adaptive hypermedia from concepts to authoring
DESCRIPTION
Adaptive Hypermedia From Concepts to Authoring. Peter Brusilovsky School of Information Sciences University of Pittsburgh [email protected] http://www.sis.pitt.edu/~peterb/. Adaptive systems. Classic loop “user modeling - adaptation” in adaptive systems. Adaptive software systems. - PowerPoint PPT PresentationTRANSCRIPT
Adaptive HypermediaFrom Concepts to Authoring
Peter Brusilovsky
School of Information Sciences
University of [email protected]
http://www.sis.pitt.edu/~peterb/
Adaptive systems
Classic loop “user modeling - adaptation” in adaptive systems
Adaptive software systems
• Intelligent Tutoring Systems– adaptive course sequencing– adaptive . . .
• Adaptive Hypermedia Systems– adaptive presentation– adaptive navigation support
• Adaptive IR systems
• Adaptive . . .
Outline
• Adaptive hypermedia– Where? – Why? – What? – How? – Who?...
• Adaptive presentation
• Adaptive navigation support
Adaptive hypermedia: Why?
Different people are differentIndividuals are different at different times"Lost in Hyperspace”Large variety of usersVariable characteristics of the usersLarge hyperspace
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
Where it can be useful?
• Web-based educationELM-ART, AHA!, KBS-Hyperbook, MANIC
• On-line information systemsPEBA-II, AHA!, AVANTI, SWAN, ELFI, ADAPTS
• E-commerceTellim, SETA, Adaptive Catalogs
• Virtual and real museumsILEX, HYPERAUDIO, HIPS, Power, Marble Museum
• Information retrieval, filtering, recommendationSmartGuide, Syskill & Webert, IfWeb, SiteIF, FAB, AIS
Adapting to what?
• Knowledge: about the system and the subject
• Goal: local and global
• Interests
• Background: profession, language, prospect, capabilities
• Navigation history
Who provides adaptation?
• User
• "Administrator"
• System itself
• Adaptive vs. adaptable systems
What can be adapted?
• Hypermedia = Pages + Links
• Adaptive presentation
– content adaptation
• Adaptive navigation support
– link adaptation
Adaptive
hypermedia
technologies
Adaptive
presentation
Adaptive
navigation support
Direct guidance
Adaptive link
sorting
Adaptive link
hiding
Adaptive link
annotation
Adaptive link
generation
Adaptive
multimedia
presentation
Adaptive text
presentation
Adaptation of
modality
Canned text
adaptation
Natural
language
adaptation
Inserting/
removing
fragments
Altering
fragments
Stretchtext
Sorting
fragments
Dimming
fragments
Map adaptation
Hiding
Disabling
Removal
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
Adaptive presentation techniques
• Conditional text filteringITEM/IP, PT, AHA!
• Adaptive stretchtextMetaDoc, KN-AHS, PUSH, ADAPTS
• Frame-based adaptationHypadapter, EPIAIM, ARIANNA, SETA
• Full natural language generationILEX, PEBA-II, Ecran Total
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
Example: Stretchtext (PUSH)
Example: Stretchtext (ADAPTS)
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
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)
Adaptive navigation support
• Direct guidance
• Restricting access– Removing, disabling, hiding
• Sorting
• Annotation
• Generation– Similarity-based, interest-based
• Map adaptation techniques
Example: Adaptive annotation
Annotations for topic states in Manuel Excell: not seen (white lens) ; partially seen (grey lens) ; and completed (black lens)
Adaptive annotation and removing
QuickTime™ and aTIFF (LZW) decompressorare needed to see this picture.
Example: Adaptive annotation
1. Concept role
2. Current concept state
3. Current section state
4. Linked sections state
4
3
2
1
v
Adaptive navigation support: major goals and relevant technologies
Direct
guidance
Sorting Hiding Annotation Map adaptation
Global
guidance
WebWatcher
ITEM/IP
ISIS-Tutor
SHIVA
Adaptive
HyperMan
CID
HYPERFLEX
Local
guidance
Land Use
Tutor
HyperTutor
Adaptive
HyperMan
ELM-PE
Hypadapter
HYPERFLEX
Hypadapter
PUSH
ISIS-Tutor
ELM-ART HYPERCASE
Local
orientation
support
(knowledge)
Hypadapter
ELM-PE
[Clibbon]
HyperTutor
Hypadapter
ISIS-Tutor
ELM-ART
ISIS-Tutor
ITEM/PG
Manuel Excel
Local
orientation
support
(goal)
Hynecosum
HyPLAN
ISIS-Tutor
PUSH
SYPROS
ELM-ART
ISIS-Tutor HYPERCASE
Global
orientation
support
[Clibbon]
Hynecosum
HyperTutor
ISIS-Tutor
SYPROS
ITEM/PG
ISIS-Tutor
ELM-ART
Manuel Excel
HYPERCASE
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
Link types and technologies
Directguidance
Sorting Hiding Annotation Mapadaptation
Contextual links OK (disabling) OK
Non-contextual links OK OK ? OK
Table of contents OK ? OK
Index OK ? OK
Local map OK OK OK OK
Global map OK OK OK OK
Adaptive navigation support: evaluation
• Sorting HYPERFLEX, 1993
• Annotation (colors) and hidingISIS-Tutor, 1995
• Annotation (icons)InterBook, 1997
• HidingDe Bra’s course, 1997
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
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)
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
Concepts, examples, and problems
Example 2 Example M
Example 1
Problem 1
Problem 2 Problem K
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Examples
Problems
Concepts
Indexing and navigation
Example 2 Example M
Example 1
Problem 1
Problem 2 Problem K
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Examples
Problems
Concepts
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 ‘+’)
Sample index page (annotation)
Sample index page (hiding)
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
Experiment design
Educational status of the node
behind the link
A. Non-
adaptive
B. Adaptive
annotation
C. Adaptive
hiding
Outside educational goal NA NA hidden
Within educational goal NA mark "-" mark "-"
Well-learned NA mark "+" mark "+"
Known NA green color green color
Ready-to-be-learned NA red color red color
Not-ready-to-be-learned NA NA hidden
Results: performance
Group Number of steps Time (sec) Concept
repetitions
"Unforced"
concept
repetitions
Task
repetitions
Non-adaptive 81.3 2196 17.3 11.2 6.2
Adaptive 65.2 1418 9.0 5.0 0.8
Restrictive 58.2 1785 8.9 4.8 0.4
Adaptive annotation makes navigation more efficient
The value of adaptivity: steps
30
40
50
60
70
80
90
100
110
120
130
Units
Steps
Hiding
Annotation
Nonadaptive
The value of adaptivity: repetitions
0
2
4
6
8
10
12
14
16
18
Concept repetitions Unforced repetitions
Hiding
Annotation
Nonadaptive
Results: navigation
Group Concept index Concept ->
concept
Concept ->
task
Task index Task ->
concept
Non-adaptive 18.5 8.1 9.8 12.2 2.0
Adaptive 18.4 1.8 6.4 9.6 2.2
Restrictive 14.0 1.6 8.1 7.0 1.8
No effect on navigation patterns due to variety of navigation styles
Results: recall
Group
Recalled concepts
correct
Recalled concepts
incorrect
Recalled links
correct
Recalled links
incorrect
Non-adaptive 7.0 0.7 8.6 5.0
Adaptive 6.2 1.2 7.5 8.5
Restrictive 6.9 0.8 5.7 5.1
No effect on recall
To hide or not to hide?
Additional value of hiding is unclear. Users prefer “freedom”
Group % users visited
non-goal tasks
% users visited
not-ready tasks
% users visited
non-goal
concepts
% users visited
not-ready concepts
visited non-
goal
concepts
Non-adaptive 20 66 33 0 1.0
Adaptive 0 100 80 20 2.8
Restrictive 0 0 0 0 0.0
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
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
Sections and concepts
Chapter 1
Chapter 2
Section 1.1
Section 1.2
Section 1.2.1 Section 1,2,2
Textbook
Sections and concepts
Chapter 1
Chapter 2
Section 1.1
Section 1.2
Section 1.2.1 Section 1,2,2
Domain model
Concept 1
Concept 2
Concept 3
Concept 4
Concept m
Concept n
Textbook
Indexing and navigation
Chapter 1
Chapter 2
Section 1.1
Section 1.2
Section 1.2.1 Section 1.2.2
Domain model
Concept 1
Concept 2
Concept 3
Concept 4
Concept m
Concept n
Textbook
Glossary view
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)
Adaptive navigation support
• Adaptive annotations– Links to sections– Links to concepts– Pages
• Adaptive sorting– Background help
• Direct guidance (course sequencing)– Teach Me
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
Adaptive annotation
• Educational status for concept unknown
known
learned
• Educational status for sections not ready to be learned
ready to be learned
suggested
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
Bookshelves and books
Book view
Glossary view
Backward learning: “help” and “teach this”
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
Preferred ways of navigation
0
2
4
6
8
10
12
14
16
18
20
Cell Mean
pCONTINUE
pBACK pTEXT
pCONTENT
pINTRODUCINGpREQUIRING
no
yes
Cell Bar Char t Split By: ANS Inclusion cr iter ia: Hits > 15 from Eklund Separated.stv
0
.1
.2
.3
.4
.5
.6
.7
.8
.9
1
Units
%Annotated % Sequential
no
yes
Box Plot Split By: ANS Inclusion cr iter ia: Hits > 15 from Eklund Separated.stv
.213 .172 .042 17 .043 .742 0
.282 .226 .080 8 .059 .742 0
.151 .072 .024 9 .043 .273 0
.583 .258 .063 17 .097 .913 0
.516 .306 .108 8 .097 .909 0
.643 .207 .069 9 .364 .913 0
Mean Std. Dev. Std. Er ror Count Minimum Maximum # Missing
%Annotated, Total
%Annotated, yes
%Annotated, no
% Sequential, Total
% Sequential, yes
% Sequential, no
Descr iptive Statistics Split By: ANS Inclusion cr iter ia: Hits > 15 from Eklund Separated.stv
The effect of “following green”with adaptation
6
6.5
7
7.5
8
8.5
9
9.5
10
Units
Score
yes, Low-negative
yes, Low-positive
yes, High-positive
Adaptation mechanisms do work!
0
20
40
60
80
100
120
140
160
Units
s1t/ s1 s2t/ s2 s3t/ s3
Box Plot Split By: Par t Inclusion cr iter ia: Hits > 15 from Eklund Separated.stv
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
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
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
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
Domain model - the key
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
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
Vector vs. network models
• Vector - no relationships
• Precedence (prerequisite) relationship
• is-a, part-of, analogy: (Wescourt et al, 1977)
• Genetic relationships (Goldstein, 1979)
Vector (set) model
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Network model
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Overlay user model: knowledge
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Simple overlay model
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept Nyesno
no
noyes
yes
Simple overlay model
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept Nyesno
no
noyes
yes
Weighted overlay model
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N103
0
27
4
Simple goal model
• Learning goal as a set of topics
More complicated models
• Sequence, stack, tree
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)
Knowledge-based hypertext
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
Indexing of nodes
Domain model
Concept 1
Concept 2
Concept 3
Concept 4
Concept m
Concept n
Hyperspace
Indexing of page fragments
Fragment 1
Fragment 2
Fragment K
Concept 1
Concept 2
Concept 3
Concept 4
Concept 5
Concept N
NodeConcepts
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
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
Hyperspace structuring
• Concept-based hyperspace• No imposed structure• Hierarchy• ASK approach - conversational
relationships
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
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-neutralAdvanced indexing (ADAPTS and COCOA)
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
ELM-ART: navigation and testing
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
Adaptivity in ELM-ART
• Adaptive navigation support
• Adaptive sequencing
• Adaptive testing
• Adaptive selection of relevant examples
• Adaptive similarity-based navigation
• Adaptive program diagnosis
ANS + Adaptive testing
Adaptive Diagnostics
Similarity-Based Navigation
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
ELM-ART: Value of ANS
With adaptive
annotation
Without adaptive
annotation
NEXT button 21.0 (N = 4) 25.0 (N = 3) 22.7 (N = 7)
No NEXT button 13.8 (N = 5) 9.5 (N = 2) 12.6 (N = 7)
17.0 (N = 9) 18.8 (N = 5) 17.7 (N = 14)
Mean number of pages which the users with no experience in programming languages completed with ELM-ART
ELM-ART: Value of ANS
With adaptive
annotation
Without adaptive
annotation
NEXT button 23.5 (N = 6) 14.0 (N = 3) 20.3 (N = 9)
No NEXT button 22.4 (N = 5) 12.6 (N = 5) 17.5 (N = 10)
23.0 (N = 11) 13.1 (N = 8) 18.8 (N = 19)
Mean number of pages which the users with experience in at least one programming language completed with ELM-ART
Adaptive annotation can:
• Reduce navigation effortsResults are not significant (variety of styles?)
• Reduce repetitive visits to learning pagesSignificant - if applied properly
• Encourage non-sequential navigation
• Increase learning outcomeFor those who is ready to follow and advice
• Make system more attractive for students
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-neutralAdvanced indexing (ADAPTS and COCOA)
ONR research project Architecture for integration of:
– Diagnostics– Technical Information– Performance-oriented Training
Technology investigation & testbed
ADAPTS
ADAPTS: What it is
IETMsTraining
Diagnostics
Use
r M
od
el
Diagnostics
Content
Navigation
What task to doSystem health
What content is applicable to thistask and this user
Levels of detail
How to display this content to this user
Experience,
Preferences,
ASSESSES: DETERMINES:
Adaptive Diagnostics
Personalized Technical Support
Video clips
(Training)Schematics
EngineeringData
Theory ofoperation
Blockdiagrams
Equipment
Simulations
(Training)
EquipmentPhotos
Illustrations
TroubleshootingStep
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?
ADAPTS - an adaptive IETM
The result
Maintenance
history Preprocessed,
condition-basedinputs
Technicianand OperatorObservations
Sensor inputs(e.g., 1553 bus)
PersonalizedDisplay
IETM TrainingStretch
text OutlineLinks
Training records
Skill assessment Experience
Preferences
Content NavigationDiagnostics
How do we make decisions?
User Model
Concept A
Concept A
ConceptB
ConceptB
ConceptC
ConceptC
SupportingInformation
Domain model
• Defines relationships between elements of technical information
• Indicates level of difficulty/ detail
• Shows prerequisites
Domain model example
CONCEPTReeling Machine
CONCEPTSonar Data Computer
CONCEPTSonar System
RemovalInstructions
TestingInstructions
IllustratedParts
Breakdown
Principles of
Operation
Principles of
Operation
Principles of
Operation
RemovalInstructions
RemovalInstructions
TestingInstructions
TestingInstructions
IllustratedParts
Breakdown
IllustratedParts
Breakdown
ReelingMachine
ReelingMachine
Sonar SystemSonar System
General Component Location
Principles of operation
Removal instructions
Principles of Operation
System Description DetailsParts List
Power Distribution
Domain model example
PART OF
Testing instructions
SUMMARY
DETAILS
TUTORIAL
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
User model example
Certified
CONCEPTReeling Machine
CONCEPTSonar Data Computer
CONCEPTSonar System
ROLERemoval
Instructions
ROLETesting
Instructions
ROLEIPB
ReviewedHands-on
Simulation
AT2 Smith
AD2 Jones
Preference
Reviewed
Hands-on+
Certified
Reviewed
Hands-on
Hands-on Reviewed
Reviewed
ROLETheory of Operatio
n
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
Integrated interface
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
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)
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
Social Navigation Support
AH Service: NavEx
AH Service: QuizGuide
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.