This document discusses generating personalized web pages for tutoring systems using knowledge-based approaches. It covers key topics like ontologies, student modeling, cognitive psychology, and hypertext. Personalized web pages can be adapted based on a student's knowledge, learning style, goals, preferences and other factors inferred from their interactions. The document argues that web pages should be designed following principles of cognitive ergonomics and rhetoric to facilitate understanding and avoid issues like high cognitive load.
Knowledge-based generation of educational web pages
1. Knowledge-Based Contents
Generation of Personalized
Web Pages Introduction
for Tutoring Web resources for learning
Stefan Trausan-Matu Web page generation
Computer Science Department, Knowledge
Bucharest "Politehnica" University,
and Computer-Human Interaction
Romanian Academy Center for Artificial Intelligence
Web page generation
ROMANIA
trausan@cs.pub.ro
http://www.racai.ro/~trausan
Stefan Trausan-Matu, ITS 2002,
Biarritz 2
Intelligent Tutoring Systems
Knowledge based systems
Student modeling
Reasoning for:
Introduction Student diagnosis
Explanations generation
Lesson planning
Intelligent interfaces
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Implied CS domains for
ITS on the web Artificial Intelligence
Computer-
Human ITS = Human learning as supervised
Interaction knowledge acquisition
Artificial
Intelligence Knowledge-based systems
Planning
Web Natural Language Processing
technologies
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2. Computer-Human Interaction Web technologies
User (learner) modeling Distributed computing
Personalization (Re)use web-based resources
Intelligent interfaces Client-server, web services
Cognitive psychology Huge amount of information available
Cognitive ergonomics on the web
Permanent evolution of the information
on the web
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Knowledge-based generation
of web pages for tutoring
Enhancing ITS with the advantages
offered by the possibility of browsing
the web :
Intelligent reuse web resources Web resources for learning
Integrate new information from the
web
Web rhetoric
Personalized web pages
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Learning on the web Resources on the web
Web is a very good place for learning
Databases
New information must be coherently
integrated in the body of knowledge in Knowledge bases (ontologies)
order to keep a holistic character of the Dictionaries, glossaries, and thesauri
body of knowledge Hypertexts and hypermedia
Specific web rhetoric Computer programs (e.g. applets)
Texts and corpora (annotated or not)
Images, films, sound
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3. Structure of resources on the
Text perspectives
web
Unstructured (e.g. TEXT, images) - Signs (Peirce, de Saussure): syntax,
hidden structure - Natural Language semantics, pragmatics - Semiotics
Processing Linguistics
Semi-structured (e.g. HYPERTEXT) - Metaphors
HTML, XML Philosophy of language
Structured (e.g. databases) Rhetoric
Psycholinguistics
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Text organization Hypertext
Linear organization - essay, story Text with extra dimensions
Hierarchical organization - treaty, Personalized reading
manual Easy browsable with computer-human
Network organization - hypertext, interfaces
hypermedia Offers the possibility of mapping to a
conceptual structure
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Hypertext - facilitator of Hypertext - facilitator of
human understanding: human understanding:
Theodor Nelson, who coined the term
Hypertext was introduced by Douglas "hypertext", defined it as the
Engelbart, in the early sixties, as a : hyperspace of concepts from a given
text or :
"Conceptual framework for augmenting "A system for massively parallel creative
human intellect" (Engelbart, 1995) work and study ... to the betterment of
human understanding" (Nelson, 1995)
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4. World Wide Web
Hypertext(media) + Internet + User Friendly
Interfaces
Text (+images ...) + Knowledge
communication, distribution, agents +
interfacing, cognitive ergonomics (HCI, CHI, HCD)
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Knowledge Knowledge-Based Systems
Learning is a knowledge centered activity: Explicit representation, in a so-called
“Knowledge Base”, of the knowledge needed
One of the main goals of a learning by the program
process is the articulation in the
The knowledge base may easy evolve - the
learner’s mind of a body of knowledge representation used must facilitate:
for the considered domain. knowledge acquisition
The skeleton of this body is usually a learning
semantic network of the main concepts The same knowledge base used in several
involved in that domain. processing regimes
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Ontologies Ontologies
"An ontology is a specification of a
Knowledge base = Ontology + …
(rules) conceptualization....That is, an ontology is
a description (like a formal specification of
Concepts + Attributes + Relations (+
Axioms) a program) of the concepts and
relationships that can exist for an agent
Multiple ontologies - Ontology
alignment ! or a community of agents" (Gruber)
Needed for agents inter-communication
(share of same concepts)
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5. PROGRAMMING_CONCEPT
PROGRAMMING_ABSTRACTION
DATA_ABSTRACTION
Ontologies - Concepts MAPPING
ARRAY
CONTAINER
TABLE
HASHTABLE
The central part of the domain ontology is a INDEXTABLE
ARRAY
taxonomically organized knowledge base of SYMBOLTABLE
COLLECTION
concepts: IMPLICITCOL
EXPLICITCOL
SET
SYMBOLTABLE
Security
BAG
Bond DISPENSER
STACK
Share QUEUE
HEAP
OrdinaryShare CURSORSTR
PreferenceShare LINKEDLIST
CURSORTREE
Stock CONTROL_ABSTRACTION
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Ontologies - Relations
Ontologies - Attributes
Each concept has attributes. For example, Each concept may be related with other
a share has the following attributes: concepts. Related terms with share are:
the shareholder,
earnings per share share capital,
share premium account dividend.
gain
issue
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Ontologies - Languages Ontologies on the web
Description logics : LOOM, CLASSIC, General lexical ontologies :
Fact WordNet
XML-Based : DAML+OIL, OML EuroWordNet
BalkanNet
MikroKosmos
FrameNet
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6. Exchange of ontologies on the
Ontologies on the web
web
Domain specific Particular ontologies are now sharable
Supper Upper Ontology on the web with XML-based languages
like DAML+OIL.
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Ontologies used in ITSs Ontologies in ITSs used for :
Domain Learner modelling - overlay, buggy
Tutoring Text processing
Test generation and selection
Human-computer interfacing
Learner diagnosys
Lexical
Authoring
Upper Level Knowledge acquisition
Course planning
Web page generation
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Computer-Human Interaction
(CHI)
Among others, it studies:
Cognitive ergonomics
Computer-Human Interaction Immersive interfaces
Learner (user) modeling
Personalization
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7. Important issues in cognitive
Cognitive ergonomics
ergonomics of web pages:
Studies the ways in which human-computer Cognitive load
interfaces can be tailored to users' cognitive
characteristics. Lack of orientation
It is very important to design cognitive Web rhetoric
ergonomic web pages.
Facilitate understanding
If you design web pages that are not
cognitive ergonomic, few people will stay
browsing them (when they have the
possibility of surfing a tremendous number of
other pages).
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Cognitive load Lack of orientation
Mental (cognitive) effort needed to You could spend even whole days surfing in
browse the web pages cyberspace, forgetting the starting point, the
path you followed, or the starting goals (all
One solution is to assure a holistic these might be one of the causes of its
character for the body of knowledge attractiveness, but it may become something
induced in the learner’s mind. The like drug-addiction).
learning process must induce the sense Therefore, a well designed structure of the
of the whole. New concepts must fit in links topology, easy to understand for
the whole. anybody is very important.
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Web rhetoric Web rhetoric
Similarly to a lawyer that uses rhetoric " In the course of designing a hyper document, an
author is generally confronted with three sub
to convince the jury, you must use problems which correspond to the classical fields of
rhetoric in your web pages in order to rhetoric, i.e. inventio, dispositio and elocutio. He
must:
obtain the best results with generate and select relevant information (inventio),
communication in your web pages structure resp. order the selected information
(dispositio), and
present the ordered information in an adequate way
(elocutio).“ (Thuering, M., Hannemann, J., Haake,
J.M., 1991)
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8. Understanding Empathy
Explanation vs. Understanding "empathy is a phenomenon in which
Understanding implies an emphatic one person can experience states,
relation, which involves the immersion thoughts and actions of another person,
of the learner in a context. (vonWright) by psychological transposition of the
Different interpreters may have self in an objective human behavior
different understandings of the same model, allowing the understanding of
sign. the way the other interprets the world “
Understanding requires experiencing (…………..)
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Very important in immersion
Immersion are the space and time
perception or imagination in
"The state of being overwhelmed or images (perceived or imagined) in
deeply absorbed; deep engagedness". which objects are identified;
(Webster Dictionary, 1999) the possibility and experience of real,
"If you immerse yourself in something, simulated or mental walkthrough in the
context of immersion;
you become completely involved in it."
(Collins Dictionary, 1999) the experience of actions (real of
imagined) done by the immersed
person.
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Immersion done by Flow state
Flow state (Alan Cooper, “About Face”), e.g.
Physically entering in a context of the domain driving a car or skiing - induced by a perfect
(for example, learning to drive a car by immersion:
entering the care, starting it and driving),
Simulations through, for example, computer sense of control
graphics facilities (starting from simple navigation
interactive computer graphic till virtual
reality); loose of the sense of time
Mentally, as a result of mental imagery, as a
consequence of reading a text or browsing
web pages.
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9. Immersion on web sites
The World Wide Web has been proved as a
very attractive and, meanwhile, very useful
space to wander for almost anyone, including
students. Therefore, it may be considered it
as a very suitable medium to provide
immersive learning
CHI - Personalization
The immersion illusion can be supported both
by a structure of web pages
Web browsing may generate a flow state
Flow state may be useful for learning
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Personalized web pages Personalized web pages
From an ideal perspective, everybody has Are adapted to each users':
to find WWW structured according to knowledge - ITS student model
his needs, goals and cognitive learning style
particularities. psychological profile
goals (e.g. lists of concepts to be learned)
level (novice, expert)
preferences (e.g. style of web pages)
context of interaction
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Student model Learning style
Keeps track of the concepts known, unknown Exploratory vs. interactional
or wrongly known by the student (………)
David Kolb’s learning styles :
Inferred from results at tests or from
Accomodator
interaction (visited web pages, topics
searched etc.) Diverger
Is usually defined in relation with the domain Converger
ontology (concept net, Bayesian net) Assimilator
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10. Psychological profile Psychological profile
Inferred from results at psychological Self-confidence
tests or from interaction (time of Motivation
visiting different types of web pages) Concentration
Personality types Social interaction
Intelligence Emotion profile
Context dependence
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Preferences Context of interaction
Explicitly chosen by the learner Avoid monotony, fatigue or cognitive
Inferred from behavior overload
Inferred from the psychological style Rhetoric schemata
Speech acts
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Web page generation
Content
Structuring
Web page generation Styling
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11. Web rhetoric
" In the course of designing a hyper document, Web page generation
…
generate and select relevant information
(inventio), Content
structure resp. order the selected information
(dispositio), and
present the ordered information in an
adequate way (elocutio).“ (Thuering, M.,
Hannemann, J., Haake, J.M., 1991)
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Content types Content types - text
Text Descriptions
Questions and tests Justifications
Explanations
Links
Questions
Images and sounds
Glossary
Programs (e.g. applets) Index
Links
Help
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Content types Content semantics
Textual Conceptual structure
Visual Semantic density
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12. Content pragmatics for learning
purposes Source of content
Created (edited) by the professor - authoring
Context tools
Reused - Information retrieval - search
Prerequisites for a content module agents
Relations to other content modules text
html
Speech act role of content
xml
jpeg, mpeg etc.
Automatically generated (text, tests)
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Dimensions of texts on the web Text structuring
1. Raw text
2. Text shown by the browser Bracketing
3. Annotated text (HTML, XML)
Knowledge extraction and semantic
4. Style of presentation (CSS, XSL)
5. Hyperlinks relations
6. Structure of web pages Text segmentation
7. Knowledge in texts Rhetoric schema identification
8. Goals of the writer
9. The history of browsing web pages Automatic link generation
10. Effect on the reader
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Text annotation Text segmentation
Syntactic Identification of structures (e.g. lexical chains
Part of speech - G. Hirst) of semantically related words
“Bracketing” Uses WordNet or other lexical ontologies,
which provides semantic relations among
Semantic words
Pragmatic synonims
Rhetoric hypernims, hiponims
meronyms, holonims
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13. Natural Language Processing Natural Language Processing
(NLP) approaches
Parsing
Annotation Grammar-based
Knowledge extraction Statistical
Document categorization
Search for relevant documents
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XML XML
“eXtensible Markup Language”
Universal markup language <Student>
<ID>7321</I
<FName>Steven</FName>
Extends HTML facilities <Name>Collins</Name>
<Year>4</Year>
Simplified SGML </Student>
Keeps 80% from SGML
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XML additional features
XML similarities with HTML
comparatively to HTML
Easy to use on Internet
Extensibility - new types of annotations
XML documents are easy to create and
may be introduced
process
Universal representation language
XML documents may be read with an
ordinary text editor Separation of content, structure and
visualization
SGML compatible
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14. XML additional features
comparatively to HTML XML encourages semantics
HTML XML
<table> <?xml version="1.0"?>
Facilities for semantic encoding <tr> <StudentsList>
<td>7612</td> <Student>
Allows different (personalized) <td>John</td>
<td>Freeman</td>
<ID>7612</ID>
<FName>John</FName>
presentations of the same document <td>3</td>
</tr>
<Name>Freeman</Name>
<Year>3</Year>
(by means of XSLT transformations) <tr>
<td>7321</td>
</Student>
<Student>
<td>Steven</td> <ID>7321</ID>
<td>Collins</td> <FName>Steven</FName>
<td>4</td> <Name>Collins</Name>
</tr> <Year>4</Year>
</table> </Student>
</StudentsList>
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XML Perspectives XML Perspectives
Allows the definition of a grammar for a Universal markup of documents (simplified
markup language: SGML)
Explicitly, with a DTD or a schema Universal document structuring - allows a
(“valid XML document”) linear representation of any structure
Implicitly, even in the absence of a DTD Universal modality of exchange of information
or schema, starting from the annotation on Internet
structure (“well formed document”) Language for federated databases
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XML languages XSLT
XSLT Transformation of XML files into other
XPointer XML, HTML or text files
Tree (source) to tree (destination)
XLink transformation rules
DAML+OIL Example-based programming
LOM XSLT programs are XML files
User defined Uses XPath language for addressing
inside XML documents
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15. XML annotation for learning
XSLT purposes
<xsl:stylesheet xmlns:xsl="http://www.w3.org/TR/WD-xsl">
<xsl:template match="/">
Universal way of content structuring
<html> <body> <h2>List of students</h2>
<xsl:apply-templates/>
and annotation
</body> </html>
Reuse of learning modules through the
</xsl:template>
<xsl:template match="StudentsList">
web
<xsl:for-each select="Student">
ID= <xsl:value-of select="ID"/> First name:<xsl:value-of select="FName"/>
Name:<xsl:value-of select="Name"/> Year:<xsl:value-of select="Year"/>
</xsl:for-each>
</xsl:template>
</xsl:stylesheet>
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Semantic editing E-learning standards
IEEE-LTSC - IEEE Learning Technology Standards
Committee (LTSC)
ARIADNE - Alliance of Remote Instructional
Authoring and Distribution Networks for Europe
IMS - Global Learning Consortium, Inc.
SCORM - Sharable Content Object Reference Model
- ADL - Advanced Distributed Learning
AICC - Aviation Industry CBT (Computer-Based
Training) Committee
DC - Dublin Core Metadata Initiative
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XML based annotation in
Learner Object Metadata
E-learning standards
XML-based Metadata - LOM (“Learning <?xml version="1.0"?>
<lom
Object Metadata”) - elementary xmlns="http://www.imsglobal.org/xsd/imsmd_rootv1
p2p1” ...>
learning module <general> ... </general>
<lifecycle> ... </lifecycle>
<metametadata> ... </metametadata>
IMS packages of learning modules <technical> ... </technical>
<educational> ... </educational>
<relation> ... </relation>
<annotation> ... </annotation>
<classification> ... </classification>
</lom>
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16. Learner Object Metadata Learner Object Metadata
<educational>
<technical> <interactivitytype>
<format>text/html</format> <langstring>Expositive</langstring>
<location type="URI"> </interactivitytype>
http://www.racai.ro/foo/c.html <learningcontext>
</location> <langstring>Higher Education</langstring>
</technical> </learningcontext>
<description>
<langstring>Online CoursePack</langstring>
</description>
</educational>
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Learner Object Metadata
<relation>
<kind>
Web page generation
<langstring>Requires</langstring>
</kind>
<resource>
<description>
Structuring
<langstring>Description of resource</langstring>
</description>
</resource>
</relation>
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Web page generation Structuring
Content Linear
Structuring Hierarchy
Styling Network
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17. Structuring Generate web pages
Usually, learning systems on the web Adaptable – with usual browsers
generate a linear, “tutorial” order, e.g. Adaptive – (Brusilovsky-AH) ELM-ART
DCG, APHID, ELM-ART, ID Generated for a group, with adaptable features
Simple hierarchical links -lessons, (reorder links, show/hide links, map adaptation)
sections, subsections, and terminal Customization vs. optimization
pages ELM-ART II Personalized (individualized) – DCG, APHID,
Very simple network links – index, Larflast
glossary, references Generated for a single person
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Scope of generation Generation horizon
Generate an entire site Local – satisfy “requires” links
Generate page by page Holistic - Larflast
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Goal of generation Generation procedure
Convert printed to electronic textbooks, Personalized generation is achieved by
e.g. ELM-ART filtering the conceptual structure
Sequencing of modules – starting from (semantic network, domain ontology)
a student model and relations among according to the learner model (known
learning modules, e.g. DCG or unknown concepts) or to the
Glossary, index, and references links abstraction level (e.g. ID)
Hypertext links – using NLP techniques Planning – AND/OR graph (DCG), Bayes
Believe Net – APHID
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18. GenWeb (Trausan-Matu, PEDAGOGICAL
KNOWLEDGE Domain knowl.
acquisition
1997) Test
generation
DOMAIN
Centered around a domain knowledge base Student
(ontology) Eval. KNOWLEDGE BASE
Adapts lesson planning according to different Rev.eng. of
predefined student personalities stud. programs
Generates simple explanations in natural language
Explanation
Generates automatically multiple answers tests generation STUDENT MODEL
(knowledge about the user)
Evaluates students results for tests, and develop a
student’s model RETHORICAL
Understands (reverse engineering) student programs KNOWLEDGE
Generates a highly structured collection of web pages HYPERTEXT
LINGUISTIC
GENERATION KNOWLEDGE
FOR WWW
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LARFLAST LARFLAST
LeARning Foreign Language Scientific
Terminology COPERNICUS EU project Browsing a holistic, understandable structure
may induce a flow state
• Leeds University – UK,
• Manchester University - UK, Adaptation of the content of the generated
• Montpellier University - France, web pages to the incoming information from
• RACAI – Romania,
• Sofia University - Bulgaria, the web. New information is extracted,
• Sinferopol University - Ukraine annotated and coherently integrated in the
body of knowledge in order to keep the
Objective: To provide a set of tools, available on the web,
for supporting the learning of foreign terminology in finance holistic character of the body of knowledge.
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Serendipitous information
LARFLAST acquisition (Cerri & Maraschi)
Dynamic generation of personalized web pages
Runs from an Apache servlet
Adapts to the learner’s model, transferred
from another web site
Parameterized, easy to configure for new
patterns of web pages and structures
Includes relevant metaphors and texts from a
corpus
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19. Semantic editing (Trausan)
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20. Web page generation
Styling
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Web page generation Styling
Content Different presentation attributes (color,
Structuring shape, highlighting, background etc.)
Styling Correspond to user’s preferences
Performed
Declaratively – CSS, XSLT
Procedural – JavaScript, Java
Client vs. server (ASP, JSP, XSP, PHP)
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References References
P. De Bra, P. Brusilovsky, G. Housen, Adaptive Hypermedia: From Kettel, Thomson, Greer, Generating Individualized Hypermedoia
Systems to Framework, ACM Computing Surveys 31(4) 1999. Apploications, Procs. Of the Int. Workshop on Adaptive and Intelligent
Clibbon, K., Conceptually Adapted Hypertext For Learning, Proceedings Web-based Educational Systems, Montrel, Canada, 2000, pp. 37-49
of CHI’95, (APHID)
http://www.acm.org/sigchi/chi95/Electronic/documnts/kc_bdy.html Sickmann and all, Adaptive Course Generation, Procs. Of the Int.
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