Knowledge-based generation of educational web pages

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Knowledge-Based Generation of Personalized Web Pagesfor Tutoring

Stefan Trausan-MatuComputer Science Department,

Bucharest "Politehnica" University,

and

Romanian Academy Center for Artificial Intelligence

ROMANIA

[email protected]

http://www.racai.ro/~trausanStefan Trausan-Matu, ITS 2002,

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Contents

Introduction

Web resources for learning

Web page generation

Knowledge

Computer-Human Interaction

Web page generation

Stefan Trausan-Matu, ITS 2002,

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Introduction


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Intelligent Tutoring Systems

Knowledge based systems

Student modeling

Reasoning for:

� Student diagnosis

� Explanations generation

� Lesson planning

Intelligent interfaces


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Implied CS domains forITS on the web

Computer-Human

InteractionArtificial

Intelligence

Web technologies


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Artificial Intelligence

ITS = Human learning as supervised knowledge acquisition

Knowledge-based systems

Planning

Natural Language Processing

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User (learner) modeling

Personalization

Intelligent interfaces

Cognitive psychology

Cognitive ergonomics


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Web technologies

Distributed computing

(Re)use web-based resources

Client-server, web services

Huge amount of information available 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

Integrate new information from the web

Web rhetoric

Personalized web pages


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Web resources for learning


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Learning on the web

Web is a very good place for learning

New information must be coherentlyintegrated in the body of knowledge inorder to keep a holistic character of thebody of knowledge

Specific web rhetoric


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Resources on the web

Databases

Knowledge bases (ontologies)

Dictionaries, glossaries, and thesauri

Hypertexts and hypermedia

Computer programs (e.g. applets)

Texts and corpora (annotated or not)

Images, films, sound

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Structure of resources on the web

Unstructured (e.g. TEXT, images) -hidden structure - Natural Language Processing

Semi-structured (e.g. HYPERTEXT) -HTML, XML

Structured (e.g. databases)


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Text perspectives

Signs (Peirce, de Saussure): syntax, semantics, pragmatics - Semiotics

Linguistics

Metaphors

Philosophy of language

Rhetoric

Psycholinguistics


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Text organization

Linear organization - essay, story

Hierarchical organization - treaty, manual

Network organization - hypertext, hypermedia


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Hypertext

Text with extra dimensions

Personalized reading

Easy browsable with computer-human interfaces

Offers the possibility of mapping to a conceptual structure


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Hypertext - facilitator ofhuman understanding:

Hypertext was introduced by Douglas Engelbart, in the early sixties, as a :

"Conceptual framework for augmenting human intellect" (Engelbart, 1995)


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Hypertext - facilitator ofhuman understanding:

Theodor Nelson, who coined the term "hypertext", defined it as the hyperspace of concepts from a given text or :

"A system for massively parallel creative work and study ... to the betterment of human understanding" (Nelson, 1995)

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Hypertext(media) + Internet + User Friendly Interfaces

Text (+images ...) +

communication, distribution, agents +

interfacing, cognitive ergonomics (HCI, CHI, HCD)

World Wide Web


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Knowledge


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Knowledge

Learning is a knowledge centered activity:

One of the main goals of a learning process is the articulation in the learner’s mind of a body of knowledge for the considered domain.

The skeleton of this body is usually a semantic network of the main concepts involved in that domain.


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Knowledge-Based Systems

Explicit representation, in a so-called “Knowledge Base”, of the knowledge needed by the program

The knowledge base may easy evolve - the representation used must facilitate:� knowledge acquisition

� learning

The same knowledge base used in several processing regimes


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Ontologies

Knowledge base = Ontology + … (rules)

Concepts + Attributes + Relations (+ Axioms)

Multiple ontologies - Ontology alignment !

Needed for agents inter-communication (share of same concepts)


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Ontologies

"An ontology is a specification of a conceptualization....That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents" (Gruber)

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Ontologies - Concepts

The central part of the domain ontology is a taxonomically organized knowledge base of

concepts:

Security

Bond

Share

OrdinaryShare

PreferenceShare

StockStefan Trausan-Matu, ITS 2002,

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PROGRAMMING_CONCEPTPROGRAMMING_ABSTRACTION

DATA_ABSTRACTIONMAPPING

ARRAYCONTAINER

TABLEHASHTABLEINDEXTABLE

ARRAYSYMBOLTABLE

COLLECTIONIMPLICITCOLEXPLICITCOLSET

SYMBOLTABLEBAG

DISPENSERSTACKQUEUEHEAP

CURSORSTRLINKEDLISTCURSORTREE

CONTROL_ABSTRACTION


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Ontologies - Attributes

Each concept has attributes. For example, a share has the following attributes:

earnings per share

share premium account

gain

issue


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Ontologies - Relations

Each concept may be related with other concepts. Related terms with share are:

the shareholder,

share capital,

dividend.


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Ontologies - Languages

Description logics : LOOM, CLASSIC, Fact

XML-Based : DAML+OIL, OML


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Ontologies on the web

General lexical ontologies :

WordNet

EuroWordNet

BalkanNet

MikroKosmos

FrameNet

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Ontologies on the web

Domain specific

Supper Upper Ontology


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Exchange of ontologies on the web

Particular ontologies are now sharableon the web with XML-based languageslike DAML+OIL.


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Ontologies used in ITSs

Domain

Tutoring

Human-computer interfacing

Lexical

Upper Level


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Ontologies in ITSs used for :

Learner modelling - overlay, buggy

Text processing

Test generation and selection

Learner diagnosys

Authoring

Knowledge acquisition

Course planning

Web page generation


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Computer-Human Interaction (CHI)

Among others, it studies:


Immersive interfaces

Learner (user) modeling

Personalization

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Studies the ways in which human-computer interfaces can be tailored to users' cognitive characteristics.

It is very important to design cognitive ergonomic web pages.

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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Important issues in cognitive ergonomics of web pages:

Cognitive load

Lack of orientation

Web rhetoric

Facilitate understanding


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Cognitive load

Mental (cognitive) effort needed to browse the web pages

One solution is to assure a holistic character for the body of knowledge induced in the learner’s mind. The learning process must induce the sense of the whole. New concepts must fit in the whole.


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Lack of orientation

You could spend even whole days surfing in cyberspace, forgetting the starting point, the path you followed, or the starting goals (all these might be one of the causes of its attractiveness, but it may become something

like drug-addiction).

Therefore, a well designed structure of the links topology, easy to understand for anybody is very important.


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Web rhetoric

Similarly to a lawyer that uses rhetoric to convince the jury, you must use rhetoric in your web pages in order to obtain the best results with communication in your web pages


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Web rhetoric

" In the course of designing a hyper document, an author is generally confronted with three sub problems which correspond to the classical fields of rhetoric, i.e. inventio, dispositio and elocutio. He must:

generate and select relevant information (inventio),

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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Understanding

Explanation vs. Understanding

Understanding implies an emphatic relation, which involves the immersion of the learner in a context. (vonWright)

Different interpreters may have different understandings of the same sign.

Understanding requires experiencing


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Empathy

"empathy is a phenomenon in which one person can experience states, thoughts and actions of another person, by psychological transposition of the self in an objective human behavior model, allowing the understanding of the way the other interprets the world “ (…………..)


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Immersion

"The state of being overwhelmed or deeply absorbed; deep engagedness". (Webster Dictionary, 1999)

"If you immerse yourself in something, you become completely involved in it." (Collins Dictionary, 1999)


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Very important in immersion are the space and time perception or imagination in

images (perceived or imagined) in which objects are identified;

the possibility and experience of real, simulated or mental walkthrough in the context of immersion;

the experience of actions (real of imagined) done by the immersed person.


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Immersion done by

Physically entering in a context of the domain (for example, learning to drive a car by entering the care, starting it and driving),

Simulations through, for example, computer graphics facilities (starting from simple interactive computer graphic till virtual reality);

Mentally, as a result of mental imagery, as a consequence of reading a text or browsing web pages.


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Flow state

Flow state (Alan Cooper, “About Face”), e.g. driving a car or skiing - induced by a perfect immersion:

sense of control

navigation

loose of the sense of time

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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

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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CHI - Personalization


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From an ideal perspective, everybody hasto find WWW structured according tohis needs, goals and cognitiveparticularities.


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Are adapted to each users':� knowledge - ITS student model

� learning style

� 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

Keeps track of the concepts known, unknown or wrongly known by the student (………)

Inferred from results at tests or from interaction (visited web pages, topics searched etc.)

Is usually defined in relation with the domain ontology (concept net, Bayesian net)


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Learning style

Exploratory vs. interactional

David Kolb’s learning styles :

� Accomodator

� Diverger

� Converger

� Assimilator

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Psychological profile

Inferred from results at psychological tests or from interaction (time of visiting different types of web pages)

Personality types

Intelligence

Context dependence


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Psychological profile

Self-confidence

Motivation

Concentration

Social interaction

Emotion profile


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Preferences

Explicitly chosen by the learner

Inferred from behavior

Inferred from the psychological style


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Context of interaction

Avoid monotony, fatigue or cognitiveoverload

Rhetoric schemata

Speech acts


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Web page generation


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Web page generation

Content

Structuring

Styling

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Web rhetoric

" In the course of designing a hyper document, …

generate and select relevant information (inventio),

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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Web page generation

Content


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Content types

Text

Questions and tests

Links

Images and sounds

Programs (e.g. applets)


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Content types - text

Descriptions

Justifications

Explanations

Questions

Glossary

Index

Links

Help


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Content types

Textual

Visual


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Content semantics

Conceptual structure

Semantic density

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Content pragmatics for learning purposes

Context

Prerequisites for a content module

Relations to other content modules

Speech act role of content


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Source of content Created (edited) by the professor - authoring tools

Reused - Information retrieval - search agents� text

� html

� xml

� jpeg, mpeg etc.

Automatically generated (text, tests)


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Dimensions of texts on the web

1. Raw text2. Text shown by the browser3. Annotated text (HTML, XML)4. Style of presentation (CSS, XSL)5. Hyperlinks

6. Structure of web pages7. Knowledge in texts8. Goals of the writer9. The history of browsing web pages10. Effect on the reader


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Text structuring

Bracketing

Knowledge extraction and semantic relations

Text segmentation

Rhetoric schema identification

Automatic link generation


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Text annotation

Syntactic

� Part of speech

� “Bracketing”

Semantic

Pragmatic

Rhetoric


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Text segmentation

Identification of structures (e.g. lexical chains - G. Hirst) of semantically related words

Uses WordNet or other lexical ontologies, which provides semantic relations among words

� synonims

� hypernims, hiponims

� meronyms, holonims

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Natural Language Processing (NLP)

Parsing

Annotation

Knowledge extraction

Document categorization

Search for relevant documents


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Natural Language Processingapproaches

Grammar-based

Statistical


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XML

“eXtensible Markup Language”

Universal markup language

Extends HTML facilities

Simplified SGML

Keeps 80% from SGML


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XML

<Student><ID>7321</I<FName>Steven</FName><Name>Collins</Name><Year>4</Year>

</Student>


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XML similarities with HTML

Easy to use on Internet

XML documents are easy to create and

process

XML documents may be read with an

ordinary text editor

SGML compatible


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XML additional features comparatively to HTML

Extensibility - new types of annotations

may be introduced

Universal representation language

Separation of content, structure and

visualization

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XML additional features comparatively to HTML

Facilities for semantic encoding

Allows different (personalized)

presentations of the same document

(by means of XSLT transformations)


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HTML XML<table> <tr> <td>7612</td> <td>John</td> <td>Freeman</td> <td>3</td> </tr> <tr> <td>7321</td> <td>Steven</td> <td>Collins</td> <td>4</td> </tr></table>

<?xml version="1.0"?><StudentsList> <Student> <ID>7612</ID> <FName>John</FName> <Name>Freeman</Name> <Year>3</Year> </Student> <Student> <ID>7321</ID> <FName>Steven</FName> <Name>Collins</Name> <Year>4</Year> </Student></StudentsList>

XML encourages semantics


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XML Perspectives

Allows the definition of a grammar for a markup language:

Explicitly, with a DTD or a schema (“valid XML document”)

Implicitly, even in the absence of a DTD or schema, starting from the annotation structure (“well formed document”)


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XML Perspectives

Universal markup of documents (simplified

SGML)

Universal document structuring - allows a

linear representation of any structure

Universal modality of exchange of information

on Internet

Language for federated databases


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XML languages

XSLT

XPointer

XLink

DAML+OIL

LOM

User defined


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XSLT

Transformation of XML files into other XML, HTML or text files

Tree (source) to tree (destination) transformation rules

Example-based programming

XSLT programs are XML files

Uses XPath language for addressing inside XML documents

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XSLT<xsl:stylesheet xmlns:xsl="http://www.w3.org/TR/WD-xsl">

<xsl:template match="/">

<html> <body> <h2>List of students</h2>

<xsl:apply-templates/>

</body> </html>

</xsl:template>

<xsl:template match="StudentsList">

<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>Stefan Trausan-Matu, ITS 2002,

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XML annotation for learning purposes

Universal way of content structuring and annotation

Reuse of learning modules through the web


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Semantic editing


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E-learning standards

IEEE-LTSC - IEEE Learning Technology Standards Committee (LTSC)ARIADNE - Alliance of Remote Instructional Authoring and Distribution Networks for EuropeIMS - Global Learning Consortium, Inc.SCORM - Sharable Content Object Reference Model - ADL - Advanced Distributed LearningAICC - Aviation Industry CBT (Computer-Based Training) CommitteeDC - Dublin Core Metadata Initiative


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XML based annotation in E-learning standards

XML-based Metadata - LOM (“Learning Object Metadata”) - elementary learning module

IMS packages of learning modules


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Learner Object Metadata

<?xml version="1.0"?>

<lom xmlns="http://www.imsglobal.org/xsd/imsmd_rootv1p2p1” ...>

<general> ... </general>

<lifecycle> ... </lifecycle>

<metametadata> ... </metametadata>

<technical> ... </technical>

<educational> ... </educational>

<relation> ... </relation>

<annotation> ... </annotation>

<classification> ... </classification>

</lom>

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<technical><format>text/html</format><location type="URI">

http://www.racai.ro/foo/c.html </location>

</technical>


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<educational><interactivitytype>

<langstring>Expositive</langstring></interactivitytype><learningcontext>

<langstring>Higher Education</langstring></learningcontext><description>

<langstring>Online CoursePack</langstring></description>

</educational>


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<relation><kind>

<langstring>Requires</langstring></kind><resource>

<description><langstring>Description of resource</langstring>

</description></resource>

</relation>


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Web page generation

Structuring


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Web page generation

Content

Structuring

Styling


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Structuring

Linear

Hierarchy

Network

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Structuring

Usually, learning systems on the web generate a linear, “tutorial” order, e.g. DCG, APHID, ELM-ART, ID

Simple hierarchical links -lessons, sections, subsections, and terminal pages ELM-ART II

Very simple network links – index, glossary, references


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Generate web pages

Adaptable – with usual browsers

Adaptive – (Brusilovsky-AH) ELM-ART

� Generated for a group, with adaptable features (reorder links, show/hide links, map adaptation)

� Customization vs. optimization

Personalized (individualized) – DCG, APHID, Larflast

� Generated for a single person


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Scope of generation

Generate an entire site

Generate page by page


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Generation horizon

Local – satisfy “requires” links

Holistic - Larflast


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Goal of generation

Convert printed to electronic textbooks, e.g. ELM-ART

Sequencing of modules – starting from a student model and relations among learning modules, e.g. DCG

Glossary, index, and references links

Hypertext links – using NLP techniques


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Generation procedure

Personalized generation is achieved by filtering the conceptual structure (semantic network, domain ontology) according to the learner model (known or unknown concepts) or to the abstraction level (e.g. ID)

Planning – AND/OR graph (DCG), Bayes Believe Net – APHID

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GenWeb (Trausan-Matu, 1997)

Centered around a domain knowledge base(ontology)

Adapts lesson planning according to differentpredefined student personalities

Generates simple explanations in natural language

Generates automatically multiple answers tests

Evaluates students results for tests, and develop astudent’s model

Understands (reverse engineering) student programs

Generates a highly structured collection of web pages


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DOMAIN

KNOWLEDGE BASE

PEDAGOGICAL KNOWLEDGE

STUDENT MODEL(knowledge about the user)

Domain knowl. acquisition

Testgeneration

Explanation generation

HYPERTEXT GENERATION

FOR WWW

RETHORICAL KNOWLEDGE

Rev.eng. of stud. programs

StudentEval.

LINGUISTIC KNOWLEDGE


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LARFLAST

LeARning Foreign Language Scientific Terminology COPERNICUS EU project

• Leeds University – UK,• Manchester University - UK,• Montpellier University - France,• RACAI – Romania,• Sofia University - Bulgaria,• Sinferopol University - Ukraine

Objective: To provide a set of tools, available on the web, for supporting the learning of foreign terminology in finance


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LARFLAST

Browsing a holistic, understandable structure

may induce a flow state

Adaptation of the content of the generated web pages to the incoming information from the web. New information is extracted, annotated and coherently integrated in the body of knowledge in order to keep the

holistic character of the body of knowledge.


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LARFLAST

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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Serendipitous information acquisition (Cerri & Maraschi)

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Semantic editing (Trausan)


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Web page generation

Styling


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Web page generation

Content

Structuring

Styling


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Styling

Different presentation attributes (color, shape, highlighting, background etc.)

Correspond to user’s preferences

Performed

� Declaratively – CSS, XSLT

� Procedural – JavaScript, Java

Client vs. server (ASP, JSP, XSP, PHP)


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References

P. De Bra, P. Brusilovsky, G. Housen, Adaptive Hypermedia: From Systems to Framework, ACM Computing Surveys 31(4) 1999.

Clibbon, K., Conceptually Adapted Hypertext For Learning, Proceedings of CHI’95, http://www.acm.org/sigchi/chi95/Electronic/documnts/kc_bdy.html

Dimitrova, V., Self, J., Brna, P., 'Maintaining a Joinly Constrcted Student Model', in S.A.Cerri (ed.), Artificial Intelligence, Methodology, Systems, Applications 2000, Springer-Verlag, ISBN 3-540-41044-9, pp.221-231.

Engelbart, D.C., Toward Augmenting the Human Intellect and Boosting our Collective IQ, Communications of the ACM, vol.38, no. 8, pp. 30-33,aug. 1995.

Gruber, T., What is an Ontology, http://www.ksl.stanford.edu/kst/what-is-an-ontology.html


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References

Kettel, Thomson, Greer, Generating Individualized Hypermedoia Apploications, Procs. Of the Int. Workshop on Adaptive and Intelligent Web-based Educational Systems, Montrel, Canada, 2000, pp. 37-49 (APHID)

Sickmann and all, Adaptive Course Generation, Procs. Of the Int. Workshop on Adaptive and Intelligent Web-based Educational Systems, Montrel, Canada, 2000 , pp. 73-84, (ID)

Nelson, T.H., The Heart of Connection: Hypermedia Unified by Transclusion, Communications of the ACM, vol.38, no. 8, pp. 31-33, aug. 1995.

Thuering, M., Hannemann, J., Haake, J.M., What’s Eliza doing in the Chinese Room? Incoherent Hyperdocuments - and how to avoid them, Hypertext'91, San Antonio, 1991, pp. 161-177.

Thuering, M., Hannemann, J., Haake, J.M., Hypermedia and Cognition: Designing for Comprehension, Communications of the ACM, vol.38, no.8, pp. 57-66, aug. 1995.

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References

Trausan-Matu, St. (1997) 'Knowledge-Based, Automatic Generation of Educational Web Pages', in Proceedings of Internet as a Vehicle for Teaching Workshop, Ilieni, June 1997, pp.141-148, See also http://rilw.emp.paed.uni-muenchen.de/99/papers/Trausan.html

Trausan-Matu, St. (2000) 'Metaphor Processing for Learning Terminology on the Web', in S.A.Cerri (ed.), Artificial Intelligence, Methodology, Systems, Applications 2000, Springer-Verlag, ISBN 3-540-41044-9, pp.232-241.

Gerhard Weber and Marcus Specht, User Modeling and Adaptive Navigation Support, in WWW-based Tutoring Systems, http://www.psychologie.uni-trier.de:8000/projects/ELM/Papers/UM97-WEBER.html - (ELM-ART)

J. Vassilieva, http://julita.usask.ca/homepage/AIED'97.ps - (DCG)

Louis Weitzman, Kent Wittenburg, Grammar-Based Articulation for Multimedia Document Design, Multimedia Systems CACM (1996) 4, pp. 99-111