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/

3. Adaptive systems Classic loop “user modeling - adaptation” in adaptive systems

4. Adaptive software systems Intelligent Tutoring Systems –adaptive course sequencing –adaptive... Adaptive Hypermedia Systems –adaptive presentation –adaptive navigation support Adaptive IR systems Adaptive...

5. Outline Adaptive hypermedia –Where? –Why? –What? –How? –Who?... Adaptive presentation Adaptive navigation support

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

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

8. Where it can be useful? Web-based education çELM-ART, AHA!, KBS-Hyperbook, MANIC On-line information systems çPEBA-II, AHA!, AVANTI, SWAN, ELFI, ADAPTS E-commerce çTellim, SETA, Adaptive Catalogs Virtual and real museums çILEX, HYPERAUDIO, HIPS, Power, Marble Museum Information retrieval, filtering, recommendation çSmartGuide, Syskill & Webert, IfWeb, SiteIF, FAB, AIS

9. Adapting to what? Knowledge: about the system and the subject Goal: local and global Interests Background: profession, language, prospect, capabilities Navigation history

10. Who provides adaptation? User "Administrator" System itself Adaptive vs. adaptable systems

11. What can be adapted? Hypermedia = Pages + Links Adaptive presentation –content adaptation Adaptive navigation support –link adaptation

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

14. Adaptive presentation techniques Conditional text filtering çITEM/IP, PT, AHA! Adaptive stretchtext çMetaDoc, KN-AHS, PUSH, ADAPTS Frame-based adaptation çHypadapter, EPIAIM, ARIANNA, SETA Full natural language generation çILEX, PEBA-II, Ecran Total

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

16. Example: Stretchtext (PUSH)

17. Example: Stretchtext (ADAPTS)

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

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)

20. Adaptive navigation support Direct guidance Restricting access –Removing, disabling, hiding Sorting Annotation Generation –Similarity-based, interest-based Map adaptation techniques

21. Example: Adaptive annotation Annotations for topic states in Manuel Excell: not seen (white lens) ; partially seen (grey lens) ; and completed (black lens)

22. Adaptive annotation and removing

23. Example: Adaptive annotation 1. Concept role 2. Current concept state 3. Current section state 4. Linked sections state 4 3 2 1 v

24. Adaptive navigation support: major goals and relevant technologies

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

26. Link types and technologies

27. Adaptive navigation support: evaluation Sorting Þ HYPERFLEX, 1993 Annotation (colors) and hiding Þ ISIS-Tutor, 1995 Annotation (icons) Þ InterBook, 1997 Hiding Þ De Bra’s course, 1997

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

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)

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

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

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

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 ‘+’)

34. Sample index page (annotation)

35. Sample index page (hiding)

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

37. Experiment design

38. Results: performance Adaptive annotation makes navigation more efficient

39. The value of adaptivity: steps

40. The value of adaptivity: repetitions

41. Results: navigation No effect on navigation patterns due to variety of navigation styles

42. Results: recall No effect on recall

43. To hide or not to hide? Additional value of hiding is unclear. Users prefer “freedom”

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

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

46. Sections and concepts Chapter 1 Chapter 2 Section 1.1 Section 1.2 Section 1.2.1Section 1,2,2 Textbook

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

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

50. Glossary view

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)

52. Adaptive navigation support Adaptive annotations –Links to sections –Links to concepts –Pages Adaptive sorting –Background help Direct guidance (course sequencing) –Teach Me

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

54. Adaptive annotation Educational status for concept unknown known learned Educational status for sections not ready to be learned ready to be learned suggested

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

56. Bookshelves and books

57. Book view

58. Glossary view

59. Backward learning: “help” and “teach this”

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

61. Preferred ways of navigation

63. The effect of “following green” with adaptation

64. Adaptation mechanisms do work!

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

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

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

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

69. Domain model - the key Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N

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

71. Vector vs. network models Vector - no relationships Precedence (prerequisite) relationship is-a, part-of, analogy: (Wescourt et al, 1977) Genetic relationships (Goldstein, 1979)

72. Vector (set) model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N

73. Network model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N

74. Overlay user model: knowledge Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N

75. Simple overlay model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N yes no yes

76. Simple overlay model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N yes no yes

77. Weighted overlay model Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N 10 3 0 2 7 4

78. Simple goal model Learning goal as a set of topics

79. More complicated models Sequence, stack, tree

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)

81. Knowledge-based hypertext Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N

82. Indexing of nodes Domain model Concept 1 Concept 2 Concept 3 Concept 4 Concept m Concept n Hyperspace

83. Indexing of page fragments Fragment 1 Fragment 2 Fragment K Concept 1 Concept 2 Concept 3 Concept 4 Concept 5 Concept N NodeConcepts

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

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

86. Hyperspace structuring Concept-based hyperspace No imposed structure Hierarchy ASK approach - conversational relationships

87. Indexing with generalized model fragments are indexed with goals –PUSH nodes are indexed with user’s tasks –HYNECOSUM: nodes are indexed with stereotypes –EPIAIM, Anatom-Tutor, C-Book goals are nodes –HYPERFLEX

88. What else? More functionality with the same knowledge çAdaptive hiding (ISIS-Tutor, AHA!) çAdaptive presentation (AHA!) çAdaptive Testing (ELM-ART) çAdaptive Recommendation (Hirashima) More functionality with more knowledge –Domain specific çKnowledge about programming (ELM-ART) –Domain-neutral çAdvanced indexing (ADAPTS and COCOA)

89. What else? More functionality with the same knowledge çAdaptive hiding (ISIS-Tutor, AHA!) çAdaptive presentation (AHA!) çAdaptive Testing (ELM-ART) çAdaptive Recommendation (Hirashima) More functionality with more knowledge –Domain specific çKnowledge about programming (ELM-ART) –Domain-neutral çAdvanced indexing (ADAPTS and COCOA)

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

91. ELM-ART: navigation and testing

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

94. Adaptivity in ELM-ART Adaptive navigation support Adaptive sequencing Adaptive testing Adaptive selection of relevant examples Adaptive similarity-based navigation Adaptive program diagnosis

95. ANS + Adaptive testing

96. Adaptive Diagnostics

97. Similarity-Based Navigation

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

99. ELM-ART: Value of ANS Mean number of pages which the users with no experience in programming languages completed with ELM-ART

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

101. Adaptive annotation can: Reduce navigation efforts çResults are not significant (variety of styles?) Reduce repetitive visits to learning pages çSignificant - if applied properly Encourage non-sequential navigation Increase learning outcome çFor those who is ready to follow and advice Make system more attractive for students

102. What else? More functionality with the same knowledge çAdaptive hiding (ISIS-Tutor, AHA!) çAdaptive presentation (AHA!) çAdaptive Testing (ELM-ART) çAdaptive Recommendation (Hirashima) More functionality with more knowledge –Domain specific çKnowledge about programming (ELM-ART) –Domain-neutral çAdvanced indexing (ADAPTS and COCOA)

103.  ONR research project  Architecture for integration of: –Diagnostics –Technical Information –Performance-oriented Training  Technology investigation & testbed ADAPTS: What it is IETMs Training Diagnostics

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

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. What’s in adaptive content?

106. ADAPTS - an adaptive IETM

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

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

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

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

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

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

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

114. Integrated interface

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

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)

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

118. Social Navigation Support

119. AH Service: NavEx

120. AH Service: QuizGuide

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.