© Simeon Keates 2009 Usability with Project Lecture 14 – 30/10/09 Dr. Simeon Keates.

Slides:



Advertisements
Similar presentations
Chapter 15: Analytical evaluation
Advertisements

AS Level – Week 11 Theory Module 1 Learning and Performance.
Chapter 12 cognitive models.
User Modeling CIS 376 Bruce R. Maxim UM-Dearborn.
Science Fair Project 2015.
Models and Theories. GOMS Example Photocopying an article Create a GOMS description of the task of photocopying an article from a journal. Assume –copy.
Lecture Roger Sutton 21: Revision 1.
Chapter 14: Usability testing and field studies. 2 FJK User-Centered Design and Development Instructor: Franz J. Kurfess Computer Science Dept.
Evaluation Types GOMS and KLM
Task Analysis (continued). Task analysis Observations can be done at different levels of detail fine level (primitives, e.g. therbligs, keystrokes,GOMS.
QUASID – Measuring Interaction Techniques Karin Nieuwenhuizen.
1 User-Centered Design and Development Instructor: Franz J. Kurfess Computer Science Dept. Cal Poly San Luis Obispo FJK 2009.
Cognition in the virtual world. Which is easiest to read? What is the time?
Instructor: Vincent Duffy, Ph.D. Associate Professor of IE Lecture 8 – Human-Computer Interaction Thurs. Feb. 8, 2007 IE 486 Work Analysis & Design II.
CS160 Discussion Section Fitts Law and KLM David Sun Sept 26 th 2007.
Testing and Modeling Users Kristina Winbladh & Ramzi Nasr.
Objectives Define predictive and descriptive models and explain why they are useful. Describe Fitts’ Law and explain its implications for interface design.
SIMS 213: User Interface Design & Development Marti Hearst Tues, April 6, 2004.
Predictive Evaluation Predicting performance. Predictive Models Translate empirical evidence into theories and models that can influence design. Performance.
Task analysis 1 © Copyright De Montfort University 1998 All Rights Reserved Task Analysis Preece et al Chapter 7.
Chapter 4 Cognitive Engineering HCI: Designing Effective Organizational Information Systems Dov Te’eni Jane M. Carey.
Some questions of hypermedia and CHI Josep Blat Universitat Pompeu Fabra.
Research Methods for HCI: Cognitive Modelling BCS HCI Tutorial 1 st September, 2008.
Predictive Evaluation Simple models of human performance.
©2011 1www.id-book.com Analytical evaluation Chapter 15.
© Simeon Keates 2009 Usability with Project Lecture 15 – 04/11/09 Dr. Simeon Keates.
Chapter 5 Models and theories 1. Cognitive modeling If we can build a model of how a user works, then we can predict how s/he will interact with the interface.
UNDERSTANDING USERS: MODELING TASKS AND LOW- LEVEL INTERACTION Human-Computer Interaction
Slides based on those by Paul Cairns, York ( users.cs.york.ac.uk/~pcairns/) + ID3 book slides + slides from: courses.ischool.berkeley.edu/i213/s08/lectures/i ppthttp://www-
1 Rensselaer Cognitive Science Keystroke-Level Model: Intro The simplest of all GOMS models: OM only!!!  No explicit goals or selection rules  Operators.
Ch 14. Testing & modeling users
Multimedia Specification Design and Production 2013 / Semester 1 / week 9 Lecturer: Dr. Nikos Gazepidis
Stanford hci group / cs October 2008 Inp ut Scott Klemmer.
User Modeling 1 Predicting thoughts and actions. Agenda Cognitive models Physical models Fall 2006PSYCH / CS
Usability Testing CS774 Human Computer Interaction Spring 2004.
Chapter 12 cognitive models. 2 Cognitive models goal and task hierarchies linguistic physical and device.
Gary MarsdenSlide 1University of Cape Town Human-Computer Interaction - 6 User Models Gary Marsden ( ) July 2002.
Testing & modeling users. The aims Describe how to do user testing. Discuss the differences between user testing, usability testing and research experiments.
Identifying needs and establishing requirements
GOMs and Action Analysis and more. 1.GOMS 2.Action Analysis.
Evaluation of User Interface Design 4. Predictive Evaluation continued Different kinds of predictive evaluation: 1.Inspection methods 2.Usage simulations.
User Modeling of Assistive Technology Rich Simpson.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Prof Jim Warren with reference to sections 7.4 and 7.6 of The Resonant Interface.
Cognitive Modeling 1 Predicting thougts and actions
Task Analysis CSCI 4800/6800 Feb 27, Goals of task analysis Elicit descriptions of what people do Represent those descriptions Predict difficulties,
ITM 734 Introduction to Human Factors in Information Systems
Evaluation Using Modeling. Testing Methods Same as Formative Surveys/questionnaires Interviews Observation Documentation Automatic data recording/tracking.
Cognitive Models Lecture # March, 2008Human Computer Intercation Spring 2008, Lecture #10 2 Agenda Cognitive models –KLM –GOMS –Fitt’s Law –Applications.
© Simeon Keates 2009 Usability with Project Lecture 13 – 28/10/09 Dr. Simeon Keates.
Chapter 15: Analytical evaluation. Aims: Describe inspection methods. Show how heuristic evaluation can be adapted to evaluate different products. Explain.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Prof Jim Warren with reference to sections 7.1 and 7.2 of The Resonant Interface.
1 1 ITM 734 Introduction to Human Factors in Information Systems Cindy Corritore This material has been developed by Georgia Tech HCI.
Unit 6 of COMP648 User Interface and Interaction Methods Dr Oscar Lin School of Computing and Information Systems Faculty of Science and Technology Athabasca.
Preparing for the Learning Experience Chapter 7. Objectives Discuss the concept of the learning experience Explain the role of the movement practitioner.
A Survey on User Modeling in HCI PRESENTED BY: MOHAMMAD SAJIB AL SERAJ SUPERVISED BY: PROF. ROBERT PASTEL.
Human Computer Interaction
Task Analysis CSCI 4800/6800 Feb 27, 2003.
CIS 376 Bruce R. Maxim UM-Dearborn
Usability with Project Lecture 13 – 29/10/08
GOMS Adapted from Berkeley Guir.
Models and Theories.
Copyright Catherine M. Burns
GOMS as a Simulation of Cognition
Evaluation.
Cognitive models linguistic physical and device architectural
Testing & modeling users
Model based design keystroke level model
Human Computer Interaction Lecture 24 Cognitive Models
Presentation transcript:

© Simeon Keates 2009 Usability with Project Lecture 14 – 30/10/09 Dr. Simeon Keates

© Simeon Keates 2009 Exercise – Part 1  Last week you were asked to prepare your user trial protocols  Today – put them into practice  Perform a pilot study of the usability of your web-site with at least 1 user  Remember – the principal aim is to “test the test” (or “trial the trial” or “evaluate the evaluation”…) Page 2

© Simeon Keates 2009 Exercise – Part 2  Prepare a progress presentation for the board for Friday  Show that good progress is being made  Summarise: The tasks performed The data collected Whether the user liked the site Whether the user could use the site (e.g. complete the tasks) What you think is working well in the design What you think needs to be looked at more closely in the design Any changes you would like to make to the site and protocol Page 3

© Simeon Keates 2009 Exercise - Practicalities  Remember to print out copies of your protocol  Allow plenty of blank space for adding observation notes  Allocate one person to do the pre-session briefing and debrief  Allocate one person to be the facilitator (the person who directs the user)  The remaining members act as observers Page 4

© Simeon Keates 2009 Cognitive models Page 5

© Simeon Keates 2009 The Power Law of Practice  T n = T 1 n - α  α = 0.4, T 1 = 60s, T 2 = 45.5s (24% faster), T 10 = 23.9s (60%faster) Page 6

© Simeon Keates 2009 Cognitive modelling – Dealing with uncertainty  The Uncertainty Principle states that decision time T increases with uncertainty about the decision to be made: T = I c H Where: H is the information-theoretic entropy of the decision; I c = 150 [0~157] ms/bit  For n equally probable alternatives (Hick’s Law) : H = log 2 (n + 1)  More generally: Page 7

© Simeon Keates 2009 Cognitive modelling – The Model Human Processor Time_taken = x τ p + y τ c + z τ m Where : x, y and z are integers τ p = time for perceptual processor τ c = time for cognitive processor τ m = time for (simple) motor function Page 8

© Simeon Keates 2009 Motor skills – Positioning time  The time to perceive something includes the time for your eye to be looking at the right thing  Similarly, motor functions also involve a “time for location”  Common sense says that: The further away something is, the longer it takes to reach it The smaller a target is, the longer it takes to “hit” it  Also, human movement is a 2 stage process  Stage 1 – gross (ballistic) movement Covers most of the distance quickly, but not very accurately  Stage 2 – fine (homing) movement Refine the position on to the target Page 9

© Simeon Keates 2009 Motor skills – Fitts’ Law  A person wishes to hit this target:  We know that a correction cycle takes: τ p + τ c + τ m ≈ 240 ms  And so n corrections takes n * 240 ms Page 10 Start x0x0 x1x1 x2x2 S D

© Simeon Keates 2009 Fitts’ Law  Now let x i be the remaining distance after the i-th correction  And let x 0 (= D) be the starting point  We will assume that the relative accuracy of movement is constant, i.e.:  Where ε < 1 and is the constant error  On 1 st cycle: x 1 = ε x 0 = ε D  On 2 nd cycle: x 2 = ε x 1 = ε (ε D) = ε 2 D  On n-th cycle: x n = ε n D  Process stops when: ε n D ≤ ½ S  Solving for n gives: Page 11

© Simeon Keates 2009 Fitts’ Law  From:  Total movement time, T pos is given by:  This can be re-written as: Where: ε has been found to be ~ 0.7 Thus I M ≈ -240 / log 2 (0.7) = 63 ms/bit [27~122 ms/bit] Page 12 Fitts’ Law

© Simeon Keates 2009 Fitts’ Law corrections  There are several modifications to Fitts’ Law  Fitt’s Law becomes less accurate for low values of log 2 (2D / S)  i.e. where the target is quite big compared with the distance  An example correction by Welford (1968): Page 13

© Simeon Keates 2009 Fitts’ Law – Implications for web-site design  Long, thin targets are not good Small S value => longer acquisition times  Example of long, thin target: Text-only hyperlinks e.g. Heinz tomato ketchup  Better to include something large e.g. an image of a ketchup bottle… Page 14

© Simeon Keates 2009 Merging the models One basic merged model is the Keystroke Level Model (KLM): T execute = T K + T P + T H + T D + T M + T R  Where T K = total time spent keystroking = n k t k (# * time per stroke) Time per stroke determined experimentally  T P = total time spent pointing (from Fitts’ Law) Assume, say, 1.1 s per pointing action  T H = total time spent homing (moving hands between devices) Assume 0.4 s per homing  T D = total time spent drawing = t D (n D, l D ) (i.e. f(#, total length)) Example: 0.9n D l D  T M = total time to mentally prepare Assume 1.35 s per preparation  T R = total system response time Page 15

© Simeon Keates 2009 Using the KLM [Note: M = mental prep, K = keyboard, P = pointing]  Rule 0: Insert Ms in front of all Ks that are not part of argument strings proper. Place Ms in front of all Ps that select commands  Rule 1: If an operator following an M is fully anticipated in an operator just previous to M, then delete the M (e.g. PMK -> PK)  Rule 2: If a string of MKs belongs to a cognitive unit (e.g. name of a command), then delete all Ms but the first one  Rule 3: If a K is a redundant terminator (e.g. terminates a command immediately following the terminator of its argument), then delete the M in front of it  Rule 4: If a K terminates a constant string (e.g. a command name), then delete the M in front of it, but if the K terminates a variable string (e.g. an argument string) then keep the M in front of it Page 16

© Simeon Keates 2009 An more generic approach - GOMS The user’s cognitive structure consists of:  A set of Goals  A set of Operators  A set of Methods  A set of Selection rules Page 17

© Simeon Keates 2009 GOMS – a quick breakdown Goals:  Symbolic structures that define a state of affairs to be achieved Examples: GOAL: EDIT-MANUSCRIPT or GOAL: MODIFY-TEXT Goals can comprise sub-goals Operators:  Elementary perceptual, motor or cognitive acts whose execution is necessary to change any aspect of the user’s mental state or to affect the task environment Examples: GET-NEXT-PAGE or GET-NEXT-TASK Page 18

© Simeon Keates 2009 GOMS – a quick breakdown Methods:  Procedures for accomplishing a goal – must be pre-learned at performance time (i.e. user already knows them) Contain sets of Operators Selection rules:  Rules for helping the user decide which method to use to accomplish the goal Example: if_such_and_such_is_true_then_use_method_M1_else_use_M2 To summarise:  Several Operators make up a Method, and  Selection rules are used to determine the best Method to reach the Goal Page 19

© Simeon Keates 2009 Using models of interaction  Fundamentally, you need to perform a comprehensive task analysis  The models indicate suggested performance for each sub-task  Those models help you to predict the performance of the interface  This can be used: In design: Estimate performance using standard parameters to optimise your design In usability trials: Estimate the performance and compare with actual observed data – investigate significant discrepancies Page 20

© Simeon Keates 2009 Exercise Page 21

© Simeon Keates 2009 Exercise  On Wednesday(-ish) you performed a pilot study  Today, make any changes you identified to your usability protocol  Also, make any changes to your web-site based on the feedback that you obtained  Please mail your finalised protocols to Stina, Susanne and me Page 22