OVERHEADS SET 3: DESIGN OF INTERACTIVE SYSTEMS TABLE OF CONTENTS SET 3 Writing & Composition TASK MODEL OF PROFESSIONAL WRITING EXPLORE MATERIALS ANALYSES OF READERS FOCUS IN I FOCUS IN II EXPLORE MATERIALS ANALYSES OF READERS FOCUS IN I FOCUS IN II WRITING 1: WRITE WRITING II: PROBLEMS FINAL STAGES DOCUMENT STRUCTURE I
 
Objectives Writers Readers
Network Exploring Remembering
Hierarchy Organizing Comprehending
Sequence Encoding Decoding

DOCUMENT STRUCTURE II

READING & BROWSING AS WRITING MISSING REQUIREMENTS FUNCTIONALITY
Hypothetical Hypertext Organization

for a Historian (Nelson, 1965)


 






OTHER MAJOR CONSIDERATIONS

USER MENTAL MODELS USER MENTAL MODELS INTERACTING OBJECTS & EVENTS     Set of states for what occurs COMMANDS: VARIABLES AND RULES Observed variables and rules between variables MENTAL MODEL GUIDELINES I

Users want to subdivide and classify (encode) system in semantic memory

MENTAL MODEL GUIDELINES II MENTAL MODEL GUIDELINES III MENTAL MODEL GUIDELINES IV MENTAL MODEL MATCHING MECHANISMS MENTAL MODEL KNOWLEDGE GOMS MENTAL MODEL TYPES I MENTAL MODELS TYPES II MENTAL MODEL INFERENCES MENTAL MODEL LEARNING METAPHOR EXAMPLES 1 METAPHOR EXAMPLES 2 MENTAL MODEL PROBLEMS 1 MENTAL MODEL PROBLEMS 2 horseless carriage, electronic mail MENTAL MODELS COGNITIVE STATES Mismatch stirs elaboration Confirmation of inferences lead to consolidation MENTAL MODEL PROPERTIES DESIGNING METAPHORS MENTAL MODEL EXAMPLE COGNITIVE APPRENTICESHIP THEORY MINIMAL MANUAL "Can you find this prompt on the screen?" MENTAL MODELS OBSERVATION

"Metaphors are not just good or bad descriptions of their targets, rather they are stimulating or unstimulating invitations to see the domain in a new light." - Carrol
 
 

COGNITIVE CONSIDERATIONS

THE MAGIC NUMBER 7+-2 1776149219181941, 1776 1492 1918 1941

553447916, 553-44-7916

 People reorganize information to overcome limitations RECIPROCAL RELATIONSHIP HUMAN FACTORS AND ENGINEERING COMMUNICATIONS AND INFORMATION THEORY MORSE CODE CODING

        Letter             Code            Probability
            e                    *                        .131
            t                     -                        .105
            a                   *-                       .082
            x                  *-**                    .0012
            z                  ****                   .0008

TRANSFORMATION FROM DATA TO INFORMATION

GENERAL CONCEPTS INFORMATION OVERLOAD CHOICE REACTION TIME (CRT) SERIAL AND PARALLEL PROCESSING SPEED-ACCURACY TRADE-OFF Slow, maximum accuracy Fast, very low accuracy SIGNAL DETECTION THEORY I SIGNAL DETECTION THEORY II RECOGNITION DECISION FLOW COGNITIVE CONSIDERATIONS I COGNITIVE CONSIDERATIONS II COGNITIVE CONSIDERATIONS III Deleting sentence should be same rule as deleting paragraph COGNITIVE CONSIDERATIONS IV USERS AND TASKS CONCEPTUAL OBSERVATIONS TASK PROBLEMS I USER & TASK PROPERTIES TASK PROBLEMS II WHORFIAN HYPOTHESIS GOOD DESIGNING ERROR ANALYSIS USER TASKS USER TASKS TASK MODEL APPROACHES I TASK MODEL APPROACHES II PROBLEM OF TASK ALLOCATION: DIALOGUE PROPERTIES INTERFACE BUGS DESIGN PRINCIPLES PROBLEM SOLVING SUBTASKS INTERACTION AS A PROBLEM SOLVING PROCESSES Nature of Problem Solving I Nature of Problem Solving II Objectives, criteria, solutions, measures, etc. Phase One: Understanding and Defining the Problem

Define problem, Preparation, Clarify problem, Represent Problem, Find Facts, Find problem, Intelligence, Get total picture, Analyze problem, List what is known, Develop problem statement, Gather information, Assess situation, Identify problem

Phase Two: Planning the solution

Suggest possible solutions, Incubation, Illumination, Devise plan, Production, Search for clues, Evaluate alternatives, Find idea, Choice, Withhold judgment, Model, List what is needed, list possible actions, Generate potential solutions, Plan strategy, Diagram problem, recall content, explore alternative strategies

Phase three: Designing and Implementing the Solution

Reason about solution, Incubation, Illumination, Carry out plan, Accept an alternative, Find solution, Design implementation, Change representation, Ask questions, Analyze information, Refine and implement solution, Implement plan, Apply content and strategies, Monitor work in progress

Phase four: Verifying and presenting the results

Test and prove, Verification, Look back, Judgment, Test Solution, Find acceptance, Doubt results, Present findings, Verify and test solution, communicate results, Assess solution product and process

Authors

Dewey, Wallas, Polya, Johnson, Kingley & Garry, Osborn & Parnes, Simon, Rubinstein, Steplen, Gallagher & Workmn, Etter, Meier, Hovde & Meier, Hartman Framework for Decision Making I

Formulation

Framework for Decision Making II

Analyses

Decision Psychological Scales & Cognitive Style Background on Psychological Scales & Cognitive Styles Factors Affecting the Decision process I Factors Affecting the Decision process II Psychological type variables: sensing oriented or sensation types vs. Intuitive oriented types


Psychological Scales & Cognitive Style

            @ Copyright 1998 Murray Turoff

Many aspects of cognitive style do not appear constant overtime even with the same decision maker. External conditions such as stress and information overload have impacts.

Decision Cognitive Style

Information Acquisition Information Evaluation and Interpretation Cognitive Complexity & Behavior Field independent / Field Dependent More scales Minimum/maximum Single decision / many possible decisions BEHAVIOR DIMENSION EXAMPLES OBSERVATION ABOUT GROUPS

Groups can be better problem solvers than the individual when they are composed of individuals with complementary talents in problem solving and can minimize group process losses. Groups made up of individuals with the same cognitive styles might not reach as good a solution.
 
Method of Problem Solving Cognitive Style
Changing problem representation Abstraction
Improving data, Consistency checking, Strategy Capture (rules) Data Driven
Alternative evaluation, Backtracking No Abstraction, Search
Strategy improvement, Decomposition & Recombination Abstraction, Conceptually driven
Alternative generation No abstraction, Search, Conceptually driven
Rapid trial & error No abstraction, Search, Data driven
Extended memory No abstraction, Search, No search, Data driven, Conceptually driven

MYERS-BRIGGS PERSONALITY TYPES

JUDGING/PERCEIVING SENSING/INTUITING THINKING/FEELING THINKING vs. FEELING
 
Thinking:

logical, analytical, scientific, dispassionate, cold, concerned with truth, rationality, theoretical, unconcerned with people’s feelings, concerned with all encompassing theorems

 Feeling:

alogical (neither logical nor illogical, atheoretical, poetic, artistic, passionate, warm, personal, concerned with matters of ethics, concerned with peoples feelings, concerned with justice, uniqueness and individuality

SENSATION vs. INTUITION
 
Sensation:

careful (risk avoider), concerned with parts and details, lives in present, specialist, factual, precise, realist, likes to develop single idea in depth, practical, conventional

 Intuition:

risk taker, concerned with whole picture, lives in future, generalist, hypothetical, vague, speculative, idealist, produces many alternative ideas, inventive, unconventional

Overlapping Categories
 
Sensation

Internal Considerations

 Intuition

External Considerations
Thinking

Efficiency

 ST: everything in its place, mathematical models, procedures and administrative policies NT: broad issues, non personal, flexible, individualistic, investment analysis, acquisitions
Feeling

Effectiveness

 SF: everyone in its place, employee evaluation, organizational structure NF: team oriented, goals and strategic alternatives

Information Concern Examples
 
ST:

units produced per man hour, rate of return on invested capital, cost of goods sold, scrap material per unit, sales per salesman or advertising dollar, inventory costs

 NT:

cost of capital, market share, cost of raw materials, labor costs, product price leadership, new product development, new market development
SF:

employee turnover, absenteeism, number of grievances, employee attitudes, organizational climate, employee commitment, interpersonal relationships

 NF:

Community satisfaction, consumer satisfaction, identification of problems or opportunities, social responsibility, corporate reputation

Human Biases I

Adjustment and Anchoring

Faced with large amounts of data individual focuses on a narrow and wrong sample Availability The information most easy to obtain or on hand is used. Base Rate * The likelihood of two events compared by the number of occurrences or most recent occurrences and not the true rate at which they occur.

* Also gets into difference between causal probabilities and the probabilistic calculus.

 Human Biases II

Conservatism

            Failure to update estimates based upon recent but contradictory information.

Data presentation context

Use of different scales, summarized as opposed to raw data, etc. Data Saturation Premature conclusions on the basis of too small a sample. Human Biases III

Desire for self-fulfilling Prophecies

Person values a certain outcome or conclusion and acquires and analyzes only information that supports it. Ease of recall Using only the data that can be easily recalled Expectations People place more weight on information that confirms what they expect than information that contradicts it. Human Biases IV

Fact-Value confusion

Strongly held values may often be regarded and presented as facts. Fundamental Attribution Error (Success/Failure error) Person associates success with personal ability and associates failure with poor luck in chance events. Gamblers fallacy False assumption that unexpected occurrence of a "run" of some events enhances the probability of the occurrence of an alternative event. Human Biases V

Habit

Familiarity with a particular rule for solving a problem results in its reuse (having a solution looking for a problem) Hindsight Easier to change view after the outcome has occurred than before. Leads to recent event having more influence than total sample. Illusion of Control A good outcome in a chance situations may have resulted from a poor decision. Feeling of control may not be reasonable. Human Biases VI

Law of Small numbers

People sometimes place to much weight on a small sample of data (e.g. success of last two or three products). They do not consider sample size and reliability (e.g. squeaky wheels). Order effects Order in which information is presented affects retention. Things at beginning and end easiest to remember. Human Biases VII

Outcome irrelevant learning system

Use of definite decision procedure can lead to confidence in result when result may be poor because of inability to evaluate outcomes of choices not examined or poor hypothesis formulation. Overconfidence Abundance of data sometimes leads to overconfidence in decision or in the accuracy of the data (e.g. poorly designed surveys). Human Biases VIII

Redundancy

The more redundancy in the data, the more confidence in the data. Reference effect People normally perceive and evaluate stimuli in accordance with their past experiential level for the stimuli. Changes in reference point often weighted more heavily than changes in the data as a whole. Regression effect The largest observed values of observation are used without regression to the mean (e.g. the small number of stocks having high growth in a year). Human Biases IX

Regression to the mean

Thinking the actions based upon a negative outcome are the cause of a good outcome the next time when it is all chance (e.g. student performance). Representativeness Results of small samples taken as representing large samples. Selective Perceptions People seek information that supports or confirms their views and values. Human Biases X

Spurious cues

Accepting the occurrence of a low probability event as a more common event. Wishful thinking Preference of a decision maker for a particular combination of decision and outcome. Ways to help avoid cognitive bias I Ways to help avoid cognitive bias II Intellectual Cripple Hypothesis Decision Dimensions Models of Decision Process I Models of Decision Process II Models of Decision Process III Group Collaboration & Composition
THE PHYSICAL SPACE FOR GROUP PROBLEM SOLVING


 






Group Problem Solving Characteristics

COMPLEXITY DIMENSION
 
Complexity Problem
Elements		 Problem
Relations		 External
Environment
Structured Known Known Known
Semi-
structured		 Known Uncertain Uncertain
Unstructured Uncertain Uncertain Ambiguous
Wicked Ambiguous Ambiguous Unknown,
Unk Unks

COORDINATION DIMENSION

Approach Characteristics
Parallel Members Independent
Information Exchanged
No Group View Imposed
Pooled Same as above BUT
Group View Generated
Sequential Group View Imposed
Group regulates discussion
Planned or static group process
Sequential problem solving phases
Reciprocal Group View Imposed
Members independent but feedback
tied to actions
Adaptive or dynamic group process
Problem Solving Phases
Interdependent
Asynchronous with functional
relationships

Trend Delphi Discourse


 







 
 


Argumentation Discourse Structure


 







 
 


Dynamic Discourse Visualization


 






Semantic Typed Hypertext
 
Guilford: Cognition Convergent Production Divergent Production
Product\ Hypertext/    
  Nodes Convergent Links Divergent
Links
units detail specification elaboration
classes collection membership exclusion
relations proposition association speculation
systems summary path branch
transformations issue alternative lateral
implications observation inference extrapolation

Group Calendar Requirements

Current Document Approaches Common Ground in Collaborative Writing I Common Ground in Collaborative Writing II Classical Model of Collaboration Writer Types Study Two philosophies of collaborative writing
 
‘en-romanticism’ emphasizes the individual nature of collaborative writing and argues that collaborative writing is essentially individual based

‘neo-classicism’ stresses the social nature of collaborative writing and suggests that collaboration activities are significantly influenced by cultural and social factors.

Most computer approaches have been based on the individual writer model (e.g. make an outline and divide it up by individual writers).
 

Classical Collaborative Writing View Current wisdom: Problems of Collaborative Writing Collaborative Writing Functions I Collaborative Writing Functions II Collaborative Requirements I Collaborative Requirements II Information Notifications and Tracking Tracking Modifications to structure Annotation requirements Collaborative Requirements Design Difficulties Many different "spaces" Why current tools have failed Collaborative Writing Structure Collaborative Data Requirements Computer Mediated Communications (CMC) CMC: A rose is a rose

e-mail, Teamware, Groupware, Message systems, Cooperative systems, Collaborative systems, Coordination systems, Bulletin Board systems, Group Communications, Teleconferencing systems, Computer Conferencing (CC), Group Support Systems (GSS), Electronic Mail Systems (EMS), Electronic Meeting Systems (EMS), Group Decision Support Systems (GDSS), Computer Supported Cooperative Work (CSCW)

Dimensions of Human Communication

Factors Influencing Structure CMC Objectives CMC Meta Processes Objects of Discourse

Problems, issues, questions, Goals, objectives, plans, Strategies, policies, agendas, Concerns, criteria, arguments, Assumptions, viewpoints, opinions, Values, interests, tradeoffs, compromises, Consequences, scenarios, impacts, proposals, Solutions, decisions, projects, tasks, Allocations, possibilities, expectations

Group Process Gains

Group Process Losses

Socializing, Domination, Free-riding, Cognitive inertia, Estimation biases, Attention blocking, Air time limitations, Loss of face or status, Failure to remember, Attenuation blocking, Information overload, Coordination problems, Concentration blocking, Evaluation apprehension, Incomplete task analysis, Incomplete use of information

CMC Design Concepts I

CMC Design Concepts II Canned Reactive Notifications Example Privileges

Create, Own, Burn. Delete, Remove, Replace/Modify, Execute, Copy, Insert/Add, Append, Contribute, View/Read, Use, Link/unlink, Target, Solicit, Render, Mark/unmark, Approve, Reply, Assign, Take, Join, Sponsor, Act, Index, Share, Trigger, Perform, System

Dictatorship Design

Superconnectivity Issues of Structure Typical Human Facilitation Functions Group Communication Structures and Functions for Information Work Organizational Human Roles

Task executor, planner, adviser, reviewer, decision maker, decision analyst, scheduler, confidante, evaluator, resource/expert, seeker, observer, negotiator, salesperson, firefighter, figurehead, leader, liaison, monitor, spokesperson, disseminator, entrepreneur, allocator, facilitator, gatekeeper, helper, author, editor, reviewer, joker, critic, designer, advocate, implementer, tester, etc.

Problem Solving Parts

Proven Applications in Organizations Organizational Crisis Situations

Strike, court case, Negotiation, cost overrun, delivery delay, new regulation, supply shortage, production delay, terrorist action, making a proposal, product malfunction, Loss of key employee, new competitive product, potential customer loss

Key Issues in CMC

Example Tradeoff HELP & DOCUMENTATION
        © Copyright 1998 Murray Turoff

Help Questions

Major Problems with Help

Cognitive Interaction Chain Many Possible Errors Slips in Formulation of the Intention Slips from faulty understanding of the interaction environment I

Activation:

Slips from faulty understanding of the interaction environment II Slips from faulty triggering of schemes Objectives for Error Handling Error Categories via Cognitive Cause Cognitive Control Levels Learning and Adaptation I Learning and Adaptation II Errors from Interference Lack of Resources Human Variability Ecological Interface Design (EID) I Ecological Interface Design (EID) II Ecological Interface Design (EID) Approach I Ecological Interface Design (EID) Approach II Error Message Design Guidelines Help Types Dynamic Help Types Ultimate Help Measure, Objective and problem Cognitive Factors Mandatory Help Situations Typical Problems with Help Minimum Help Functionality Guidelines on Writing Documentation I Guidelines on Writing Documentation II Problems with Intelligent Help Approaches
 
Understanding of the domain or application has never been sufficient to deal with novel use of the system. A novel use is one that no one predicted would occur before the system was designed and put in place.

Understanding of the user has never been sufficient to deal with cognitive differences among users. Difficult to come up with suggestions for the user that are consistent with his or her mode of problem solving.

Understanding of the state of the help system with respect to the realization of the "goodness" of the interaction is a major problem.


Observations on Documentation

Objectives of Documentation I Objectives of Documentation II Objectives of Documentation III Objectives of Documentation IV Types of Information Needed Recommendations on Documentation I Recommendations on Documentation II Recommendations on Documentation III Recommendations on Documentation IV Recommendations on Documentation V Learning Models Metaphors Sequential Models Transition State Models GOMS Steps to Produce a GOMS Help System Typical Results of GOMS Oriented Documentation Problems with GOMS Approach General Guidelines for Help I General Guidelines for Help II General Guidelines for Help III General Guidelines for Help IV General Guidelines for Help V General Guidelines for Help VI General Guidelines for Help VII General Guidelines for Help VIII General Guidelines for Help IX General Guidelines for Help X END OF THIRD SET