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Slide 2 Adaptive Hypermedia From Concepts to Authoring Peter Brusilovsky School of Information Sciences University of Pittsburgh peterb@pitt.edu http://www.sis.pitt.edu/~peterb/ Slide 3 Adaptive systems Classic loop user modeling - adaptation in adaptive systems Slide 4 Adaptive software systems Intelligent Tutoring Systems adaptive course sequencing adaptive... Adaptive Hypermedia Systems adaptive presentation adaptive navigation support Adaptive IR systems Adaptive... Slide 5 Outline Adaptive hypermedia Where? Why? What? How? Who?... Adaptive presentation Adaptive navigation support Slide 6 Adaptive hypermedia: Why? Different people are different Individuals are different at different times "Lost in Hyperspace Large variety of users Variable characteristics of the users Large hyperspace Slide 7 Where it can be useful? Web-based Education ITS, tutorials, Web courses On-line information systems classic IS, information kiosks, encyclopedias E-commerce Museums virtual museums and handheld guides Information retrieval systems classic IR, filtering, recommendation, services Slide 8 Where it can be useful? Web-based education ELM-ART, AHA!, KBS-Hyperbook, MANIC On-line information systems PEBA-II, AHA!, AVANTI, SWAN, ELFI, ADAPTS E-commerce Tellim, SETA, Adaptive Catalogs Virtual and real museums ILEX, HYPERAUDIO, HIPS, Power, Marble Museum Information retrieval, filtering, recommendation SmartGuide, Syskill & Webert, IfWeb, SiteIF, FAB, AIS Slide 9 Adapting to what? Knowledge: about the system and the subject Goal: local and global Interests Background: profession, language, prospect, capabilities Navigation history Slide 10 Who provides adaptation? User "Administrator" System itself Adaptive vs. adaptable systems Slide 11 What can be adapted? Hypermedia = Pages + Links Adaptive presentation content adaptation Adaptive navigation support link adaptation Slide 12 Slide 13 Adaptive presentation: goals Provide the different content for users with different knowledge, goals, background Provide additional material for some categories of users comparisons extra explanations details Remove or fade irrelevant piece of content Sort fragments - most relevant first Slide 14 Adaptive presentation techniques Conditional text filtering ITEM/IP, PT, AHA! Adaptive stretchtext MetaDoc, KN-AHS, PUSH, ADAPTS Frame-based adaptation Hypadapter, EPIAIM, ARIANNA, SETA Full natural language generation ILEX, PEBA-II, Ecran Total Slide 15 Conditional text filtering If switch is known and user_motivation is high Fragment 2 Fragment K Fragment 1 Similar to UNIX cpp Universal technology Altering fragments Extra explanation Extra details Comparisons Low level technology Text programming Slide 16 Example: Stretchtext (PUSH) Slide 17 Example: Stretchtext (ADAPTS) Slide 18 Adaptive presentation: evaluation MetaDoc: On-line documentation system, adapting to user knowledge on the subject Reading comprehension time decreased Understanding increased for novices No effect for navigation time, number of nodes visited, number of operations Slide 19 Adaptive navigation support: goals Guidance: Where I can go? Local guidance (next best) Global guidance (ultimate goal) Orientation: Where am I? Local orientation support (local area) Global orientation support (whole hyperspace) Slide 20 Adaptive navigation support Direct guidance Restricting access Removing, disabling, hiding Sorting Annotation Generation Similarity-based, interest-based Map adaptation techniques Slide 21 Example: Adaptive annotation Annotations for topic states in Manuel Excell: not seen (white lens) ; partially seen (grey lens) ; and completed (black lens) Slide 22 Adaptive annotation and removing Slide 23 Example: Adaptive annotation 1. Concept role 2. Current concept state 3. Current section state 4. Linked sections state 4 3 2 1 v Slide 24 Adaptive navigation support: major goals and relevant technologies Slide 25 What can be adapted: links Contextual links (real hypertext) Local non-contextual links Index pages Table of contents Links on local map Links on global map Slide 26 Link types and technologies Slide 27 Adaptive navigation support: evaluation Sorting HYPERFLEX, 1993 Annotation (colors) and hiding ISIS-Tutor, 1995 Annotation (icons) InterBook, 1997 Hiding De Bras course, 1997 Slide 28 Evaluation of sorting HYPERFLEX: IR System adaptation to user search goal adaptation to personal cognitive map Number of visited nodes decreased (significant) Correctness increased (not significant) Goal adaptation is more effective No significant difference for time/topic Slide 29 Adaptive Hypermedia: our approach ISIS-Tutor, MSU (1992-1994)ITEM/PG, MSU (1991-1993) SQL-Tutor, MSU (1995-1998)ELM-ART, Trier (1994-1997) InterBook, CMU (1996-1998) ELM-ART II, Trier (1997-1998) ITEM/IP, MSU (1986-1994) ADAPTS, CMU (1998-1999)COCOA, CTE (1999-2000) Slide 30 Annotation and hiding: ISIS-Tutor An adaptive tutorial for CDS/ISIS/M users Domain knowledge: concepts and constructs Hyperspace of learning material: Description of concepts and constructs Examples and problems indexed with concepts (could be used in an exploratory environment) Link annotation with colors and marks Removing links to not relevant pages Slide 31 Concepts, examples, and problems Example 2Example M Example 1 Problem 1 Problem 2Problem K Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Examples Problems Concepts Slide 32 Indexing and navigation Example 2Example M Example 1 Problem 1 Problem 2Problem K Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Examples Problems Concepts Slide 33 Student modeling and adaptation States for concepts: not ready (may be hidden) ready (red) known (green) learned (green and +) State for problems/examples: not ready (may be hidden) ready (red) solved (green and +) Slide 34 Sample index page (annotation) Slide 35 Sample index page (hiding) Slide 36 ISIS-Tutor: Evaluation 26 first year CS students of MSU 3 groups: control (no adaptation) adaptive annotation adaptive annotation + hiding Goal: 10 concepts (of 64), 10 problems, all examples Slide 37 Experiment design Slide 38 Results: performance Adaptive annotation makes navigation more efficient Slide 39 The value of adaptivity: steps Slide 40 The value of adaptivity: repetitions Slide 41 Results: navigation No effect on navigation patterns due to variety of navigation styles Slide 42 Results: recall No effect on recall Slide 43 To hide or not to hide? Additional value of hiding is unclear. Users prefer freedom Slide 44 Evaluation of hiding Adaptive course on Hypertext (De Bra) Hiding not ready links Hiding obsolete links Small-scale evaluation No significant differences Students are not comfortable with disappearing links Slide 45 InterBook: concept-indexed ET Knowledge behind pages Structured electronic textbook (a tree of sections) Sections indexed by domain concepts Outcome concepts Background concepts Concepts are externalized as glossary entries Shows educational status of concepts and pages Slide 46 Sections and concepts Chapter 1 Chapter 2 Section 1.1 Section 1.2 Section 1.2.1Section 1,2,2 Textbook Slide 47 Sections and concepts Chapter 1 Chapter 2 Section 1.1 Section 1.2 Section 1.2.1Section 1,2,2 Domain model Concept 1 Concept 2 Concept 3 Concept 4 Concept m Concept n Textbook Slide 48 Indexing and navigation Chapter 1 Chapter 2 Section 1.1 Section 1.2 Section 1.2.1Section 1.2.2 Domain model Concept 1 Concept 2 Concept 3 Concept 4 Concept m Concept n Textbook Slide 49 Slide 50 Glossary view Slide 51 Navigation in InterBook Regular navigation Linear (Continue/Back) Tree navigation (Ancestors/Brothers) Table of contents Concept-based navigation Glossary (concept -> section) Concept bar (section -> concept) Hypertext links (section -> concept) Slide 52 Adaptive navigation support Adaptive annotations Links to sections Links to concepts Pages Adaptive sorting Background help Direct guidance (course sequencing) Teach Me Slide 53 User modeling Overlay student model for domain concepts Knowledge states for each concept unknown (never seen) known (visited some page) learned (passed a test) Information for sections visited/not visited time spent Information for tests: last answers Slide 54 Adaptive annotation Educational status for concept unknown known learned Educational status for sections not ready to be learned ready to be learned suggested Slide 55 Adaptive annotation in InterBook 1. State of concepts (unknown, known,..., learned) 2. State of current section (ready, not ready, nothing new) 3. States of sections behind the links (as above + visited) 3 2 1 v Slide 56 Bookshelves and books Slide 57 Book view Slide 58 Glossary view Slide 59 Backward learning: help and teach this Slide 60 InterBook: Evaluation Goal: to find a value of adaptive annotation Electronic textbook about ClarisWorks 25 undergraduate teacher education students 2 groups: with/without adaptive annotation Format: exploring + testing knowledge Full action protocol Slide 61 Preferred ways of navigation Slide 62 Slide 63 The effect of following green with adaptation Slide 64 Adaptation mechanisms do work! Slide 65 Results No overall difference in performance Sequential navigation dominates...but... Adaptive annotation encourage non- sequential navigation The effect of following green The adaptation mechanism works well Slide 66 Where is the magic? No magic: Knowledge behind material Knowledge about domain (subject) Knowledge about documents Simple concept indexing Knowledge about students Learning goal model Overlay student model Straightforward techniques of user modeling and adaptation Slide 67 Adaptive Hypertext: The Secret Adaptive hypertext has knowledge behind the pages A network of pages like a regular hypertext plus a network of concepts connected to pages Pool of Learning ItemsDomain Model Slide 68 Adaptive Hypertext: Design Design and structure knowledge space Design a generic user model Design a set of learning goals Design and structure the hyperspace of educational material Design connections between the knowledge space and the hyperspace of educational material Slide 69 Domain model - the key Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Slide 70 Domain model - the key Knowledge about domain is decomposed into a set of fragments - domain knowledge elements (DKE) Also called topics, knowledge items, concepts, learning outcomes Most often DKE is just a name denoting a piece of knowledge, sometimes it has internal structure Semantic relationships between DKE can be established Slide 71 Vector vs. network models Vector - no relationships Precedence (prerequisite) relationship is-a, part-of, analogy: (Wescourt et al, 1977) Genetic relationships (Goldstein, 1979) Slide 72 Vector (set) model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Slide 73 Network model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Slide 74 Overlay user model: knowledge Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Slide 75 Simple overlay model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N yes no yes Slide 76 Simple overlay model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N yes no yes Slide 77 Weighted overlay model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N 10 3 0 2 7 4 Slide 78 Simple goal model Learning goal as a set of topics Slide 79 More complicated models Sequence, stack, tree Slide 80 Indexing: the key to AH Types of indexing Knowledge-based hypertext (concept = node) Indexing of nodes Indexing of Fragments How to get the hyperspace indexed? Manual indexing (closed corpus) Computer indexing (open corpus) Slide 81 Knowledge-based hypertext Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N Slide 82 Indexing of nodes Domain model Concept 1 Concept 2 Concept 3 Concept 4 Concept m Concept n Hyperspace Slide 83 Indexing of page fragments Fragment 1 Fragment 2 Fragment K Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N NodeConcepts Slide 84 Overlay model + indexing Indexing nodes with concepts InterBook, ELM-ART, ISIS-Tutor, AHA Indexing anchors with concepts StrathTutor Indexing fragments with concepts MetaDoc, AHA, PT Nodes are concepts InterBook, ELM-ART, ISIS-Tutor Slide 85 Generalized overlay model Knowledge overlay for set of concepts, network of concepts Goals overlay for set of possible goals, tree of goals Stereotypes overlay for set of stereotypes Slide 86 Hyperspace structuring Concept-based hyperspace No imposed structure Hierarchy ASK approach - conversational relationships Slide 87 Indexing with generalized model fragments are indexed with goals PUSH nodes are indexed with users tasks HYNECOSUM: nodes are indexed with stereotypes EPIAIM, Anatom-Tutor, C-Book goals are nodes HYPERFLEX Slide 88 What else? More functionality with the same knowledge Adaptive hiding (ISIS-Tutor, AHA!) Adaptive presentation (AHA!) Adaptive Testing (ELM-ART) Adaptive Recommendation (Hirashima) More functionality with more knowledge Domain specific Knowledge about programming (ELM-ART) Domain-neutral Advanced indexing (ADAPTS and COCOA) Slide 89 What else? More functionality with the same knowledge Adaptive hiding (ISIS-Tutor, AHA!) Adaptive presentation (AHA!) Adaptive Testing (ELM-ART) Adaptive Recommendation (Hirashima) More functionality with more knowledge Domain specific Knowledge about programming (ELM-ART) Domain-neutral Advanced indexing (ADAPTS and COCOA) Slide 90 ELM-ART: Lisp ITS on WWW Model: adaptive electronic textbook hierarchical textbook tests examples problems programming laboratory Extra for Web-based teaching messages to the teacher chat room Slide 91 ELM-ART: navigation and testing Slide 92 Knowledge representation Domain knowledge conceptual network for Lisp problem solving plans debugging knowledge Student model overlay model for Lisp concepts episodic model for problem-solving knowledge Slide 93 Slide 94 Adaptivity in ELM-ART Adaptive navigation support Adaptive sequencing Adaptive testing Adaptive selection of relevant examples Adaptive similarity-based navigation Adaptive program diagnosis Slide 95 ANS + Adaptive testing Slide 96 Adaptive Diagnostics Slide 97 Similarity-Based Navigation Slide 98 ELM-ART: Evaluation No formal classroom study Users provided their experience Drop-out evaluation technology 33 subjects visited more than 5 pages have no experience with Lisp did not finish lesson 3 14/19 with/without programming experience Slide 99 ELM-ART: Value of ANS Mean number of pages which the users with no experience in programming languages completed with ELM-ART Slide 100 ELM-ART: Value of ANS Mean number of pages which the users with experience in at least one programming language completed with ELM-ART Slide 101 Adaptive annotation can: Reduce navigation efforts Results are not significant (variety of styles?) Reduce repetitive visits to learning pages Significant - if applied properly Encourage non-sequential navigation Increase learning outcome For those who is ready to follow and advice Make system more attractive for students Slide 102 What else? More functionality with the same knowledge Adaptive hiding (ISIS-Tutor, AHA!) Adaptive presentation (AHA!) Adaptive Testing (ELM-ART) Adaptive Recommendation (Hirashima) More functionality with more knowledge Domain specific Knowledge about programming (ELM-ART) Domain-neutral Advanced indexing (ADAPTS and COCOA) Slide 103 ONR research project Architecture for integration of: Diagnostics Technical Information Performance-oriented Training Technology investigation & testbed ADAPTS: What it is IETMs Training Diagnostics Slide 104 User Model Diagnostics Content Navigation What task to do System health What content is applicable to this task and this user Levels of detail How to display this content to this user Experience, Preferences, ASSESSES:DETERMINES: Adaptive Diagnostics Personalized Technical Support Slide 105 Video clips (Training) Schematics Engineering Data Theory of operation Block diagrams Equipment Simulations (Training) Equipment Photos Illustrations Troubleshooting Step Troubleshooting step plus hypermedia support information, custom-selected for a specific technician within a specific work context. ADAPTS dynamically assembles custom-selected content. Whats in adaptive content? Slide 106 ADAPTS - an adaptive IETM Slide 107 The result Maintenance history Preprocessed, condition-based inputs Technician and Operator Observations Sensor inputs (e.g., 1553 bus) Personalized Display IETMTraining Stretch text Outline Links Training records Skill assessment Experience Preferences ContentNavigationDiagnostics How do we make decisions? User Model Slide 108 Concept A Concept B Concept C Supporting Information Domain model Defines relationships between elements of technical information Indicates level of difficulty/ detail Shows prerequisites Slide 109 Domain model example CONCEPT Reeling Machine CONCEPT Sonar Data Computer CONCEPT Sonar System Removal Instructions Testing Instructions Illustrated Parts Breakdown Principles of Operation Removal Instructions Removal Instructions Testing Instructions Testing Instructions Illustrated Parts Breakdown Illustrated Parts Breakdown Slide 110 Reeling Machine Reeling Machine Sonar System General Component Location Principles of operation Removal instructions Principles of Operation System Description Details Parts List Power Distribution Domain model example PART OF Testing instructions SUMMARY DETAILS TUTORIAL Slide 111 User model Characterizes user ability at each element of the domain model Size of model is bounded by domain Weights on different types of elements account for learning styles and preferences Can be time sensitive Constrains the diagnostic strategy Slide 112 User model example Certified CONCEPT Reeling Machine CONCEPT Sonar Data Computer CONCEPT Sonar System ROLE Removal Instructions ROLE Testing Instructions ROLE IPB Reviewed Hands-on Simulation AT2 Smith AD2 Jones Preference Reviewed Hands-on + Certified Reviewed Hands-on Reviewed ROLE Theory of Operation Slide 113 Adaptive content selection Information is custom-selected for a user Level of detail offered depends upon who the user is (i.e., his level of expertise) Selected at a highly granular level, e.g., for each step within a procedure Performance-oriented training is presented as part of content Slide 114 Integrated interface Slide 115 Summary Concept-based approach to adaptive hypertext and adaptive WBS Concept indexing: Knowledge behind pages Explored Different levels of model complexity Different application domains Different adaptation techniques Slide 116 The complexity issue ISIS-Tutor, MSU (1992-1994) COCOA, CTE (1998-1999) ELM-ART, Trier (1994-1997) InterBook, CMU (1996-1998) ADAPTS, CMU, (1997-1998) ITEM/IP, MSU (1986-1994) Slide 117 Open Corpus AH Current AH techniques are based on manual page/fragment indexing How to work with open Web and Digital Libraries? Ways being explore Add open corpus content, but index it manually Use IR techniques for content-based adaptation without manual indexing Use social navigation approaches for adaptation Slide 118 Social Navigation Support Slide 119 AH Service: NavEx Slide 120 AH Service: QuizGuide Slide 121 More information... Adaptive Hypertext and Hypermedia Home Page: http://wwwis.win.tue.nl/ah/ Brusilovsky, P., Kobsa, A., and Vassileva, J. (eds.) (1998), Adaptive Hypertext and Hypermedia. Dordrecht: Kluwer Academic Publishers. Brusilovsky, P. (2001) Adaptive hypermedia. User Modeling and User Adapted Interaction, Ten Year Anniversary Issue (Alfred Kobsa, ed.) 11 (1/2), 87-110 Brusilovsky, P. (2003) Developing adaptive educational hypermedia systems: From design models to authoring tools. In: T. Murray, S. Blessing and S. Ainsworth (eds.): Authoring Tools for Advanced Technology Learning Environment. Dordrecht: Kluwer Academic Publishers.