UNDERSTANDING USERS: MODELING TASKS AND LOW- LEVEL INTERACTION Human-Computer Interaction

Agenda  Project Part 1 notes  Task analysis review  An overview of HCI design  Fitts’s Law  Project group time

Project Part 1  Problem or task description, revised and refined  Description and justification of how the information above was collected  Description of the users and their tasks related to the problem, including a task analysis of a single task  Description of the larger system and environment  Analysis of the existing systems and tools used in the problem  Initial list of three usability criteria you will focus on, with justification  Support your claims with specific data and examples!  Prepare a poster about your work

Task Analysis review

Task analysis  Hierarchical Task Analysis (HTA)  Knowledge-Based Task Analysis

Hierarchical Task Analysis (HTA)  Two parts  Task breakdown – a listing of all tasks broken down into subtasks  Plans – specifications of the order of subtasks within the supertask  Often represented as a graphical diagram for clarity Example: changing a light bulb

Hierarchical Task Analysis

Knowledge-based analysis: TAKD  Focused on categorizing objects by action, function, or other properties  List all objects associated with the task  Build taxonomy using AND, OR, and XOR branching

Knowledge Based Analysis Kitchen item AND shape XOR dished mixing bowl, saucepan, soup bowl, glass flat plate, chopping board, frying pan function OR preparation mixing bowl, saucepan eating XOR for food plate, soup bowl for drink glass

Knowledge-based analysis TAKD – Task Analysis for Knowledge Description Taxonomic - unique categorizations of items, characteristics, and functions Good for understanding a problem language or an environment

Task analysis  When to use which?

Design process basics

Why is HCI Design Difficult? Difficult to deeply analyze human behavior May be too close to the domain Multiple clients with different needs Co-evolution of technology and users

Software life cycles – Waterfall Model Requirements Specification Architectural Design Detailed Design Coding and Unit Testing Integration and Testing Operation and Maintenance

Limitations of the waterfall model

 You can’t determine all requirements from the start  Some tasks will only be known after the user has interacted with the system  Users will perform tasks that weren’t intended by the designer  Doesn’t support the user’s perspective of the system

Software Life cycles – Iterative Waterfall Model Requirements Specification Architectural Design Detailed Design Coding and Unit Testing Integration and Testing Operation and Maintenance

Iterative design

Applying HCI in the cycle  Formative  Strategies to build a better interface prior to creating the technology  Summative  Assessing an existing interface after creating the technology

Formative techniques  Apply principles  “Don’t assume the user is right-handed”  Build prototypes  Apply design rules / standards  Java look and feel  Create usability specifications  The XYZ dialog takes < 5 sec.  Study potential users to understand their needs

Summative techniques  Empirical / laboratory evaluation  Expert review  Field study or deployment

Iterative design

Understanding users: Modeling

Modeling takes many forms  Interaction  Low-level/physical actions  Complex activities/tasks  Cognitive  Contextual

What is a model?

 A constructed representation intended to help understand and reason about the world  Abstracted and simplified  Generalized  Not necessarily reflective of how the world actually works

Fitts’s Law – Modeling physical actions

Physical modeling: Using Fitts’s Law  Models movement time for selection tasks  Quantitative modeling technique  A summative technique

Fitts’s Law Live!

Fitts’s Law demo  Tap back and forth between the two rectangles as quickly as you can!  Don’t worry about where in the rectangle you tap- just tap as many times as you can somewhere within the shape

Basics Movement time (MT) is proportional to Index of Difficulty (ID) of a selection task

The big picture The movement time for a well-rehearsed selection task: increases as the distance A to the target increases; and decreases as the size of the target W increases

Index of difficulty (ID) Measure difficulty of selection task ID = log 2 (2A/W) “bits” A = distance between targets W = target width

Movement time (MT) MT = a + b ID (a=0 if line passes through the origin) MT Difficulty

How MT is determined Empirical measurement establishes constants a and b Different for different devices and different ways the same device is used.

Original application of Fitts’s Law 1-dimensional selection task

Original application of Fitts’s Law W A

Extending to 2D  What is W when we consider 2 dimensions of movement? θ

Extending to 2D  What is W when we consider 2 dimensions of movement?  Same as usual? θ W’

Extending to 2D  What is W when we consider 2 dimensions of movement?  Smallest dimension? θ W’

Extending to 2D  What is W when we consider 2 dimensions of movement?  Distance from edge to centroid? θ W’

Application of Fitts’s law? When does it apply? When does it not?

Application of Fitt’s law? When does it apply? When does it not?  Used for predicting performance low-level physical actions  Automated tasks and actions  Minimal cognition – you don’t have to “think” about it

A possible example

How is Fitts’s Law used in UI design?  Predicting performance with an interface  May substitute for empirical testing, particularly in early stages  Comparing alternative UI layouts

KLM- another way of modeling physical action

Keystroke-Level Model (KLM)  Another way of doing physical modeling  Decompose tasks into low-level elements with time values  Calculate prediction for total execution time  Best for automated behavior

Keystroke-Level Model (KLM)  K – striking keys  B – pressing a mouse button  P – pointing (dragging a pointer to a target)  H – homing – switching the hand between the mouse and keyboard  D – drawing lines using the mouse  M – mentally preparing for a physical action  R – system response time

Keystroke-Level Model (KLM)  Calculate time required for individual generic actions  Decompose tasks into individual actions  Calculate the total time for a task as a sum of the time for each action  Can be used for comparing alternate ways of executing a task  Does not take time for cognition into account

For next week  Read “The Model Human Processor” by Card, Moran, and Newell