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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
                   Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                               Biarritz                 3                             Biarritz             4




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



                   Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                               Biarritz                 5                             Biarritz             6




                                                                                                               1
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
             Stefan Trausan-Matu, ITS 2002,                     Stefan Trausan-Matu, ITS 2002,
                         Biarritz             7                             Biarritz              8




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
             Stefan Trausan-Matu, ITS 2002,                     Stefan Trausan-Matu, ITS 2002,
                         Biarritz             9                             Biarritz             10




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
             Stefan Trausan-Matu, ITS 2002,                     Stefan Trausan-Matu, ITS 2002,
                         Biarritz             11                            Biarritz             12




                                                                                                      2
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
               Stefan Trausan-Matu, ITS 2002,                     Stefan Trausan-Matu, ITS 2002,
                           Biarritz             13                            Biarritz             14




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


               Stefan Trausan-Matu, ITS 2002,                     Stefan Trausan-Matu, ITS 2002,
                           Biarritz             15                            Biarritz             16




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)

              Stefan Trausan-Matu, ITS 2002,                      Stefan Trausan-Matu, ITS 2002,
                          Biarritz              17                            Biarritz             18




                                                                                                        3
World Wide Web
  Hypertext(media) + Internet + User Friendly
                  Interfaces

              Text (+images ...) +                     Knowledge
      communication, distribution, agents +

interfacing, cognitive ergonomics (HCI, CHI, HCD)

                 Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                             Biarritz             19                                Biarritz             20




   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

                 Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                             Biarritz             21                                Biarritz             22




   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)
                 Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                             Biarritz             23                                Biarritz             24




                                                                                                              4
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
                  Stefan Trausan-Matu, ITS 2002,                                  Stefan Trausan-Matu, ITS 2002,
                              Biarritz             25                                         Biarritz                  26




                                                             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
                  Stefan Trausan-Matu, ITS 2002,                                  Stefan Trausan-Matu, ITS 2002,
                              Biarritz             27                                         Biarritz                  28




Ontologies - Languages                                       Ontologies on the web

  Description logics : LOOM, CLASSIC,                         General lexical ontologies :
  Fact                                                         WordNet
  XML-Based : DAML+OIL, OML                                    EuroWordNet
                                                               BalkanNet
                                                               MikroKosmos
                                                               FrameNet

                  Stefan Trausan-Matu, ITS 2002,                                  Stefan Trausan-Matu, ITS 2002,
                              Biarritz             29                                         Biarritz                  30




                                                                                                                               5
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.




            Stefan Trausan-Matu, ITS 2002,                      Stefan Trausan-Matu, ITS 2002,
                        Biarritz             31                             Biarritz             32




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
            Stefan Trausan-Matu, ITS 2002,                      Stefan Trausan-Matu, ITS 2002,
                        Biarritz             33                             Biarritz             34




                                                  Computer-Human Interaction
                                                  (CHI)
                                                  Among others, it studies:
                                                   Cognitive ergonomics
Computer-Human Interaction                         Immersive interfaces
                                                   Learner (user) modeling
                                                   Personalization



            Stefan Trausan-Matu, ITS 2002,                      Stefan Trausan-Matu, ITS 2002,
                        Biarritz             35                             Biarritz             36




                                                                                                      6
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).
               Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                           Biarritz             37                                Biarritz               38




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.
               Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                           Biarritz             39                                Biarritz               40




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)

               Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                           Biarritz             41                                Biarritz               42




                                                                                                              7
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                  (…………..)
               Stefan Trausan-Matu, ITS 2002,                       Stefan Trausan-Matu, ITS 2002,
                           Biarritz             43                              Biarritz             44




                                                     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.

               Stefan Trausan-Matu, ITS 2002,                       Stefan Trausan-Matu, ITS 2002,
                           Biarritz             45                              Biarritz             46




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.
               Stefan Trausan-Matu, ITS 2002,                       Stefan Trausan-Matu, ITS 2002,
                           Biarritz             47                              Biarritz             48




                                                                                                          8
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

               Stefan Trausan-Matu, ITS 2002,                       Stefan Trausan-Matu, ITS 2002,
                           Biarritz             49                              Biarritz             50




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

               Stefan Trausan-Matu, ITS 2002,                       Stefan Trausan-Matu, ITS 2002,
                           Biarritz             51                              Biarritz             52




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


               Stefan Trausan-Matu, ITS 2002,                       Stefan Trausan-Matu, ITS 2002,
                           Biarritz             53                              Biarritz             54




                                                                                                          9
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


             Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                         Biarritz             55                               Biarritz             56




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




             Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                         Biarritz             57                               Biarritz             58




                                                   Web page generation
                                                     Content
                                                     Structuring
Web page generation                                  Styling




             Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                         Biarritz             59                               Biarritz             60




                                                                                                         10
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)

               Stefan Trausan-Matu, ITS 2002,                           Stefan Trausan-Matu, ITS 2002,
                           Biarritz             61                                  Biarritz             62




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
               Stefan Trausan-Matu, ITS 2002,                           Stefan Trausan-Matu, ITS 2002,
                           Biarritz             63                                  Biarritz             64




Content types                                        Content semantics

  Textual                                              Conceptual structure
  Visual                                               Semantic density




               Stefan Trausan-Matu, ITS 2002,                           Stefan Trausan-Matu, ITS 2002,
                           Biarritz             65                                  Biarritz             66




                                                                                                              11
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)


               Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                           Biarritz             67                               Biarritz             68




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
               Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                           Biarritz             69                               Biarritz             70




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


               Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                           Biarritz             71                               Biarritz             72




                                                                                                           12
Natural Language Processing                        Natural Language Processing
(NLP)                                              approaches
  Parsing
  Annotation                                         Grammar-based
  Knowledge extraction                               Statistical
  Document categorization
  Search for relevant documents



             Stefan Trausan-Matu, ITS 2002,                     Stefan Trausan-Matu, ITS 2002,
                         Biarritz             73                            Biarritz             74




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


             Stefan Trausan-Matu, ITS 2002,                     Stefan Trausan-Matu, ITS 2002,
                         Biarritz             75                            Biarritz             76




                                                   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


             Stefan Trausan-Matu, ITS 2002,                     Stefan Trausan-Matu, ITS 2002,
                         Biarritz             77                            Biarritz             78




                                                                                                      13
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>

             Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                         Biarritz             79                               Biarritz             80




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

             Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                         Biarritz             81                               Biarritz             82




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
             Stefan Trausan-Matu, ITS 2002,                        Stefan Trausan-Matu, ITS 2002,
                         Biarritz             83                               Biarritz             84




                                                                                                         14
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>
                               Stefan Trausan-Matu, ITS 2002,                                         Stefan Trausan-Matu, ITS 2002,
                                           Biarritz                              85                               Biarritz             86




 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

                               Stefan Trausan-Matu, ITS 2002,                                         Stefan Trausan-Matu, ITS 2002,
                                           Biarritz                              87                               Biarritz             88




 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>
                               Stefan Trausan-Matu, ITS 2002,                                         Stefan Trausan-Matu, ITS 2002,
                                           Biarritz                              89                               Biarritz             90




                                                                                                                                            15
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>



                 Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                             Biarritz             91                                Biarritz               92




Learner Object Metadata
<relation>
   <kind>
                                                         Web page generation
    <langstring>Requires</langstring>
   </kind>
   <resource>
     <description>
                                                         Structuring
      <langstring>Description of resource</langstring>
     </description>
   </resource>
</relation>




                 Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                             Biarritz             93                                Biarritz               94




Web page generation                                      Structuring
   Content                                                 Linear
   Structuring                                             Hierarchy
   Styling                                                 Network




                 Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                             Biarritz             95                                Biarritz               96




                                                                                                                16
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


              Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                          Biarritz             97                                Biarritz                   98




Scope of generation                                  Generation horizon
  Generate an entire site                              Local – satisfy “requires” links
  Generate page by page                                Holistic - Larflast




              Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                          Biarritz             99                                Biarritz               100




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
              Stefan Trausan-Matu, ITS 2002,                         Stefan Trausan-Matu, ITS 2002,
                          Biarritz             101                               Biarritz               102




                                                                                                                 17
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
                         Stefan Trausan-Matu, ITS 2002,                                        Stefan Trausan-Matu, ITS 2002,
                                     Biarritz             103                                              Biarritz                         104




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.

                         Stefan Trausan-Matu, ITS 2002,                                        Stefan Trausan-Matu, ITS 2002,
                                     Biarritz             105                                              Biarritz                         106




                                                                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

                         Stefan Trausan-Matu, ITS 2002,                                        Stefan Trausan-Matu, ITS 2002,
                                     Biarritz             107                                              Biarritz                         108




                                                                                                                                                  18
Semantic editing (Trausan)




          Stefan Trausan-Matu, ITS 2002,         Stefan Trausan-Matu, ITS 2002,
                      Biarritz             109               Biarritz             110




          Stefan Trausan-Matu, ITS 2002,         Stefan Trausan-Matu, ITS 2002,
                      Biarritz             111               Biarritz             112




          Stefan Trausan-Matu, ITS 2002,         Stefan Trausan-Matu, ITS 2002,
                      Biarritz             113               Biarritz             114




                                                                                        19
Web page generation

                                                                              Styling




                      Stefan Trausan-Matu, ITS 2002,                                               Stefan Trausan-Matu, ITS 2002,
                                  Biarritz                             115                                     Biarritz                          116




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)
                      Stefan Trausan-Matu, ITS 2002,                                               Stefan Trausan-Matu, ITS 2002,
                                  Biarritz                             117                                     Biarritz                          118




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.
  Dimitrova, V., Self, J., Brna, P., 'Maintaining a Joinly Constrcted           Workshop on Adaptive and Intelligent Web-based Educational Systems,
  Student Model', in S.A.Cerri (ed.), Artificial Intelligence, Methodology,     Montrel, Canada, 2000 , pp. 73-84, (ID)
  Systems, Applications 2000, Springer-Verlag, ISBN 3-540-41044-9,              Nelson, T.H., The Heart of Connection: Hypermedia Unified by
  pp.221-231.                                                                   Transclusion, Communications of the ACM, vol.38, no. 8, pp. 31-33,
  Engelbart, D.C., Toward Augmenting the Human Intellect and Boosting           aug. 1995.
  our Collective IQ, Communications of the ACM, vol.38, no. 8, pp. 30-          Thuering, M., Hannemann, J., Haake, J.M., What’s Eliza doing in the
  33,aug. 1995.                                                                 Chinese Room? Incoherent Hyperdocuments - and how to avoid them,
  Gruber, T., What is an Ontology,                                              Hypertext'91, San Antonio, 1991, pp. 161-177.
  http://www.ksl.stanford.edu/kst/what-is-an-ontology.html                      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.
                      Stefan Trausan-Matu, ITS 2002,                                               Stefan Trausan-Matu, ITS 2002,
                                  Biarritz                             119                                     Biarritz                          120




                                                                                                                                                         20
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
                     Stefan Trausan-Matu, ITS 2002,
                                 Biarritz                          121




                                                                          21

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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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 3 Biarritz 4 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 5 Biarritz 6 1
  • 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 7 Biarritz 8 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 9 Biarritz 10 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 11 Biarritz 12 2
  • 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 13 Biarritz 14 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 15 Biarritz 16 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) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 17 Biarritz 18 3
  • 4. World Wide Web Hypertext(media) + Internet + User Friendly Interfaces Text (+images ...) + Knowledge communication, distribution, agents + interfacing, cognitive ergonomics (HCI, CHI, HCD) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 19 Biarritz 20 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 21 Biarritz 22 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) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 23 Biarritz 24 4
  • 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 25 Biarritz 26 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 27 Biarritz 28 Ontologies - Languages Ontologies on the web Description logics : LOOM, CLASSIC, General lexical ontologies : Fact WordNet XML-Based : DAML+OIL, OML EuroWordNet BalkanNet MikroKosmos FrameNet Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 29 Biarritz 30 5
  • 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. Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 31 Biarritz 32 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 33 Biarritz 34 Computer-Human Interaction (CHI) Among others, it studies: Cognitive ergonomics Computer-Human Interaction Immersive interfaces Learner (user) modeling Personalization Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 35 Biarritz 36 6
  • 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). Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 37 Biarritz 38 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. Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 39 Biarritz 40 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) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 41 Biarritz 42 7
  • 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 (…………..) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 43 Biarritz 44 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. Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 45 Biarritz 46 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. Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 47 Biarritz 48 8
  • 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 49 Biarritz 50 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 51 Biarritz 52 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 53 Biarritz 54 9
  • 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 55 Biarritz 56 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 57 Biarritz 58 Web page generation Content Structuring Web page generation Styling Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 59 Biarritz 60 10
  • 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) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 61 Biarritz 62 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 63 Biarritz 64 Content types Content semantics Textual Conceptual structure Visual Semantic density Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 65 Biarritz 66 11
  • 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) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 67 Biarritz 68 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 69 Biarritz 70 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 71 Biarritz 72 12
  • 13. Natural Language Processing Natural Language Processing (NLP) approaches Parsing Annotation Grammar-based Knowledge extraction Statistical Document categorization Search for relevant documents Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 73 Biarritz 74 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 75 Biarritz 76 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 77 Biarritz 78 13
  • 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> Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 79 Biarritz 80 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 81 Biarritz 82 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 83 Biarritz 84 14
  • 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> Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 85 Biarritz 86 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 87 Biarritz 88 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> Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 89 Biarritz 90 15
  • 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> Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 91 Biarritz 92 Learner Object Metadata <relation> <kind> Web page generation <langstring>Requires</langstring> </kind> <resource> <description> Structuring <langstring>Description of resource</langstring> </description> </resource> </relation> Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 93 Biarritz 94 Web page generation Structuring Content Linear Structuring Hierarchy Styling Network Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 95 Biarritz 96 16
  • 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 97 Biarritz 98 Scope of generation Generation horizon Generate an entire site Local – satisfy “requires” links Generate page by page Holistic - Larflast Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 99 Biarritz 100 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 101 Biarritz 102 17
  • 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 103 Biarritz 104 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. Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 105 Biarritz 106 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 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 107 Biarritz 108 18
  • 19. Semantic editing (Trausan) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 109 Biarritz 110 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 111 Biarritz 112 Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 113 Biarritz 114 19
  • 20. Web page generation Styling Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 115 Biarritz 116 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) Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 117 Biarritz 118 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. Dimitrova, V., Self, J., Brna, P., 'Maintaining a Joinly Constrcted Workshop on Adaptive and Intelligent Web-based Educational Systems, Student Model', in S.A.Cerri (ed.), Artificial Intelligence, Methodology, Montrel, Canada, 2000 , pp. 73-84, (ID) Systems, Applications 2000, Springer-Verlag, ISBN 3-540-41044-9, Nelson, T.H., The Heart of Connection: Hypermedia Unified by pp.221-231. Transclusion, Communications of the ACM, vol.38, no. 8, pp. 31-33, Engelbart, D.C., Toward Augmenting the Human Intellect and Boosting aug. 1995. our Collective IQ, Communications of the ACM, vol.38, no. 8, pp. 30- Thuering, M., Hannemann, J., Haake, J.M., What’s Eliza doing in the 33,aug. 1995. Chinese Room? Incoherent Hyperdocuments - and how to avoid them, Gruber, T., What is an Ontology, Hypertext'91, San Antonio, 1991, pp. 161-177. http://www.ksl.stanford.edu/kst/what-is-an-ontology.html 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. Stefan Trausan-Matu, ITS 2002, Stefan Trausan-Matu, ITS 2002, Biarritz 119 Biarritz 120 20
  • 21. 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 Stefan Trausan-Matu, ITS 2002, Biarritz 121 21