Advantages:
  • Cost-effective and portable (requires minimal equipment ).
  • Provides rapid insights into decision-making processes.
  • Flexible across different domains and contexts.
• Limitations:
  • Subjective nature of data can lead to interpretation biases.
  • Requires skilled facilitation for effective data collection and analysis.
  • Limited generalizability due to focus on specific incidents rather than broader trends.

Practical Applications of CDM

• Design and Development: Guides the design of systems, controls, and decision aids that align with user mental models and decision strategies.
• Training and Expertise Development: Inform training programs by identifying critical decision points and the knowledge/skills required for effective decision-making.

Conclusion

• Key Takeaways:
  • CDM is a valuable tool for understanding complex decision-making processes in real-world settings.
  • It enhances user-centered design by aligning system interfaces with user cognitive processes.
  • Despite challenges with data analysis, CDM provides rich insights into human factors influencing decision-making.

User Research Notes

Understanding User Work and Needs (Personas)

• User Persona Definition: A user persona represents a specific work role with characteristics that make them realistic and relatable, aiding in focused design decisions.
• Creation and Refinement: Personas are initially created based on research and data, periodically refined to align with evolving user needs and behaviors.
• Components of a Good Persona:
  • Goals, expectations, motivations, and behaviors derived from research data.
  • Example: An experienced manager focusing on content curation and social media presence due to time constraints.

Designing Solutions

• HCI Principles in UI Design: Utilize Human-Computer Interaction principles for designing and critiquing interfaces.
• Systematic UI Evaluation: Plan and conduct user research to comprehend user needs and activities.

Prototyping

• H-V-T Prototypes: High-Value-Testing prototypes facilitate iterative design improvements.

Evaluating UX

• Analytical Methods: Includes heuristic evaluation, cognitive walkthroughs, and expert inspections.
• Specialized Methods: Alpha and beta testing involving selected users for feedback.
• Automatic UX Evaluation: Utilizing large-scale usage data from tools like Google Analytics for quantitative insights.

Data Analysis for UX

• Quantitative Data Analysis: Includes metrics such as timings, error counts, and questionnaire ratings. Techniques involve descriptive and inferential statistics.
• System Usability Scale (SUS): A standardized questionnaire to assess usability, calculated from user responses to statements on system usage.