Information Architecture & Design Construction of IA and Web Rosenfeld Chapters Other Readings Presentations

IA Methodology AnalysisDesign VerificationConstructionMaintenance Planning

The Construction Phase Construction is building the product. - Using all of the information from the preceding phases to make a product suited to the users and their environment. - Following structured information engineering principles to provide rigor and metrics.

Construction Developing Content Organizing Content - Version Control - Conventions Construction Methods Templates File Names Cooperative Development Backups Revision to Project Plan

Construction Tips Don’t Use “Bad” Tools - Interfaces - Functionality - Import – Exports – File Formats Don’t Rely on Tool-Generated Markup Fine-Tune Generated Markup Save Multiple (All) Versions Prepare Different Formats Backups Coordination - Version Control - Serial Development - Communication

Construction Resources htmlhttp://builder.cnet.com/webbuilding/ html

More Construction Resources

Constructing the IA Product Planning and Designing Are Over – What’s Next? Construction (Depending on Size) Uses the Most Project Resources - Time - People Selling the Project - “Making the Case” To Management - “Business Strategy” for Developers & IA Project Team IA Is A Resource

Making the Case for IA The Methods Save Resources - Less Design Errors - Faster Construction - Following the Plan Over 90% of Software Projects Are Never Completed Requirements Aren’t Tracked for Subsequent Versions

IA ROI (Construction Goals) - Find Information Faster - Find Information (!) - Make Sense of Information Found - Less Time Searching for Documents - More Completed Purchases - Fewer Navigation Errors - Better Understanding of Information (Context and Content) - Fewer System Resources - Less Technical Support - Higher Quality Final Design - Difficult To Verify and Measure

IA Value (& Design) Checklist Reduces the Cost of Finding Information Reduces the Cost of Finding Wrong Information Reduces the Cost of Not Finding Information at All Provides a Competitive Advantage Increases Product Awareness Increases Sales Makes Using a Site A More Enjoyable Experience (IAv2 p 344-5)

IA Value Checklist, Part Two Improves Brand Loyalty - Ease of Use - Strong, Unique Design Reduces Reliance on Documentation Reduces Maintenance Costs - Sensible IA Structures For the User For the Developers Reduces Training Costs Reduces Staff Turnover - Better Development Methods -Comments -File Sharing -Backups

IA Value Checklist, Part Three Reduces Organizational Upheaval - Design Goals are Explained and Agreed Upon Early - Good Development Reduces Surprises Reduces Organizational Politicking Improves Knowledge Sharing - Group Communication - File Sharing - Development Standards Templates Tools Reduces Duplication of Effort Solidifies Business Strategies (IAv2 p 344-5)

IA & Business Strategy Business Goals vs. IA IA Exposes Business Goals - New Models for Organizing Information - New Tasks With Old Information - New Ways of Working The IA Project Plan as a Business Plan - Focus on the Users/Customers - Focus on Goals (in addition to Tasks) Corporate Sponsorship - Business Needs - Executive Clarity

The Verification Phase Verification is ensuring the usefulness of the product. - Testing the product with the target user to uncover weaknesses in the product. - Implementing solutions to iron out these weaknesses - Planning when to return to the Construction phase to iron out these weaknesses.

Verification/Evaluation Error Tracking - Logging - Notification User Testing - Test Plan Functional tests Completeness tests Evaluating Test Results - Metrics

The Maintenance Phase Maintenance is providing for future releases of the product. - Establishing some intervals and responsibilities to keep the product up to date. - Deciding if it is necessary to return to or modify other phases to improve the product or the methodology itself.

Maintenance Support Post-Mortem Versions Mixed Lifecycle Versioning Maintenance is always more difficult than planned

MS Web Intranet Study 3 Million Pages 50,000 (Potential) Users 74 Countries 8,000 Separate Intranet Sites 2.3 Hours a Day Used 50% of User’s Time Looking for Information

MS Web Intranet Problems Starting Points Navigation Systems Labels Answers & Resolution Portal Design Diverse Authoring Tools Diverse Authorship Age of Information Massive Team Approach To Solving Problems

MS Web Taxonomies The “Language of Clients” Descriptive Vocabularies - Server Log Analysis - Pre-Existing Work - Political and Content Experts - Universal Applicability Metadata - Basics (URL, Desc, Dates, Contact, Status) - Extensions (Importance, Categories, Keywords) Category Labels - Site Maps - Page Terms

MS Web Construction/Evaluation Search Log Analysis for Taxonomy Development Controlled Vocabulary Use Set of Tools - Metadata Registry - Vocabulary Manager - URL Catalog Tools Enforce Processes What Other Tools Would Be Appropriate for Construction, Evaluation and Maintenance?

MS Web Verification For Improvement “Helping Where It Hurts” (p 403) Fix Major Broken Areas Search - Often the Most Broken - Often the First To Be Fixed Collection and Analysis Services Portable Search Technologies - Any Tool With Import and Export - XML Analysis Fixes Problems and Helps Future Design “Best Bets” – Most Likely Applicable Result Interaction Analysis – Before and After

evolt.orgevolt.org – Adaptive Verification evolt.org Online Community Atypical Users Atypical Development? Different Possible Users & Tasks Site Functions Added Variably Gradual Shift in User Functions IA Should Support Community by Sharing and Monitoring Let Members Verify IA Structures and Construct Content Use Determines What Gets Fixed or Added

IA Evaluation Using Heuristics Nielsen’s Discount Usability Engineering - Quick - Dependent on Experience of Eval Team - Done Throughout the IA Methodology Group Work – Different People Find Different Problems Follow Basic Usability PrinciplesUsability Principles Find More Problems Than Time To Fix IA Plan Determines Ranking Problems to Fix - Severity Ratings Good, But Ranking is Better Severity Ratings - Often Too Arbitrary - Tie to IA Plan and User Analysis

Web Usage Mining VL Verification Data Mining to Discover Patterns of Use - Pre-Processing - Pattern Discovery - Pattern Analysis Site Analysis, Not User Analysis Srivastava, J., Cooley, R., Deshpande, M., & Tan, P.N

Web Usage Discovery - Content Text Graphics Features - Structure Content Organization Templates and Tags - Usage Patterns Page References Dates and Times - User Profile Demographics Customer Information

Web Usage Collection Types of Data - Web Servers - Proxies - Web Clients Data Abstractions - Sessions - Episodes - Clickstreams - Page Views The Tools for Web Use Verification

Web Usage Preprocessing Usage Preprocessing - Understanding the Web Use Activities of the Site - Extract from Logs Content Preprocessing - Converting Content Into Formats for Processing - Understanding Content (Working with Dev Team) Structure Preprocessing - Mining Links and Navigation from Site - Understanding Page Content and Link Structures

Web Usage Pattern Discovery Clustering for Similarities - Pages - Users - Links Classification - Mapping Data to Pre-defined Classes - Rule Discovery - Rule Rules - Computation Intensive - Many Paths to the Similar Answers Pattern Detection - Ordering By Time - Predicting Use With Time

Web Usage Applications Application Goals - Improved Design - Improved Delivery - Improved Content Personalization (XMod Data) System Improvement (Tech Data) Site Modification (IA Data) Business Intelligence (Market Data) Usage Characterization (User Behavior Data)

Real Life Information Retrieval Real Life Information Retrieval 51K Queries from Excite (1997) Search Terms = 2.21 Number of Terms - 1 = 31% - 2 = 31% - 3 = 18% (80% Combined) Logic & Modifiers (by User) - Infrequent - AND, “+”, “-” Logic & Modifiers (by Query) - 6% of Users - Less Than 10% of Users - Lots of Mistakes

Real Life Information Retrieval Sessions - Flawed Analysis (User ID) - Some Revisits to Query (Result Page Revisits) Page Views - Accurate, but not by User Use of Relevance Feedback - Not Used Much (~11%) Terms Used Typical Mistakes - Typos - Misspellings - Bad (Advanced) Query Formulation Jansen, B. J., Spink, A., Bateman, J., & Saracevic, T. (1998)

Analysis of a Very Large Search Log 280 GB – Six Weeks of Web Queries 1 Billion Search Requests 285 Million User Sessions Web Users: - Use Short Queries - Mostly Look at the First Ten Results only - Seldom Modify Queries Traditional IR Isn’t Accurately Describing Web Search Phrase Searching Could Be Augmented Silverstein, Henzinger, Marais, Moricz (1998)

Analysis of a Very Large Search Log 2.35 Average Terms Per Query - 0 = 20.6% (?) - 1 = 25.8% - 2 = 26.0% = 72.4% Operators Per Query - 0 = 79.6% Terms Predictable First Set of Results Viewed Only = 85% Some (Single Term Phrase) Query Correlation - Augmentation - Taxonomy Input - Robots vs. Humans

Scent of a (Web) Site Exploring Hypotheses About Web Site Use Goals: Analysis and Prediction Predicting Usability of Alternate Designs - What is the Overall Site Traffic Flow? - Where Do Visitors Come From? - What Pages Are Related? - What Are the User Interests for a Page? Information Foraging and Information Scent - Paths of Web Use Captures User Goals and Behavior

Scent of a (Web) Site Look for Longest Repeating Subsequences - Among Different Users - The Same User Over Time - For One Web Site Only Assume User Has Information Goal Users Like Ants Exploring and Foraging Paths are Links from Page to Page Analyze All the Paths and What Were Used Visualization Methods Prediction

Using Web Use Evaluation for IA How Can These Ideas Be Used for IA? Verification for Design and Construction Web Usage Clustering and Classification Web Site Design Rules Web Searching Web Scent and Foraging Web Use Goal Prediction

Evaluation the Utility & Usability For Adaptive Hypermedia System As Web Sites, Web Users & IA Advance – How Do You Evaluate Them? Help With Large Info Structures Somewhere between System & User Control Adaptive Systems Influence User Behavior - Less Actions - Less Decisions - Preferred

Adaptive Systems Evaluation Ways to Evaluate - Part of Iterative Design Process - Time to Task Measurement - Diagnostic Testing - Goal Measurement How Is This Different? - User Perceptions of Adaptation - Variable Experience for Each User - Longer Evaluation Times - Selected Goals and Tasks That Show Adaptation - Interfaces and Content Changes! - More Users and Evaluations May Be Needed - Work Environments, Not Labs - Real Content

Let’s Talk about IA Tools What Are You Using? - HTML/XML - Graphics - Navigation - Image Maps - Javascripts - Forms - Site Maps - Directories

Class Work: Card Sorting Open Card Sorting - No established groups - Show all the cards Task = Navigation & Understanding the Site Put the cards in groups that seem similar Name the groups Put the groups in “order” Describe what you understand from the cards

Card Sorting Analysis What were the groups? Were labels unclear? What was the general understanding of the site? Did you get more groups or less? What tasks does this sorting support? - Navigation - Understanding - Wayfinding (Mental Model) - Metaphor